Zuhara Chavez

Jannicke Baalsrud Hauge

Monica Bellgran
email: bellgran@kth.se

A conceptual model for deploying digitalization in SMEs through capability building

Keywords: Sustainable Production, Managerial Capabilities, Digital Transformation, SME, Manufacturing Industry

This paper proposes a conceptual implementation model for small and medium enterprises (SMEs) to follow as part of their digitalization implementation. It can later be translated into a practical step by step guide for SMEs to practice during their digital transformation. The model is based on gradually developing industrial capabilities that can influence production processes performance. The model development was based on a critical literature review and a real case industry application. The case data served as direct feedback to the model to assess both the model validity and the actual SMEs needs. The capabilities included in the model are proved to directly influence the performance positively. In comparison with existing models and frameworks, this model envisions the company a full digital shift by proposing an achievable sequence which SMEs in a resource-efficient way could start deploying in compliance with their business needs. SMEs can utilize the capabilities as a foundation for a system that supports continuous improvement in the whole factory.

Introduction

The Industry 4.0 (I4.0) concept is based on the emergence of new technologies so that business process and engineering process are deeply integrated enabling production to operate in a flexible, efficient, and green way with high quality at low cost [START_REF] Wang | Implementing Smart Factory of Industrie 4.0: An Outlook[END_REF], [START_REF] Mukhopadhyay | PCB inspection in the context of smart manufacturing[END_REF]. Such technologies should improve the transmission of information throughout the entire system, and thereby enable better control and operations to be adapted in real-time according to varying demand [START_REF] Moeuf | The industrial management of SMEs in the era of Industry 4.0[END_REF]. The concept is being widely spread around the world, given the trust that incorporating emerging technical advancement can improve the industry's ability to deal with global challenges [START_REF] Wang | Implementing Smart Factory of Industrie 4.0: An Outlook[END_REF]. However, the SME perspective has not sufficiently been taken into account in terms of framing the appropriate I4.0 policies [START_REF] Mittal | Towards a Smart Manufacturing Maturity Model for SMEs ( SM3E )[END_REF] even though they form the backbone of the European Economy.

The terminology and the so-called nine elements i.e. "foundational technology advances" [START_REF] Rüßmann | Industry 4.0: The future of productivity and growth in Manufacturing Industries[END_REF] connected to I4.0 are defined conceptually and treated as single elements both in research and in application [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0based manufacturing systems[END_REF]- [START_REF] Gomes | Cloud manufacturing as a sustainable process manufacturing route[END_REF]. However, the reality of SMEs positioning in such a context is not clearly defined. I4.0 projects in SMEs often remain cost-driven initiatives and there is still no evidence of real business model transformation at this time [START_REF] Moeuf | The industrial management of SMEs in the era of Industry 4.0[END_REF]. The practical cases of deployment found in research are often centered around showing single applications, highlighting the low-cost factor as a main advantage and strength of their application [START_REF] Mukhopadhyay | PCB inspection in the context of smart manufacturing[END_REF], [START_REF] Ud Din | Agent-oriented smart factory (AOSF): An MAS based framework for SMEs under industry 4.0[END_REF]- [START_REF] Wang | An empirical study on the key factors of intelligent upgrade of small and medium-sized enterprises in China[END_REF].

One major challenge faced by the implementation of the I4.0 concept is the robustness and resilience of the manufacturing production system, that is, the ability to absorb manufacturing disruptions without failing or breaking and to adapt to major variations/disturbances and gradually return to its original level of performance [START_REF] Martínez-Olvera | A Max-Plus Algebra Approach to Study Time Disturbance Propagation within a Robustness Improvement Context[END_REF], [START_REF] Spiegler | A control engineering approach to the assessment of supply chain resilience[END_REF]. New technologies enable an entirely new set of capabilities, which according to [START_REF] Porter | How Smart , Connected Products Are Transforming Competition[END_REF] can be grouped into four areas: monitoring, control, optimization, and autonomy. There is a close relation between these capabilities and the technologies or technical resources available in order to achieve the performance targets. Given the defined four capabilities, little is said about how can SMEs develop each one of them to achieve "self-awareness and autonomy" and how they can connect to production performance.

SMEs might struggle to adapt to external market forces and to make the next step in expanding their business due to a lack of expertise and resources [START_REF] Gomes | Cloud manufacturing as a sustainable process manufacturing route[END_REF]. Therefore, it is necessary to embrace a well assessed and affordable path for the adoption of new technologies that allow them to develop the needed managerial capabilities and create the ability to better handle deviations in their production system. In order to contribute to knowledge building within the area, this paper focuses on a main research question: How can SMEs conduct a digital transformation that supports and aligns with deviation handling for production system performance improvement? The results will be demonstrated by a model that connects the mentioned elements and depicts the digitalization deployment for SME; which to the best of our knowledge is lacking.

2

Theoretical background

Managerial capabilities of production processes

Acknowledging the technical, managerial and operational aspects of digitalization will make SMEs more aware of their own organizational dimensions like finance, product, process, and people. This awareness will result in informed decision making [START_REF] Mittal | Smart manufacturing: Characteristics , technologies and enabling factors[END_REF], which leads to more accurate judgments and results in close coordination with production plans, operations and supply chains. New technologies are designed to support processes such as data collection, integration, storage, analysis and visualization [START_REF] Tao | Data-driven smart manufacturing[END_REF] and they can be deployable in a variety of industries. However, those technologies can generally not be adopted independently, and they require management involvement and support to succeed. It is also important to understand the circumstances of technologies being adopted, the extend of the technology adoption i.e. incremental or radical [START_REF] Mittal | Smart manufacturing: Characteristics , technologies and enabling factors[END_REF], [START_REF] Stoldt | Planning for Digitalisation in SMEs using Tools of the Digital Factory[END_REF], as well as the system, process or the activity that needs to be improved by technologies. SMEs, in particular, may not always be aware of the understanding and capabilities required from management to implement such technologies. In a model [START_REF] Moeuf | The industrial management of SMEs in the era of Industry 4.0[END_REF] connect [START_REF] Porter | How Smart , Connected Products Are Transforming Competition[END_REF] managerial capabilities with the concept of I4.0 (as means of implementation) their analytical model stated the importance of classifying I4.0 elements in terms of the desired performance objective and the corresponding managerial capacity that needs in place. It shows a close relationship between the targeted objectives, the levels of managerial capacity pursued and the technical resources required to achieve them.

2.2

Disturbance handling as part of the Industry 4.0 paradigm

The ability to absorb manufacturing disruptions and to adapt is a crucial characteristic to achieve with the support of the I4.0 concept and elements implementations. According to [START_REF] Martínez-Olvera | A Max-Plus Algebra Approach to Study Time Disturbance Propagation within a Robustness Improvement Context[END_REF], [START_REF] Kristianto | Building the 'Triple R' in global manufacturing[END_REF] robustness and resilience do not necessarily co-exist, but both provide production systems with a sustainable competitive advantage. When trying to connect disturbances and deviations to I4.0, the terms are assessed in works that address resilience and robustness [START_REF] Martínez-Olvera | A Max-Plus Algebra Approach to Study Time Disturbance Propagation within a Robustness Improvement Context[END_REF], [START_REF] Boorla | Product robustness philosophy -A strategy towards zero variation manufacturing (ZVM)[END_REF], [START_REF] Morisse | Design of a Business Resilience Model for Industry 4.0 manufacturers[END_REF], with focus on a) analyzing the variation and its adjustability, b) analyzing variation in terms of disturbances propagation their effects and that leads to c) concentrating on the characteristics necessary to build resilience.

Research highlights the principle of adjustability as one of the aspects that comply with I4.0 demands when focusing on proactive communication [START_REF] Boorla | Product robustness philosophy -A strategy towards zero variation manufacturing (ZVM)[END_REF]. The deviation is mentioned only as variation in the physical product specification related to quality and design. On their work [START_REF] Martínez-Olvera | A Max-Plus Algebra Approach to Study Time Disturbance Propagation within a Robustness Improvement Context[END_REF] studies the propagation of disturbances, making a clear distinction between resilience and robust system and highlights the objective of systems for I4.0, which are intended to be robust i.e. have the ability to absorb disruptions without failing or breaking and resilient i.e. have the ability to adapt to major disturbances and gradually return to its original level of performance. According to [START_REF] Morisse | Design of a Business Resilience Model for Industry 4.0 manufacturers[END_REF], six characteristics are necessary to achieve resilience in I4.0: flexibility, diversity, connectivity, knowledge, redundancy, and robustness. Resilience is highlighted as a competitive approach, and robustness is mentioned as one of the characteristics for resilience. One common factor presented in the mentioned studies is the variation analysis in the production processes and operations; they all agree that it is necessary to control and stabilize variation to reduce disturbances and deviations. However, the spectrum is closed to product variation, which is far from the aim of full integration with I4.0 or full digital transformation. The characteristics to be resilient may be identified in works like [START_REF] Morisse | Design of a Business Resilience Model for Industry 4.0 manufacturers[END_REF], but models and guidelines on how exactly this can be achieved are lacking.

A model for digitalization deployment in SMEs

The literature review indicates that analytical models for managerial capabilities exist but there is a practical approach missing in terms of sequence and concrete simplified guidance on how to integrate performance objectives. For our theoretical model, we adopted the analytical model proposed by [START_REF] Moeuf | The industrial management of SMEs in the era of Industry 4.0[END_REF] as a building base, due to the elements it comprehends: capabilities, means of implementation and operational performance objectives. The four capabilities defined initially by [START_REF] Porter | How Smart , Connected Products Are Transforming Competition[END_REF] are to be linked together, each capability builds on the next one, as valuable in its own right and also sets the stage for the next level. In that sense, a company won't be able to control and optimize without first having a monitoring system in place.

Figure 1 represents the elements in three main categories: 1. managerial capabilities, 2. means of implementation and 3. operational performance objectives. They are arranged in a suggested deployment order from left to right. SMEs are sometimes lacking formal systems to control and measure their performance. That was the reason why we considered the operational performance to be initially measured by the three elements of the OEE KPI. To our knowledge, most SMEs independent of industry and production strategy, often have an initial understanding of these three elements. The OEE data is not always analyzed, but that data itself is often generated. With the introduction of OEE as an initial step, a gradual connection to production disturbances is expected, as is the propagation and their effects on performance. A reactive approach is often common practice among SMEs, therefore our model's goal is to make the shift to a proactive approach possible, by developing capability three and four.

Practical application on the capabilities building Practical case: A deployment process

The case selected is a Swedish SME with more than 75 years of experience in manufacturing of fasteners and industrial components for the automotive and engineering industries. In 2019, with the objective to improve its disturbance handling and become more autonomous, the company formally started its digital transformation by the development and implementation of a low cost and tailor-made digital tool. The project was interlinked with a research team (advisors) and a couple of students (developers). Table 1 describes the practical implications of the implemented elements in the case study as illustrated in figure 2, which is a visual representation of the elements that the prototype tool covers. A local network was built around a system containing a PLC and HMI with attached sensors, the local network then connected to a server that processed the data i.e. "means for implementation". Changing how data was processed (from manual to digital), was a major step to build on the control capability. It made monitoring in real-time possible, and the operators to start interacting with the system. Manual data was initially necessary to get an overview and understanding of the environment to develop the tool. However, for a system that was required to be resilient and robust, that was not enough, trustworthy data was also vital. Due to the length of this paper, we omit details on the actual OEE calculations.

Table 1. Practical details on the conceptual model elements.

Element Practical details

Monitoring

Starting level. Production data needed to be recorded. The goal was to have the data automatically recorded and avoid human error by manual logs, however, it was necessary to start working with manual data while the sensors were in place. In about 8 weeks, the company migrated to automatic logs.

Control

Aiming to define production targets. Using the recorded data, the company performed an analysis that allowed them to set targets that could be used as comparison points when obtaining new data from the production processes. OEE was introduced.

Optimization

On plan -not deployed at the moment. The goal will be to improve by monitoring data, system models, simulation systems, production system, and resources. Autonomy On plan and set as the ultimate goal, when the system can learn from the inputs and its own behavior and can adapt itself. Shift from reactive behavior to proactive response will be enabled.

Operational performance objectives OEE in terms of AxPxQ A: Availability %. Data became available digitally from the machines (configurable, initial data was collected manually by the operators, special attention on downtime). P: Performance efficiency %. Data became available digitally from the machines (configurable, initial data was collected manually since the only existing records were in ERP system and not updated, special attention on cycle times). Q: Quality. Data is both available from manual records and digitally, depending on the machine. Some quality measures are digitalized but not part of this calculation.

Discussion

Being the topic of digitalization in a novel field, it becomes harder to create a model that fits in a higher set of manufacturing/production environments. Nonetheless, the deployment case allows us to offer some analytical generalization as a result of the lessons learned:

 Means of implementation in higher capabilities. The path for deploying the four managerial capabilities in the proposed conceptual model is set systematically and in a defined order. However, we can infer that the means of implementation may not necessarily follow a strict order of deployment, especially for the last five blocks: internet of things, CPS, cybersecurity, virtual reality, and autonomous robots. The deployment order may highly depend on the company strategy and business needs.

For instance, a company may decide to prioritize virtual reality for training purposes without necessarily have a CPS in place.  Adapting I4.0 elements. Taylor-made digital solutions could be a more reliable way for SMEs to develop managerial capabilities. The key lays in incorporating their needs into a digital system that integrates the generated data for analysis purposes to support decision making.  Resilience and robustness coexisting in I4.0 for SMEs. Robustness is a priority when discussing disturbance and deviation handling. With the introduction of digital tools, SMEs can little by little develop their production systems to have the desired abilities.  OEE as building base for a performance improvement system. OEE gives a good enough overview of any factory, based on production data that most of the SMEs are interested to track and in most cases already are generating: availability, performance, and quality. It allows setting realistic thresholds to integrate into the digital tools and to monitor against.

Conclusion and future work

This paper presents a conceptual model for the deployment of digitalization in SMEs, based on the development of four managerial capabilities of a production process. A real industry case was used as base for both developing and testing the early stages of the conceptual model, exemplifying how SMEs with limited resources could initiate their digital transformation process. The model proposes a deployment sequence that connects directly the elements of I4.0 and suggests the measurement of performance objectives by using OEE as initial metric (KPI). The limitation of the research presented in this paper is that only two practical capabilities have been tested due to the early stage of the company's digitalization journey. Future work could be devoted to following the progression of the manufacturing company in achieving the higher capabilities presented in the model. The characteristics of the practical side when talking about resilience and robustness is furthermore interesting to explore, to build new knowledge ensuring that SMEs build on their digitalization based on a good theoretical foundation.

Fig. 1 .

 1 Fig. 1. Model for deploying manufacturing digitalization in SMEs based on [3].

Fig. 2 .

 2 Fig. 2. Exemplification of model deployment: Industrial case.

Acknowledgements

This work is part of the project ASPIRE "Automation solutions for production deviation management", funded by Sweden's Government Agency for Innovation VINNOVA (Programme Produktion2030). The authors thankfully acknowledge the funding agency, the good collaboration and support from the case company and the feedback from Alvis Sokolovs which helped to improve the manuscript.


Fabian Dietrich
email: fabian.dietrich@reutlingen-university.de

Ali Turgut

Daniel Palm

Louis Louw

Smart contract-based blockchain solution to reduce supply chain risks

Keywords: Blockchain, Smart Contract, Supply Chain Risk Management, Sustainability, Supply Chain Control

Companies are becoming aware of the potential risks arising from sustainability aspects in supply chains. These risks can affect ecological, economic or social aspects. One important element in managing those risks is improved transparency in supply chains by means of digital transformation. Innovative technologies like blockchain technology can be used to enforce transparency. In this paper, we present a smart contract-based Supply Chain Control Solution to reduce risks. Technological capabilities of the solution will be compared to a similar technology approach and evaluated regarding their benefits and challenges within the framework of supply chain models. As a result, the proposed solution is suitable for the dynamic administration of complex supply chains.

Introduction

Companies are becoming increasingly aware of the potential risks arising from supply chains (SCs). Studies reveal that SC risks and associated business interruptions are considered to be one of the most important global business risks [START_REF] Dobie | Allianz Risk Barometer -Results Appendix 2020[END_REF][START_REF] Dobie | Allianz Risk Barometer[END_REF]. This results in a growing need for proactive risk management in the SC across industries. Companies need to respond to these challenges in order to fully identify and manage risks in their SCs in order to understand and manage the risks and vulnerabilities [START_REF] Palm | Efficiency-oriented risk prioritisation method for supply chains in series production[END_REF].

Innovative and Industry 4.0 technologies like tracking and tracing with RFID, sensors and IoT-devices can be used to overcome these challenges [START_REF] Kersten | Potenziale der Digitalisierung für das Supply Chain Risikomanagement: Eine empirische Analyse[END_REF][START_REF] Ivanov | The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics[END_REF]. One of the current emerging technologies is blockchain technology, a distributed and immutable database that allows transactions to be conducted directly and transparently between parties [START_REF] Bosch | Blockchain als Treiber im modernen Supply Chain Management 4[END_REF] while at the same time permitting the programming of algorithms and rules of so-called Based on the potential of the blockchain technology, research approaches use the technology as an innovative solution for challenges in the area of Supply Chain Risk Management (SCRM). Layaq et al. [START_REF] Layaq | Blockchain Technology as a Risk Mitigation Tool in Supply Chain[END_REF] for example, examined the potential of the blockchain in the area of supply risks which revealed that the use of blockchain and smart contracts can lead to improvements in material and information flows in order to reduce disturbance risks. This is enabled by increasing visibility (accessing or sharing information with participants of the SC network [START_REF] Barratt | Antecedents of supply chain visibility in retail supply chains[END_REF]), transparency (disclosure of information to all stakeholders, including customers [START_REF] Doorey | The Transparent Supply Chain: from Resistance to Implementation at Nike and Levi-Strauss[END_REF] and transaction automation along the SC. Due to their structural design, traditional SCRM tend to take action reactively after damage has occurred. With blockchain technology it is possible to design action proactively and preventively as well as manage non-physical risks -such as cyberattacks or miscommunications -more efficiently [START_REF] Min | Blockchain technology for enhancing supply chain resilience[END_REF].

This paper focuses on the area of network control risks, since the proposed smart contract-based blockchain solution aims to enhance the transparency and efficiency of information flows. Therefore, the paper briefly presents current problems with conventional token standards in this context. Subsequently, a new extended token-based approach is proposed. Finally, the characteristics of the two solutions are compared.

2

Background and rationale of the paper Nowadays, companies have to deal with the growing interests of customers, governments, and non-governmental organizations in having a greater transparency of brands, manufacturers, and producers throughout the SC [START_REF] New | The Transparent Supply Chain[END_REF][START_REF] Chen | Impact of Supply Chain Transparency on Sustainability under NGO Scrutiny[END_REF]. As a result, social and environmental sustainability issues have become increasingly important for manufacturers in order to maintain the flawless reputation of their brand [START_REF] Lemke | Managing Risks in Supply Chains[END_REF]. However, as SCs become more global, many suppliers in the network can be located in developing economies where governments have only limited ability and willingness to enforce their own laws [START_REF] Chen | Impact of Supply Chain Transparency on Sustainability under NGO Scrutiny[END_REF]. In addition to the risk of being unintentionally involved in social and environmental sustainability issues, a lack of transparency also increases the probability of counterfeit components being introduced into a SC. For example, the counterfeiting of electronic parts causes potential risks including safety and loss of profits to companies, as well as maligning the reputation of manufacturers and distributors [START_REF] Collier | Supply Chains[END_REF][START_REF] Pecht | The Counterfeit Electronics Problem[END_REF][START_REF] Dimase | Traceability and Risk Analysis Strategies for Addressing Counterfeit Electronics in Supply Chains for Complex Systems[END_REF]. It can therefore be summarized that the dispersed nature of today's SCs creates increasing levels of network control risks for multinational businesses, making transparency of SCs both critical and complex [START_REF] Linich | The path to supply chain transparency[END_REF][START_REF] Brandon-Jones | A contingent resource-based perspective of supply chain resilience and robustness[END_REF]. Notably, the problem of incorporating tampered counterfeit parts in assemblies introduces a vulnerability that must be prevented and a gap in present research [START_REF] Collier | Supply Chains[END_REF].

In particular, when applying the blockchain technology to SC management, companies have high expectations to solve transparency and auditability issues of complex collaborative SCs [START_REF] Iansiti | The Truth About Blockchain[END_REF][START_REF] Hackius | Blockchain in logistics and supply chain: trick or treat? Hamburg[END_REF]. A first approach by Abeyratne et.al. proposes that physical assets in combination with their 'unique digital profiles' on the blockchain can solve transparency problems of manufacturing SCs [START_REF] Abeyratne | Blockchain ready manufacturing supply chain using distributed ledger[END_REF]. Based on this approach, Westerkamp et.al. developed a solution using special non-fungible tokens in order to represent batches of manufactured products on the blockchain [START_REF] Westerkamp | Tracing manufacturing processes using blockchainbased token compositions[END_REF]. However, conventional nonfungible token standards such as the ERC-721 [START_REF] Entriken | EIP 721: ERC-721 Non-Fungible Token Standard[END_REF] are not specifically designed to map SCs. In combination with the immutability of the blockchain technology [START_REF] Westerkamp | Tracing manufacturing processes using blockchainbased token compositions[END_REF], nonfungible tokens enable a static reconstruction of SCs but show weaknesses when mapping dynamic changes in the composition of products or the structure of the SCs.

Extended token-based solution

This paper proposes a new solution aiming to create a smart contract with a static address on the network, but still enabling a dynamic mapping of changes in the SC. In conventional token standards, a transaction to change a token's ownership does not send the token itself to a new address; it is an interaction with the token contract assigning a new owner address to the token. This fundamental functionality of tokens is used to create a new approach specifically designed to adopt supply chain characteristics.

From the very beginning, this extended smart contract-based approach includes the identification and mapping of all stakeholders in the supply chain. For this purpose, the SC participants are divided into SC partners (suppliers, producers and service providers), customers and certifiers [START_REF] Westerkamp | Tracing manufacturing processes using blockchainbased token compositions[END_REF][START_REF] Bechini | Patterns and technologies for enabling supply chain traceability through collaborative e-business[END_REF] Similar to the token standard-based approach [START_REF] Westerkamp | Tracing manufacturing processes using blockchainbased token compositions[END_REF], the creation of virtual identities on the blockchain is strictly connected to dependencies reflected in their 'recipes'. In this context, a distinction can be made between two types of assets:

 Assets without dependencies: can be created without depending on previous actions (e.g. raw materials or certificates).  Assets with dependencies: can only be created when previous actions have been successfully conducted.

In order to map SC processes, the virtual identities of assets must have the same ownership and conversion characteristics as their physical counterparts. They must be able to change their owners when the physical product is transferred in the SC or sold. Furthermore, the virtual identities must be able to be summarized when combining several components into a new product. These events can then be tracked in the blockchain and are accessible for all partners in the SC. All nodes of the network agree on a common time by implementing a consensus algorithm. As a result, the immutability of the network guarantees that a certain event on the network happened at a certain time Fig. 2.

Fig. 2. Blockchain technology as verified public timestamps (based on Grossman [28])

To ensure this traceability by using the blockchain technology, a link between the blockchain and the physical product must be established. Accordingly, this approach assumes that all data can refer to an asset itself. For this purpose, the smart contract generates unique identification identities (IDs). A unique ID refers to the virtual identity of an asset. For the uniqueness of these IDs the extended solution proposes hashing the asset's information. Therefore, Hash IDs are a logical result of their input data and provide initial information about the origin, composition, and time of creation. Hash IDs can also be referred to as the smart contract version of conventional primary keys. Within the smart contract, these Hash IDs refer to the virtual identity of their physical counterparts. Similar to other blockchain approaches, these Hash IDs can be attached to the physical parts in the form of barcodes, RFIDs or QR codes [START_REF] Abeyratne | Blockchain ready manufacturing supply chain using distributed ledger[END_REF]. The smart contract logic of a function creating an asset with dependencies is shown in Fig. 3. To map such a logic, it requires the modelling, planning, and definition of further various smart contract functions. Therefore, it is necessary to carefully design all required conditions to create the virtual identity of an asset and its associated Hash ID. The creation of assets is firmly bound to the address of the responsible entity. This means that only a selected authorized entity is able to create the respective virtual identity. The address of the creator is then authorized to send this virtual identity to a new owner. As soon as the transaction has been successfully confirmed by the network, only the new owner has the rights to cause further actions related to the sent virtual identity. In this way, the same functionality in terms of asset 'moving' and asset 'recipes' as with non-fungible token standards can be achieved, except that there is now more scope to link the respective smart contract functions to defined authorities and to execute dynamic adjustments.

Fig. 3. Model to create virtual identities for assets with dependencies

Since the structure of SCs is subject to dynamic influences, the smart contract must also allow dynamic changes of content. This not only affects the modification of structural elements but also the composition of products. An initial design of such a dynamic smart contract structure is presented in section 3.1.

Holistic smart contract structure

The assigned authorities and the functions for creating virtual identities are embedded into algorithms allowing the mapping of dynamic changes in the SC while the smart contract's address remains static. For these complex algorithms, this paper proposes to include existing approaches to continuously update and generate smart contracts [START_REF] Bechini | Patterns and technologies for enabling supply chain traceability through collaborative e-business[END_REF][START_REF] Frantz | From Institutions to Code: Towards Automated Generation of Smart Contracts[END_REF]. Fig. 4 shows the design of the holistic smart contract structure. It is important to state, that such smart contract constructs are affected by the immutability of the blockchain technology. Consequently, any eventuality that could affect the SC or product structure must be considered in the program code before its deployment. If, for example, the addition of new suppliers is not taken into account in the code, they cannot be added. A new smart contract with a new address must be deployed. Furthermore, the proposed structure allows involving privacy approaches to encrypt the content of the smart contract [START_REF] Yuan | ShadowEth: Private Smart Contract on Public Blockchain[END_REF]. In this way, the interactions with the contract would remain visible to the public to provide SC transparency. The content of these transactions, however, would be encrypted and only visible for a defined group of authorities.

Comparison of solutions and conclusion

This section compares the characteristics of blockchain-based Supply Chain Control Solutions. On the one hand, these are the characteristics of the ERC-721-based smart contracts [START_REF] Entriken | EIP 721: ERC-721 Non-Fungible Token Standard[END_REF] (non-fungible token-based standard) which was adopted by Westerkamp et al. [START_REF] Westerkamp | Tracing manufacturing processes using blockchainbased token compositions[END_REF] for manufacturing SCs and on the other hand, the characteristics of the extended token-based solution proposed in this paper (see Table 1). An analysis of the two solutions in terms of suitability for SC types (see, for example, Lee [START_REF] Lee | Aligning Supply Chain Strategies with Product Uncertainties[END_REF]) shows that the conventional non-fungible token standard is mainly suitable for simpler SCs following an efficient strategy with a single source approach with a low number of changes over time. This makes it much easier to implement and manage downstream changes in the SC. In responsive scenarios the flexibility also means that the product must be constantly adapted and changed according to the requirements of the customer [START_REF] Lee | Aligning Supply Chain Strategies with Product Uncertainties[END_REF]. Therefore, a responsive strategy is preferably suitable in combination with the proposed smart contract-based solution allowing continuous configurations. This also applies to companies choosing more flexible strategies, such as the risk-hedging strategy and agile strategy. Especially in complex SCs, this solution allows a continuous administration of the SC and the adoption of structural changes. To what extent the changes of such complex SCs can be foreseen when designing the smart contract, should be further investigated. The limitations of the dynamics in the SC in terms of product composition and SC structure also need to be researched. Since the flexibility and the resulting complexity of SCs depicts a challenge [START_REF] Ivanov | The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics[END_REF], the proposed solution represents a promising approach for industry sectors with a high degree of product customization like the automotive industry or sectors following Industry 4.0 concepts. Further research on this topic is currently being conducted. Furthermore, it is necessary to investigate the scalability of such solutions, since the scalability currently represents a limitation of the blockchain technology.

Fig. 1 .

 1 Fig. 1. Areas of supply chain risks (own illustration based on [4, 9, 10])

Fig. 4 .

 4 Fig. 4. Holistic smart contract structure Such a structure allows a wide range of possibilities in terms of the allocation of authorities, which is determined in the dynamic updating algorithm related to the list of authorities. It is possible to virtually represent decentralized and open SCs with an even distribution of power, as well as strictly regulated SC structures with only one central authority. As illustrated in Fig. 4, this determines to what extent the authorities can have an impact on changes in the virtual identity logic.

Table 1 .

 1 Comparison of blockchain-based Supply Chain Control Solutions


Bahrudin Hrnjica
email: bahrudin.hrnjica@unbi.ba

Selver Softic
email: selver.softic@campus02.at

Explainable AI in Manufacturing: A Predictive Maintenance Case Study

Keywords: Explainable AI, Predictive Maintenance, Production Management

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.

Introduction

Predictive Maintenance (PdM) anticipates maintenance needs to avoid costs associated with unscheduled downtime. By connecting to devices and monitoring the data the devices produce, we can identify patterns that lead to potential problems or failures. Those insights can be used to address issues before they happen. This ability to predict when equipment or assets need maintenance allows us to optimize equipment lifetime and minimize downtime [START_REF] Lee | Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data[END_REF].

The fundamental litmus test for explainable AI (XAI) -that is, machine learning algorithms and other Artificial Intelligence systems that produce outcomes that humans can readily understand and track backwards to the origins [START_REF] Kim | Explainable artificial intelligence (xai), the goodness criteria and the grasp-ability test[END_REF].

In this case study we will consider the field of maintenance in manufacturing. More precisely we will deal with PdM by involving explainable AI outputs as base for our decisions and predictions.

Explainable AI -XAI

Recent success of Machine Learning (ML) led to series of application scenarios for Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users.

The Explainable AI (XAI) program introduced by DARPA3 aims to create a suite of ML techniques that:

-Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and -Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

For decision makers who rely upon Data Analytics and Data Science, explainability is a real issue. If the computational system relies on a simple decision model such as logistic regression, they can understand it and convince executives who have to sign off on a system because it seems reasonable and fair. They can justify the analytical results to shareholders, regulators, and other involved stakeholders. But for "Deep Nets" and ML systems, this is no longer possible. There is a need to find ways to explain the system to the decision maker so that they know that their decisions are going to be reasonable. The goals of explanation involves reaching a persuasion, but that comes only as a consequence of understanding the how the AI works, the mistakes the system can make, and the safety measures surrounding it.

Meanwhile, AI is increasingly allowed to make and take more autonomous decisions and actions. Justifying these decisions will only become more crucial, and there is little doubt that this field will continue to rise in prominence and produce exciting and much needed work in the future [START_REF] Biran | Explanation and justification in machine learning : A survey or[END_REF].

The importance of explanation, and especially explanation in AI, has been emphasized in numerous popular press outlets over the past decades, with considerable discussion of the explainability of "Deep Nets" and ML systems in both the technical literature and the recent popular press [START_REF] Kim | Explainable artificial intelligence (xai), the goodness criteria and the grasp-ability test[END_REF][START_REF] Bornstein | Is artificial intelligence permanently inscrutable[END_REF][5][START_REF] Core | Building explainable artificial intelligence systems[END_REF][START_REF] Hawkins | Special report: Can we copy the brain?-what intelligent machines need to learn from the neocortex[END_REF][START_REF] Marcus | Deep learning: A critical appraisal[END_REF][START_REF] Monroe | Ai, explain yourself[END_REF][START_REF] Sheh | why did you do that?" explainable intelligent robots[END_REF][START_REF] Voosen | How ai detectives are craking open the black box of deep learning[END_REF][START_REF] Wang | Designing theory-driven user-centric explainable ai[END_REF][START_REF] Weinberger | Our machines now have knowledge we'll never understand[END_REF].

Predictive Maintenance

PdM extracts insights from the data produced by the equipment on the shop floor and acts on these insights. The idea of PdM goes back to the early 1990's and augments regularly scheduled, preventive maintenance. PdM requires the equipment to provide data from sensors monitoring the equipment as well as other operational data. Humans act based on the analysis. Simply speaking, it is a technique to determine (predict) the failure of the machine component in the near future so that the component can be replaced based on the maintenance plan before it fails and stops the production process. The PdM can improve the production process and increase the productivity. By successfully handling with PdM we are able to achieve the following goals:

-Reduce the operational risk of mission-critical equipment.

-Control cost of maintenance by enabling just-in-time maintenance operations. -Discover patterns connected to various maintenance problems.

-Provide Key Performance Indicators.

Usually PdM uses descriptive, statistical or probabilistic approach to drive analysis and prediction. There are also several approaches which used Machine Learning (ML) [START_REF] Lee | Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data[END_REF][START_REF] Susto | Machine learning for predictive maintenance: A multiple classifier approach[END_REF]. Through the literature [START_REF] Selcuk | Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers[END_REF] there can be found the following types of PrM in the production: reactive, periodic, proactive and predictive (Figure 1).

Case Study

In order to handle and use this technique we need a various data from the machines in production. In this case study we used the freely available data from a data source generated as test data set for PdM containing information about: telemetry, errors, failures and machine properties. The data can be found at Azure blob storage. The data is maintained by Azure Gallery Article4 . Once the data is downloaded from the blob storage, local copies will be used for further observations in this contribution.

Methodology

Usually, every PdM technique should proceed by the following three main steps:

-Collect Data -collect all possible descriptions, historical and real-time data, usually by using IoT (Internet of Things) devices, various loggers, technical documentation, etc. -Predict Failures -collected data can be used and transformed into ML ready data sets, and build a ML model to predict the failures of the components in the set of machines in the production. -React -by obtaining the information which components will fail in the near future, we can activate the process of replacement so the component will be replaced before it fails, and the production process will not.

Data Preparation

In order to predict failures in the production process, a set of data transformations, cleaning, feature engineering, and selection must be performed to prepare the data for building a ML model. The data preparation part plays a crucial role in the model building process because quality of the data and its preparation will directly influences the model accuracy and reliability. The data used for this PdM use case can be classified to:

-Telemetry -which collects historical data about machine behavior (voltage, vibration, etc). -Errors -the data about warnings and errors in the machines.

-Maint -data about replacement and maintenance for the machines.

-Machines -descriptive information about the machines.

-Failures -data when a certain machine is stopped, due to component failure.

Errors data represents the most important information in every PdM system. The errors are non-breaking recorded events while the machine is still operational. In the experimental data set the error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate. What we get to insight is shown in the left chart of figure 2.

Failures data represents the replacements of the components due to the failure of the machines. Once the failure is happened the machine is stopped. This is a crucial difference between errors and failures. Failure distribution produced by certain component across machines is shown in the right chart of figure 2.

Maintenance data tells us about scheduled and unscheduled maintenance. The data set contains the records which correspond to both, regular inspection of components as well as failures. To add the record into the maintenance table a component must be replaced during the scheduled inspection or replaced due to a breakdown. In case the records are created due to breakdowns are called failures. Maintenance contains the data from 2014 and 2015 years.

Feature Engineering

First, several lagged telemetry data was created, since telemetry data are classic time series data. In the following, the rolling mean and standard deviation of the telemetry data over the last 3-hours lag window is calculated for every 3 hours. For capturing a longer term effect 24 hours lag features rolling average and standard deviation were calculated. Once we have rolling lag features calculated, we can merge them into one data frame. Now that we have basic data frame, we merged previously calculated data frames with this one. At the end of the merging process, the relevant columns are selected. Unlike telemetry that had numerical values, errors have categorical values denoting the type of error that occurred at a time-stamp. This was used to aggregate categories of the error with different types of errors that occurred in the lag window. The main task here was to create a relevant feature in order to create a quality data set for the machine learning part.

One of the good features that has been chosen was the number of replacements of each component in the last 3 months to incorporate the frequency of replacements. Furthermore, we calculated how long it has been since a component is last replaced as that would be expected to correlate better with component failures since the longer a component is used, the more degradation should be expected. The machine data set contains descriptive information about machines like the type of machines and their ages which is the years in service.

As the last step in feature engineering, we are performing merging all features into one data set. The label in PdM should be the probability that a machine will fail in the near future due to a failure certain component. If we take 24 hours to be the period (task) for this problem, the label construction consists of a new column in the feature data set which indicate if certain machine will fail or not in the next 24 hours due to failure one of several components.

In this way, we are defining the label as a categorical variable containing:none -if the machine will not fail in the next 24 hours, -comp1 to comp4 if the machine will fail in the next 24 hours due to the failure of certain components. Since we can experiment with the label construction by applying different conditions, we can implement methods that take several arguments in order to define the general problem.

Preliminary Results

We analyzed 5 data sets with information about telemetry, data, errors and maintenance as well as failure for 100 machines. The data were transformed and analyzed in order to create the final data set for building a machine learning model for PdM.

Once we created all features from the data sets, as a final step is to create the label column so that it describes if a certain machine will fail in the next 24 hours due to failure a comp1, comp2, comp3, comp4 or it will continue to work. In this part, we performed a part of the ML task and start training a ML model for predicting if a certain machine will fail in the next 24 hours due to failure, or it will be in functioning normal in that time period.

The model which we built was multi-class classification model since it has 5 values to predict: comp1, comp2, comp3, comp4 or none -means it will continue to work. We used the DART Booster hyper-parameter tuning along with Light-GBM5 [START_REF] Ke | Lightgbm: A highly efficient gradient boosting decision tree[END_REF] which is a gradient boosting framework that uses tree based learning algorithm. It is especially efficient on small data sets. We evaluated the trained model first with training data set (see Table 1 the model has overall accuracy 99%, and 95% average per class accuracy which is very promising for experimental case.

Conclusions, Limitations and Outlook

In this paper we conducted a case study in the field of Predictive Maintenance (PdM) with sample machine data to demonstrate how explainable AI can be reached in the field of manufacturing. Although the study is strictly experimental, we can conclude that explainable AI in form of reliable prediction model and visualizations can reasonably contribute to avoiding unnecessary costs associated with unscheduled downtime caused through machine errors or tool failures.

The basic limitation of this contribution is that this experiment was conducted with generic data set, however the presented concept shows high maturity with promising results.

The next step in the future would be to engage the trained model with some data collected directly in real world manufacturing settings and involving data

Fig. 1 .

 1 Fig. 1. Different types of maintenance in the production

Fig. 2 .Fig. 3 .

 23 Fig. 2. Error and failure distributions across machines

Table 1 .

 1 ). Results on training data set.As can be seen the model predicts the values from the training data set correctly in most cases. In order to see how the model predicts unknown data entries we used the test data. The result is shown in Table2. We can see, that

	PREDICTED TRUTH none comp1 comp2 comp3 comp4 Recall
	none	165371 0	0	0	0	1,0000
	comp1	0	844	26	4	8	0,9588
	comp2	0	22	1097 8	31	0,9473
	comp3	0	4	8	576	13	0,9548
	comp4	0	16	25	11	772	0,9369
	Precision	1,0000 0,9546 0,949 0,9616 0,9369
	PREDICTED TRUTH none comp1 comp2 comp3 comp4 Recall
	none	120313 15	0	0	6	0,9998
	comp1	2	464	24	24	14	0,8788
	comp2	0	22	1097 8	39	0,9382
	comp3	0	0	0	412	4	0,9904
	comp4	1	10	17	4	552	0,0,9452
	Precision	1,0000 0.9489 0.9532 0.9035 0.8976

Table 2 .

 2 Results on test data set.

https://www.darpa.mil/program/explainable-artificial-intelligence

https://gallery.azure.ai/Experiment/Predictive-Maintenance-Implementation-Guide-Data-Sets-1

https://lightgbm.readthedocs.io/en/latest/

from different manufacturer. In this way the reliability of presented results could be approved and through the comparison of results from different data sources and adjustment of the prediction model.


Jan Duda

Sylwester Oleszek
email: sylwester.oleszek@gmail.com

Concept of PLM Application Integration with VR and AR Techniques

Keywords: Augmented Reality, Virtual Reality, Product Lifecycle Management

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

VR is a medium composed of interactive computer simulations that determine the position and actions of the participant and replace or extend the feedback to one or more his senses, giving him a sense of immersion or mental presence in the simulation, that is, in the virtual world [START_REF] Sherman | Understanding virtual reality: Interface, application, and design. The Morgan Kaufmann Series in Computer Graphics[END_REF]. The key elements of the ability to experience virtual reality are: virtual world, immersion, sensory feedback on user actions and interactivity [START_REF] Sherman | Understanding virtual reality: Interface, application, and design. The Morgan Kaufmann Series in Computer Graphics[END_REF]. Virtual reality is also a scientific and technical field that uses IT tools and behavioural interfaces to simulate the behaviour of 3D objects in the virtual world so they can interact with each other in real time in pseudo-natural immersion through sensomotor channels.

Augmented reality, unlike virtual reality, includes techniques for adding virtual objects to the real world [START_REF] Mann | Intelligent image processing. Adaptive and learning systems for signal processing, communications, and control[END_REF]. Augmented reality techniques are therefore defined as those which, in real time and in a three-dimensional environment, make it possible to connect the real world with the virtual one [START_REF] Barfield | Fundamentals of wearable computers and augmented reality[END_REF][START_REF] Januszka | Metoda wspomagania procesu projektowania i konstruowania z zastosowaniem poszerzonej rzeczywistości[END_REF]. In other words, the AR system is a system that connects the real world with a computer-generated world.

Virtual Manufacturing (VM) is a concept of planning production activities using virtual models and simulation tools instead of objects and their operations in the real world [START_REF] Fuchs | Virtual reality: Concepts and technologies[END_REF]. The way to implement this concept is a computer system that generates information about the structure, states and behaviour of the manufacturing system as observed in the natural production environment. In other words, the process of virtual production does not involve materials or physical products, but information about them. Virtual manufacturing can therefore be defined as an integrated computer model that represents the physical and logical scheme and behaviour of the actual manufacturing system [START_REF] Keyvani | A Virtual Manufacturing Approach for Integrating Fixture Design with Process Planning[END_REF]. Some scholars define virtual manufacturing in relation to virtual reality by describing this technology as a virtual world for manufacturing at the same time presenting virtual reality as a visualisation tool for virtual manufacturing. With this approach in mind, we can define VM as an application of integrated technologies of simulation, modelling and analysis with the use of models and virtual processes in a virtual reality environment. This integration makes it possible to identify potential problems before the actual production takes place. As a result, it is possible to improve the design of the production system, make decisions and control all stages of this process [START_REF] Barfield | Fundamentals of wearable computers and augmented reality[END_REF]. Thus, the general premise present in most definitions is that "Virtual production is nothing more than production on a computer".

The Virtual Factory (VF) paradigm is the basis for dealing with the problem of implementing methodologies to support the design of production systems, processes, simulation, production control, visualization and other. The ideal implementation of a virtual factory should therefore include the creation of a comprehensive, integral, upgradeable and scalable representation of the actual factory. It should provide a virtual representation of buildings, resources, processes and products. The entire factory should be simulated as a continuous and coherent digital model that can be used throughout the entire product lifecycle -from the product idea to the final disassembly of production plants and buildings [START_REF] Duda | Zarządzanie rozwojem wyrobów w ujęciu systemowym[END_REF].

Interactive product development cycles

A modern company has to cope with the co-evolution of products, processes and production systems through strategic and operational management, promotion of engineering changes depending on market requirements and the dynamics of legislative changes. The essence of these changes is to manage the implementation of development phases based on a common model of product, process and resource data (Product, Process, Resource). Fig. 1.

Fig. 1. Management of development phases based on a common model of product, process and resource data

One way to implement the idea of a virtual factory is to use PLM class systems. They integrate a set of applications supporting product development which include [START_REF] Bharath | Virtual Manufacturing: A Review[END_REF]:

 Product and Portfolio Management (PPM),  Computer-aided technologies (CAx),  Manufacturing Process Management (MPM),  Product Data Management (PDM). Although these systems provide support for most areas related to the planning and design phases, and effectively support product data management and process virtualization, they do not provide certain functionalities relevant to business needs, especially in the area of interoperability [START_REF] Oleszek | Metoda wspomagania projektowania naczyń szklanych z zastosowaniem konfiguratora w środowisku poszerzonej rzeczywistości[END_REF][START_REF] Sharma | Concept of Virtual Manufacturing[END_REF][START_REF] Sherman | Understanding virtual reality: Interface, application, and design. The Morgan Kaufmann Series in Computer Graphics[END_REF]. As a result, the whole process of virtual factory realisation cannot be carried out with the use of PLM systems in a continuous and coherent way. This is particularly visible in the lack of integration between technologies and data model used in the product design and the production systems design phases [START_REF] Ghielmini | Virtual Factory Manager for semantic data handling[END_REF].

One of the important features of the product development process is concurrency. The essence of this process is the implementation of product development phases and the possibility of evaluating possible product, process and system variants in the context of the implementation of subsequent development phases. The achievement of this postulate has been made possible by separating the conceptual and detailed design stages in subsequent development stages [START_REF] Bharath | Virtual Manufacturing: A Review[END_REF]. The assessment of the obtained design solutions, apart from the application of simulation programs, may be supported by the application of VR and AR virtual manufacturing techniques. Assuming that the product development process consists of subsequent stages, where the quality of each stage depends directly on the quality of data from the previous one, the above mentioned approach allows the performance of interactive design cycles using CAx (Comuter Aided x) systems and then testing using DFx (Design for x) systems, AR and VR techniques, using digital data at each individual stage. As a result, it allows to significantly increase the speed of the whole process, and also move the detection and resolution of potential errors or problems to earlier stages. In the virtual environment process, 3D models are created and tested at a very early stage, much earlier than any physical prototypes could be produced and tested. What is more, in a virtual environment we can afford to quickly and relatively inexpensively (without the use of physical raw materials) manufacture and test many variants of both the product and the tools used for its production. We also have the ability to simulate the entire production process using various parameters [START_REF] Mann | Intelligent image processing. Adaptive and learning systems for signal processing, communications, and control[END_REF]. This is schematically shown in Figure 2.

However, effectively designing and integrating VR and AR with Virtual Manufacturing Systems (VMS) in such a way that they can improve production and product development processes, leading to shorter lead times, lower costs, efficiency and quality improvement along with a clean and eco-friendly process, is a major challenge. One of the biggest difficulties to overcome is the absence of system and data interoperability, i.e. the lack of consistency in data transfer between systems (in case of many softwareintegrated system platforms) or between system modules performing different tasks (in case of e.g. PLM systems).

Fig. 2. Interactive product development cycles

3

PLM system architecture integrated with the service performing tasks related to VR and AR technologies

Modern PLM systems operate according to the client-server model, and communication between system clients (the client can be a web browser, another system or system component), and the system is based on HTTP protocol, i.e. in requestresponse mode. Thus, it is possible to propose a generalised PLM system architecture integrated with modules performing tasks related to VR or AR technologies. The architectural pattern of WWW application for client-server system in the most general sense assumes organizing the whole system into three layers: database, processing (business) and presentation (client) [START_REF] Lafleur | An Onto-Based Interoperability Framework for the Connection of PLM and Production Capability Tools[END_REF]. At the level of the processing layer, where PLM system services realizing business logic are located, an application performing tasks related to the preparation of data for use in VR or AR environments may be integrated. These tasks may include e.g. conversion of 3D data from native engineering formats (.prt, .CATPart, .sldprt, etc.) to a neutral or lightweight format, which enables easier transfer via HTTP protocol and easier display in mobile multimedia applications. They can also be more complex tasks, i.e. processing and prepar-ing 3D models in such a way that they can be used directly in client applications, e.g. in mobile applications or augmented or virtual reality glasses. Fig. 3. Generalised architecture of the PLM system integrated with the service performing tasks related to VR and AR technologies Response information from the client application, e.g. in the form of metadata, can be sent directly to the PLM system business layer -for further processing -via communication protocol requests. A proposal of a generalized PLM system architecture integrated with the service performing tasks related to VR and AR technologies is presented in Figure 3.

4

The proposal of the procedure for technical implementation of PLM system integration with VR and AR techniques

This chapter presents the concept of the procedure for the technical implementation of the integration of the PLM commercial system with the proprietary AR system, where the integration of an advanced CAx class system with a mobile application -in which the AR technology has been applied -was accomplished. This solution enables 3D modeling activities (based on techniques adopted from product configurators) directly in AR environment. Simultaneously, in the CAx system, a parametric 3D model is created in a fully automated way [START_REF] Januszka | Metoda wspomagania procesu projektowania i konstruowania z zastosowaniem poszerzonej rzeczywistości[END_REF]. The AR system consists of three components:

1. A mobile application created in Unity3D environment, in which the Vuforia augmented reality library was used, 2. CAx class system, 3. An integration module in the form of an application created with the use of the CAx system programming interface in VB.net language, the aim of which is to provide communication with the mobile application and control the automated operations of the CAx system. The goal of the presented concept of PLM and AR systems integration is:

 implementation of centralised management of standard elements, which are used in the process of modeling and configuration, based on the principles of standards management in PLM system (permission control, versioning etc.),  integration of the developed configuration method in the AR environment with a consistent product lifecycle management process,  implementation of history tracking, status management and versioning of modeled product concepts during their development,  introducing the possibility of a formalised version approval process for the product created by the customer during the configuration process. According to the authors' intention, an integrated, uniform environment including the PLM and AR systems should allow for all configuration and modelling activities in the AR environment, as developed in [START_REF] Januszka | Metoda wspomagania procesu projektowania i konstruowania z zastosowaniem poszerzonej rzeczywistości[END_REF]. However, all these activities should be performed within a PLM environment. To enable this, it is necessary to automate operations such as:

 establishing a connection between the AR and the PLM systems,  loading elements used in the configuration process from the PLM system standard library,  saving the 3D model developed in the process of configuration to the development container of the PLM system,  management of the lifecycle state of the 3D model developed in the configuration process,  end of session and logout from PLM system. In order to implement the above mentioned actions, the following steps are necessary:

 developing a method of automatic login into the PLM system using credentials (login and password),  creating a standard library of elements used in the configuration process in the PLM system and modifying the AR system to enable loading these elements directly from the created library,  developing a method of automatic saving the 3D model developed in the process of configuration directly to the development container of the PLM system,  developing a method to automatically change the state of the 3D model lifecycle developed during the configuration process (this will also enable version management). The diagram in the BPMN notation of the product configuration process in the AR system with integrated PLM system is shown in Figure 4.

Summary

This paper presents practical definitions of virtual and augmented reality, virtual manufacturing and virtual factory. The authors highlighted that one of the possible, although not perfect, implementations of the virtual factory paradigm is using modern PLM systems. In these systems, AR and VR technologies can be implemented in the presentation layer level. However, this requires the development and integration of additional services with the PLM system, the purpose of which is to carry out tasks related to data conversion and processing in a specific way (depending on the technology and purpose), so that they can be displayed in AR or VR devices.

The paper also proposes a concept of PLM system integration with VR and AR techniques based on generalised PLM system architecture. This concept is based on the assumption that at the level of processing layer integrated with PLM system there is a software service, by means of which tasks related to VR and AR technologies are performed.

The last part of the paper presents the proposal of the procedure of technical realization of integration based on the example of a commercial PLM and a multimodule proprietary AR systems. The aim and benefits of obtaining a uniform PLM and AR environment have been defined and additional functionalities that are possible to achieve (e.g. version control, status management, management of elements used in the configuration process based on the principles of the standards library in the PLM system) have been highlighted. This part of the paper also presents actions that need to be performed in order to create a prototype version of the integration. The authors intend to continue their work in order to implement the concept mentioned and also to examine the functionality and practical usefulness of such a solution.

Fig. 4 .

 4 Fig. 4. Diagram of the product configuration process in the AR system integrated with the PLM system


Aguinaldo Souza

João Reis

Antonio Carlos Estender

Jorge Agia

Oduvaldo Vendrametto

Luciana Melo Costa

Paula Cruz Correia

Aguinaldo Eduardo De Souza

João Gilberto -6100-752x]

Mendes Dos Reis

Antonio Carlos

Jorge Luiz

Dias Agia

Paula Ferreira

Cruz Correia

Short Agri-Food Supply Chains: A Proposal in a Food Bank

Keywords: Agri-Food Chains, Family Farm, Food Bank, Logistics

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Food supply chains suffer from a high number of wastes and losses between farm to fork. Around 30% of the food produced in the world is wasted or lost mainly due to the logistics process [START_REF] Maldonado | In establishing food security and alternatives to international trade in emerging economies[END_REF][START_REF] Todorovic | Solutions for more sustainable distribution in the short food supply chains 10[END_REF]. Therefore, the efficiency alongside supply chain management is fundamental as a source of social well-being, reduction of the use of natural resources, and to increase food access [START_REF] Gonzalez-Feliu | Local productionbased dietary supplement distribution in emerging countries: Bienestarina distribution in colombia[END_REF].

Contrary to the common belief the agri-food networks are lead by Family Farms [START_REF] Hill | The 'myth' of the family farm: Defining the family farm and assessing its importance in the european community 9[END_REF][START_REF] Toader | Family farming -examples for rural communities development 6[END_REF][START_REF] Graeub | The state of family farms in the world 87[END_REF][START_REF] Godoy-Durán | Assessing eco-efficiency and the determinants of horticultural family-farming in southeast spain 204[END_REF][START_REF] Usda | Family farms | national institute of food and agriculture[END_REF] that are negatively affected by the global supply chains [START_REF] Hill | The 'myth' of the family farm: Defining the family farm and assessing its importance in the european community 9[END_REF][START_REF] Goodman | The quality 'turn' and alternative food practices: reflections and agenda 19[END_REF][START_REF] Bloom | Moving local food through conventional food system infrastructure: Value chain framework comparisons and insights 26[END_REF][START_REF] King | Comparing the structure, size, and performance of local and mainstream food supply chains[END_REF][START_REF] Todorovic | Solutions for more sustainable distribution in the short food supply chains 10[END_REF][START_REF] Chiffoleau | The participatory construction of new economic models in short food supply chains[END_REF][START_REF] Mancini | Producers' and consumers' perception of the sustainability of short food supply chains: The case of parmigiano reggiano[END_REF] . These supply chains usually raise the product price to consumers due to intermediaries' agents but reduce profitability to small producers.

On the other hand, studies point out a new paradigm of global consumption, where consumers have preferred healthy foods, produced locally, with their own cultural characteristics and guaranteeing social justice [START_REF] Hill | The 'myth' of the family farm: Defining the family farm and assessing its importance in the european community 9[END_REF][START_REF] Goodman | The quality 'turn' and alternative food practices: reflections and agenda 19[END_REF][START_REF] Bloom | Moving local food through conventional food system infrastructure: Value chain framework comparisons and insights 26[END_REF][START_REF] King | Comparing the structure, size, and performance of local and mainstream food supply chains[END_REF][START_REF] Todorovic | Solutions for more sustainable distribution in the short food supply chains 10[END_REF][START_REF] Chiffoleau | The participatory construction of new economic models in short food supply chains[END_REF][START_REF] Mancini | Producers' and consumers' perception of the sustainability of short food supply chains: The case of parmigiano reggiano[END_REF].

Currently, one alternative to this scenery is the Alternative Food Networks -AFN which is an option to the traditional agri-food system. The AFNs have as characteristics the development of local initiatives, use of local input, and low production scale with a focus on quality, to the detriment of high productivity [START_REF] Pivoto | Cadeias curtas de suprimentos de alimentos: Uma oportunidade para os produtores rurais? 54[END_REF].

It is in this context that emerges the Short Food Supply Chain -SFSC concept. A model substitute for conventional supply chain where foods reflect the characteristics of "local", "natural", "healthy" and "reliable" [START_REF] Aguiar | Short food supply chain: characteristics of a family farm 48[END_REF].

According to [START_REF] Scarabelot | As cadeias agroalimentares curtas e desenvolvimento local -um estudo de caso no município de nova veneza[END_REF] Short Agri-Food Chains, they are specific chains of agricultural products intended for human consumption. The authors reveal that these chains excel in proximity between producers and consumers. Far beyond the spatial aspect, but a connection that allows interactivity so that both know each other's purposes and needs

The aim of this study is to demonstrate the role of Food Bank as a catalyst for the model of Short Agri-Food Chains. To this end, we chose to conduct field research through a case study with the Itanhaém Food Banks, located on the southern coast of the state of São Paulo.

Background

Food Bank

Food Banks are intended to combat hunger and food insecurity [START_REF] Knoblock-Hahn | How community food banks support school breakfast: Strategies used and lessons learned 116[END_REF]. They have physical structures that aim to capture, select and distribute free foodstuff that would be wasted throughout the production chain [START_REF] Davis | Scheduling food bank collections and deliveries to ensure food safety and improve access[END_REF]. These entities may be public or private, under the management of civil society organizations of social interest, non-profit organizations [START_REF] Cidadania | SISAN -sistema nacional de segurança alimentar e nutricional[END_REF].

The Food Bank originated in 1967 in the United States in Phoenix city, through the initiative of volunteers who requested donations of foodstuffs, which would be discarded by supermarkets and industry. Initially, the donations were intended to prepare meals for those in need. However, the volume of donations was exceeding the capacity to prepare meals, starting to be stored and distributed to philanthropic organizations [START_REF] Belik | Crise dos alimentos e estratégias para a redução do desperdício no contexto de uma política de segurança alimentar e nutricional no brasil 0[END_REF].

In Brazil, the National System of Food and Nutrition Security aims to guarantee the Human Right to Adequate Food. For this purpose, the government articulates public policy at the three levels of government: federal, state, municipality, through different sectors and civil society, with a view to promoting Food and Nutrition Security and combating hunger. These actions are operationalized through public food and nutrition security equipment. Public Equipment is structures aimed at serving and supporting citizens in nutritional vulnerability [START_REF] Cidadania | SISAN -sistema nacional de segurança alimentar e nutricional[END_REF].

The first movements of Food Bank in Brazil emerged from the initiative of the Social Trade Service -SESC, with the urban harvesting program in 1997. Later in 2000, the first Food Bank was inaugurated in the city of Rio de Janeiro [START_REF] Belik | Crise dos alimentos e estratégias para a redução do desperdício no contexto de uma política de segurança alimentar e nutricional no brasil 0[END_REF].

Therefore, Food Banks are important arrangements for promoting food security in Brazilian municipalities, aiming to eradicate hunger.

Short Agri-Food Chains

Sustainable agri-food systems play a key role in combating malnutrition [START_REF] Gillespie | Nutrition and the governance of agrifood systems in south asia: A systematic review[END_REF]. However, the Short Supply Chains have proved to be an option for traditional supply chains as well as an alternative to AF's sustainability. They can nullify two problems faced by farmers: the increase of the production cost; and the new global trend of consumption, with a strong call for healthy eating, based on sustainable production, aiming at food security [START_REF] Scarabelot | As cadeias agroalimentares curtas e desenvolvimento local -um estudo de caso no município de nova veneza[END_REF].

Moreover, Short Supply Chains propose a direct relationship between the producer and consumer, reducing the number of intermediaries and the distance between the producer and the final consumer [START_REF] Pivoto | Cadeias curtas de suprimentos de alimentos: Uma oportunidade para os produtores rurais? 54[END_REF].

The small farmers are the main benefit of short supply chains, the obtaining of fairer recipes. The relationship between producer and consumer is the main characteristic of the SFSC, and, therefore, contribute to the economic development of the locations where they are inserted, besides providing the autonomy of producers [START_REF] Aguiar | Short food supply chain: characteristics of a family farm 48[END_REF].

Family Farm in Brazil

The legal definition of Family Agriculture is contained in Decree No. 9,064 [START_REF] Government | [END_REF]. In order to be framed as a family farmer, the producer must develop his practices in rural areas. What characterizes the family agriculture of the non-family is the fact that is management is shared between family members and agricultural production activities are the main source of income of the family [START_REF] Medina | Development conditions for family farming: Lessons from brazil[END_REF].

Regarding the profile of Family Agriculture in Brazil, data from the Brazilian Institute of Geography and Statistics -IBGE [START_REF] Ibge | Agricultural census[END_REF] indicate that of the 5.07 million rural establishments located in 351.2 million hectares, The Family Farm accounts for 3.9 million establishments (77%), which together occupy a total area of 80.9 million hectares, i.e. 23% of the country's agricultural area.

Methods

To meet the objectives of this work, we opted for an exploratory and descriptive methodological approach. The study was developed in the municipality of Itanhaém (SP) through field research with the Association of Rural Producers of the Rio Branco Hydrographic Watershed, Artisanal Fishermen, Aquaculture and Indigenous Peoples of Itanhaém and Region -AMIBRA, composed of 52 family farmers and the Itanhaém Food Bank.

For data collection, a semi-structured questionnaire was applied in order to quantify the volume of food purchase, food type, main suppliers (farmers), logistics flow (receipt and distribution), recipients, related to the operations of 2018. The study population comprises family farmers from the AMIBRA farmers association and Itanhaém Food Bank. The collected data was compiled into an electronic database of the Microsoft Excel program R . From there it was possible to structure and understand a model of Short Agri-Food Chains.

Result and Discussion

The Itanhaém Food Bank is responsible for operationalizing Food and Nutrition Security -SAN programs in the municipality through the Food Acquisition Program -PAA. Created in 2007, the Itanhaém Food Bank is the result of the agreement between the Municipality of Itanhaém and the Federal Government.

In 2019, BRL 300,000.00 from the Ministry of Social Development was contributed, which ensured the socioeconomic inclusion of family farmers in the municipality, registered as suppliers of the Food Acquisition Program. Together the farmers provided more than 90 tonnes of food to the municipality's program, which were transferred to ten entities registered with the Municipal Social Assistance Council -CMAS (Fig. 1) including about 2,500 families. As shown in Figure 1, about 95% of the food purchased was hortifrúti, especially banana (40 t), the most produced item due to the characteristics of the region. The remaining 5% was intended for the purchase of fish, offered by fishermen from Itanhaém city.

The income of the 52 farmers was around BRL 5,679.00 (1,750 kg/month) where the lowest income was BRL 2,000.00 (454 kg/month) and the highest, BRL 6,500.00 (2.600 kg/month).

Another source of acquisition is food from "urban collection". Products without commercial value, but that retain their nutritional characteristics, which are donated by local retailers to Itanhaém Food Bank. About two tons per month of hortifrúti food are received, selected, packaged, stored and subsequently destined for the "Capillarity Project" (Fig. 2).

The "Capillarity Project" serves people in a state of social and nutritional vulnerability, who are informally referred to Itanhaém Food Bank through the Social Care Reference Center -CRAS, Health Secretary (sick with diabetes, can-Fig. 2: Capillarity Project. Source: Authors . cer, HIV) and other entities that are not registered by CMAS. Once routed, these families are registered in the project and become benefited from food baskets.

Given its logistical functional ability (reception, selection, storage, and distribution), the Food Bank can be presented as an important instrument for consolidating the Short Agri-Food Chains, contributing to the development of Family Farm and social rise of the small producer [START_REF] Goodman | The quality 'turn' and alternative food practices: reflections and agenda 19[END_REF][START_REF] Scarabelot | As cadeias agroalimentares curtas e desenvolvimento local -um estudo de caso no município de nova veneza[END_REF][START_REF] Aguiar | Short food supply chain: characteristics of a family farm 48[END_REF].

In the specific case of Itanhaém Food Bank, its logistics structure ensures the development of a model of Short Chains, as can be observed in Figure 3. The Short Chain model elaborated in Figure 3 presents Itanhaém Food Bank as the main articulator of the logistics activities of this chain. However, three activities gain greater prominence, supplier management, food acquisition, storage, and distribution. In addition, it accounts for dimensions of economic, environmental and social sustainability [START_REF] Martins | Redesigning a food bank supply chain network in a triple bottom line context 214[END_REF].

Two operations are performed in this model presented. In the management of the PAA, o Itanhaém Food Bank is responsible for defining food purchase items, considering the specificities of local agricultural culture, as well as the productive potential of farmers, in addition to the receipt, storage and shipment of food acquired for registered entities.

In the Operation Capillarity Project, Itanhaém Food Bank is responsible for maintaining the agreement with donor companies. In addition, it manages the logistics functions of the project such as receiving donated foods, selection, and separation of products, the composition of baskets, storage and distribution for registered families that are withdrawn from the bank itself.

Fig. 1 :

 1 Fig. 1: Entities benefited from the PAA in 2019. Source: Adapted Itanhaém Food Bank.

Fig. 3 :

 3 Fig. 3: Structure of a Short Agri-Food Chain. Source: Authors

Final ConsiderationsIn order to present the Food Bank as a proposal of Short Agri-Food Chains, it can be concluded that the Itanhaém Food Bank in its attributions, develops the main activities that make up the premises of Short Agri-Food Chains.In this model Itanhaém, the Bank is the only intermediate agent, allowing a direct connection between the field and the table, and therefore, fairer revenues for farmers. Moreover, institutional sale to the PAA is an opportunity for market diversification for Family Farm.In view of this, the Food Banks can be a viable alternative for structuring Short Chains in small locations, ensuring the socio-economic empowerment of the small farmer, in addition to the economic development of the local community.However, the limitations of this study were not to answer whether the model is feasible for chains that serve other markets. Considering that this model addressed (Fig.3) is exclusively linked to government acquisition. The next studies could be based on Short Agri-Food Chains based on the structure of a Food Bank is feasible for other marketing channels.

Acknoledgments

This study was financed in part by the Coordination of Improvement of Personal Higher Education -Brazil (CAPES) -Finance Code 001