Computer-aided cycle time analysis and / or availability analysis of recipe-controlled processes in production plants

Abstract

The invention relates to a computer-implemented method for analysing the cycle time and / or availability of a recipe-controlled process in a production plant, preferably in a production plant in the fields of the chemical industry, the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automobile industry, the electronics industry or the aerospace industry, in particular in a chemical production plant. The invention also relates to an associated data processing device and to a computer program.

Description

[0001] Computer-aided cycle time and / or availability analysis of prescription-controlled processes in production plants

[0002] TECHNICAL AREA

[0003] The present invention relates generally to the field of production, in particular chemical production, and specifically to a method for cycle time and / or availability analysis of a recipe-controlled process in a production plant, preferably in a production plant in the chemical industry, the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automotive industry, the electrical industry or the aerospace industry, in particular in a chemical production plant.

[0004] BACKGROUND

[0005] In the manufacturing industry, and especially in the processing industry (e.g., the chemical, pharmaceutical, food, cosmetics, semiconductor, biotechnology, plastics, metal, glass, textile, automotive, electrical, and aerospace industries), numerous products are manufactured using batch processes. This typically involves combining several pieces of equipment into a linear or branched process. Often, this process involves producing a single product in numerous batches over several months or even years. Each batch goes through this process in a defined sequence. The added value of the entire process increases when more batches are produced per unit of time under otherwise identical conditions (batch size, yield, quality).For this to happen, the process (e.g., with regard to average cycle time and cycle time variation) must be optimized using technical measures. At the same time, a reduction in cycle time variation leads to better predictability of the plant.

[0006] A prerequisite for optimizing cycle times in recipe-driven batch processes is transparency regarding the process and its individual recipe steps. This allows for the identification of which equipment and recipe steps are the process speed determinants. These machines are referred to as bottlenecks in the process. The slowest of these bottlenecks (i.e., the slowest machine in the entire process) can also be called the "bottleneck." Intentional and unintentional changes to the overall process lead to dynamic changes and result in few batches exhibiting a comparable time profile.

[0007] CN111913449A discloses a method for monitoring production cycles, characterized in that it is applied to a production system in a manufacturing plant with multiple stations. While a material handling device, which can transport, for example, a material or a part, moves sequentially to each station, a timestamp is generated at the first moment when the material handling device arrives at each station and at the second moment when the material handling device leaves each station. The time difference calculated from these timestamps is defined in CN111913449A as the cycle time per station. By comparing the cycle times of the individual stations, bottleneck stations that lead to an increase in the overall production time are identified. This is possible because, according to the method,

[0008] CN111913449A states that the sending station (i.e., the station from which a part was vacated) and the receiving station (i.e., the station to which a part is transferred) are not simultaneously occupied by that part. This is because, under normal operating conditions, the part, on its material handling device, can leave the sending station freely and independently of the receiving station. The sending station can thus complete its entire processing of the part regardless of the status of the receiving station. Therefore, the transfer between the stations does not, at least not under normal operating conditions, result in a temporal coupling of the stations, nor does it, at least not under normal operating conditions, result in a temporal coupling of the cycle times of the stations.

[0009] Example from the chemical industry: In recipe-controlled batch processes in chemical production plants, it is not an individual part that is processed, but rather a substance or mixture of substances that undergoes a chemical reaction during the production process. This results in one or more products being created from a starting material or mixture of starting materials, possibly via various intermediate stages. The substances or mixtures involved can be solid, liquid, or gaseous and pass through the equipment of the production process in a defined sequence.

[0010] In recipe-controlled batch processes in chemical production plants, a temporal coupling of equipment or of recipes for equipment typically occurs. This coupling can be due to the technical design of the chemical production plant or the recipe-controlled execution of the chemical process. The technical design of the chemical production plant generally involves the transfer of substances or mixtures via lines that directly connect the respective equipment. These lines can lead to a temporal coupling of the individual equipment or their recipes. The transfer of the substance or mixture from one piece of equipment to the next, for example, from equipment A to equipment B, occupies both pieces of equipment simultaneously and can only take place if equipment A can discharge its contents and if equipment B can receive them.In other words, both the recipe of apparatus A and the recipe of apparatus B must have reached the transition conditions required for the transfer. If the transition conditions required for the transfer have not yet been reached in one of the apparatuses, the other apparatus must inevitably wait for it due to the temporal coupling of the recipes of the respective apparatuses. For example, if apparatus B is ready to receive the contents of apparatus A, but apparatus A is not yet ready for the transfer, e.g., because the chemical conversion in apparatus A has not yet completed to the point where the transition conditions have been reached, the transfer cannot occur until the transition conditions are met. In the example shown, apparatus A thus induces a waiting time in apparatus B and consequently extends the cycle time of apparatus B for reasons that lie outside of apparatus B.Due to the chemical processes and procedures taking place within the apparatus, such induced waiting times can occur regularly or even always, i.e., for numerous or all batches. This type of coupling is also possible between multiple apparatuses or their recipes. For example, a recipe for one apparatus may query parameter values ​​from multiple apparatuses and / or multiple recipes and must wait until all queried parameter values ​​have reached the required transition conditions. In the example above, the transfer time is part of the cycle time for both apparatus A and apparatus B.

[0011] Even the recipe-driven execution of a chemical process can lead to a temporal coupling of apparatus or recipes. This coupling can also be one-sided. It may be that one recipe queries parameter values ​​from another apparatus and / or its recipe and must wait for certain parameter values ​​of the other apparatus and / or recipe to be reached, as these are part of its transition conditions, while the other recipe can be carried out completely independently of the first apparatus and recipe; thus, parameter values ​​of the first apparatus and recipe are not part of the transition conditions of the second recipe. For example, in the chemical synthesis of a thermally labile substance in apparatus C, it may be essential that a solvent is already present in the subsequent apparatus D.The recipe for apparatus C therefore queries parameter values ​​from apparatus D and / or its recipe, while the solvent addition step in apparatus D can be completely independent of apparatus C and its recipe. If, in the example, the parameter values ​​required to start the synthesis of the thermally labile substance from apparatus D and / or its recipe have not yet been reached in the recipe for apparatus C, apparatus C must inevitably wait for apparatus D due to this temporal coupling. Thus, in this example as well, a waiting time and cycle time extension induced by another apparatus (apparatus D) occurs in one apparatus (apparatus C), for reasons that lie outside of that apparatus (apparatus C).

[0012] Chemical stability, chemical yield, or requirements for the quality of the substance or mixture can also be reasons for temporal coupling of apparatus or recipes in the recipe-controlled execution of a chemical process.

[0013] A temporal coupling is also possible between multiple devices or their recipes. It can happen that a device's recipe queries parameter values ​​from multiple devices and / or multiple recipes and must wait until all queried parameter values ​​have reached the required transition conditions. Due to the chemical processes taking place in the devices and the chemical procedure itself, this can regularly or even always result in induced waiting times, i.e., for numerous or all batches.

[0014] Even in production facilities in sectors other than the chemical industry, for example in the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automotive industry, the electrical industry or the aerospace industry, a temporal coupling of equipment or its recipes is possible, which can also lead to induced waiting times.

[0015] Example from the pharmaceutical industry: In pharmaceutical production facilities, for example, a crystallization apparatus and a centrifuge, or the formulations involved, may be linked. An apparatus in which an active ingredient is crystallized may not be able to be emptied until, on the one hand, the crystallization process in the apparatus is complete, and on the other hand, the receiving centrifuge has completed its previous process, been emptied and cleaned, and is ready to receive the active ingredient suspension. Due to this resulting temporal link, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0016] Example from the food industry: In food production facilities, for example, the equipment for pasteurizing and cooling jam, or the recipes involved, may be linked. The jam from the pasteurizer may only be transferred to the cooling container once the pasteurization process is complete, the jam has reached its target viscosity, and the cooled jam from the previous cooling process has reached the required temperature, the cooling container has been emptied, and it is ready to receive the next batch of jam. Due to this resulting temporal linkage, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0017] Example: Cosmetics industry. In cosmetics production facilities, for example, the equipment for emulsion formation and filling, or the recipes involved, may be linked. The production of emulsions may require, on the one hand, that the emulsion has reached a target droplet size before being transferred to the filling system, and on the other hand, that the filling system is ready to receive it. Due to the resulting temporal linkage, induced waiting times can occur regularly or even always, i.e., in numerous or all batches.

[0018] Example: Semiconductor industry. In semiconductor production facilities, for example, the etching and cleaning equipment, or the associated processes, may be linked. It may be necessary to thoroughly clean wafers promptly after etching. On the one hand, the etching process, including all required inspections, must be completed before the wafer can be transferred to the cleaning station. On the other hand, the cleaning station must have finished its cleaning and be ready to receive the wafer. Due to this resulting time coupling, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0019] Example from the biotechnology industry: In biotechnology production facilities, for example, the fermentation and membrane filtration equipment, or the recipes involved, can be linked. In the fermentation process, it may be necessary to achieve the target cell number and cell viability when producing a sensitive cell suspension. Simultaneously, the membrane filtration apparatus may need to be cleaned, tested, and ready to receive the sensitive cell suspension in a timely manner before it can be transferred to the membrane filtration apparatus to ensure process efficiency and cell purity. Due to this resulting temporal linkage, induced waiting times can occur regularly or even continuously, i.e., for numerous or all batches.

[0020] Example: Plastics Industry. In plastics production plants, the equipment for polymerization and wet milling, or the processes involved, can be linked. After polymerization, the polymer may need to reach a target molecular weight and viscosity before it can be wet milled. Additionally, the polymer suspension may need to be milled promptly, as otherwise sedimentation problems and consequently poorer processability can occur. Furthermore, the mill must be ready to receive the polymer suspension in a timely manner. Due to this resulting temporal coupling, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0021] Example: Metal Industry. In metal production plants, the melting and casting equipment, or the processes involved, can be linked. On the one hand, it may be necessary for the metal to be completely and homogeneously melted before being transferred to the molds to ensure the quality of the castings. On the other hand, it may be necessary for the melting process to only begin when it is foreseeable that the molds will be ready to receive the molten metal at the scheduled completion time, in order to avoid unnecessarily high energy consumption. Due to this resulting temporal linkage, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0022] Example: Glass Industry. In glass production plants, the melting and casting equipment, or the recipes involved, can be linked. On the one hand, it may be necessary for the glass to be completely and homogeneously melted before being transferred to the molds to ensure the quality of the glass products. On the other hand, it may be necessary for the melting process to only begin when it is foreseeable that the molds will be ready to receive the molten glass at the scheduled completion time, in order to avoid unnecessarily high energy consumption. Due to this resulting temporal linkage, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0023] Example: Textile industry. In textile production facilities, the dyeing and fixing equipment, or the recipes involved, can be linked. It may be necessary for textile fibers to reach a target color index and pH value. Simultaneously, the fixing chamber may need to reach a target temperature and humidity and be ready to receive the textile fiber before the dyed fiber can be transferred to the fixing chamber for the next step, in order to prevent color changes. Due to this resulting temporal linkage, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0024] Example: Automotive industry. In automotive production facilities, the equipment for carburizing and quenching engine blocks, or the processes involved, can be linked. During carburizing, the enrichment of carbon in the engine block may require that the block has reached a target temperature. Simultaneously, the quench tank may need to be ready to receive the engine block promptly to achieve the desired surface properties. Due to this resulting temporal coupling, induced waiting times can occur regularly or even continuously, affecting numerous or all production batches.

[0025] Example from the electronics industry: In electronics production facilities, for example, the equipment for flux application and selective wave soldering, or the processes involved, can be linked. It may be necessary that the printed circuit boards from the flux station can only be transferred to the soldering station once, on the one hand, the flux application, including all necessary process controls, is complete to ensure optimal soldering quality. On the other hand, it is necessary that the soldering station has completely processed the previous batch and is ready to accept the next one. Due to this resulting time linkage, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0026] Example: Aerospace industry. In aerospace production facilities, for example, an autoclave for curing a fiber composite part and a vacuum wet paint booth, or the processes involved, may be linked. The fiber composite part coming from the autoclave may only be transferred to the vacuum wet paint booth once, on the one hand, the curing process in the autoclave is complete and the component has reached a predetermined residual moisture content or surface temperature, and on the other hand, the vacuum wet paint booth has already reached its target parameters. Due to the resulting temporal coupling, induced waiting times can occur regularly or even always, i.e., for numerous or all batches.

[0027] When applying the principles of CN111913449A to recipe-driven batch processes with time-coupled equipment or recipes, induced waiting times are part of the equipment's cycle time and extend it, even if the cause of these waiting times lies outside the respective equipment. Thus, cycle time analysis can identify equipment as a bottleneck simply because it had to wait for another piece of equipment in the production process. If induced waiting times occur regularly or consistently, this can lead to incorrect equipment being considered bottlenecks and / or constraints. Improvements to these machines would then generally be inefficient and would only increase waiting times within the production process, without improving the overall cycle time.For this reason, the teaching of CN111913449A is not applicable to recipe-controlled batch processes with time-coupled equipment or recipes.

[0028] Furthermore, CN111913449A does not consider transfer times or the times of upstream and downstream work steps, such as setup and cleaning, when calculating cycle time. In contrast, in batch processes, such as chemical batch processes, all recipe steps can be relevant for cycle time analysis and process optimization. All recipe steps for each individual piece of equipment can have a unique duration for each batch and can vary from batch to batch. In particular, value-adding or non-value-adding recipe steps can occur within the equipment before, during, between, or even after the transfer from the equipment. It is important to note that batch processes, such as chemical batch processes, can be both linear and branched.The sum of the durations of the individual value-adding and non-value-adding recipe steps of the bottleneck determines its cycle time, which in turn influences the cycle time of the entire process.

[0029] The procedure according to CN111913449A has the following disadvantages: 1. It is not suitable for production processes in which the stations (e.g., equipment or workstations) or their recipes are subject to at least a temporal coupling.

[0030] 2. No work steps or recipe steps relevant for the analysis of root causes are recorded, identified, prioritized, and made available for monitoring the success of implemented measures at each station (e.g., per apparatus or per workstation).

[0031] Therefore, there was a need to provide a method for cycle time and / or availability analysis of recipe-driven batch processes that at least partially overcomes the disadvantages of the state of the art. This method should enable the identification of the process bottleneck, the identification of other bottleneck components, the prioritization of these bottleneck components, the identification and prioritization of the most relevant recipe steps for root cause analysis, and the monitoring of the success of implemented measures at the most relevant recipe steps, bottleneck components, and the bottleneck. This should be possible dynamically with minimal effort, such as time expenditure, and allow for the consideration of both short-term and long-term trends in a dynamic process.

[0032] SUMMARY OF THE INVENTION

[0033] This is achieved by the subject matter defined in the independent claims. Advantageous modifications of embodiments of the present disclosure are defined in the dependent claims, as well as in the description and the figures.

[0034] One aspect of the invention relates to a method for cycle time and / or availability analysis of a recipe-controlled process in a production plant, preferably in a production plant in the chemical, pharmaceutical, food, cosmetics, semiconductor, biotechnology, plastics, metal, glass, textile, automotive, electrical, or aerospace industries, and in particular in a chemical production plant. The method can be computer-implemented.In the chemical, pharmaceutical, food, cosmetics, semiconductor, biotechnology, plastics, metal, glass, textile, automotive, electrical, or aerospace industries, a "production plant" can be understood as a technical facility used to carry out biological, chemical, physical, and / or process engineering activities with the aim of producing products typical of these technical fields.

[0035] A "chemical production plant" can be understood as a technical facility used to carry out chemical, physicochemical, biochemical, and / or process engineering operations with the aim of producing chemical products, intermediates, formulations, or similar products. Such a plant typically comprises one or more "units of equipment." These units may include specific equipment such as reactors, mixers, destination or rectification columns, evaporators, filters, dryers, and / or tanks.

[0036] A process carried out by such a production plant can be a batch process. The term "batch process" (or "batch process") can be understood, for example in the context of a chemical production plant, as a production method in which a finite, defined quantity of starting materials or mixtures of starting materials undergoes a defined sequence of process steps or recipe steps within a limited period of time to produce an equally finite quantity of intermediate product or product (the batch). Batch processes are typically contrasted with continuous processes, in which the substance or mixture flows continuously through the chemical production plant and the process steps take place simultaneously in different parts of the plant, spatially separated and overlapping in time.Batch processes are frequently used in the specialty chemicals, pharmaceuticals, fine chemicals, biotechnology, and food industries, particularly in the production of high-value products, in smaller production volumes, when high flexibility in product variety is required on the same equipment, or when traceability of each individual production batch is important. They are often recipe-driven.

[0037] The term "recipe" can be understood as a step-by-step instruction or manufacturing procedure for carrying out a process in a device. The recipe can define any number of recipe steps and a specific sequence of process steps, phases, actions, parameter values, transition conditions, and / or logical branches.

[0038] The term "recipe step" can be understood as a subset of a recipe. A recipe step can be a process step, the sum of several process steps, or a subset of a process step. Recipe steps can be completed when defined transition conditions are met or remain active if these transition conditions are not met. Recipe steps can be, for example, "Filling the apparatus with 1,000 liters of water," "Waiting for transfer from apparatus B," or "Heating the contents of the apparatus to 80°C within 1 hour."

[0039] The term "transition condition" can be understood as a set of predefined parameter values, such as sensor data, and conditions whose fulfillment triggers the completion of one recipe step and the transition to the next defined recipe step. Transition conditions and the resulting action could be, for example, "If the temperature is below 20 °C, the total fill level is above 80%, and the downstream device is in the recipe step 'Waiting for Dosing', switch to the recipe step 'Transfer to Downstream Device'" or "If the temperature is below 20 °C, the total fill level is above 80%, and the downstream device is not in the recipe step 'Waiting for Dosing', switch to the recipe step 'Waiting for Downstream Device'."

[0040] Similarly, the terms "batch process", "recipe", "recipe step" and "transition condition" can also be understood in relation to production facilities in the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automotive industry, the electrical industry or the aerospace industry, with the process steps and equipment typical in these areas.

[0041] The term "recipe-controlled process" can be understood as a process whose execution follows predefined manufacturing instructions (recipes).

[0042] The term "recipe-controlled" can be understood here, in particular, as a recipe-based sequence control of a batch process, in which a recipe is followed as a sequence, especially a hierarchically structured sequence, of recipe steps. This control can be used equally in fully automated systems, semi-automated systems, and manual process execution. The term "time-coupled" can be understood as the presence of transition conditions in the recipe of one device, which also relate to the parameter values ​​of another device and / or its recipe. Time-coupled processing is also possible between multiple devices or their recipes. It can happen that the recipe of one device queries parameter values ​​from multiple devices and / or multiple recipes and must wait until all queried parameter values ​​have met the required transition conditions.Temporal couplings can include, for example, "Start dosing when the downstream device has finished dispensing" or "Start transfer when the downstream device is ready to receive".

[0043] The procedure may include providing a process definition. The process definition may define at least a number of pieces of equipment in the production plant and / or a number of individual recipe steps. If there is more than one piece of equipment, the process definition may additionally define their sequence in the process (i.e., if the number of pieces of equipment in the production plant is greater than 1). If there is more than one recipe step, the process definition may additionally define their sequence in the recipe (i.e., if the number of recipe steps in the recipe is greater than 1).

[0044] The process may include receiving production data from the production plant. This production data may include information about completed batches and / or recipe steps for multiple batches passing through a specified number of machines. The completed batches and / or recipe steps may include currently completed batches and / or recipe steps (i.e., the batches and / or recipe steps currently passing through a specified number of machines), already completed batches and / or recipe steps (i.e., the batches and / or recipe steps that have already passed through a specified number of machines), and / or future recipe steps (i.e., the recipe steps that will be passed through a specified number of machines after the currently completed recipe steps).

[0045] Production data can be received in the form of a stream and / or a log. In the case of a stream, the production data can be recorded or measured (e.g., using appropriate sensors) and sent and received via a stream (e.g., in real time). Thus, the production data can also be understood as measurement data representing the current, physical state of the production plant (i.e., a real, physical production plant in which a real, physical production process is taking place). In the case of a log, the production data can be recorded or measured (e.g., using appropriate sensors), written to a log file (e.g., stored in a database), and retrieved and received from it. The production data can be in a format that includes time information (e.g., a timestamp) associated with each batch and / or recipe step.The batch can be represented by a name and / or an identifier (e.g., a unique ID, an index, a number, etc.). An example batch might be included in the production data as follows: "Time information: ID". Similarly, the recipe step can be represented by a name and / or an identifier (e.g., a unique ID, an index, a number, etc.). An example recipe step might be included in the production data as follows: "Time information: ID".

[0046] The process can include the automatic determination of at least one cycle time and / or waiting time and / or batch time. The determination of the cycle time and / or waiting time and / or batch time can be based on information about executed batches and / or recipe steps for a plurality of batches passing through a number of machines.

[0047] The cycle time of a batch can be defined as the total duration of all recipe steps for that batch within a machine. The cycle time of a machine can vary from batch to batch. Within a machine, a batch typically goes through various recipe steps, each of which can be value-adding or non-value-adding (waiting times). The duration of the recipe steps can differ from batch to batch. The sum of the value-adding recipe steps of a batch can be referred to as the batch time. The batch time of a machine also varies from batch to batch. The characteristics and reasons for these variations differ and can change dynamically over time. This applies to each individual machine in the process. The sum of the non-value-adding recipe steps can be referred to as the waiting times of a batch. Such waiting times arise, for example, because machines are waiting for other (e.g.,B. equipment preceding the process needs to be maintained. Waiting times can also occur for reasons outside the process.

[0048] This results in the average cycle time and the cycle time variation of the entire system.

[0049] The process consists of the cycle times and cycle time fluctuations of the individual machines, which in turn result from the batch times and waiting times as well as their fluctuations, which in turn are composed of the recipe step durations as well as their fluctuations.

[0050] The procedure can include an automatic evaluation of at least the batch time and / or the waiting time and / or the cycle time of at least one of the devices to identify at least one device from the number of devices as a bottleneck device that affects a cycle time and / or availability of the process.

[0051] The method may include displaying analytical information on an electronic display device for use by a user in optimizing the process and / or the production plant. The analytical information may include at least one identified bottleneck device.

[0052] Batch time can be used to prioritize bottleneck equipment, with the bottleneck equipment having the highest priority also being referred to as the bottleneck.

[0053] Thus, the method provides a solution for analyzing even highly complex production plants, preferably those in the chemical, pharmaceutical, food, cosmetics, semiconductor, biotechnology, plastics, metal, glass, textile, automotive, electrical, or aerospace industries, and especially chemical production plants, and for providing a transparent, objective overview of the production plant using the analytical information. In other words, the method provides computer-based support for users in carrying out the analysis of the process and / or the production plant (i.e., targeted human-machine interaction in the technical task of production process analysis).Based on this analysis, process inefficiencies can be identified that would otherwise remain hidden due to the process's complexity. These inefficiencies might include an excessively long or fluctuating duration of a recipe step within a piece of equipment, such as a bottleneck. The information is presented in a user-friendly format of hours and minutes. Accordingly, efficient measures for optimizing the process and / or production plant can be determined based on the identified root causes. At the same time, inefficient measures based on subjective assessments can be avoided.Surprisingly, the application of the method resulted in double-digit percentage increases in plant capacity through more efficient use of existing resources in more than ten production processes, averaging 20 percent, without requiring extensive investment.

[0054] The recipe-controlled process can be a pure batch process and / or a process that may include elements of continuous processes. A batch process comprises at least one piece of equipment with batch operation. A batch process can be linear or branched.

[0055] Apart from chemical production, the method can also be used for cycle time and / or availability analysis of production plants in other domains, such as:

[0056] • Pharmacy,

[0057] • Automobile,

[0058] • Electronics,

[0059] • Biotechnology,

[0060] • Aerospace,

[0061] • metalworking industry,

[0062] • plastics processing industry,

[0063] • Food industry.

[0064] The following describes, using a chemical production plant as an example, the coupling of equipment within a production plant, or the coupling of the recipes for this equipment. It may be intended that at least some of the equipment within the production plant, or the recipes for this equipment, are interconnected. Such coupling can be temporal. This temporal coupling can be based, for example, on the technical design of the chemical production plant or on the recipe-driven execution of the chemical process.

[0065] The technical design of a chemical production plant may, for example, involve transferring substances or mixtures through lines, such as pipes or hoses, that connect the respective equipment directly and without buffering. These lines can lead to a temporal coupling of the respective equipment and its processes, since the physical transfer typically requires the simultaneous execution of specific recipe steps in the respective equipment. For this to occur, both the recipe of the supplying equipment and the recipe of the receiving equipment must have met the transition conditions required for the transfer recipe step, which may also include parameter values ​​of the other equipment and / or the other recipe. Temporal coupling is also possible between multiple pieces of equipment and their recipes.It can happen that a machine's recipe queries parameter values ​​from multiple machines and / or multiple recipes and has to wait until all queried parameter values ​​have met the required transition conditions. This leads to a dependency between the machines and / or their recipes, and can result in one or more machines having to wait for another machine or machines regularly, i.e., in many batches.

[0066] The temporal coupling of equipment or recipes can also be due to the recipe-controlled execution of the chemical process. This coupling can also be one-sided. A recipe for one piece of equipment might query parameter values ​​from another piece of equipment and / or its recipe and must wait for certain parameter values ​​of the other piece of equipment and / or its recipe to be reached, as these are part of its transition conditions. Meanwhile, the other recipe can be executed completely independently of the first; parameter values ​​of the first piece of equipment and / or recipe are therefore not part of the transition conditions of the second recipe. This also leads to a dependency of the equipment or its recipes and can, for example, with many batches, result in regular waiting times in one or both pieces of equipment. These waiting times are part of the cycle time. Temporal coupling also exists between several pieces of equipment or recipes.This is possible with their recipes. It can happen that a machine's recipe queries parameter values ​​from multiple machines and / or multiple recipes and has to wait until all queried parameter values ​​have reached the required transition conditions. This leads to a dependency between the machines or their recipes and can result in one or more machines having to wait for another machine or machines regularly, i.e., in many batches. Time-coupled systems contrast with production systems where material is moved between stations in a time-decoupled manner and can be buffered, for example, by material transport devices and storage facilities.The temporal coupling means that one apparatus often has to wait for the readiness of another apparatus, which affects the cycle time and cannot be adequately represented in a cycle time analysis for temporally uncoupled production processes due to the focus on decoupled stations.

[0067] It may be possible for the automatic determination and / or evaluation of the cycle time of at least one of the machines to also take waiting times into account. In production environments with time-coupled machines or recipes, such as those typical in chemical batch processes, waiting times often arise when a machine's recipe, due to a time coupling, must wait for certain parameter values ​​of another machine and / or its recipe, or of several other machines and / or their recipes, to reach their own transition conditions before it can begin its next recipe step. These waiting times are often difficult to identify manually, but can significantly influence the cycle time and can mask the true bottlenecks in the process chain. Waiting times are preferably represented as a separate recipe step within the recipe and / or a separate tag value and systematically (i.e., e.g.,The waiting times are mapped (before each transfer of a substance or mixture, before each manual operation, before each use of an auxiliary medium or resource). The automatic, targeted analysis of these waiting times enables the precise identification of the truly limiting equipment, the bottleneck, and the congestion points, thus forming the basis for accurately revealing inefficiencies, e.g., caused by specific recipe steps. Waiting times can change dynamically over time or batches, disappear, arise anew, and occur regularly and / or irregularly. For example, it is possible that for years, over numerous batches, one piece of equipment always had to wait for the following piece of equipment until its cycle time was improved, e.g., by shortening one of its most time-sensitive recipe steps, and that since then, the following piece of equipment has had to wait for the preceding piece of equipment.

[0068] Waiting times can result from a variety of specific circumstances, for which the process in the respective apparatus is not necessarily the cause, and are non-value-adding to the process in that apparatus. These circumstances include, for example:

[0069] • Waiting for at least one coupled downstream apparatus: The transition conditions of a recipe for a particular apparatus include parameter values ​​from that apparatus, its recipe, a downstream apparatus, and / or the downstream apparatus's recipe. The parameter values ​​of the apparatus and / or its recipe have met the transition conditions, while the parameter values ​​of the downstream apparatus and / or its recipe have not yet met them. Consequently, the apparatus waits for the downstream apparatus and / or its recipe until the latter has met the required transition conditions. This can occur regularly, for example, with many or all batches, or only sporadically, for example, in exceptional cases. The relationships described also apply analogously to multiple downstream apparatus and their recipes and are explained using a chemical production plant as an example.

[0070] Example 1: A reactor has finished synthesis and is waiting to be emptied, but the downstream filter is not yet ready to filter the batch. A possible recipe step could be: "Waiting for Filter 1".

[0071] Example 2: A reactor has already supplied solvent, is heated, and could start the synthesis of a thermally labile intermediate, but the downstream apparatus is not yet ready for immediate takeover because the aqueous solution for quenching the reaction mixture has not yet been supplied. A possible recipe step could be: "Waiting for quench apparatus 22".

[0072] • Waiting for at least one coupled predecessor apparatus: The transition conditions of an apparatus's recipe include parameter values ​​from that apparatus, its recipe, a predecessor apparatus, and / or its recipe. The parameter values ​​of the apparatus and / or its recipe have met the transition conditions, while the parameter values ​​of the predecessor apparatus and / or its recipe have not yet met them. Consequently, the apparatus waits for the predecessor apparatus and / or its recipe until it has met the required transition conditions. This can occur regularly, for example, with many or all batches, or only sporadically, for example, in exceptional cases. The relationships described also apply analogously to multiple predecessor apparatuses and their recipes.

[0073] Example 3: A crystallization reactor has already discharged its suspension and is now waiting for the next batch of reaction mixture from the preceding apparatus. A possible recipe step could be: "Waiting for reaction mixture from reactor 3".

[0074] Example 4: A reactor is waiting for the exact chemical yield of its predecessor apparatus in order to dose precisely a stoichiometric amount of solvent, but the chemical yield of the predecessor apparatus has not yet been determined. A possible recipe step could be: "Waiting for yield from reactor R4".

[0075] • Waiting for auxiliary media and / or resources: The transition conditions of a recipe in an apparatus include parameter values ​​from a source of an auxiliary medium (e.g., steam, cooling water, inert gas) or another resource (e.g., a reservoir, a tank), and these parameter values ​​have not yet met the transition conditions. Consequently, the apparatus waits until the required parameter values ​​of the source of the auxiliary medium or other resource have met the transition conditions. This can occur regularly, e.g., for many or all batches, or only sporadically, e.g., in exceptional cases. The relationships described also apply analogously to multiple auxiliary media and / or resources.

[0076] Example 5: A reactor is waiting for cooling water to be provided in order to start an exothermic reaction. A possible recipe step could be: "Waiting for cooling water".

[0077] • Waiting for at least one operator intervention: The transition conditions of a recipe for a machine include parameter values ​​that reach the transition conditions as soon as a specific manual intervention in the process begins (e.g., manual addition of a solid, sampling of the reactor, removal of the dryer, manual opening of a valve, confirmation by the operator in the control system), and these parameter values ​​have not yet reached the transition conditions. Consequently, the machine waits until the parameter values ​​required for the manual intervention have reached the transition conditions. This can happen regularly, e.g., for many or all batches, or only sporadically, e.g., in exceptional cases. The relationships described also apply analogously to multiple operator interventions.

[0078] Example 6: Waiting for the dryer to be emptied manually. A possible recipe step could be: "Waiting for the dryer to be emptied".

[0079] Combinations of the examples shown here are also possible.

[0080] Examples of waiting times in batch processes in production plants from the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automotive industry, the electrical industry or the aerospace industry can be derived analogously, for example from the examples mentioned above on the topic of “coupling recipes or devices”.

[0081] Systematically considering all potential waiting times as separate recipe steps and / or values ​​of a day makes it possible to identify the root causes of long and / or fluctuating cycle times of the bottleneck and of bottlenecks in batch production, preferably in batch production in the chemical industry, pharmaceutical industry, food industry, cosmetics industry, semiconductor industry, biotechnology industry, plastics industry, metal industry, glass industry, textile industry, automotive industry, electrical industry or aerospace industry, especially in a chemical production plant, and to trace these back to the level of recipe steps in order to take targeted measures for process optimization and efficiency improvement that go beyond a simple overall time analysis.For example, a cycle time analysis might reveal that in a bottleneck unit E, the recipe step of transferring the product to the subsequent filter drier F takes a particularly long time. This could potentially be optimized by increasing the pressure or using a variable pressure filtration method. On the other hand, it could also be that this recipe step in unit E, the bottleneck, is sometimes preceded by a fluctuating waiting time for the filter drier F, which is not the bottleneck itself. This waiting time might occur because the filter drier F occasionally needs to wait to be emptied. This could be resolved by adjusting personnel scheduling priorities, demonstrating that even the bottleneck can experience waiting times. Accurately identifying an inefficient recipe step can already contribute to solving the problem.

[0082] One aspect of the process involves an availability analysis of a production plant. If the availability of the production plant or a piece of equipment is limited, this results in a cycle time that is significantly higher than the expected range of cycle times. The availability analysis can identify the batch responsible for this increased cycle time, as well as the duration (absolute and / or relative) of the availability loss. Simultaneously, the analysis allows for the identification of the responsible piece of equipment and its corresponding recipe step, thereby enabling the identification of potential root causes. For example, a significantly increased cycle time for a batch could be due to a blocked line if the recipe step "transfer to the next piece of equipment" is significantly increased, or to another root cause, such as a delayed cooling process.as a result of a leak in the coolant circuit, if the recipe step "Cooling" is significantly increased.

[0083] It may be possible to automatically determine and / or evaluate the cycle time of at least one of the machines and also take into account the duration of transfers between the machines. For example, in chemical batch processes where substances or mixtures are transferred between machines, the duration of the transfer can be an integral part of the recipe duration of the machines involved. Thus, an example result of the cycle time analysis might be that, in the bottleneck (e.g., machine H), the recipe step of transferring the product from machine G to machine H takes a particularly long time. This could potentially be optimized by using a more powerful pump, larger cross-sections of the transfer lines, parallelizing this recipe step with other recipe steps, or by fundamentally redesigning the process.On the other hand, it is also possible, for example, that this recipe step of the transfer in the bottleneck apparatus H is preceded by a fluctuating waiting time, which becomes visible by introducing the waiting time as a recipe step or a tag value. This could be because analytical data of the mixture from apparatus G first need to be available, which could be solved by prioritized sample analysis, online analysis, or soft sensing. Including these transfer times can thus improve cycle time calculations, as the transfer times can influence the cycle time of the overall process and can be particularly helpful in time-coupled systems for identifying bottlenecks and waiting times caused by the transfer process itself or the readiness of the equipment involved.

[0084] It may be intended that the automatic determination and / or evaluation of the cycle time of at least one of the machines also takes into account all recipe steps of the machine, in particular upstream and / or downstream recipe steps that take place before, during, between, or after transfers of substances or mixtures, such as setup, inerting, cooling, heating, filtration during transfer, or cleaning. Recipes for batch processes, for example in the chemical industry, often include a multitude of recipe steps that go beyond the pure processing or transfer of substances, such as machine preparation (setup), cleaning or sterilization between batches, temperature control phases, solvent addition, or waiting for quality releases. The duration and variation of these recipe steps can contribute significantly to the duration and variation of the cycle time.Therefore, taking all recipe steps into account when calculating the cycle time allows for a particularly comprehensive and realistic analysis, since each of these recipe steps can potentially be a root cause of long and fluctuating cycle times in the bottleneck or constricted equipment of the process.

[0085] The method may include the analysis of linear and / or branched process flows. Batch processes often exhibit complex, non-linear structures that extend beyond simple sequential sequences. These include parallel process paths and conditional branches based on process parameters. Therefore, for a particularly meaningful cycle time and availability analysis, it can be advantageous to be able to reliably identify the correct bottleneck devices despite complex plant configurations.

[0086] The method can be designed to be applicable to production processes where substances or mixtures are divided into subsets or combined from multiple sources at a single apparatus. Chemical batch processes, for example, often involve operations where a quantity of material is divided (e.g., for parallel processing, filling into different containers) or where quantities from different sources (e.g., products from parallel reactors) are combined at a single apparatus. Such non-linear material flows and the associated resource allocations can be considered in cycle time and availability analysis to accurately track and aggregate the time profiles and equipment utilization across the various branches and merging stages of the process.

[0087] In a further aspect, the process can also include the automatic determination of at least one recipe step duration. This automatic determination can be based on information about the executed recipe steps for the majority of batches passing through the specified number of machines. The automatic determination of at least the cycle time and / or the waiting time and / or the batch time can be based on the recipe step durations.

[0088] One or more recipe step durations can be determined, for example, based on the time information that can be assigned to each recipe step. As described above, the production data can have a format that includes, for each recipe step, time information associated with the recipe step, as well as a name and / or an identifier. These can be retrieved under a recipe step tag. Similarly, as described above, the production data can have a format that includes, for each batch, time information associated with that batch, as well as a name and / or an identifier. These can be retrieved under a batch tag.

[0089] Based on these recipe step tags, the recipe step durations can be determined by defining a start and end time for the same recipe step using the recipe step tag (i.e., recipe step duration = end time - start time). An alternative approach is to determine the recipe step durations by adding or...

[0090] To determine the combination of time information, the time information of a batch tag and a corresponding recipe step tag is added / combined until the batch tag and / or the recipe step tag changes its value.

[0091] Determining the recipe step durations enables the efficient determination of the process steps or recipe steps which, within a device, have a major influence (i.e., a significant influence) on the duration and / or fluctuation of the cycle time of the device (e.g., of the identified at least one bottleneck device).

[0092] In another aspect, the analysis information can include information about the boundary conditions of the production plant and / or information about the identified at least one bottleneck device and / or information about a set of further bottleneck devices and / or information about the executed recipe steps and / or information about the completeness of the time information used.

[0093] In another aspect, the information about the boundary conditions of the production plant can include a configurable period over which the production data were measured and / or information about the number of devices.

[0094] In another aspect, the information about the at least one identified bottleneck device can include recipe step duration information of the bottleneck device and / or batch time information of the bottleneck device and / or cycle time information of the bottleneck device and / or waiting time information of the bottleneck device and / or information about the availability of the bottleneck device and / or a target value for the cycle time of the bottleneck device and / or the process.

[0095] In particular, the recipe step duration information of the bottleneck device can include an average recipe step duration and / or a variation in the recipe step duration and / or a time course of the recipe step duration. In particular, the batch time information of the bottleneck device can include an average batch time and / or a batch time variation and / or a time course of the batch time.

[0096] In particular, the cycle time information of the bottleneck apparatus can include an average cycle time and / or cycle time variation and / or a time-related cycle time profile. In particular, the waiting time information of the bottleneck apparatus can include an average waiting time and / or waiting time variation and / or a time-related waiting time profile and / or waiting frequency and / or the relevance of waiting to the process cycle time.

[0097] In another aspect, the information about the set of additional bottleneck devices can include a configurable number of devices for the set of additional bottleneck devices and / or recipe step duration information of the set of additional bottleneck devices and / or batch time information of the set of additional bottleneck devices and / or information about the availability of the set of additional bottleneck devices and / or a prioritization specification of the set of additional bottleneck devices with regard to their relevance to the cycle time of the process.

[0098] In particular, the recipe step duration information for the set of additional constriction devices can include an average recipe step duration and / or a variation in the recipe step duration and / or a time-based profile of the recipe step duration. In particular, the batch time information for the set of additional constriction devices can include an average batch time and / or a batch time variation and / or a time-based profile of the batch time.

[0099] In another aspect, the information about the executed recipe steps can include the names of the executed recipe steps and / or the duration of each recipe step.

[0100] Providing comprehensive analysis information simplifies the identification of optimization potential within the process. In particular, information about boundary conditions (e.g., the configured time period) allows a user to determine whether the analysis was based on a meaningful set of production data (e.g., one that is sufficiently large or that considers both short-term and long-term trends). Specifically, information about the at least one identified bottleneck device and / or other bottleneck devices enables a user to efficiently identify and determine the extent of the influence of this bottleneck device and / or other bottleneck devices on the cycle time and / or availability of the process. A "set," as used here, can describe a group of one or more elements. In other words, a "set" can also be understood as "one or more" or as "at least one."

[0101] In another aspect, the display of the analysis information can be based on a graphical and / or statistical representation.

[0102] In another aspect, the graphical representation can include a box plot diagram and / or a time series diagram and / or a correlation diagram and / or a probability network diagram and / or a table with bars showing the recipe step durations.

[0103] In another aspect, the statistical representation can include a frequency indication of a recipe step and / or a cycle time and / or a waiting time and / or a batch time and / or a minimum value of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time and / or a maximum value of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time and / or an indication of one or more quartiles, in particular a first and / or a third quartile.include a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time and / or an arithmetic mean of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time and / or a median of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time and / or a stability factor of a recipe step and / or a cycle time and / or a waiting time and / or a batch time.

[0104] In another aspect, the procedure can also include receiving user input indicating a desired way of displaying the analysis information and providing the analysis information based on the desired way of display indicated by the user input.

[0105] The ability to select from a wide variety of different combinations of visual representations (e.g., various graphical representations combined with statistical descriptors) allows the complex production process to be viewed and evaluated from diverse perspectives. This enables the user to perform a rapid, comprehensive analysis, based on which efficient measures for process optimization can be derived and determined, while inefficient measures can be avoided.In another aspect, user input can include the duration of a configurable period for which the production data should be used for automatic evaluation, and / or a selection of the number of devices to be used for automatic evaluation, and / or a selection of statistical descriptors to be used for automatic evaluation, and / or a specification of a target value for the cycle time of the process.

[0106] By changing the duration of the configurable time period, it is possible, for example, to determine whether an identified bottleneck device has only recently or has been a speed-limiting factor for the process for a longer period. This allows not only short-term but also long-term trends to be identified. By selecting a specific number of devices (e.g., all devices, only a subset, or even just one device), certain devices can be excluded. This reduces the amount of production data to be analyzed, thereby saving computing resources and utilizing them more efficiently.

[0107] In another aspect, the process can include automatic saving of user input.

[0108] This ensures user-friendly operation.

[0109] In another aspect, information about executed recipe steps for the majority of batches passing through the number of machines can be received from a database containing the information, and / or information about executed recipe steps for the majority of batches passing through the number of machines can be received from sensors and / or actuators and / or data interfaces of the production plant.

[0110] In another aspect, the identified bottleneck apparatus may have the highest quotient of batch time and target value and / or the highest batch time.

[0111] In another aspect, the procedure may include performing a measurement system analysis (MSA) to determine the completeness of the time information used.

[0112] By specifying the completeness of the time information used (i.e., cycle times and / or batch times and / or waiting time(s) and / or recipe step duration(s) and / or production data), it can be ensured that, for example, no gaps exist in the timeline that could render the provided analysis information of little to no value (depending on the degree of incompleteness). Furthermore, the process can include determining that additional production data is available, re-executing the automatic determination and / or evaluation based on this additional production data, and updating the displayed analysis information.

[0113] This ensures that all relevant production data (e.g., relevant with regard to the configurable time period and / or the selected equipment) is used for analysis. This allows for the provision of up-to-date and complete analytical information, on the basis of which optimization measures can be determined.

[0114] In another aspect, the process can include providing a recommendation for a measure to optimize the process and / or the production plant based on the analysis information. The analysis information can provide clues to root causes. Additionally or alternatively, the process can include providing an analysis for an implemented measure to optimize the process and / or the production plant based on the analysis information. Additionally or alternatively, the process can include receiving a command input from the user regarding a measure to optimize the process and / or the production plant.

[0115] By providing a recommendation for a measure to optimize the process and / or the production plant, the user is offered an additional support measure. This can be done, for example, based on historical data (e.g., using an AI system trained on the historical data, such as a recommendation system). The historical data can include data pairs consisting of analytical information and recommendations for optimization measures that have been rated as efficient (i.e., labeled as such).

[0116] Providing the analysis of an implemented optimization measure also enables a kind of success control (i.e., an evaluation of whether the measure has actually optimized the process).

[0117] The ability to directly receive command input from the user for process optimization allows for immediate intervention in the process. This can be based, for example, on the provided recommendation. Furthermore, after a command input has been received, a corresponding analysis of the optimization measures implemented according to the command input can be provided (e.g., after a predefined number of batches have been processed).

[0118] In one aspect, the invention relates to a data processing device comprising means for carrying out the methods according to any of the mentioned aspects.

[0119] In one aspect, the invention relates to a computer program or a computer-readable medium on which a computer program is stored, wherein the computer program comprises instructions which, when the computer program is executed by a computer, cause the computer to execute the method according to any of the mentioned aspects.

[0120] BRIEF DESCRIPTION OF THE FIGURES

[0121] The invention can be better understood with the help of the following figures:

[0122] Fig. 1: A flowchart of a computer-implemented method for cycle time and / or availability analysis according to an exemplary embodiment of the present invention.

[0123] Fig. 2: A data processing device according to an exemplary

[0124] embodiment of the present invention.

[0125] Fig. 3: A statistical and graphical display of analysis information from an automatic bottleneck analysis according to an exemplary embodiment of the present invention.

[0126] Fig. 4: A graphical display of analysis information using a

[0127] Boxplot diagrams comprising a target value for the cycle time of the process according to an exemplary embodiment of the present invention.

[0128] Fig. 5: A graphical display of analysis information using a

[0129] Boxplot diagrams according to an exemplary embodiment of the present invention for identifying root causes.

[0130] Fig. 6: A graphical display of analysis information using a

[0131] Time series diagram according to an exemplary embodiment of the present invention for monitoring the success of implemented technical measures. Fig. 7: A graphical display of analysis information using a

[0132] Probability network diagram according to an exemplary embodiment of the present invention.

[0133] Fig. 8: A graphical user interface of an electronic display device for

[0134] Input of user input according to an exemplary embodiment of the present invention.

[0135] Fig. 9: A statistical and graphical display of analysis information using a

[0136] Table with bars indicating the recipe step durations, according to an exemplary embodiment of the present invention.

[0137] Fig. 10: A measurement system analysis (MSA) according to an exemplary embodiment of the present invention.

[0138] DETAILED DESCRIPTION

[0139] The following section describes representative embodiments illustrated in the accompanying drawings. It should be understood that the illustrated embodiments and the following descriptions are examples and are not intended to limit the embodiments to a preferred embodiment.

[0140] Fig. 1 shows a flowchart of a computer-implemented method 100 for cycle time and / or availability analysis according to an exemplary embodiment of the present invention. The method 100 can be used, for example, for cycle time and / or availability analysis of a recipe-controlled process in a production plant, preferably in a production plant in the chemical, pharmaceutical, food, cosmetics, semiconductor, biotechnology, plastics, metal, glass, textile, automotive, electrical, or aerospace industries, particularly in a chemical production plant. The method 100 can also be referred to as automatic bottleneck analysis.

[0141] Procedure 100 can include providing (step 102) a process definition that defines a number of pieces of equipment in the production plant and, if there is more than one piece of equipment, their sequence in the process. Procedure 100 can also include receiving (step 104) production data from the production plant, which contains information about executed recipe steps for multiple batches that pass through the specified number of pieces of equipment.

[0142] Method 100 can include an automatic determination (step 106) of at least a cycle time and / or a waiting time and / or a batch time based on information about executed recipe steps for a plurality of batches passing through the number of machines.

[0143] Procedure 100 can include an automatic evaluation (step 108) of at least the batch time and / or the waiting time and / or the cycle time of at least one of the devices to identify at least one device from the number of devices as a bottleneck device which affects a cycle time and / or availability of the process.

[0144] Method 100 may include displaying (step 110) analytical information, which includes at least the identified at least one constriction apparatus, on an electronic display device for use in process and / or production plant optimization by a user.

[0145] Procedure 100 may also include any of the aspects mentioned above.

[0146] Fig. 2 shows a data processing device 200 according to an exemplary embodiment of the present invention.

[0147] The data processing device may include means for carrying out the method according to the present disclosure (e.g., method 100). The means may be a processor 202 and a memory 204. The processor 202 and the memory 204 may be operatively connected. A computer program 206 may be stored in the memory 204, wherein the computer program 206 comprises instructions which, when the computer program 206 is executed by a computer, cause the computer to execute the method according to any of the aspects mentioned (e.g., method 100).

[0148] Fig. 3 shows a statistical and graphical display of analysis information 300 from an automatic bottleneck analysis according to an exemplary embodiment of the present invention. The analysis information 300 includes information 302 about the boundary conditions of the production plant and information 304 about the identified at least one bottleneck device. The information 302 includes information about the number of devices, a configurable period over which the production data were measured, and a condition under which the analysis was performed.

[0149] The information about the number of devices includes, in addition to the number (e.g., 10), a list of the selected devices of the process under consideration. For example, 10 devices might be listed, where each device is represented by an ID (e.g., Unit A, Unit B, etc.) and is uniquely identifiable.

[0150] The information about the configured period indicates the timeframe for which the measured production data should be considered for analysis. For example, the period could be configured from July 1, 2023 to September 30, 2023.

[0151] Information 304 concerning the identified bottleneck device contains an ID (e.g., the ID of bottleneck device Unit A). Furthermore, information 304 includes batch time, cycle time, and waiting time information presented in a statistical display. For example, the information 304 includes a frequency value of the batches (e.g., 106), a target value (e.g., for cycle and batch time, e.g., 17 hours), a median (e.g., for batch and cycle time 18:11 and 19:09 hours respectively, and 0:07 hours for waiting time), information about several quartiles (e.g., the first quartile Q1 and the third quartile Q3 for batch, cycle, and waiting time respectively), an arithmetic mean (e.g., for batch and cycle time 18:54 and 20:40 hours respectively, and 1:46 hours for waiting time), and a stability factor (e.g., 0.85 for batch time, 0.82 for cycle time, and 0.02 for waiting time).Furthermore, the information 304 about the identified bottleneck unit contains information about the bottleneck unit's waiting time. For example, bottleneck unit Unit A may frequently exhibit waiting times (e.g., in 105 of the 106 batches recorded during the period), which can have an average impact of 1 hour and 46 minutes on the process cycle time. The evaluation can be performed, for example, using a rule-based approach, which includes thresholds regarding the relationship between the impact value and the total process duration, or using an artificial intelligence-based approach, such as a trained classification system. The impact value can, for example, be determined as "high".

[0152] The display of the analysis information 300 can also be based on a graphical display 306 and 308. In the example shown, the graphical display 306 can, for example, be a

[0153] The box plot diagram and graphical display 308, for example, contain a time series diagram. The box plot diagram shows the batch times of all machines of a given number (e.g., 10). This provides an overview of the environment surrounding the identified bottleneck machine. The time series diagram also shows the temporal development of the identified bottleneck machine as a progression of batch and cycle times.

[0154] Based on the batch time of the equipment (e.g., Unit A, Unit B, Unit C, etc.), a prioritization can be determined regarding the relevance of the equipment (e.g., Unit A, Unit C, Unit B, etc.) to the cycle time of the process. If further bottleneck equipment has been identified, the analysis information 300 can also include this information. This can be done in the same way as shown for the single identified bottleneck equipment. However, this has not been shown in this figure for the sake of clarity.

[0155] The automatic bottleneck analysis can be displayed, for example, in response to a corresponding user input. This can be done, for instance, from the graphical user interface 800.

[0156] Fig. 4 shows a graphical display of analysis information by means of a boxplot diagram 400 comprising a target value for the cycle time of the process according to an exemplary embodiment of the present invention.

[0157] In this diagram, the X-axis lists equipment (e.g., the equipment unit AJ), while the Y-axis shows the respective duration, in this example, the duration of a batch time. In other examples, the duration could refer to a cycle time, a waiting time, or a recipe step. The analysis information underlying the boxplot diagram 400 corresponds to the analysis information 300 shown in Fig. 3. In addition to the automatic evaluation shown in Fig. 3, the boxplot diagram 400 also allows for manual evaluation by the user. However, the boxplot diagram 400 additionally includes a visualization of a target value 402 (e.g., selected by the user or predefined) by means of a horizontal, dashed line. Furthermore, an individual target value 402 can be defined for each piece of equipment in the process. This is preferable for processes in which equipment has different target cycle times.This allows the analysis to run on these types of processes as well. Based on the analysis information displayed in a box plot diagram (400), which includes the number of machines, their corresponding batch times, and, in response to user input (e.g., a mouse click), the associated cycle times (not shown here), the user can evaluate production data for the process. By displaying a suitable target value (402), the analysis not only helps identify which machines in the process are bottlenecks, but also allows the user to assess which machines are exceeding the target value and by how much. For example, machines whose median lies above the plotted line may be relevant for optimizing the process and / or the production plant to achieve the target cycle time. In the example shown, this is machine Unit A.For devices whose box is intersected by the line (e.g., Unit I), it must be individually assessed to what extent they need to be considered in the optimization. For example, the configurable time period can be increased or decreased to identify further trends (i.e., whether the corresponding boxes move above or below the line).

[0158] The box plot diagram, which includes a target value for the cycle time of the process, can be displayed, for example, in response to a corresponding user input. This can be done, for example, from the graphical user interface 800.

[0159] Fig. 5 shows a graphical display of analysis information using a boxplot diagram 500 according to an exemplary embodiment of the present invention for identifying root causes.

[0160] In this diagram, the x-axis lists recipe steps (e.g., recipe steps A01-A11), while the y-axis shows the respective duration, in this example, the duration of a recipe step. In other examples, the duration could refer to a cycle time, a batch time, or a waiting time. Boxplot diagram 500 contains a user-configured selection of recipe steps for a machine (e.g., a bottleneck machine, Unit A). By analyzing this, a user can identify recipe step durations and their variations. For example, it can be seen that recipe step A02 in the example has the greatest variation in recipe step duration (interquartile range approximately 5 hours), and recipe step A08 has the next greatest variation (interquartile range approximately 1.5 hours). It is also evident that recipe step A04 in the example has the longest recipe step duration of approximately...Recipe step A01 has a median duration of 7.5 hours, followed by recipe step A02 with a median duration of approximately 6.5 hours. Furthermore, it is evident that recipe steps A01, A03, A05, A09, A10, and A11 exhibit comparatively low fluctuations and that recipe steps A01, A03, A05, A06, A09, A10, and A11 are relatively quick, i.e., they have a low median duration. The box plot diagram can be displayed, for example, in response to user input. This can be done, for instance, from the graphical user interface 800.

[0161] Fig. 6 shows a graphical display of analysis information using a time series diagram 600 according to an exemplary embodiment of the present invention for monitoring the success of implemented technical measures.

[0162] The time series diagram is particularly suitable for identifying changes in cycle time, batch time, and recipe step time, as well as for tracking trends. In this diagram, the X-axis lists the individual batches according to their start time, while the Y-axis shows the respective duration. In the example shown, the solid line 602 represents the batch time of Unit B, while the dashed line 604 represents the recipe step duration of recipe step B03 executed by the Unit B machine. The example also illustrates how an implemented technical measure to optimize the Unit B machine with regard to recipe step B03 can be tracked and its success monitored. For example, from approximately September 10, 2023 (606), recipe step B03 runs significantly faster and with less fluctuation than before. At the same time, the batch time of the Unit B machine exhibits shorter durations and less fluctuation.By executing the computer-implemented method for cycle time and / or availability analysis of a recipe-controlled process according to aspects of the present invention, fluctuations within the production process could be quickly and efficiently identified and corrected by means of appropriate measures to optimize the process.

[0163] If, in addition to the batch time of Unit B, the cycle time of Unit B is also displayed (not shown here), it is possible, for example, to identify when a unit was responsible for the cycle time of the entire process (i.e., when its cycle and batch times are the same) and when this unit had waiting times (i.e., cycle time vs. batch time). The user can view which batches this applies to within the graphic.

[0164] In another example, the cycle time of a bottleneck could be extended if the average cycle time of the process is longer than the bottleneck's batch time. In other words, the bottleneck already takes the longest for all its value-adding process steps and also has to wait for other process equipment from time to time. The time series diagram can be displayed, for example, in response to a corresponding user input. This can be done, for instance, from the graphical user interface 800. The time series diagram shown here only displays the batch time of equipment Unit B and the recipe step duration of recipe step B03. The other equipment is hidden (e.g., via a corresponding user input for selection). A data point in each time series includes a batch (e.g., name and / or ID), a batch start time (e.g., timestamp), and a duration (e.g., e-time).B. 40:13 hours). A corresponding display can be implemented, for example, by means of a hover.

[0165] Fig. 7 shows a graphical display of analysis information using a probability network diagram (700) according to an exemplary embodiment of the present invention.

[0166] The probability plot (700) is particularly suitable for illustrating the fluctuation of cycle and batch times as well as availability. Here, the duration of the batch is plotted on the x-axis, while the double-logarithmic y-axis describes the percentage of batches. The probability plot shows what percentage of the considered batches exceed or fall short of a given cycle or batch time and how stable the process is. In the selected example, approximately 50% of the batches are faster than about 3:00 hours, with an interquartile range of approximately 10 minutes.

[0167] The probability diagram (700) additionally enabled the user to assess what percentage of the batches were subject to a fluctuation in cycle or batch time that was acceptable to the user, e.g. 90%, and in what percentage of the batches there may have been large fluctuations in cycle and batch time that the user could interpret as availability losses of the process.

[0168] The probability diagram (700) enabled the user to additionally assess, based on its shape, whether the data presented were subject to a normal distribution or not.

[0169] The probability plot (700) can be displayed, for example, in response to user input. This can be done, for instance, from the graphical user interface (800). The probability plot shown here displays only the batch time of one machine. The other machines are hidden (e.g., by user input for selection). A data point (702) of the probability plot includes a game (e.g., name and / or ID, e.g., game 417), a percentage indicating how many games are faster than the selected game (here, approximately 95%), and the game duration (e.g., 4:35 hours). This display can be implemented, for example, via a hover effect.

[0170] Fig. 8 shows a graphical user interface 800 of an electronic display device for inputting user input according to an exemplary embodiment of the present invention.

[0171] The 800 graphical user interface allows a user to initiate user and command inputs. For example, the 800 graphical user interface can be a start screen from which user input can be entered. This input can include, for example, the duration of a configurable period for which the production data should be used for automatic evaluation, and / or a selection of the number of devices to be used for automatic evaluation, and / or a selection of statistical descriptors to be used for automatic evaluation, and / or a target value (402) for the cycle time of the process. The 800 user interface also allows user input to specify a desired way of displaying analysis information. In other words, starting from the graphical user interface, the display of analysis information (e.g.,graphical or statistical representations) can be customized. For example, by means of user input (e.g., a mouse click on one of the displayed devices 802), analysis information (e.g., a table with bars showing the recipe step durations) can be opened (see Figure 9). The display of further displays 804 (e.g., a time series diagram, a box plot diagram, a probability network diagram, a correlation diagram) can also be initiated by corresponding user input. Furthermore, a desired analysis (e.g., an automatic bottleneck analysis 806 or a measurement system analysis 808) can be initiated by corresponding user input.

[0172] Fig. 9 shows a statistical and graphical display of analysis information using a table 900 with bars, which displays the recipe step durations, according to an exemplary embodiment of the present invention.

[0173] As described in Fig. 8, Table 900 can be implemented as a bar chart to display analysis information. To create this table, the received production data can be used, as described above; for example, the time series of the batch and recipe step tags defined for the configured period, especially those with irregular entries. The table can contain a top row 902 containing recipe step names (e.g., recipe step names B01, B02, etc.), several rows 904 containing statistical parameters, and several rows 906 containing batch numbers.

[0174] Line 904 may contain a statistical display of the analysis information. In the example shown, the statistical display includes a frequency value (here "N") of the recipe step, which indicates how often the recipe step is longer than a predefined duration (e.g., at least 1 minute), a maximum value of the recipe step duration ("Max", not shown here), an arithmetic mean of the recipe step duration (not shown here), a median of the recipe step duration, a first quartile of the recipe step duration ("Q1", not shown here), a third quartile of the recipe step duration (here "Q3"), a minimum value of the recipe step duration ("Min", not shown here), and a stability factor (e.g., the quotient of the first quartile Q1 and a third quartile Q3 of the recipe step duration, not shown here) of the recipe step.The statistical display can contain analogous statistical information on cycle times, batch times, waiting times and hidden recipe steps.

[0175] Displaying this statistical analysis can be done via user input (e.g., by selecting the relevant statistical descriptors from a drop-down menu, etc.). The production data underlying the analysis can be limited to complete batches within the considered (i.e., configurable) time period. For example, the newest and oldest batches can be excluded because data completeness cannot be guaranteed for them.

[0176] Time series with irregular entries can be interpolated (e.g., using the fill-forward principle) to create time series with regular (e.g., minute-by-minute) entries. In other words, a time series in which the value of a game tag (e.g., a game ID and / or a game name) and a recipe step tag (e.g., a recipe step ID and / or a recipe step name) repeats at a regular interval (e.g., every minute) until this value changes for the first time (i.e., a different recipe step starts) and the value of the new recipe step repeats. This ensures that each point in time in the time series is uniquely assigned to a game and a specific recipe step, preventing any time from being lost in the visualization.

[0177] Subsequently, the entries in the time series associated with the same recipe step tag value can be combined into a single time series, allowing the precise determination of the recipe step duration for the corresponding recipe step. In particular, unlike other approaches (e.g., determining start and end times based on recipe step tags), this does not require prior knowledge of the existing recipe step names, making the determination more robust and flexible. This can be especially important when optimizations are dynamically performed simultaneously at many points in the process, often resulting in frequent changes to recipe step names. Once the recipe step durations are determined, the data can be grouped by batch and recipe step.The specified recipe step durations can then be displayed within the table next to the corresponding recipe steps as bars indicating the associated step duration. The format of the recipe step durations can be specified, for example, in "hh:mm" (i.e., hours and minutes). The bars can be displayed in color. The length of a bar can be determined proportionally to a threshold (e.g., 24:00, i.e., 24 hours) and can then reach full scale. This display provides an initial visual analysis within the table, showing how stable or variable the individual recipe steps are and how long they take. Furthermore, by sorting the recipe steps, identical recipe steps are displayed one above the other in the table, making trends in the development of individual recipe step durations easily recognizable.

[0178] This allows the system to display, at periodic (e.g., minute-by-minute) resolution, the duration of any given recipe step in any given production batch. Batches can be sorted in reverse chronological order. Recipe steps can be sorted numerically and / or alphabetically, automatically determining the order of the steps (e.g., because they are numbered sequentially). Users can also select individual recipe steps via additional input. For example, user input can include a selection between value-adding and non-value-adding recipe steps. Selected recipe steps can be highlighted in color within the table (e.g., recipe step B05, which has been selected as a non-value-adding step). Additionally or alternatively, the selection can include showing or hiding recipe steps (e.g., irrelevant ones).The recipe steps selected in this way via user input can be automatically saved (e.g. in a user profile or user settings) so that they can be automatically loaded when used again.

[0179] The last four columns on the right side of Table 900 can display the cycle time, batch time, waiting time, and the sum of all hidden recipe steps (here labeled "Hidden"). The displayed cycle time can refer to the sum of all recipe step durations for a batch. The displayed batch time can refer to the sum of all value-adding recipe step durations for a batch. The displayed waiting time can refer to the sum of all waiting times for a batch. Although hidden, the hidden recipe step durations can still be considered when determining cycle times, batch times, and / or waiting times. A target value for the process cycle time can be specified, for example, by the user via a drop-down menu. This value can be set for all machines in the process, for a group of machines, or for each machine individually.The drop-down menu can appear, for example, in response to a click on a graphical user element.

[0180] Fig. 10 shows a measurement system analysis (MSA) 1000 according to an exemplary embodiment of the present invention.

[0181] MSA can be used to determine the completeness of the analysis information, particularly the timing information used (e.g., cycle and / or batch times and / or recipe step durations). This prevents analyses from being performed based on incomplete information (i.e., information with time gaps). To this end, MSA can check the information used for errors. Errors (such as time gaps) can arise from connection problems, interference, or sensor malfunctions. In such cases, production data may be missing at the corresponding time points.

[0182] The MSA 1000 can include evaluation information for a tested piece of equipment. In the example shown, the MSA 1000 includes evaluation information for Unit A. This evaluation information includes the duration of the configurable period (i.e., the period over which the analysis information was used for the MSA). The evaluation information can also include a number of hidden recipe steps within and / or outside the configurable period, a number of waiting times within and / or outside the configurable period, hidden waiting times within and / or outside the configurable period, a number of hidden batches within and / or outside the configurable period, and / or a number of process steps within and / or outside the configurable period.

[0183] The MSA 1000 results can include a granularity (e.g., 1 minute).

[0184] Granularity can be predefined and / or flexibly adjustable by the user (e.g., before performing the MSA via a parameterization configuration). The result can include an indication of the completeness of the information used (e.g., in percent, here 100%, which signals that no errors such as data gaps exist in the information used). Additionally, the MSA result can include information about hidden batches (e.g., an absolute number) that fall below a predefined duration (e.g., 10 minutes).

[0185] Performing the MSA (Production Site Analysis) allows for periodic queries of production data according to the granularity of the configurable time period. This enables, for example, the determination of a value for a used batch tag and recipe step tag every minute (i.e., granularity = 1 minute). If a unique value can be assigned to the batch tag and / or recipe step tag of a batch for each minute within the configurable time period, then 100% completeness can be reported. This would ensure a complete evaluation within the configurable time period.

[0186] A complete evaluation may not be possible, for example, if errors prevent a batch tag and / or recipe step tag from being written for a certain period within the configurable timeframe. Any resulting gaps can be displayed in the MSA (Methodological Analysis). The incorrect data points can also be marked accordingly so that a user can quickly identify them and, if necessary, hide or remove them.

[0187] A complete evaluation may also be impossible if, due to errors, the recipe step tag and / or the batch tag contain no value for a certain period within the configurable timeframe. In this case, interpolation (e.g., using forward filling) can be performed. Forward filling uses the last written value of the batch tag and / or recipe step tag to fill the specified period. This results in a noticeably long recipe step duration (e.g., indicated by a correspondingly long bar) and / or a gap in necessary recipe steps—that is, recipe steps that the process must have completed. This error can then be easily corrected by the user (e.g., by removing the conspicuously long recipe step).

[0188] The measurement system analysis can be displayed, for example, in response to a corresponding user input. This can be done, for instance, from the graphical user interface 800. The term "and / or" used here includes all combinations of one or more of the listed aspects and can be abbreviated with " / ".

[0189] Although some aspects related to a device have been described, it is clear that these aspects also constitute a description of the corresponding process, where a block or device corresponds to a process step or a feature of a process step. Similarly, aspects described in connection with a process step also constitute a description of a corresponding block, element, or feature of a corresponding device.

[0190] Embodiments of the present disclosure can be implemented on a computer system. The computer system may be a local computing device (e.g., a personal computer, laptop, tablet computer, or mobile phone) with one or more processors and one or more memory devices, or a distributed computing system (e.g., a cloud computing system with one or more processors and one or more memory devices distributed across different locations, such as a local client and / or one or more remote server farms and / or data centers). The computer system may comprise any circuit or combination of circuits. In one embodiment, the computer system may comprise one or more processors, which may be of any type. The term "processor" as used herein can refer to any type of computing circuit, e.g.,a microprocessor, a microcontroller, a CISC (Complex Instruction Set Computing) microprocessor, a RISC (Reduced Instruction Set Computing) microprocessor, a VLIW (Very Long Instruction Word) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, an FPGA (Field Programmable Gate Array), or any other type of processor or processing circuit. Other types of circuitry that may be included in the computer system could be a custom-designed circuit, an application-specific integrated circuit (ASIC), or similar, such as one or more circuits (e.g., a communications circuit) for use in wireless devices like mobile phones, tablet computers, laptop computers, two-way radios, and similar electronic systems.The computer system may include one or more storage devices, which may comprise one or more storage elements suitable for the specific application, such as main memory in the form of random-access memory (RAM), one or more hard disks, and / or one or more drives that handle removable media such as compact discs (CDs), flash memory cards, digital video discs (DVDs), and the like. The computer system may also include a display device, one or more speakers, and a keyboard and / or a control device, which may include a mouse, trackball, touchscreen, speech recognition device, or any other device that enables a system user to input information into and receive information from the computer system.

[0191] Some or all of the process steps can be performed by (or using) a hardware device, such as a processor, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, some or more of the key process steps can be performed by such a device.

[0192] Depending on specific implementation requirements, embodiments of the present disclosure can be implemented in hardware or in software. The implementation can be carried out using a non-transferable storage medium such as a digital storage medium, for example, a floppy disk, DVD, Blu-ray disc, CD, ROM, PROM, EPROM, EEPROM, or FLASH memory, on which electronically readable control signals are stored that interact (or can interact) with a programmable computer system to execute the respective method. Therefore, the digital storage medium can be computer-readable.

[0193] Some embodiments according to the present disclosure include a data carrier with electronically readable control signals that can interact with a programmable computer system to perform one of the methods described herein. In general, embodiments of the present disclosure can be implemented as a computer program product with program code, wherein the program code serves to execute one of the methods when the computer program product is running on a computer. The program code can, for example, be stored on a machine-readable medium.

[0194] Other embodiments include the computer program for carrying out one of the methods described herein, which is stored on a machine-readable medium.

[0195] In other words, one embodiment of the present disclosure is therefore a computer program with program code for carrying out one of the methods described herein when the computer program is running on a computer. Another embodiment of the present disclosure is therefore a storage medium (or a data carrier or a computer-readable medium) on which the computer program for carrying out one of the methods described herein is stored when executed by a processor. The data carrier, the digital storage medium, or the recorded medium is typically tangible and / or non-transferable. A further embodiment of the present disclosure is a device as described herein comprising a processor and the storage medium.

[0196] Another embodiment of the present disclosure is therefore a data stream or a sequence of signals that represents the computer program for carrying out one of the methods described herein. The data stream or sequence of signals can, for example, be configured to be transmitted via a data communication link, e.g., via the Internet.

[0197] Another embodiment comprises a processing means, e.g. a computer or a programmable logic device, configured or adapted to perform one of the methods described herein.

[0198] Another embodiment comprises a computer on which the computer program for carrying out one of the methods described herein is installed.

[0199] Another embodiment according to the present disclosure comprises a device or system configured to transmit a computer program for carrying out one of the methods described herein to a receiver (e.g., electronically or optically). The receiver may be, for example, a computer, a mobile device, a storage device, or the like. The device or system may, for example, include a file server for transmitting the computer program to the receiver.

[0200] In some embodiments, a programmable logic device (e.g., a field-programmable gate array) can be used to perform some or all of the functions of the methods described herein. In some embodiments, a field-programmable gate array can cooperate with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by any hardware device.

Claims

REQUIREMENTS 1. A computer-implemented method (100) for cycle time and / or availability analysis of a recipe-controlled process in a production plant, preferably in a production plant in the chemical industry, the pharmaceutical industry, the food industry, the cosmetics industry, the semiconductor industry, the biotechnology industry, the plastics industry, the metal industry, the glass industry, the textile industry, the automotive industry, the electrical industry or the aerospace industry, in particular in a chemical production plant, wherein the method comprises at least: Providing (102) a process definition which defines a number of apparatus (802) of the production plant and, in the case of more than one apparatus (802), their sequence in the process; Receiving (104) production data from the production plant, which includes information about executed recipe steps for a plurality of batches passing through the number of apparatuses (802); automatically determining (106) at least one cycle time and / or waiting time and / or batch time (602) based on the information about executed recipe steps for a plurality of batches passing through the number of apparatuses (802); automatically evaluating (108) at least the batch time (602) and / or the waiting time and / or the cycle time of at least one of the apparatuses (802) to identify at least one apparatus (802) from the number of apparatuses (802) as a bottleneck apparatus that affects a cycle time and / or availability of the process; and Displays (110) of analytical information (300), which include at least the identified at least one constriction apparatus, on an electronic display device for use in process and / or production plant optimization by a user.

2. The method of claim 1, wherein the method further comprises: Automatic determination of at least one recipe step duration (604) based on information about the executed recipe steps for the majority of batches passing through the number of machines; wherein the automatic determination of at least the cycle time, waiting time and batch time is based on the recipe step duration.

3. Method according to any one of claims 1-2, wherein the analysis information (300) further comprises: Information (302) about boundary conditions of the production plant; and / or Information (304) about the identified at least one constriction apparatus; and / or Information about a set of additional constriction devices; and / or Information about the recipe steps performed; and / or Information regarding the completeness of the time information used, i.e., at least cycle time and / or batch time and / or waiting time.

4. The method of claim 3, wherein the information (302) about boundary conditions of the production plant comprises: a configurable period over which the production data were measured; and / or Information about the number of devices.

5. Method according to any one of claims 3-4, wherein the information (304) comprises the at least one identified constriction apparatus: Prescription step duration information of the constriction apparatus, in particular an average prescription step duration and / or a fluctuation in the prescription step duration and / or a temporal course of the prescription step duration.

6. Method according to any one of claims 3-5, wherein the information (304) comprises the at least one identified constriction apparatus: Batch time information of the bottleneck apparatus, in particular an average batch time and / or a batch time fluctuation and / or a time course of the batch time.

7. Method according to any one of claims 3-6, wherein the information (304) comprises the at least one identified constriction apparatus: Cycle time information of the bottleneck apparatus, in particular an average cycle time and / or a cycle time fluctuation and / or a temporal profile of the cycle time.

8. Method according to any one of claims 3-7, wherein the information (304) comprises the at least one identified constriction apparatus: Waiting time information of the bottleneck apparatus, in particular an average waiting time and / or a waiting time fluctuation and / or a temporal course of the waiting time and / or a frequency of waiting and / or a relevance of waiting for the cycle time of the process.

9. Method according to any one of claims 3-8, wherein the information (304) about the at least one identified constriction apparatus comprises: Information on the availability of the bottleneck device.

10. Method according to any one of claims 3-9, wherein the information (304) about the at least one identified constriction apparatus comprises: a target value (402) for the cycle time of the process.

11. Method according to any one of claims 3-10, wherein the analysis information (300) comprises information about the set of further constriction devices and wherein the information about the set of further constriction devices comprises: a configurable number of devices for the set of further constriction devices.

12. Method according to any one of claims 3-11, wherein the analysis information (300) comprises information about the set of further constriction devices and wherein the information about the set of further constriction devices comprises: Prescription step duration information of the set of further constriction devices, in particular an average prescription step duration and / or a fluctuation of the prescription step duration and / or a temporal course of the prescription step duration.

13. Method according to any one of claims 3-12, wherein the analysis information (300) comprises information about the set of further constriction devices and wherein the information about the set of further constriction devices comprises: Batch time information of the set of further constriction devices, in particular an average batch time and / or a batch time fluctuation and / or a time course of the batch time.

14. Method according to any one of claims 3-13, wherein the analysis information (300) comprises information about the set of further constriction devices and wherein the information about the set of further constriction devices comprises: Information on the availability of the set of additional constriction devices.

15. Method according to any one of claims 3-14, wherein the analysis information (300) comprises information about the set of further constriction devices and wherein the information about the set of further constriction devices comprises: a prioritization indication of the set of further constriction devices with respect to their relevance to the cycle time of the process.

16. Method according to any one of claims 1-15, wherein the information on the recipe steps performed comprises: Names of the executed recipe steps; and a recipe step duration (604) of each recipe step.

17. Method according to any one of claims 1-16, wherein the display of the analysis information (300) is based on a graphical (306, 308) and / or statistical representation (304).

18. Method according to claim 17, wherein the graphical representation comprises: a box plot diagram (400, 500) and / or a time series diagram (600) and / or a correlation diagram and / or a probability network diagram (700) and / or a table (900) with bars indicating the recipe step durations.

19. Method according to claim 17 or 18, wherein the statistical representation comprises: a frequency specification of a recipe step and / or a cycle time and / or a waiting time and / or a batch time; and / or a minimum value of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time; and / or a maximum value of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time; and / or a specification of one or more quartiles, in particular a first and / or third quartile, of a recipe step duration and / or a cycle time and / or a Waiting time and / or a batch time; and / or an arithmetic mean of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time; and / or a median of a recipe step duration and / or a cycle time and / or a waiting time and / or a batch time; and / or a stability factor of a recipe step and / or a cycle time and / or a waiting time and / or a batch time.

20. A method according to any one of claims 1-19 further comprising: Receiving user input indicating a desired way of displaying the analysis information (300); and Providing the analysis information (300) based on the desired type of display shown by the user input.

21. Method according to claim 20, wherein the user input comprises: a duration of a configurable period for which the production data are to be used for automatic evaluation.

22. Method according to claim 20 or 21, wherein the user input comprises: a selection of apparatus (802) of the number of apparatus to be used for automatic evaluation.

23. Method according to any one of claims 20-22, wherein the user input comprises: a selection of statistical descriptors to be used for automatic evaluation.

24. Method according to any one of claims 20-23, wherein the user input comprises: a specification of a target value (402) for the cycle time of the process.

25. Method according to any one of claims 1-24, further comprising: Performing a measurement system analysis, MSA, (1000, 808) to determine the completeness of the time information used.

26. A method according to any one of claims 1-25 further comprising: Determine that further production data is available; repeat the automatic determination (106) and / or evaluation (108) based on the further production data; and Updating the displayed analysis information (300).

27. Method according to any one of claims 1-26, further comprising: Providing a recommendation for a measure to optimize the process and / or the production plant based on the analysis information (300).

28. Method according to any one of claims 1-27, further comprising: Providing an analysis for an implemented measure to optimize the process and / or the production plant based on the analysis information (300).

29. Method according to any one of claims 1-28, further comprising: Receiving a command input from the user includes a measure to optimize the process and / or the production plant.

30. A data processing device (200) comprising means (202, 204) for carrying out the method (100) according to any one of the preceding claims 1-29.

31. A computer program (206) or a computer-readable medium on which a computer program (206) is stored, wherein the computer program (206) comprises instructions which, when the computer program (206) is executed by a data processing device (200), cause the latter to execute the method (100) according to any one of the preceding claims 1-29.

Images