Productivity prediction method and device, equipment, storage medium and program product

Abstract

The invention relates to a productivity prediction method and device, equipment, a storage medium and a program product. The method comprises the steps that an interface used for receiving productivity prediction parameters is provided; in response to productivity prediction parameters, received based on the interface, of a target scheme, the productivity prediction parameters are input to a pre-constructed productivity prediction model, productivity plan arrangement information of the target scheme is obtained, the target scheme comprises a simulation commissioning scheme of a target production line climbing stage, and the simulation commissioning scheme comprises a simulation commissioning scheme of a target production line climbing stage. The productivity plan arrangement information is used for predicting the productivity of the target production line in a climbing stage; and outputting the productivity plan arrangement information. The difficulty of climbing management can be reduced, and then the accuracy and efficiency of productivity prediction in the climbing stage of the production line can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a capacity forecasting method, device, equipment, storage medium and program product. Background Art

[0002] The production line ramp-up phase refers to the phase when the production speed and output of the production line are increased. Capacity forecasting during the production line ramp-up phase at the time of project establishment can provide a basis for the formulation of subsequent factory management decisions such as actual production plans, which is of great significance.

[0003] In the related technologies, the capacity forecasting in the ramp-up phase of the production line still relies on the manual work of the management personnel (e.g., the managers in the ramp-up phase of the production line), which strongly relies on the manual observation and manual scheduling of the management personnel. However, due to the large number of factors that need to be comprehensively considered, the accuracy and efficiency of the capacity forecasting are easily affected by factors such as the scheduling experience of the management personnel, which in turn affects the quality of the subsequent factory management decision-making. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the embodiments of the present disclosure provide a capacity prediction method, device, equipment, storage medium and program product to solve the defects in the related art.

[0005] According to a first aspect of an embodiment of the present disclosure, a capacity prediction method is provided, the method comprising:

[0006] Provide an interface for receiving capacity forecast parameters;

[0007] In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage;

[0008] Output the production capacity planning information.

[0009] In some embodiments, the capacity prediction parameters include assembly line mode, assembly climbing slope, assembly climbing efficiency, first-time tightening ratio, packaging line mode, and packaging climbing slope;

[0010] The capacity planning arrangement information includes assembly input quantity, assembly output quantity, packaging input quantity, packaging output quantity and storage quantity;

[0011] The method of constructing the production capacity prediction model includes:

[0012] Establishing the relationship between the assembly input amount and the assembly line mode and the assembly climbing slope;

[0013] Establishing the relationship between the assembly output and the assembly ramping efficiency and the first tightening ratio;

[0014] Constructing the relationship between the packaging input amount and the packaging line-aligning mode and the packaging climbing slope;

[0015] Constructing the relationship between the packaging output, the packaging input and the packaging climbing slope;

[0016] Build a relationship between incoming inventory and outgoing packaging.

[0017] In some embodiments, the capacity prediction parameters also include quality standard level, ramp start time and off-shift strategy;

[0018] The production capacity planning arrangement information also includes a production scheduling timeline;

[0019] The method for constructing the production capacity prediction model also includes:

[0020] Construct the relationship between the production scheduling timeline and the quality standard level, ramp-up start time, and off-shift strategy.

[0021] In some embodiments, the capacity prediction parameters further include assembly team mode, packaging team mode, number of assembly lines, number of packaging lines, assembly line opening rhythm, and packaging line opening rhythm;

[0022] The capacity planning arrangement information also includes production line and team information;

[0023] The method for constructing the production capacity prediction model also includes:

[0024] Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

[0025] In some embodiments, the capacity forecast parameters further include assembly UPH, EMS foundry peak, and packaging UPH;

[0026] The method for constructing the production capacity prediction model also includes:

[0027] Constructing the relationship between the number of assembled lines and the assembled UPH;

[0028] Construct the relationship between the number of packaging line bodies and the peak value of the EMS foundry, packaging UPH and the number of assembly line bodies.

[0029] In some embodiments, the capacity prediction parameter further includes at least one of the following:

[0030] Project name, plan name, production area, first delivery quantity, first delivery time, EMS OEM factory quantity, OEM factory name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information, packaging start time, packaging first piece quantity, set equipment static quantity.

[0031] In some embodiments, the outputting the capacity planning arrangement information includes:

[0032] The capacity planning arrangement information is presented in a set form, and the set form includes a graphical form and / or a tabular form.

[0033] In some embodiments, the method further comprises:

[0034] An early warning is issued for the balance between the output of each section in the ramp-up phase of the target production line and the input of the subsequent section.

[0035] In some embodiments, the outputting the capacity planning arrangement information includes:

[0036] The capacity planning arrangement information is saved as an offline file in a set format.

[0037] According to a second aspect of an embodiment of the present disclosure, a production capacity prediction device is provided, the device comprising:

[0038] A parameter interface providing module is used to provide an interface for receiving capacity forecast parameters;

[0039] an arrangement information determination module, configured to, in response to receiving the capacity forecast parameters of the target solution based on the interface, input the capacity forecast parameters into a pre-built capacity forecast model to obtain capacity plan arrangement information of the target solution, wherein the target solution includes a simulated production plan for the ramp-up phase of the target production line, and the capacity plan arrangement information is used to predict the capacity of the target production line in the ramp-up phase;

[0040] The arrangement information output module is used to output the production capacity plan arrangement information.

[0041] In some embodiments, the capacity prediction parameters include assembly line mode, assembly climbing slope, assembly climbing efficiency, first-time tightening ratio, packaging line mode, and packaging climbing slope;

[0042] The capacity planning arrangement information includes assembly input quantity, assembly output quantity, packaging input quantity, packaging output quantity and storage quantity;

[0043] The method of constructing the production capacity prediction model includes:

[0044] Establishing the relationship between the assembly input amount and the assembly line mode and the assembly climbing slope;

[0045] Establishing the relationship between the assembly output and the assembly ramping efficiency and the first tightening ratio;

[0046] Constructing the relationship between the packaging input amount and the packaging line-aligning mode and the packaging climbing slope;

[0047] Constructing the relationship between the packaging output, the packaging input and the packaging climbing slope;

[0048] Build a relationship between incoming inventory and outgoing packaging.

[0049] In some embodiments, the capacity prediction parameters also include quality standard level, ramp start time and off-shift strategy;

[0050] The production capacity planning arrangement information also includes a production scheduling timeline;

[0051] The method for constructing the production capacity prediction model also includes:

[0052] Construct the relationship between the production scheduling timeline and the quality standard level, ramp-up start time, and off-shift strategy.

[0053] In some embodiments, the capacity prediction parameters further include assembly team mode, packaging team mode, number of assembly lines, number of packaging lines, assembly line opening rhythm, and packaging line opening rhythm;

[0054] The capacity planning arrangement information also includes production line and team information;

[0055] The method for constructing the production capacity prediction model also includes:

[0056] Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

[0057] In some embodiments, the capacity forecast parameters further include assembly UPH, EMS foundry peak, and packaging UPH;

[0058] The method for constructing the production capacity prediction model also includes:

[0059] Constructing the relationship between the number of assembled lines and the assembled UPH;

[0060] Construct the relationship between the number of packaging line bodies and the peak value of the EMS foundry, packaging UPH and the number of assembly line bodies.

[0061] In some embodiments, the capacity prediction parameter further includes at least one of the following:

[0062] Project name, plan name, production area, first delivery quantity, first delivery time, EMS OEM factory quantity, OEM factory name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information, packaging start time, packaging first piece quantity, set equipment static quantity.

[0063] In some embodiments, the arrangement information output module is further used to present the capacity planning arrangement information in a set form, and the set form includes a graphical form and / or a tabular form.

[0064] In some embodiments, the arrangement information output module is also used to issue an early warning of the balance between the output of each section and the input of the subsequent section during the ramp-up phase of the target production line.

[0065] In some embodiments, the arrangement information output module is further used to save the capacity planning arrangement information as an offline file in a set format.

[0066] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, the device comprising:

[0067] a processor and a memory for storing a computer program;

[0068] Wherein, the processor is configured to implement, when executing the computer program:

[0069] Provide an interface for receiving capacity forecast parameters;

[0070] In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage;

[0071] Output the production capacity planning information.

[0072] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the following is implemented:

[0073] Provide an interface for receiving capacity forecast parameters;

[0074] In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage;

[0075] Output the production capacity planning information.

[0076] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements:

[0077] Provide an interface for receiving capacity forecast parameters;

[0078] In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage;

[0079] Output the production capacity planning information.

[0080] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0081] The present disclosure provides an interface for receiving capacity forecast parameters, and in response to receiving the capacity forecast parameters of a target solution based on the interface, inputs the capacity forecast parameters into a pre-built capacity forecast model to obtain capacity planning information of the target solution, wherein the target solution includes a simulated production plan for the ramp-up phase of a target production line, and the capacity planning information is used to predict the capacity of the target production line during the ramp-up phase, and then outputs the capacity planning information. Since it avoids the need for managers to conduct capacity forecasts based on manual observations and manual scheduling, it is possible to unify the ramp-up management methods of different regions and projects, reduce the difficulty of ramp-up management, and thereby improve the accuracy and efficiency of capacity forecasts during the ramp-up phase of production lines, and improve the quality of subsequent factory management decisions.

[0082] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0084] Figure 1A is a flow chart of a production capacity prediction method according to an exemplary embodiment of the present disclosure;

[0085] Figure 1B is a schematic diagram of a capacity prediction model according to an exemplary embodiment of the present disclosure;

[0086] Figure 1Cis a schematic diagram showing the relationship between various capacity forecast parameters and capacity planning arrangement information according to an exemplary embodiment of the present disclosure;

[0087] Figure 1D is a logic diagram of input and output in the ramp-up phase of a production line according to an exemplary embodiment of the present disclosure;

[0088] Figure 2 is a schematic diagram of an interface for receiving capacity forecast parameters according to an exemplary embodiment of the present disclosure;

[0089] Figure 3 is a schematic diagram of a production capacity prediction process according to an exemplary embodiment of the present disclosure;

[0090] Figure 4A is a schematic diagram showing the production capacity planning arrangement information in a graphical form according to an exemplary embodiment of the present disclosure;

[0091] Figure 4B is a schematic diagram showing the production capacity planning arrangement information in a table form according to an exemplary embodiment of the present disclosure;

[0092] Figure 5 is a block diagram of a production capacity prediction device according to an exemplary embodiment of the present disclosure;

[0093] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0094] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0095] Figure 1A is a flow chart of a capacity prediction method according to an exemplary embodiment; the method of this embodiment can be executed by a capacity prediction device, and the capacity prediction device can be configured in an electronic device, such as a server, a workstation, a personal computer, a mobile terminal (such as a mobile phone, a tablet computer, etc.), a wearable device (such as glasses, a watch, etc.), etc. Figure 1A As shown, the method includes the following steps S101-S103:

[0096] In step S101, an interface for receiving capacity forecast parameters is provided.

[0097] In this embodiment, the production capacity forecasting device (such as a production capacity forecasting application APP) installed on the electronic device may provide the above-mentioned interface for receiving production capacity forecasting parameters, so that the user can input the corresponding production capacity forecasting parameters in each interface.

[0098] In step S102, in response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain the capacity planning arrangement information of the target solution.

[0099] Figure 1B is a schematic diagram of a capacity prediction model according to an exemplary embodiment of the present disclosure; in this embodiment, after an interface for receiving capacity prediction parameters is provided, the electronic device can input the capacity prediction parameters into the target solution after receiving the capacity prediction parameters of the target solution based on the interface. Figure 1B The capacity prediction model shown is used to obtain the capacity planning arrangement information of the target solution. The target solution may include a simulated production plan for the ramp-up phase of the target production line, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up phase.

[0100] It is understandable that the above capacity forecast model may include the relationship between each capacity forecast parameter and the capacity planning arrangement information. In other words, the construction method of the capacity forecast model may include constructing a direct and / or indirect relationship between each capacity forecast parameter and the capacity planning arrangement information. In other embodiments, the relationship between each capacity forecast parameter and the capacity planning arrangement information can be seen in Figure 1C As shown, no detailed description is given here.

[0101] In step S103, the production capacity planning arrangement information is output.

[0102] In this embodiment, after the capacity forecasting parameters are input into a pre-built capacity forecasting model to obtain the capacity planning arrangement information of the target solution, the capacity planning arrangement information can be output.

[0103] In some embodiments, when the capacity planning arrangement information is output, the capacity planning arrangement information can be presented in a set form. Exemplarily, the set form includes a graphical form (e.g., a line graph, a bar graph, etc.) and / or a table form. It is understandable that the specific form in which the capacity planning arrangement information is presented can be determined based on the user's choice, such as the user's desired presentation form can be known through the option selected by the user in the tab (e.g., a line graph or a bar graph or a table, etc.), so as to present it based on the form.

[0104] In other embodiments, while outputting the capacity planning arrangement information, an early warning can also be issued for the balance between the output of each section and the input of the subsequent section in the ramp-up phase of the target production line. For example, when the output of section A cannot meet the input of section B (the subsequent section of section A), an early warning message is generated to inform the management personnel of the situation. Among them, the above-mentioned sections are also called process sections. It can be understood that the segment division result of the entire manufacturing process of a product from raw materials to finished products is the process section, such as assembly, packaging, warehousing, etc., which is not limited in this embodiment.

[0105] In addition, the above-mentioned method of outputting the capacity planning arrangement information may also include saving the capacity planning arrangement information as an offline file in a set format, such as saving it as an Excel format file, etc., for sharing with third-party personnel for reference, which can enrich the flexibility of sharing the capacity planning arrangement information.

[0106] For example, Figure 1C is a schematic diagram showing the relationship between various capacity forecast parameters and capacity planning arrangement information according to an exemplary embodiment of the present disclosure; Figure 1C As shown, in some embodiments, the above-mentioned capacity forecasting parameters may include assembly line mode, assembly climbing slope, assembly climbing efficiency, first-time tightening ratio, packaging line mode and packaging climbing slope; furthermore, the capacity planning arrangement information may include assembly input, assembly output, packaging input, packaging output and warehousing quantity.

[0107] On this basis, the construction method of the capacity forecasting model can include constructing the following relationships:

[0108] The relationship between the assembly input quantity and the assembly line mode and the assembly climbing slope, the relationship between the assembly output quantity and the assembly climbing efficiency and the first-time tightening ratio, the relationship between the packaging input quantity and the packaging line mode and the packaging climbing slope, the relationship between the packaging output quantity and the packaging input quantity and the packaging climbing slope, and the relationship between the warehousing quantity and the packaging output quantity.

[0109] Figure 1D is a logical diagram of input and output during the ramp-up phase of a production line according to an exemplary embodiment of the present disclosure; Figure 1DAs shown in the figure, the assembly input is not a one-time investment of the maximum capacity, but the capacity can be slowly increased every day according to the assembly ramp slope until the maximum capacity is reached. The assembly input to the assembly output will be affected by the assembly ramp efficiency and the first tightening ratio, which will make the assembly output less than the assembly input. From the packaging input to the packaging output, it is not affected by the packaging ramp efficiency (the loss is very small), but only by the packaging ramp slope, that is, the packaging input is gradually increased. There is usually no loss of output from the packaging output to the warehousing stage, but the numerical transition time between the two may be delayed (for example, one day).

[0110] Furthermore, the above-mentioned capacity forecasting parameters may also include quality standard level, ramp-up start time and off-shift strategy, and the capacity planning arrangement information may also include a production scheduling timeline. On this basis, the construction method of the capacity forecasting model may also include constructing a relationship between the production scheduling timeline and the quality standard level, ramp-up start time and off-shift strategy.

[0111] In other embodiments, the above capacity forecasting parameters may also include assembly team mode, packaging team mode, assembly line quantity (such as assembly theoretical peak line quantity, assembly actual line quantity, etc.), packaging line quantity (such as packaging theoretical peak line quantity, packaging actual line quantity, etc.), assembly line opening rhythm and packaging line opening rhythm; further, the capacity planning arrangement information may also include production line and team information. On this basis, the construction method of the capacity forecasting model may also include:

[0112] Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

[0113] In addition, the above capacity forecast parameters can also include assembly UPH, EMS foundry peak, and packaging UPH;

[0114] Furthermore, the construction method of the capacity forecasting model may also include constructing the following relationship:

[0115] The relationship between the number of assembly lines and assembly UPH, and the relationship between the number of packaging lines and the EMS foundry peak, packaging UPH and the number of assembly lines.

[0116] It is understandable that the above capacity forecast parameters are only used for illustrative purposes and can be added or deleted based on actual application requirements during application. For example, the above capacity forecast parameters can also include at least one of the following:

[0117] Project name, plan name, production area, first delivery quantity, first delivery time, EMS foundry quantity, foundry name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information (such as whether to skip software testing time and start packaging directly), packaging start time, first piece quantity, set device static quantity, such as OLED soft screen static quantity, etc. (OLED soft screen has special properties, and hidden problems such as black spots on the screen can be easily discovered by letting it stand for a period of time).

[0118] The values, functions and explanations of the above-mentioned capacity forecast parameters can be found in Table 1 below, which will not be described in detail here.

[0119] Table 1

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] It is worth noting that the data in Table 1 above are only used for exemplary description. In actual applications, the specific values ​​of the parameters can be set based on application requirements, which is not limited in this embodiment.

[0130] For example, Figure 2 is a schematic diagram of an interface for receiving capacity forecast parameters according to an exemplary embodiment of the present disclosure; Figure 3 FIG. 1 is a schematic diagram of a production capacity prediction process according to an exemplary embodiment of the present disclosure. Figure 2 As shown in Figure 1, when it is necessary to predict the production capacity of the production line during the ramp-up phase, managers can Figure 2 Input the corresponding capacity forecast parameters into each interface shown in Figure 3 As shown, steps 301-332 are an exemplary capacity prediction process of this embodiment. The "balance" in step 332 may refer to the balance between the output of the previous stage and the input of the next stage.

[0131] Figure 4A FIG. 1 is a schematic diagram showing the production capacity planning arrangement information in a graphical form according to an exemplary embodiment of the present disclosure. Figure 4A As shown, this embodiment can present the output of each work section, such as the SMT material input quantity (unit is k), the cumulative assembly achievement (unit is k), the cumulative packaging achievement (unit is k) and the cumulative warehousing achievement (unit is k).

[0132] When the user clicks Figure 4A In the upper left corner of the "Legend" tab, you can Figure 4A In the upper right corner, select the type of graph (e.g., line graph or bar graph) and the time unit (e.g., day, week, month, etc.). When the user selects "bar graph" and "day", you can see Figure 4A The "assembly achieved", "packaging achieved" and "warehousing achieved" of each time period are shown in the figure. The three quantities of the bar chart can be distinguished by different colors.

[0133] Figure 4B FIG. 1 is a schematic diagram showing the capacity planning arrangement information in a table format according to an exemplary embodiment of the present disclosure. Figure 4B As shown, this embodiment can clearly and accurately present the cumulative achievements of each process section (assembly, packaging, warehousing) and the daily output of each line (such as benchmark line, replication line, etc.) in the form of a table. It is understandable that due to space limitations, Figure 4B The content shown in is only a part of the capacity planning arrangement information, which is only used for exemplary description.

[0134] As can be seen from the above description, the method of this embodiment provides an interface for receiving capacity forecast parameters, and in response to receiving the capacity forecast parameters of the target solution based on the interface, inputs the capacity forecast parameters into a pre-built capacity forecast model to obtain the capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan for the target production line ramp-up phase, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up phase, and then outputs the capacity planning arrangement information. Since it avoids the management personnel from making capacity forecasts based on manual observations and manual scheduling, it is possible to unify the ramp-up management methods of different regions and projects, reduce the difficulty of ramp-up management, and thereby improve the accuracy and efficiency of capacity forecasts during the ramp-up phase of the production line, as well as improve the quality of subsequent factory management decision-making, and it is also possible to intuitively present the delivery quantity in the form of graphics and / or tables, so that a horizontal comparison can be made for ramp-up evaluation plans with different focuses.

[0135] Figure 5is a block diagram of a production capacity prediction device according to an exemplary embodiment of the present disclosure; the device of this embodiment can be configured in electronic devices, such as servers, workstations, personal computers, mobile terminals (such as mobile phones, tablet computers, etc.), wearable devices (such as glasses, watches, etc.), etc. Figure 5 As shown, the device may include: a parameter interface providing module 110, an arrangement information determining module 120 and an arrangement information output module 130, wherein:

[0136] A parameter interface providing module 110 is used to provide an interface for receiving capacity forecast parameters;

[0137] The arrangement information determination module 120 is used for, in response to receiving the capacity forecast parameters of the target solution based on the interface, inputting the capacity forecast parameters into a pre-built capacity forecast model to obtain the capacity plan arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity plan arrangement information is used to predict the capacity of the target production line during the ramp-up stage;

[0138] The arrangement information output module 130 is used to output the production capacity plan arrangement information.

[0139] In some embodiments, the capacity prediction parameters may include assembly line mode, assembly ramp rate, assembly ramp efficiency, first-run tightening ratio, packaging line mode, and packaging ramp rate;

[0140] Furthermore, the capacity planning arrangement information may include assembly input quantity, assembly output quantity, packaging input quantity, packaging output quantity and storage quantity;

[0141] On this basis, the construction methods of the capacity forecasting model can include:

[0142] Establishing the relationship between the assembly input amount and the assembly line mode and the assembly climbing slope;

[0143] Establishing the relationship between the assembly output and the assembly ramping efficiency and the first tightening ratio;

[0144] Constructing the relationship between the packaging input amount and the packaging line-aligning mode and the packaging climbing slope;

[0145] Constructing the relationship between the packaging output, the packaging input and the packaging climbing slope;

[0146] Build a relationship between incoming inventory and outgoing packaging.

[0147] In some embodiments, the capacity forecast parameters may also include quality standard level, ramp start time, and off-shift strategy;

[0148] Furthermore, the capacity planning arrangement information also includes the production scheduling timeline;

[0149] On this basis, the construction methods of the capacity forecasting model can also include:

[0150] Construct the relationship between the production scheduling timeline and the quality standard level, ramp-up start time, and off-shift strategy.

[0151] In some embodiments, the capacity prediction parameters may also include assembly team mode, packaging team mode, number of assembly lines, number of packaging lines, assembly line opening rhythm, and packaging line opening rhythm;

[0152] Furthermore, the capacity planning arrangement information may also include production line and team information;

[0153] On this basis, the construction methods of the capacity forecasting model can also include:

[0154] Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

[0155] In some embodiments, the capacity forecast parameters may also include assembly UPH, EMS foundry peak, and packaging UPH;

[0156] Furthermore, the construction method of the capacity forecasting model may also include:

[0157] Constructing the relationship between the number of assembled lines and the assembled UPH;

[0158] Construct the relationship between the number of packaging line bodies and the peak value of the EMS foundry, packaging UPH and the number of assembly line bodies.

[0159] In some other embodiments, the capacity forecast parameter may further include at least one of the following:

[0160] Project name, plan name, production area, first delivery quantity, first delivery time, EMS OEM factory quantity, OEM factory name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information, packaging start time, packaging first piece quantity, set equipment static quantity.

[0161] In some embodiments, the arrangement information output module 130 may also be used to present the capacity planning arrangement information in a set form, wherein the set form includes a graphical form and / or a tabular form.

[0162] In some embodiments, the arrangement information output module 130 can also be used to warn of the balance between the output of each section and the input of the subsequent section during the ramp-up phase of the target production line.

[0163] In some embodiments, the arrangement information output module 130 may also be used to save the capacity planning arrangement information as an offline file in a set format.

[0164] It can be seen from the above description that the device of this embodiment provides an interface for receiving capacity forecast parameters, and in response to receiving the capacity forecast parameters of the target plan based on the interface, inputs the capacity forecast parameters into a pre-built capacity forecast model to obtain the capacity planning arrangement information of the target plan, wherein the target plan includes a simulated production plan for the target production line ramp-up phase, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up phase, and then outputs the capacity planning arrangement information. Since it avoids the management personnel from making capacity forecasts based on manual observation and manual scheduling, it can unify the ramp-up management methods of different regions and projects, reduce the difficulty of ramp-up management, and thus improve the accuracy and efficiency of capacity forecasts during the ramp-up phase of the production line, as well as improve the quality of subsequent factory management decisions.

[0165] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0166] Figure 6 900 is a block diagram of an electronic device according to an exemplary embodiment. For example, the device 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0167] Reference Figure 6 , device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0168] The processing component 902 generally controls the overall operation of the device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the above-mentioned capacity forecasting method. In addition, the processing component 902 may include one or more modules to facilitate the interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate the interaction between the multimedia component 908 and the processing component 902.

[0169] The memory 904 is configured to store various types of data to support operations on the device 900. Examples of such data include instructions for any application or method operating on the device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0170] The power supply component 906 provides power to the various components of the device 900. The power supply component 906 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 900.

[0171] The multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display panel and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0172] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0173] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0174] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the device 900. For example, the sensor assembly 914 can detect the open / closed state of the device 900, the relative positioning of components, such as the display panel and keypad of the device 900, and the sensor assembly 914 can also detect the position change of the device 900 or a component of the device 900, the presence or absence of user contact with the device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the device 900. The sensor assembly 914 can also include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0175] The communication component 916 is configured to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, 4G or 5G or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0176] In an exemplary embodiment, the device 900 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned capacity prediction method.

[0177] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the instructions can be executed by the processor 920 of the device 900 to complete the above-mentioned capacity prediction method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0178] In an exemplary embodiment, a computer program product including instructions is also provided. The instructions can be executed by the processor 920 of the device 900 to implement the above-mentioned capacity prediction method.

[0179] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0180] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A capacity forecasting method, characterized in that: The method comprises: Provide an interface for receiving capacity forecast parameters; In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage; Output the production capacity planning information.

2. The method according to claim 1, characterized in that The capacity prediction parameters include assembly line mode, assembly ramp rate, assembly ramp efficiency, first-time tightening ratio, packaging line mode and packaging ramp rate; The capacity planning arrangement information includes assembly input quantity, assembly output quantity, packaging input quantity, packaging output quantity and storage quantity; The method of constructing the production capacity prediction model includes: Establishing the relationship between the assembly input amount and the assembly line mode and the assembly climbing slope; Establishing the relationship between the assembly output and the assembly ramping efficiency and the first tightening ratio; Constructing the relationship between the packaging input amount and the packaging line-aligning mode and the packaging climbing slope; Constructing the relationship between the packaging output, the packaging input and the packaging climbing slope; Build a relationship between incoming inventory and outgoing packaging.

3. The method according to claim 2, characterized in that The capacity forecast parameters also include quality standard level, ramp start time and off-shift strategy; The production capacity planning arrangement information also includes a production scheduling timeline; The method for constructing the production capacity prediction model also includes: Construct the relationship between the production scheduling timeline and the quality standard level, ramp-up start time, and off-shift strategy.

4. The method according to claim 2, characterized in that: The capacity prediction parameters also include assembly team mode, packaging team mode, number of assembly lines, number of packaging lines, assembly line opening rhythm and packaging line opening rhythm; The capacity planning arrangement information also includes production line and team information; The method for constructing the production capacity prediction model also includes: Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

5. The method according to claim 4, characterized in that The capacity forecast parameters also include assembly UPH, EMS foundry peak and packaging UPH; The method for constructing the production capacity prediction model also includes: Constructing the relationship between the number of assembled lines and the assembled UPH; Construct the relationship between the number of packaging line bodies and the peak value of the EMS foundry, packaging UPH and the number of assembly line bodies.

6. The method according to claim 2, characterized in that The capacity forecast parameter also includes at least one of the following: Project name, plan name, production area, first delivery quantity, first delivery time, EMS OEM factory quantity, OEM factory name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information, packaging start time, packaging first piece quantity, set equipment static quantity.

7. The method according to claim 1, characterized in that The outputting the production capacity planning arrangement information includes: The capacity planning arrangement information is presented in a set form, and the set form includes a graphical form and / or a tabular form.

8. The method according to claim 7, characterized in that The method further comprises: An early warning is issued for the balance between the output of each section in the ramp-up phase of the target production line and the input of the subsequent section.

9. The method according to claim 1, characterized in that: The outputting the production capacity planning arrangement information includes: The capacity planning arrangement information is saved as an offline file in a set format.

10. A production capacity prediction device, characterized in that: The device comprises: A parameter interface providing module is used to provide an interface for receiving capacity forecast parameters; an arrangement information determination module, configured to, in response to receiving the capacity forecast parameters of the target solution based on the interface, input the capacity forecast parameters into a pre-built capacity forecast model to obtain capacity plan arrangement information of the target solution, wherein the target solution includes a simulated production plan for the ramp-up phase of the target production line, and the capacity plan arrangement information is used to predict the capacity of the target production line in the ramp-up phase; The arrangement information output module is used to output the production capacity plan arrangement information.

11. The device according to claim 10, characterized in that The capacity prediction parameters include assembly line mode, assembly ramp rate, assembly ramp efficiency, first-time tightening ratio, packaging line mode and packaging ramp rate; The capacity planning arrangement information includes assembly input quantity, assembly output quantity, packaging input quantity, packaging output quantity and storage quantity; The method of constructing the production capacity prediction model includes: Establishing the relationship between the assembly input amount and the assembly line mode and the assembly climbing slope; Establishing the relationship between the assembly output and the assembly ramping efficiency and the first tightening ratio; Constructing the relationship between the packaging input amount and the packaging line-aligning mode and the packaging climbing slope; Constructing the relationship between the packaging output, the packaging input and the packaging climbing slope; Build a relationship between incoming inventory and outgoing packaging.

12. The device according to claim 11, characterized in that The capacity forecast parameters also include quality standard level, ramp start time and off-shift strategy; The production capacity planning arrangement information also includes a production scheduling timeline; The method for constructing the production capacity prediction model also includes: Construct the relationship between the production scheduling timeline and the quality standard level, ramp-up start time, and off-shift strategy.

13. The device according to claim 11, characterized in that The capacity prediction parameters also include assembly team mode, packaging team mode, number of assembly lines, number of packaging lines, assembly line opening rhythm and packaging line opening rhythm; The capacity planning arrangement information also includes production line and team information; The method for constructing the production capacity prediction model also includes: Construct the relationship between the production line and team information and the assembly team mode, packaging team mode, assembly line quantity, packaging line quantity, assembly line opening rhythm and packaging line opening rhythm.

14. The device according to claim 13, characterized in that The capacity forecast parameters also include assembly UPH, EMS foundry peak and packaging UPH; The method for constructing the production capacity prediction model also includes: Constructing the relationship between the number of assembled lines and the assembled UPH; Construct the relationship between the number of packaging line bodies and the peak value of the EMS foundry, packaging UPH and the number of assembly line bodies.

15. The device according to claim 11, characterized in that The capacity forecast parameter also includes at least one of the following: Project name, plan name, production area, first delivery quantity, first delivery time, EMS OEM factory quantity, OEM factory name, assembly copy mold line quantity, assembly first piece quantity, packaging time, packaging time impact information, packaging start time, packaging first piece quantity, set equipment static quantity.

16. The device according to claim 10, characterized in that The arrangement information output module is further used to present the capacity planning arrangement information in a set form, and the set form includes a graphical form and / or a table form.

17. The device according to claim 16, characterized in that The arrangement information output module is also used to issue an early warning of the balance between the output of each section and the input of the subsequent section during the ramp-up phase of the target production line.

18. The device according to claim 10, characterized in that The arrangement information output module is also used to save the capacity planning arrangement information as an offline file in a set format.

19. An electronic device, characterized in that: The device comprises: a processor and a memory for storing a computer program; Wherein, the processor is configured to implement, when executing the computer program: Provide an interface for receiving capacity forecast parameters; In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage; Output the production capacity planning arrangement information.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it realizes: Provide an interface for receiving capacity forecast parameters; In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage; Output the production capacity planning arrangement information.

21. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it realizes: Provide an interface for receiving capacity forecast parameters; In response to receiving the capacity forecast parameters of the target solution based on the interface, the capacity forecast parameters are input into a pre-built capacity forecast model to obtain capacity planning arrangement information of the target solution, wherein the target solution includes a simulated production plan of the target production line ramp-up stage, and the capacity planning arrangement information is used to predict the capacity of the target production line during the ramp-up stage; Output the production capacity planning information.

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