Method for assisting in improving a production operation of a participant in a food protein value chain

Abstract

The invention refers to a method for assisting in improving a production operation in a food protein value chain with respect to an environmental impact. Product data of a food product from a participant is received. The received product data is stored on a database The product data is aggregated to enable further processing by an operation assessment system A request for an assessment of a parameters in a production operation of a participant is received. The aggregated product data and the parameter are forward to the operation assessment system. The parameter is assessed based on the aggregated product data, wherein the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to the parameter and an environmental impact. The improvement data associated with the parameter is provided to a requesting node of the participant.

Description

[0001] Method for assisting in improving a production operation of a participant in a food protein value chain

[0002] FIELD OF THE INVENTION

[0003] The invention refers to a computer implemented method, apparatus and computer program product for assisting in improving a production operation of a participant in a food protein value chain.

[0004] BACKGROUND OF THE INVENTION

[0005] The production of food protein can have a strong impact on the environment. Thus, improving the production of food protein with respect to its environmental impact is of high importance.

[0006] SUMMARY OF THE INVENTION

[0007] The food protein value chain is a complex value chain with a plurality of participants that interact in a complex manner to produce respective food protein. For example, the value chain of a food product can comprise fertilizer producers, plant protection producers, crop farmers, livestock farmers, food packaging providers, regulators, certifiers, and retailers. In such a complex value chain it is nearly impossible for a participant to really assess an environmental impact of the production of the food protein due to the complex interactions of the participants and missing information. In particular, the data necessary for determining an environmental impact is not only spread through all participants of the food protein value chain but also exists in a plurality of formats and comes from a plurality of sources, for instance, structured data can be provided from sensors like humidity sensors, temperature sensors, etc. or from unstructured sources, for instance, an electrical bill, a transportation protocol, etc. Thus, in order to assess an environmental impact which then allows to improve the production such that the environmental impact is decreased, it is necessary to generate a possibility to assess the data provided by the plurality of sources such that suggestions for respective improvements of a food protein production, in particular, with respect to the environmental impact, can be derived. However, the accessing of the data of the plurality of participants is hindered by data security and data safety requirements. In most cases a participant of the food protein value chain is not interested in sharing data directly with competitor in the value chain. Making it difficult to access in particularthe data interesting for improving a production operation in a certain part of the food protein value chain.

[0008] Since the invention refers to a method, as described in more detail in the following, that comprises receiving product data associated with the protein value chain on one or more participants of the food protein value chain, aggregating the product data and, based on a request for an assessment of one or more parameters of a production operation of a participant, accessing the one or more parameters of the participant based on the aggregated product data, wherein the assessment comprises generating improvement data indicative of determined improvements of the production operation of participant with respect to one or more parameters, information from a plurality of participants of the food protein value chain can be made available for the assessment of the environmental impact of one participant. Moreover, since the data is not directly available to a participant, but only in form of the improvement recommendation, data security can be guaranteed to each participant. This allows more participants with strict data security requirements to share the respective data making it available for deriving respective improvements. Thus, the environmental impact of a participant of a food protein value chain can be assessed based on and in a relation to the production of other participants of the food protein value chain while maintaining a high data security standard. The environmental impact of a food product can hence be improved throughout the complete food protein value chain.

[0009] In an aspect of the invention a computer-implemented method is presented for assisting in improving a production operation of a participant in a food protein value chain with respect to an environmental impact of the production operation, wherein the method comprises a) receiving product data associated with a food protein value chain of a food product from one or more participants of the food protein value chain, b) storing the received product data on an electronic database, c) aggregating the product data to enable further processing by an operation assessment system, d) receiving, from a requesting node associated with a participant, a request for an assessment of one or more parameters in a pro- duction operation of the participant associated with the food product value chain, e) forwarding the aggregated product data and the one or more parameters to the operation assessment system, f) assessing, by the operation assessment system, the one or more parameters of the participant of the food protein value chain based on the aggregated product data, wherein the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to the one or more parameters and an environmental impact of the production operation of the participant, and g) providing the improvement data associated with the one or more parameters to the requesting node of the participant.

[0010] The computer implemented method can be performed by any computer system comprising one or more data processors. For example, the computer implemented method can be performed by a standard general computer, dedicated hardware, or distributed computing in which a plurality of computing systems work together to perform the functions defined in the computer implemented method.

[0011] A food protein value chain can refer to a representation of the various processes and participants involved in producing a food product, starting, for instance, with respective raw materials and ending with the respective consumer product. However, also only parts of the food protein value chain can be assessed. The food protein can refer to any food product that provides protein for human or animal consumption. For example, the food protein can refer to plant based protein or to animal based protein or a combination thereof. In an embodiment, the food product is an animal protein product and the food protein value chain is an animal protein value chain. However, in another embodiment, the food product is a plant protein product and the food protein value chain is a plant protein value chain. Moreover, in an embodiment, the food product can also be a combination of an animal protein product and a food protein product and the value chain can be a respective combination of an animal protein value chain and a plant protein value chain.

[0012] The environmental impact can be any environmental impact, for example, can be a carbon footprint, a general emission footprint, a land use, an energy consumption, water use, etc. In an embodiment, the environmental impact refers to a carbon footprint that can be defined as an amount of greenhouse gases emitted or removed in the production process of the participant or only parts of the production operation of the participant. In particular, the environmental impact is associated with at least one of the one or more parameters to assessed. For example, if an energy consumption parameters is to be assessed the environmental impact assessed can refer to an energy consumption or a quantity derived from an energy consumption. Product data can refer to any data that is associated with the food product in the food protein value chain. In particular, the product data can comprise production data and / or environmental impact data. Production data refers to data that is collected from a food protein value chain process in order to monitor and / or optimize a production. For example, the production data can include data on fertilizers, plant protection, soil quality and type, crop yields, water usage, feed quality, feed quantity, additives, animal type, animal number, energy consumption, final product, and production rates. Production data is collected and used to track performance and identify areas for improvement to increase efficiency and reduce costs of a specific production step in the food protein value chain. Environmental impact data refers to data that is collected to assess the environmental impact of practices in the food protein value chain. The environmental impact data can include, for instance, data on greenhouse gas emissions, water usage, soil erosion, nutrient runoff, energy consumption, and waste production. Environmental impact data is used to identify the environmental impact of at least a part of the food protein value chain and to develop strategies for reducing negative impacts and promoting sustainability. In the food protein value chain, both production data and environmental impact data can be utilized for optimizing production processes and promoting sustainability. By collecting and analyzing this type of data, the participants of the food protein value chain can make informed decisions about how to manage their resources and reduce the environmental impact while maintaining or improving the yields.

[0013] The product data can be provided, transferred or received in a digital context in form of a digital representation of the product data. A digital representation of the product data can refer to any kind of digital representation that comprises the information provided by the product data. In particular, the digital representation can refer to any digital format, type or structure. For example, a digital representation of energy consumption data can refer to an image of a scan of an electricity bill. In another example, a digital representation of temperature sensor measurements can refer to time series data containing the measurements of the sensor together with respective time stamps and sensor identifiers. Thus, a digital representation generally allows to represent the real-world information provided by the data in a digital environment such that it can be processed by digital processes.

[0014] A participant of the food protein value chain can be any participant that performs a process or produces a product that is part of the food protein value chain. The participant has access to the respective product data produced during the process or production that the participant of the food protein value chain performs. The product data in the digital domain can be digital represented in a form that includes both structured data and unstructured data. For example, the participant might receive structured data from a temperature sensor and at the same time receive a PDF containing an electricity bill as an unstructured data. In this example, both data contain information about energy consumption and environmental impact of the process or production performed by the participant and can thus be of interest for an assessment and provided as product data to the operation assessment system. The production operation can be any part of the food protein value chain that contributes to the food protein value chain by producing a food product, a part of a food product or an intermediate product utilised for the production of a food product or part of the food product. For example, if the food protein refers to animal protein, a production operation can refer to the production of crops utilised in the feed of an animal, the production of the feed itself, a breeding operation for breeding an animal, the production of the animal protein, the production of a consumer good from the animal protein, etc.

[0015] The method comprises receiving, at a respective operation assessment system, product data from one or more participant, for instance, via a participant node. The participant node can be any computational environment that allows a participant to access and upload the respective digital representation of the product data to the operation assessment system. For example, the participant node can be realized in form of a smartphone, tablet, laptop, personal computer or any other computational hardware or computational interface. The receiving can then refer, for instance, to receiving an upload of the respective product data but also to accessing a respective storage unit on which the uploaded product data has been stored.

[0016] Preferably, product data is received from more than one participant of the food protein value chain which allows for assessing parameters of a production operation of a participant utilising a wider data basis. Even more preferably, product data is received by a plurality of participants. However, also product data received from only one participant can be helpful and utilised for accessing the parameters of a participant. In an embodiment additional product data from sources outside of participants of the food protein value chain can also be received and utilised by the method in the same way as the product data. Such data can come, for instance, from free available sources like official statistics, studies and articles, press releases, specialist literature, specialists and operator knowledge, etc. For example, such additional product data can often be helpful for replacing missing data in the assessment, for setting standard values during the assessment or for widen the data basis used in the assessment.

[0017] The received product data can then be stored on an electronic database and aggregated to enable further processing by the operational assessment system. For example, the aggregating of the received product data can comprise structuring and labelling the product data of different participants to generate the aggregated data. The structuring and labelling allows to make the product data more assessable and finable for later processing. Moreover, aggregating can comprise converting the received product data into a predetermined data content format. Utilising a predetermined data content format allows an easier processing by the operation assessment system when assessing, for instance, the one or more parameters and generating the improvement data. For example, the converting of the received product data into a predetermined content format can comprise extracting information from the product data, classifying the extracted information into one or more predetermined product data types, and processing the information into a respective predetermined data content format based on the predetermined product datatypes. The product data types can, for instance, be based on the product data source, the product data content, a participant providing the product data, etc. The classification of the digital representation into the respective product data types then allows to provide respective different data normalization functions for the different product data types. Data normalization functions can be any functions that process the data to make the content of the data assessable by the operation assessment system. For example, a data normalization function can change a data format of the data, restructure the data, extract or remove data, etc. For example, if a digital representation of an electricity bill is classified in the product data type “bills for energy consumption”, a data normalization function can be provided specifically for the content of this kind of product data type. For instance, specific predetermined keywords, values, text format schemes, etc. are expected for this kind of product data type and can be utilized by a data normalization function to remove or reformat the extracted information.

[0018] In an embodiment, the aggregating can further comprise processing the product data to remove duplicates, correct errors, and / or add missing information. This processing can be performed before storing the product data, for instance, as part of deploying a data normalization function. For example, if in the same document the same information is provided twice, the duplicate of the information can be removed at each stage of the aggregating of the product data. Errors can be corrected, for instance, by utilizing a correction function that can detect obvious errors and, if possible, correct the respective obvious errors. An example of a detection of an obvious error could be the comparing of provided values with predefined expected values stored on a database, wherein, if the values deviate from the expected value above a predetermined limit, a respective error can be detected. A correction of such an error can then, for instance, depend on respective rules that can, for instance, determine that instead of the erroneous value the respective expected value is utilized. However, error correction can also refer to making an operator aware of the error and requesting a respective correction action. Missing information can be added, for instance, by utilizing a respective information model depending on the respective expected information content. For example, databases can be accessed by the information model to find the missing information, for instance, in previous product data of the participant, in the product data of other similar participants or in a generated database. Moreover, the missing information can also be noted to the participant via the participant node and the participant can be requested to add the missing information. Moreover, the aggregating can comprise converting physical quantities provided by the product data into a predetermined system of units. For example, a function can be applied to the product data that determines the system of units of a respective physical quantity and converts all physical quantities that are not in the predetermined system of units into the respective predetermined system of units. In particular, it can be technical sensible to convert all physical quantities into an SI System of units referring to the international standard. This ensures that the operational assessment system is provided with values for physical quantities that are in the same system units and thus can be utilised in the same calculations without further processing.

[0019] Further, the method comprises receiving, from a requesting node that can be associated with the participant of the food protein value chain, a request for assessing on or more parameters of the production operation of the participant. For example, a participant of the food protein value chain can utilize a respective user interface on a requesting node that can refer to any computing system communicatively coupled with an API or other interface of the operation assessment system for providing the request.

[0020] The requesting participant of the food protein value chain can be any participant that performs a production operation as part of the food protein value chain. The participant has access to the respective product data acquired during the production operation that the participant of the food protein value chain performs. The requesting node can be any computational environment that allows a participant to access and upload request to the operation assessment system. For example, the requesting node can be realized in form of a smartphone, tablet, laptop, personal computer or any other computational hardware or computational interface. The receiving can then refer, for instance, to receiving an upload of the respective request.

[0021] The one or more parameters can refer to any of the parameters that are utilised by the participant to monitor, control or manage the production operation. In particular the one or more parameters can be associated with a predetermined improvement goal of the participants. For example, the one or more parameters can refer to an energy consumption and comprise values like an overall energy consumption, a temperature in a part of the production process, indications of an utilised energy mix, a fuel consumption, etc. and the improvement goal can be to decreased the overall energy consumption or to decrease a CO2production of the consumed energy. However, the one or more parameters provided by the participant can also be associated with different improvement goals. For example, the participant can select a respective improvement goal from a suggested predetermined improvement goals, e.g. in a drop down menu. In this case the one or more parameters to be assessed can be automatically selected and presented to the participant such that the user can provide the respective data for the assessment. However, an improvement goal can also be automatically selected based on the one or more parameters provided by the participant. Moreover, the improvement goal can determined the environmental impact assessment performed. For instance, if the improvement goal is a CO2emission reduction, the environmental impact assessment can be performed to determine the CO2emission of the production operation. However, if no environmental goal is provided by the user, also a default environmental goal can be set or the environmental impact can be assessed on a generally for a plurality of aspects or as a averaged of different aspects.

[0022] The method then further comprises forwarding the aggregated product data and the one or more parameters to the operation assessment system. For example, an API or interface receiving the product data and / or the one or more parameters can be communicatively coupled with the operation assessment system and provide aggregated product data and / or the one or more parameters such that it can be further processed by the operation assessment system. For example, the structure, content and format of the aggregated product data and / or the one or more parameters can be such that the operation assessment system can directly process the product data and the one or more parameters to generate improvement data.

[0023] The method then further comprises assessing, by the operational assessment system, the one or more parameters of the participant of the food protein value chain based on the aggregated product data. In particular, the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to the one or more parameters and an environmental impact of the production operation of the participant. Improvements, when implemented in the production operation, can, for instance, refer to changes in the operation of the participant that also lead to changes in the respective one or more parameters and thus can lead to meeting predetermined improvement goals. In particular, the improvement goal refers to improving one or more aspects that lead to a decrease of the environmental impact of the production operation of the participant. Such aspects can be a CO2reduction, a methane reduction, an energy consumption reduction, an optimisation and various trade-offs influencing an environmental impact, etc. The assessing performed by the operation assessment system can be performed in a plurality of different ways. For example, respective rules, functions and models can be utilised that utilised information provided by the aggregated product data and the one or more parameters of the participant.

[0024] In an embodiment, the assessing comprises performing a benchmark based on the one or more parameters of the participant and the aggregated product data and generating the improvement data based on the benchmark. Generally, a benchmark can be defined as an evaluation of a production operation in comparison to other production operations. In particular, the benchmark can utilise the one or more parameters as performance indicators providing a measure of the performance of the production operation of the participant such that the performance can be compared to other participants utilising the one or more parameters. For the benchmark the one or more parameters can be used directly, for instance, can directly be compared to respective parameters of other participants or to a standard. However one or more parameters can also be utilised to derive a predetermined measure of the performance of the production operation, wherein than the respective measure is compared to other participants or a standard. Preferably, the performance is determined with respect to one or more predetermined improvement goals that are associated with an environmental impact of the production operation. For example, a performance goal can referto decreasing the CO2emission of the production operation, wherein in this case the one or more parameters refer to a measure that is indicative of the CO2emission and the benchmark can refer to determining the performance of the production operation of the participant with respect to the CO2emission. The parameters of other participants or the standard for benchmarking the production operation of the participant can be derived from the aggregated product data which has been provided by one or more participants, preferably, by a plurality of participants. Thus, respective one or more parameters forthe comparison with the one or more parameters of the participant can be selected from the aggregated product data utilising predetermined rules and functions. For example the benchmark can comprise selecting a benchmark reference from one or more predetermined benchmark references and comparing the one or more parameters of the participant to parameters identified in the aggregated product data as belonging to the selected benchmark reference. The benchmark references refers to the reference with respect to which the benchmark is performed thus to the set of data and parameters that are utilised for the comparison with the one or more parameters of the participant. For example, the predetermined benchmark references can referto parameters defined by industry average, industry standard and / or best in class or can refer to parameters of one or more other participants identified in the aggregated product data. An industrial average benchmark reference refers to an average performance with respect to the one or more parameters in a predetermined industry, e.g. a respective part of the food protein value chain referring to the production operation of the participant. Such an industrial average can be derived, for example, from the aggregated product data utilising respective statistical methods. Further, additional product data from other sources, for instance, from official statistics, literature, studies, etc. for this part of the food protein value chain can be utilised for deriving respective industrial average values for the one or more parameters of the participants. An industry standard can refer to a predetermined standard set in the respective part of the food protein value chain of the production operation of the participant. Such a standard can be set, for example, by respective industrial organisations, e.g., standard organisations, by legislation, official regulations, etc. A best in class benchmark reference indicates that the participant intends a benchmark comparison with the best performances in a respective industry. For example, in this case the participant can select if the benchmark is performed with respect to the best 10 %, the best 15 %, the best 20 %, or the best 30 % in the respective industry with respect to a predetermined improvement goal. Respective comparison values for the benchmark can then be derived from the aggregated product data by identifying the respective participants that fulfil the predetermined criteria and defining the respective measured and parameters. However, also in this case additional sources with additional product data can be taken into account. Moreover, a participant can select to perform the benchmark with respect to parameters of other participants identified in the aggregated product data. In this case a direct comparison can be performed with respect to specific selected participants orto all participants that have provided the aggregated product data associated with the one or more parameters of the participants. The other participants utilised for comparison are generally associated with the same production operations performed by the participant. A result of such a comparison can be, for instance, a ranking of the participants with respect to other participants. Preferably, the benchmark is performed with respect to a predetermined product or product category. For example, the utilized parameters and / or the benchmark reference are related to the predetermined product or product category. The product or product category can refer to any product or product category associated with the food protein value chain, for example, a food product itself or a product utilized for producing the food product.

[0025] The improvement data can be generated based on the performed benchmark. For example, a result of a benchmark can referto whether or not a participant meets a predetermined industrial standard, wherein the improvement data can then comprise indication to the participant on how to reach the industrial standard or how to keep the reached industrial standard. For example, the main cause for not meeting an industrial standard can be identified based on the one or more parameters of the participant and the improvement data can indicate to the participant to remove this cause. For example, if a participant does not meet an industrial standard with respect to a CO2emission during animal protein production, it can be determined by the one or more parameters provided by the participant that the main cause is the utilising of a specific feed product forthe animals that has a high CO2footprint, whereas all other parameters do not strongly contribute to the CO2footprint of the participant. In this case the improvement data can indicate to the participant that changing the feed product forthe animals to a feed product with less CO2footprint improves the production operation. Moreover, also other rules and functions can be utilised for generating improvement data indicating respective improvement to the participant. For example, the improvement data can be generated further based on the benchmark reference. In this case the participant might be provided with respective parameters utilised by the respective benchmark reference such that the participant can directly identify respective differences. Moreover, the benchmark reference can be utilised to identify a main cause of a not met benchmark goal and then be utilized for suggesting a respective improvement.

[0026] In an embodiment, the assessing comprises utilizing an environmental impact model and providing the one or more parameters to the environmental impact model, wherein the environmental impact model is configured for determining assessment data based on the one or more parameters, wherein assessment data is indicative of an environmental impact of at least a part of a food protein value chain associated with the participant, wherein the improvement data is generated further based on the assessment data. The environmental impact model can be based on a plurality of different model architectures. For example, the environmental impact model can be a machine learning based model that has been trained utilising, for example, historical aggregated product data and respective environmental assessments of the historical aggregated product data, to determine a respective assessment data based on one or more parameters of participants. Utilizing a machine learning algorithm can for example decrease the number of parameters that have to be provided as input. However, the model can also be a rule based model that utilises predetermined functions and rules, like physical laws, to derive from the one or more parameters and, optionally, at least a part of the aggregated product data the assessment data. For example, the environmental impact assessment system can be configured to apply an environmental impact assessment generation function to the one or more parameters to generate assessment data indicative of an environmental impact assessment of a production operation. The utilized impact assessment generation function can be configured to utilize known physical relationships, predetermined rules and other functional relationships to utilize the one or more parameters to generate assessment data. In particular, a mass balance for predetermined substances can be utilized for determining the amount of a substance emission relevant for an environmental impact. Also functions determined by official regulations and laws can be utilized for specific aspects of an environmental impact assessment. Moreover, if the one or more parameters do not provide enough information for the assessment the aggregated product data can be utilized to provide the missing information, for example, in form of default values, average values, etc. The specific environmental impact assessment generation function utilized can depend on the respective information available and / or on the respective request of the participant. For example, if the participant is a farmer, requesting an assessment of a crop production operation other information is relevant than if the participant is a food product producer or an animal protein producer. Moreover, also for participants belonging to the same category, for instance, farmers, different information can be available and / or relevant. Thus, the environmental impact assessment generation function can comprise different parts and aspects that are only utilized forthe environmental impact assessment if the respective information to which they refer is available and / or depending on the respective request. The one or more parameters can then be enriched by information provided by the aggregated product data and based on the information available a respective environmental impact assessment generation function can be utilized. However, it can also be determined which information is needed for a specific environmental impact assessment generation function and the information not provided by the one or more parameters can be either requested from the participant or can be derived from the aggregated product data. The environmental impact assessment generation function then generates assessment data that is indicative of an environmental impact assessment of the product. For example, the assessment data can refer to one or more values of respective environmental impact metrics. In one example, the environmental impact assessment can refer to the value of a carbon footprint of the product. Moreover, the assessment data can also comprise more information than the simple environmental impact metric. For example, the assessment data can also comprise an analysis on the main sources of the respective environmental impact. Thus, the assessment data can be utilized for generating the improvement data that is then utilized for controlling and / or monitoring at least a part of a production operation of the requesting participant in the food protein value chain. The assessment data can be provided by the operation assessment system to the participant as part of the assessment data. For example, a carbon footprint value can be provided as part of the improvement data to the participant via a connection to a smartphone of the participant.

[0027] Moreover, the environmental impact model can also be derived from a plurality of impact modules which each are configured to generate a different part of a production operation assessment. In particular, different modules can be configured to utilise different parameters, assess different parts of an operation, refer to different production processes, etc. such that the environmental impact model can be adapted to a specific application by selecting the respective modules. In particular the modules can be selected with respect to the specific application, for instance, allowing forthe respective provided input parameters, relating to the specific production processes, etc. For example, a module for assessing an energy consumption of a production process can be different if the energy consumption is referring to a crop production where most of the energy will be consumed in the work performed by the machines on the field, than of the energy consumption is referring to an animal protein production where most of the energy consumption comes from the heating of a barn in which the animals are kept. Moreover, for assessing an operation for producing crop modules can be provided that assess the utilisation of plant protection, whereas for an animal protein production modules can be provided that assess the consumption of food and water. Thus, providing and storing a plurality of such modules allows to adapt the utilised environmental impact model specifically to the respective application. However, in another example a plurality of different environmental impact models can be stored and the respective environmental impact model utilised for a specific application can be selected based on a plurality of criteria, for instance, based on the provided parameters, the requesting applicant, the production of the applicant, etc.

[0028] In an embodiment the assessment data is a benchmark metric for the one or more parameters and the assessing comprises performing a benchmark based on the benchmark metric and the aggregated product data and generating the improvement data based on the benchmark. In this embodiment the environmental impact model can be combined with performing a benchmark by utilising the environmental impact model to generate as assessment data a benchmark metric from the one or more parameters and utilising the benchmark metric in a respective benchmark as described above. In particular, all embodiments with respect to the benchmark described above can also be utilised in this combined embodiment. For example, a benchmark reference can be selected, as described above, and the benchmark metric can be compared to the respective benchmark reference. Thus, the specific environmental impact like a CO2emission, a water consumption, an overall energy consumption etc. can be directly utilised as metric in the benchmark and for generating based on the benchmark results the improvement data.

[0029] In an embodiment, the received product data is received and provided at an application program interface (API) respectively, wherein the received product data is defined by a data model of the API. An application programming interface refers to a software interface that regulates and defines the communication and data utilization between different computer programs. Thus, an API can provide a regulated and defined access to functions and data of a computer program by another computer program. A data model of the API refers to a definition on how information and data is stored and retrieved in communication with and by the API. For example, the data model can define a data format, communication protocol, data structure, etc. that can be utilized in communication with the API. Preferably, the data model defines at least one of a structure, a format and a data type of the generated structured product data. In a preferred example, the data model defines at least one of a structure, a format and a data type of the generated structured product data.

[0030] In an further aspect a computer-implemented method is presented for improving a production operation of a participant in a food protein value chain, wherein the method comprises a) collecting information associated with a food protein value chain of a food product of a participant of the food protein value chain, b) generating product data based on the collected information and providing the product data to an operation assessment system, c) providing a request to the operation assessment system for an assessment of one or more parameters in a production operation of the participant, wherein the request comprises the one or more production parameters, d) receiving, by the operation assessment system, improvement data associated with the one or more parameters and generated according to as described above, wherein the improvement data is indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and e) implementing the information included in the improvement data into a production of the food product.

[0031] In an further aspect an environmental impact assessment system is presented for assisting in improving a production operation of a participant in a food protein value chain, wherein the apparatus comprises one or more processors configured to a) receiving product data associated with a food protein value chain of a food product from one or more participants of the food protein value chain, b) storing the received product data on an electronic database, c) aggregating the product data to enable further processing by an operation assessment system, d) forwarding the aggregated product data to the operation assessment system, e) receiving, from a requesting node associated with a participant, a request for an assessment of one or more parameters in a production operation of the participant associated with the food product value chain, f) assessing, by the assessment system, the one or more parameters of the participant of the food protein value chain based on the received product data, wherein the assessment comprises generating improvement data indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and g) providing the improvement data associated with the one or more parameters to the requesting node of the participant.

[0032] In an further aspect an apparatus is presented for improving a production operation of a participant in a food protein value chain, wherein the apparatus comprises one or more processors configured for a) collecting information associated with a food protein value chain of a food product of a participant ofthe food protein value chain, b) generating product data based on the collected information and providing the product data to an operation assessment system, c) providing a request to the operation assessment system for an assessment of one or more parameters in a production operation of the participant, wherein the request comprises the one or more production parameters, d) receiving, by the operation assessment system, improvement data associated with the one or more parameters and generated according to any of claims 1 to 16, wherein the improvement data is indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and e) implementing the information included in the improvement data into a production of the food product.

[0033] In an further aspect a computer program product is presented for assisting in improving a production operation of a participant in a food protein value chain, wherein the computer program product causes an apparatus according to claim 18 to perform the method according to any of claims 1 to 16 when carried out on the apparatus.

[0034] In an further aspect a computer program product is presented for improving a production operation of a participant in a food protein value chain, wherein the computer program product causes an apparatus according to claim 19 to perform the method according to claim 17 when carried out on the apparatus.

[0035] It shall be understood, that the method described above, the apparatus as described above, and the computer program product as described as above have similar and / or identically preferred embodiments, in particular, as defined in the dependent claims.

[0036] It shall be understood that the preferred embodiment of the present invention can also be any combination of the dependent claims or above embodiments with a respective independent claim.

[0037] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described here after.

[0038] BRIEF DESCRIPTION OF DRAWINGS

[0039] Fig. 1 shows schematically and exemplary an ecosystem of distributed data sources in a food protein value chain;

[0040] Fig. 2 shows schematically and exemplary a data flow in a food protein value chain; Fig. 3 shows schematically and exemplary embodiments of a method for assisting in improving a production operation of a participant in a food protein value chain;

[0041] Figs. 4a to c show benchmarking architectures for a food protein value chain for different cases;

[0042] Fig. 5 shows schematically and exemplary a model architecture of an environmental impact model;

[0043] Fig. 6 shows schematically and exemplary possible modules of an impact assessment model for a food protein value chain referring to broilers;

[0044] Fig. 7 shows schematically and exemplary a calculation for an N2O, and CH4emissions in broiler production;

[0045] Fig. 8 shows schematically and exemplary a method combining the utilisation of an impact assessment model and a benchmark;

[0046] Fig. 9 shows functions of a graphical user interface;

[0047] Fig.10 shows schematically and exemplary a data layer structure of an environmental impact assessment system;

[0048] Figs. 11 , 12 show schematically and exemplary a decentralised database structure for a food protein value chain; and

[0049] Figs. 13a, b show schematically and exemplary product data utilized by the environmental impact assessment system.

[0050] DETAILED DESCRIPTION OF EMBODIMENTS

[0051] Fig. 1 shows schematically and exemplary an ecosystem of distributed data sources for a food protein value chain. In the left row different participants of the food protein value chain are shown like crop / feed crop farmers, crop / feed processors, animal farms, food producers and retailers. The following arrows indicate input data referring to different product data that is acquired by the respective participants and provided to a computing system referring to an operation assessment system that is configured for processing the product data input from the respective participants. In particular, the operation assessment system processes a product data to provide respective output data that can refer to a plurality of different outputs. In particular, the output data can refer to improvement data that allows the participants to improve their production processes and operations. The improvement data can comprise, for instance, a determined environmental impact of the production operation of the participant, a respective benchmark of the environmental impact, comparisons between different improvement scenarios and respective recommendations.

[0052] Fig. 2 shows schematically and exemplary a common data flow in a food protein value chain. Generally, a crop / feed crop farmer produces a plurality of product data related, for instance, to utilities, transportation, utilised protection, utilised fertilisation, irrigation, harvest yield, etc. At least a part of this product data can be provided together with the crop to a crop feed processor that mixes feed or further processes the crop, for instance, in a crop mill. The data generated by the crop / feed processor can refer to utilities, transportation, utilised raw materials, packaging, energy consumption, etc. Depending on the respective food protein value chain a part of this data togetherwith the product produced is then either provided to an animal farm as feed or to a food producer for further processing. Also an animal farm again produces a plurality of product data that is related to the utilised utilities, transportation, medicines, animal feed formulation, animal performance, energy consumption, water consumption, etc. The produced animal products can also be provided together with part of the data to the food producers and can be utilised in the products together with the processed crop. A food producerthen produces again a plurality of product data related to utilities, transportation, raw materials, packaging, energy consumption, etc. related to the produced food product. The such produced food product is then provided togetherwith a part of the data to respective retailers that again generate data that can be related, for instance, to utilities, transportation, storage, etc. At each station of the food protein value chain as exemplary described above, the respective product data can be provided to the next participant and at the end of a respective food protein value chain data from the complete food protein value chain can be available for one or more participants. However, often the chain of product data is broken at one or more points due to data system inconsistencies between the participants. Moreover, often only the necessary product data is provided to the next participant for data security reasons. Thus, in reality it is difficult for a participant of the food protein value chain to access the complete product data that would be necessary to assess the respective complete protein value chain with respect to an environmental impact. Fig. 3 shows schematically and exemplary a method for assisting and improving a production operation of a participant in a food protein value chain with respect to environmental impact of the production operation. The method comprises in a first step receiving product data associated with the food protein value chain of a food product from one or more participants of the food protein value chain. For example, the product data can relate to the data described to the data in Fig. 1 and 2 and can be provided by one or more respective participants of the food protein value chain. Moreover, it is preferred that the product data is provided by a plurality of participants of the food protein value chain. For example, product data can be provided by a plurality of feed / crop farmers, plurality of food processors, etc. The received product data will then be stored on an electronic database and aggregated to enable further processing by an operation assessment system. The storing and aggregating of the product data can refer to structuring and labelling the product data such that it can be easily accessed for further processing. Moreover, the product data can be anonymised and associated instead with one or more characteristics of the respective participant providing the product data. For example, the identity and name of a specific farmer can be removed from the product data and the product data can instead be associated with a characteristic, like that the farmer is a milk producer, has a certain operation size, produces in a certain area, or any other characteristic that does not identify the participant, but allows for statistical grouping of the product data. Further, it is determined if a request has been received for an assessment of parameters in a production operation of a participant. For example, a participant of the food protein value chain can provide one or more parameters which should be utilized to assess the production operation. If such a request has been received, the aggravated product data and the one or more parameters are forwarded to the operation assessment system. The operation assessment system can then assess the one or more parameters of the participant of the food protein value chain based on the aggregated product data. In particular, the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to one or more parameters and an environmental impact of the production operation of the participant. Different embodiments for such an assessment will be described in more detail with respect, for instance, to Fig. 4 to 9. In a last step the respective result, e.g. the improvement data, associated with the one or more parameters is than provided to the requesting note of the participant.

[0053] In an embodiment for assessing the one or more parameters of the participant a benchmark is utilised comparing the one or more parameters directly or in form of a derived metric with respect to one or more parameters of the aggravated product data. Figs. 4a to 4c show different possibilities for the benchmark in form of different comparisons. For example, Fig. 4a shows the possibility of performing the benchmark with respect to another participant with respective similarities. For example, in this case two crop / feed crop farms are compared in the benchmark with respect to one or more parameters requested. However, the benchmark can also be performed with respect to other benchmark references like best in class, average and standard. For example, the crop / feed crop farmer can compare the performance of the production operation to the performance of the best in the class of crop / feed crop farmers with respect to the one or more parameters and with respect to respective environmental impact. For example, the comparison can be performed with respect to the 10% of crop / feed crop farmers with the least CO2emission in the respective class of the requesting crop / feed crop farmer. The benchmark can also be performed with respect to an average that can be derived, for instance, from the aggregated product data or with respect to a standard that can be derived from the aggregated product data and / or from additional product data from respective additional sources like literature, studies, further statistics, etc. Fig. 4b shows the same benchmarks exemplary for a retailer. Fig. 4c shows possibilities of benchmarking and selecting respective benchmark references for crop / feed processors, animal farms and food producers. Also in this case comparisons can be performed with respect to another participant that can also be from a different class and with respect to other classes.

[0054] In a further embodiment the assessment of the one or more parameters is performed utilising an impact assessment model. The impact assessment model can be based on any kind of machine learning algorithm that is trained based on the aggregated product data and respective associated environmental impact metrics or can refer to any kind of environmental impact assessment function that is based on physical laws and rules derived for respective production processes and production operations. The environmental assessment model utilises the one or more parameters of the participant and optionally further a part of the product data, for example, as default values. The generated output assessment data relates to an environmental impact with respect to the one or more parameters. For example, the product data can be utilised to set default values of quantities needed for a respective environmental impact metric calculation that are not known from the product data provided by the participant and do not refer to the one or more parameters to be assessed. Preferably the environmental impact assessment model is modular and thus utilises a plurality of calculation modules for calculating different parts of the environmental impact assessment. An example for such modules are provided in Figs. 5 to 8.

[0055] For example, Fig. 5 shows a flowchart for an environmental impact assessment model in an animal protein production example. A first module refers to receiving the input parameters referring to the respective one or more parameters but also to further known product data of the participant or to default values from the aggregated product data. The received and / or set product data can be amended to enable further processing, for instance, can be converted, mathematically merged, like averaged, etc. After this initial processing the input parameters can be provided to a further module that refers to calculating farm emissions referring in this case to pork farm. Farm emission can refer to any emission of the farm, in particular, all mass going out of the farm, like, the amount of pork, the manure, the gas emissions, etc. For example the input parameters can be utilised to model the output kilogram pork per feed input, a methane output per kilogram pork, the output kilogram pork per consumed electricity, etc. The output can be calculated by mass balancing the in- and output of the farm. The next model is than utilised for mapping the farm output to elementary flows, for instance, N2O, CH4, SO2 cycles within the pork production. In the module for the lifecycle inventory analyses the calculated elementary flows are utilised for determining an environmental impact. For example, ten different environmental impacts can be calculated like CO2emission, CH4emission, energy consumption, etc. In a further module the result of the environmental impact calculations can then be normalised and aggregated, for instance, weighting the different environmental impact factors to calculated an overall environmental impact result that can then be utilised for assessing the overall environmental impact of the production operation of the participant.

[0056] Fig. 6 shows exemplary another example for respective modules of the environmental impact assessment model with respect to poultry production. In this case the different production parts of the animal protein value chain for poultry production are schematically and exemplary shown in the upper part referring to the market ingredients utilising, for instance, for feeding, medicating, housing, etc. further, the feed production is shown in which the respective feed for the poultries is produced. In the next step of the animal protein value chain the animal / meat itself is produced by breeding, feeding and housing the poultry. In a next step the respective meat processing is performed in which poultry is slaughtered and the meat processed. In each of this parts of the food protein value chain different aspects can be identified that go in or out of the protein value chain and that can have an impact on the environmental impact assessment. For examples, utilities can be taken into account in almost each of the steps whereas during the animal meat production further medication can play a role and manure has to be disposed. In the lower part of the exemplary Fig. 6 different modules of an environmental impact assessment model for the poultry production are shown and which aspects and which parts of the food protein value chain are taken into account by these modules. Module 1 refers to a referenciation module that scales parameters and calculated results with respect to a functional unit, here exemplary to 1000 kg poultry. Thus module 1 can be utilized by other modules throughout the whole calculation of the food product value chain. Module 2 refers to a module that calculates the ingredient mass and thus takes into account all of the products, in particular, feed products, that are utilised until the end of the animal / meat production. Module 3 calculates a nutrition input / balance between the feed production and the meat output. Module 4 calculates a nutrition distribution and model 5 a manure management. Further, module 6 calculates the respective environmental impact of a transportation during the whole of the food protein value chain and a module 8 refers to taking the utilities into account.

[0057] A very specific example for the calculations that can be performed by the respective modules is given in Fig. 7. In this case the modules are utilised to calculate the N2O and CH4emissions for poultry raised at a respective poultry farm. In this Fig. 7 it is shown how the input data of a participant and in this case additional default data from aggregated product data is utilised to calculate the respective environmental impact of the poultry farm in the form of the respective N2O and CH4emissions.

[0058] Fig. 8 shows schematically and exemplary an embodiment in which the assessment of the one or more parameters of the participant is performed utilising the environmental impact model and the benchmark together. In this flowchart of the exemplary method first the input for the environmental impact assessment model is collected, for instance, as described above by utilising the one or more parameters of the participant, product data of the participant and / or aggregated product data. The respective collected data can then be stored in a database and can further be aggregated, for instance, the labelling the data or by removing any personal data. The aggregated data can then also be stored in a database. An impact assessment model can then be utilised to calculate the assessment data based on the aggregated product data and the one or more parameters of the participant. The assessment data can for instance, refer to a respective environmental impact metric as described above. The calculated assessment data can then be utilised as matric in a benchmark as described, for instance, with respect to Figs. 4 and 5. For example, the calculated assessment data can be compared to other participants or to another benchmark reference. The result of the benchmark can then be expressed on a respective product and operations level as an output to the participant. For example, a footprint of respective parts of the production operation can be provided to the participant. Moreover, the result can be visualized and further include information on how to improve the respective production operation, for example, in form of recommendations for the participant.

[0059] Fig. 9 shows schematically and exemplary functions of a user interface for interfacing with a respective operation assessment system as described above performing the respective functions as described above. For example, the interface can be configured to allow a user to select a benchmark reference like industry average, best in class, etc. Further, a user, like a participant, can select, for instance, a respective environmental impact goal with respect to which the environmental assessment should be performed. However, also a default scenario can be utilised in which preselected parameters are used for assessing the environmental impact. Further, the user interface can be configured to give one or more recommendations as results of the above assessment and depending on the selected reference. Moreover, the user interface can be configured to give respective recommendation based on the environmental impact goal or target that can also be selected from a dropdown-menu with different categories like CO2footprint, formulation optimisation, water consumption, emission, energy consumption, etc. Further, the user interface can be configured to provide the recommendation for a service or an equipment that might improve environmental impact. Also, statistical assessments like heat maps for the performance of a participant for the one or more parameters can be provided.

[0060] Fig. 10 shows schematically and exemplary date levels of the operation assessment system. In this figure, the operation assessment system is schematically shown in the middle comprising, referred to as rule base, an operation assessment unit that can be utilized for the operation assessment. However, additionally, the operation assessment system can comprise data processing parts that can be configured for data receiving, aggregation and consolidation such that the data can then be utilized by the rule base, in particular, by the operation assessment unit. Preferably, at least a part of the data processing part refers to an API utilizing a data model. On the left side, as first level, different possible data sources and formats are exemplary shown that can be provided as input to the operation assessment system. For example, the data represented on the left side can refer to manual input data, excel upload data, third party application data, literature data, etc. This data can refer to product data which can comprise production data and also environmental impact data. The data utilized by the environmental impact assessment system can come from databases shown, for instance, for the upper three data sources but can also come from individual sources, for instance, a participant node, as shown for the lower four examples. The operation assessment system then utilizes, for instance, an API, and a data model that allows to represent the received data that is exchanged between the API and the respective data sources. For example, the data model can define the structure, format and data type of the information that is exchanged utilizing the API. The API itself can be utilized as an interface that allows participants to access the environmental impact assessment system, for instance, its provided databases and functionalities that are provided on one or more respective servers. The API then defines the operations that the respective participants can perform and the data that they can access or modify. The relationship between the data model utilized by the API and the API can be regarded such that the data model defines the structure and format of the data that is exchanged through the API, while the API provides the interface for the participants to access that data and perform operations on it. Thus, the data model can be regarded as a component of the API that ensures that the data exchange between the participant, for instance, the participant node, and a server, is consistent and interoperable. In an example embodiment, the data model can be based on an XML file or a similar specification that defines for different data types the structure and format with which the data types can be exchanged with the environmental impact assessment system.

[0061] Fig. 1 1 shows schematically and exemplary a decentralized data organization for an operation system. The operation system can comprise a plurality of databases on which the respective product data is stored. A data manager can be utilized for managing, controlling and monitoring the access to the respective databases. This structure allows to distribute the data to different data owners and at the same time guarantee a central access organization.

[0062] Fig. 12 shows schematically and exemplary a decentralized data structure of the food protein value chain, wherein each of the respective databases 2200, 2202 and 2208 can be a source for product data or other information that can be usable for the environmental impact assessment of a food product. Moreover, the environmental impact assessment system can be configured to store at least some of the environmental impact assessment data on one or more of the respective databases 2200, 2202 and 2208 based on an ID of a food protein product or a chain of associated food protein product IDs. A food product or an intermediate ingredient 2720, e.g. a feed mix, arriving at a respective facility of a participant 220 can be identified. For example, a physical identifier, like a QR code can be scanned utilizing scanner 2206 and associated with a digital identifier. Based on the received intermediate ingredient or final food product 2720 utilizing the determined identifier 2222 a request to access product or environmental impact data associated with the intermediate ingredient or final food product can be triggered by a data consuming service 2210 of a participant. For example, the identifier may be provided 2212 to a data providing service 2208 associated with or of the producer of the intermediate ingredient or final food product. In addition, authentication and / or authorization information may be provided. The environmental impact data or assessment data associated with the intermediate ingredient or final food product can then be assessed via the identifier 2218 in the database 2202 of the producer. The database 2202 is a database associated with the producer and data owner of the intermediate ingredient or final food product. The one or more environmental impact data can refer to a product carbon footprint, N2, water consumption, recycled content or bio-based content and can be stored in database 2202 associated with the producer of the intermediate ingredient or final food product, e.g., feed mix, feed supplement, final food product e.g., a package of milk, eggs, meat, sausage, pizza, and the identifier. The associated data can then be provided via the data providing service 2208 to the data consuming service 2210 as signified by arrows 2220, 2216. The environmental impact data associated with the intermediate ingredient or final food product can then be stored in the data base 2208 associated with the user of the intermediate ingredient or final food product, e.g., a farm using the feed mix, as signified by arrow 2222.

[0063] Figs. 13a and 13b show schematically and exemplary product data that can be utilized for an environmental impact assessment, for example, for the benchmarking and / or for the model. In the first row of the tables the parameter name is provided. The following rows indicate a status of the parameter and typical or alternative sources. For example, a mandatory parameter is a parameter that in this example is necessary for the environmental assessment in this example case and thus has to be provided by the participant, if possible. Default values can be either set based as default, for instance, as an average from other data sources or from literature, or can be set to a specific value is available for the participant. Background values can be utilized, if available, but are only relevant for specific assessments or improvement goals. The data type and possible sources can, for instance, be defined in the data model. Generally, it is preferred that as many parameters as possible are provided by the participant to tune the assessment specifically to the production process of the participant. However, except of the mandatory parameters all other parameters utilized for the environmental impact assessment can be set to a default value. Generally, for other cases and environmental impact assessments also different parameter sets can be defined.

[0064] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0065] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in differing order. Furthermore, the outlined operations are only provided as examples, and some of the operations may be optional, combined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without detracting from the essence of the disclosed embodiments.

[0066] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0067] Procedures like the receiving of product data, the extracting of information, the applying of an environmental impact assessment function, the providing of assessment data, etc. performed by one or several units or devices can be performed by any other number of units or devices. These procedures can be implemented as program code means of a computer program and / or as dedicated hardware.

[0068] A computer program product may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0069] Any units described herein may be processing units that are part of a classical computing system. Processing units may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may include any computer-readable storage media such as a non-volatile mass storage. If the computing system is distributed, the processing and / or memory capability may be distributed as well. The computing system may include multiple structures as “executable components”. The term “executable component” is a structure well understood in the field of computing as being a structure that can be software, hardware, or a combination thereof. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component may include software objects, routines, methods, and so forth, that may be executed on the computing system. This may include both an executable component in the heap of a computing system, or on computer- readable storage media. The structure of the executable component may exist on a computer-readable medium such that, when interpreted by one or more processors of a computing system, e.g., by a processor thread, the computing system is caused to perform a function. Such structure may be computer readable directly by the processors, for instance, as is the case if the executable component were binary, or it may be structured to be interpretable and / or compiled, for instance, whether in a single stage or in multiple stages, so as to generate such binary that is directly interpretable by the processors. In other instances, structures may be hard coded or hard wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Accordingly, the term “executable component” is a term for a structure that is well understood by those of ordinary skill in the art of computing, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to acts that are performed by one or more processing units of the computing system. If such acts are implemented in software, one or more processors direct the operation of the computing system in response to having executed computer-executable instructions that constitute an executable component. Computing system may also contain communication channels that allow the computing system to communicate with other computing systems over, for example, network. A “network” is defined as one or more data links that enable the transport of electronic data between computing systems and / or modules and / or other electronic devices. When information is transferred or provided over a network or another communications connection, for example, either hardwired, wireless, or a combination of hardwired or wireless, to a computing system, the computing system properly views the connection as a transmission medium. Transmission media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or specialpurpose computing system or combinations. While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for use in interfacing with a user. User interfaces act as input or output mechanism to users for instance via displays.

[0070] Those skilled in the art will appreciate that at least parts of the invention may be practiced in network computing environments with many types of computing system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, pagers, routers, switches, datacenters, wearables, such as glasses, and the like. The invention may also be practiced in distributed system environments where local and remote computing system, which are linked, for example, either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0071] Those skilled in the art will also appreciate that at least parts of the invention may be practiced in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and / or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources, e.g., networks, servers, storage, applications, and services. The definition of “cloud computing” is not limited to any of the other numerous advantages that can be obtained from such a model when deployed. The computing systems of the figures include various components or functional blocks that may implement the various embodiments disclosed herein as explained. The various components or functional blocks may be implemented on a local computing system or may be implemented on a distributed computing system that includes elements resident in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or less than the components illustrated in the figures and some of the components may be combined as circumstances warrant.

[0072] Any reference signs in the claims should not be construed as limiting the scope.

[0073] The invention refers to a method for assisting in improving a production operation in a food protein value chain with respect to an environmental impact. Product data of a food product from a participant is received. The received product data is stored on a database The product data is aggregated to enable further processing by an operation assessment system A request for an assessment of a parameters in a production operation of a participant is received. The aggregated product data and the parameter are forward to the operation assessment system. The parameter is assessed based on the aggregated product data, wherein the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to the parameter and an environmental impact. The improvement data associated with the parameter is provided to a requesting node of the participant.

Claims

Claims1 . A computer-implemented method for assisting in improving a production operation of a participant in a food protein value chain with respect to an environmental impact of the production operation, wherein the method comprises: receiving product data associated with a food protein value chain of a food product from one or more participants of the food protein value chain, storing the received product data on an electronic database, aggregating the product data to enable further processing by an operation assessment system, receiving, from a requesting node associated with a participant, a request for an assessment of one or more parameters in a production operation of the participant associated with the food product value chain, forwarding the aggregated product data and the one or more parameters to the operation assessment system, assessing, by the operation assessment system, the one or more parameters of the participant of the food protein value chain based on the aggregated product data, wherein the assessment comprises generating improvement data indicative of determined improvements of the production operation of the participant with respect to the one or more parameters and an environmental impact of the production operation of the participant, and providing the improvement data associated with the one or more parameters to the requesting node of the participant.

2. The method according to claim 1 , wherein the assessing comprises performing a benchmark based on the one or more parameters of the participant and the aggregated product data and generating the improvement data based on the benchmark.

3. The method according to claim 2, wherein the benchmark comprises selecting a benchmark reference from one or more predetermined benchmark references and comparing the one or more parameters of the participant to parameters identified in the aggregated product data as belonging to the selected benchmark reference.

4. The method according to any of claims 2 and 3, wherein the predetermined benchmark references refer to parameters defined by industry average, industry standard and / or best in class or refer to parameters of one or more other participants identified in the aggregated product data.5 The method according to any of claims 3 and 4, wherein the improvement data is generated further based on the benchmark reference.

6. The method according to any of the preceding claims, the assessing comprises utilizing an environmental impact model and providing the one or more parameters to the environmental impact model, wherein the environmental impact model is configured for determining assessment data based on the one or more parameters, wherein assessment data is indicative of an environmental impact of at least a part of a food protein value chain associated with the participant, wherein the improvement data is generated further based on the assessment data.

7. The method according to claim 6, wherein the assessment data is a benchmark metric for the one or more parameters and wherein the assessing comprises performing a benchmark based on the benchmark metric and the aggregated product data and generating the improvement data based on the benchmark.

8. The method according to any of the preceding claims, wherein aggregating the received product data comprises structuring and labeling product data of different participants to generate the aggregated data.

9. The method according to any of the preceding claims, wherein the food product comprises animal protein and / or plant based protein.

10. A computer-implemented method for improving a production operation of a participant in a food protein value chain, wherein the method comprises: collecting information associated with a food protein value chain of a food product of a participant of the food protein value chain,generating product data based on the collected information and providing the product data to an operation assessment system, providing a request to the operation assessment system for an assessment of one or more parameters in a production operation of the participant, wherein the request comprises the one or more production parameters, receiving, by the operation assessment system, improvement data associated with the one or more parameters and generated according to any of claims 1 to 9, wherein the improvement data is indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and implementing the information included in the improvement data into a production of the food product.

11. An environmental impact assessment system for assisting in improving a production operation of a participant in a food protein value chain, wherein the apparatus comprises one or more processors configured to: receiving product data associated with a food protein value chain of a food product from one or more participants of the food protein value chain storing the received product data on an electronic database, aggregating the product data to enable further processing by an operation assessment system, forwarding the aggregated product data to the operation assessment system, receiving, from a requesting node associated with a participant, a request for an assessment of one or more parameters in a production operation of the participant associated with the food product value chain, assessing, by the assessment system, the one or more parameters of the participant of the food protein value chain based on the received product data, whereinthe assessment comprises generating improvement data indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and providing the improvement data associated with the one or more parameters to the requesting node of the participant.

12. An apparatus for improving a production operation of a participant in a food protein value chain, wherein the apparatus comprises one or more processors configured for: collecting information associated with a food protein value chain of a food product of a participant of the food protein value chain, generating product data based on the collected information and providing the product data to an operation assessment system, providing a request to the operation assessment system for an assessment of one or more parameters in a production operation of the participant, wherein the request comprises the one or more production parameters, receiving, by the operation assessment system, improvement data associated with the one or more parameters and generated according to any of claims 1 to 9, wherein the improvement data is indicative of possible improvements of the production operation of the participant with respect to the one or more parameters, and implementing the information included in the improvement data into a production of the food product.

13. A computer program product for assisting in improving a production operation of a participant in a food protein value chain, wherein the computer program product causes an apparatus according to claim 11 to perform the method according to any of claims 1 to 9 when carried out on the apparatus.

14. A computer program product for improving a production operation of a participant in a food protein value chain, wherein the computer program product causes an apparatus according to claim 12 to perform the method according to claim 10 when carried out on the apparatus.

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