Marko Vještica
email: marko.vjestica@uns.ac.rs

Vladimir Dimitrieski
email: dimitrieski@uns.ac.rs

Sonja Ristić
email: sdristic@uns.ac.rs

Ivan Luković

An Application of a DSML in Industry 4.0 Production Processes

Keywords: Production Process Modeling, Domain-Specific Modeling Languages, Model-Driven Software Engineering, Industry 4.0, Process Execution

One of the goals of Industry 4.0 is to enable mass customization of products and to satisfy specific needs of customers. This goal is often hard to achieve in traditional manufacturing systems. To enable fast production changes, an automatic and flexible production is needed. In this context we propose a Model-Driven Software Development (MDSD) approach and a Domain-Specific Modeling Language (DSML) to model production processes. The language supports two levels of abstraction. A Master-Level (ML) model is used by a process designer to model process steps. A Detail-Level (DL) model is used by Orchestrator, a cluster of industrial computers that manages production, to fill existing ML models with a specification of production logistic and smart resources. A code generator is used to generate machine-readable or human-readable instructions from DL models. Generated code is used for automatic execution of production processes within a simulation or a shop floor. In this paper we provide an application of a DSML, which is capable of modeling production processes that are ready for automatic code generation.

Introduction

Industry 4.0 aims to establish a flexible production in the traditional manufacturing [START_REF] Lu | Industry 4.0: A survey on technologies, applications and open research issues[END_REF], moving from mass production to mass customization [START_REF] Crnjac | From Concept to the Introduction of Industry 4.0[END_REF]. Introducing variability to the production process in traditional manufacturing systems requires either stopping the production to reconfigure machines or having multiple production lines for every product variant. To achieve aimed production flexibility, production process variability need to be enabled during manufacturing, without a need to stop the production [START_REF] Dorofeev | Agile Operational Behavior for the Control-Level Devices in Plug&Produce Production Environments[END_REF]. Thus, a customized product's price could be lower, as one of the key aspects of the mass customization is to provide products with prices that are near to mass production prices [START_REF] Zhao | Why do we not buy mass customised products?" -An investigation of consumer purchase intention of mass customised products[END_REF]. In order to establish production flexibility at the lower cost and to enable automatic production, we proposed a Model-Driven Software Development (MDSD) approach to model and automate the execution of production processes [START_REF] Vještica | Towards a formal description and automatic execution of production processes[END_REF]. The overview of the approach is given in Fig. 1 and described in short below.

Fig. 1. The proposed MDSD approach

In the presented MDSD approach, a process designer uses a Domain-Specific Modeling Language (DSML) to specify production processes. This production process specification is a technical description of a production process, which we call a Master-Level (ML) model. It includes specification of: (i) process steps, (ii) required capabilities, i.e. skills to execute the step, (iii) input and output products, i.e. transformed resources like raw materials, components or finished goods, (iv) constraints and (v) capability parameters. In order to use such a production process model for automatic code generation and execution, additional information needs to be added. This information includes: (i) specific smart resources, e.g. storage, machines, robots and humans, which execute process steps, (ii) logistic information for product and resource movement and (iii) configuration tasks of smart resources, e.g. software setup, changing grippers, plugging into a charger or a workstation. This enrichment is automatically performed by Orchestrator and we denote the enriched process model as a Detail-Level (DL) model. It is up to process designers to either mark the process model for execution or perform manual interventions and optimization where they deem necessary.

Orchestrator is a software run on top of a cluster of industrial computers that is able to orchestrate smart resources and assign them to process steps for their execution [START_REF] Keddis | Capability-Based System-Aware Planning and Scheduling of Workflows for Adaptable Manufacturing Systems[END_REF]. It also needs to detect and configure new smart resources or reconfigure existing ones [START_REF] Pisarić | Towards a Plug-and-Play Architecture in Industry 4.0[END_REF]. Each process step is specified with a required capability, a skill required to execute the process step. On the other hand, each resource offers a set of capabilities, i.e. skills it is able to perform. Using a knowledge base and the required and offered capabilities, Orchestrator is able to identify smart resources which are able to execute specific process steps. It is also able to identify storages that contain required products and process steps that need to be changed or added, e.g. transportation or machine configuration process steps, in order to execute the production process with the available resources. A DL model is used by the code generator to automatically generate instructions that are to be performed by humans or machines. The executor sends generated instructions to the digital twin that comprises both the simulation and the command proxies to the shop floor. In our case, the digital twin can be used as the simulation only or it can forward instruction to shop floor smart resources via the built-in proxies.

As a DSML is an integral part of the proposed MDSD approach which aims to improve flexibility and automation of a factory, and as no appropriate DSML has been found for modeling all the aspects of a production process, we decided to create a new one which fits this need. In this paper we present basic concepts of the proposed DSML that is used to model production processes. In this paper an application of this DSML in an assembly use case is presented. The DSML is used by domain experts to model production processes using familiar, domain concepts. This should result in faster, easier and comprehensive modeling of production processes which are suitable for automatic execution that yields less faults. Using the DSML within our MDSD approach should also improve the manufacturing flexibility and the level of overall automation of production.

Apart from Introduction, this paper is structured as follows. An overview of the related work is presented in Section 2. Basic concepts of the proposed DSML for production process modeling and an assembly production use case are described in Section 3. Conclusions and future work are given in Section 4.

Related Work

Modeling production processes in Industry 4.0 is an important industrial informatics research topic [START_REF] Xu | Enterprise Systems: State-of-the-Art and Future Trends[END_REF], as production processes are digitally supported, and they need to be integrated with Cyber-Physical Production Systems [START_REF] Xu | Industry 4.0: state of the art and future trends[END_REF]. There are many process modeling languages, but most of them are neither tailored for production processes, nor ready to model execution-ready production processes. There is the manufacturing process chart standard named KS A 3002 [START_REF]KS A 3002 Standard[END_REF], however it lacks a tooling support for modeling and automatic execution [START_REF] Ahn | Measuring Similarity for Manufacturing Process Models[END_REF]. Companies often utilize process charts or Bill of Materials (BOM) specifications, but none of them can fully describe production processes that could be automatically executed. BOM specifications are not sufficient to understand a production flow [START_REF] Ahn | Measuring Similarity for Manufacturing Process Models[END_REF]. Bill of Materials and Operations (BOMO) specifications include the production flow, but cannot specify the selection and iteration patterns or smart resources.

Conceptual process modeling languages like Business Process Modeling and Notation (BPMN), Unified Modeling Language (UML) Activity Diagram, Petri Nets and Event-Driven Process Chains usually cannot support the material flow concept, which makes modeling production processes very difficult. To model production processes, BPMN extensions have been created [START_REF] Zor | A Proposal of BPMN Extensions for the Manufacturing Domain[END_REF], but a depiction of material flow is still hard to achieve [START_REF] Lütjen | GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes[END_REF] and there was an absence of uniformity [START_REF] Ahn | Measuring Similarity for Manufacturing Process Models[END_REF]. Also, BPMN extensions were created for production process similarity measurements [START_REF] Ahn | Measuring Similarity for Manufacturing Process Models[END_REF], but it is not possible to model selection or iteration patterns or smart resources. Using Systems Modeling Language (SysML) or Petri Nets to model production processes usually lack to model complex production processes or the material flow. To overcome usual lack of the material flow concept, a new material flow-oriented process modeling language -GRAMOSA has been created using UML profiles, but the material flow-oriented approach was complex [START_REF] Lütjen | GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes[END_REF]. Some languages lack production logistic specifications like DELMIA Process Engineer (DPE), while others are limited to simple linear process sequences like Value Stream Design (VSD) [START_REF] Lütjen | GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes[END_REF]. We have identified the lack of a modeling language capable to specify all relevant aspects of a production process in the context of its formal definition suitable for automatic execution. That is the main reason why we have decided to create a new DSML instead of applying an existing one in our MDSD approach. The main goal of introducing such a DSML is to improve the production process flexibility and to enable formal specification of production processes that will allow automatic generation of program code aimed at automation of process execution.

Application of the DSML in an Assembly Use Case

In our previous work [START_REF] Vještica | Towards a formal description and automatic execution of production processes[END_REF], we discussed a DSML usage for modeling production processes in Industry 4.0. We also identified concepts that must be supported by a DSML for production processes modeling that will enable automatic code generation and execution from such models. In this section, we present a use case to demonstrate the application of the developed DSML. For each DSML concept we provide an example of its use in the presented use case. The language is created by using Ecore meta-metamodel, while the graphical syntax and the modeling tool are created by using Eclipse Sirius. Concepts of the graphical syntax are presented on the left side of Fig. 2. The presented use case describes an assembly process of a custom LEGO flag. The process is modeled by our DSML and the model is then passed for code generation which is executed both in the simulation and on the shop floor. Bricks of various colors are stored in a smart shelf while a human worker and an industrial mobile robot pick different bricks in parallel and assemble the flag on a brick pedestal at an assembly table. A human worker is using a tablet or a smart watch in order to receive instructions and to send feedback when an activity is finished.

Our DSML supports modeling at two levels of abstraction in order to make the modeling easier for process designers. They are creating higher-level abstraction models, i.e. ML models. An ML model can be extended with additional specifications of execution details. In that way a DL model, at a lower level of abstraction, is created. Execution details can be added to an ML model manually or automatically by means of Orchestrator, as it is illustrated in Fig. 1. Based on these two abstraction levels, we have classified the language elements into two groups: (i) elements that are needed to model ML production processes and (ii) elements that are needed to be added into existing ML models to create DL models. The ML model of our LEGO use case is presented in the central part of Fig. 2, while the DL model is presented on the right side of Fig. 2.

At the higher level of abstraction, process designers model process steps that denote either an operation or an inspection activity. A process step can include (i) an input product, that can be e.g. raw material or component, and that can be picked from a storage or is a result of the previous process step, (ii) a capability, i.e. skill required to execute the step and (iii) an output product, which is a result of executed capability on the input product and is either placed in a storage or transferred to the next process step. Every product and capability can have constraints, e.g. width, height, color, which are considered by Orchestrator when it decides which resources are able to perform a process step. Some capabilities require parameters to be specified e.g. position must be specified in order to place a brick on a brick pedestal. Also, a material flow can be specified by defining if a product needs to be picked from a storage, placed to a storage, or it is a result of the previous process step. As specified process steps need to be connected using relationshipslinks between process steps, the language has a concept to model a workflow. Process steps can be connected so as to form a sequence or a set of parallel workflow branches. Additionally, selection and iteration patterns can be also added to the workflow. For this purpose, we are using a gate concepta modeling concept that is used to connect multiple workflow branches. The language also supports a message flow, i.e. collaboration of resources. This concept is used if two or more process steps need to be executed in parallel, but one process step must not finish its activity or start the next one before it gets a message that another process step finished its activity. Using described concepts, a process designer can be focused only on process steps that must be executed and need not to worry about production logistic and resources that will execute the process steps.

Fig. 2. A production process model example

In our LEGO use case, the ML model has: (i) the start process step, (ii) parallelism gates (PAR) and between them parallel assembly process steps, (iii) the inspection process step, (iv) decision gates (DEC), which have two branches that leads to product discard or packaging, (v) collaboration gates (COL) with packaging process steps and (vi) the end process step. An assembly process step is an operation process step, which is denoted by a circle on the left side of a process step name. Flow Process Chart (FPC) is broadly used to specify the production process flow and process designers are familiar with its graphical and symbolic representation. That is why we have decided to take over some common symbols from FPC for corresponding concepts in the DSML, like circle symbol for an operation process step. An operation process step needs to have input and output products and a capability. Products and a capability are graphically represented by rectangles of different color connected to a process step. They can be hidden from a diagram using a +/-button at the top left corner of a process step, so a process model could be more or less complex depending on the designer needs. Due to length limitations, these detailed specifications are depicted just for one process step in the central part of Fig. 2, while for the rest they are specified, but not shown. In the depicted ML model, the input product can be seena blue brick that is gathered from a storage. A storage is presented by a triangle icon, same as in FPC, on the left side of a product name, representing that a product must be picked from a storage. The assembly capability needs to be executed on the input product, and the output product is the assembled blue brick on the brick pedestal, basically the partially assembled flag with this brick in it. After assembly process steps, the inspection process step is needed, which notation is presented by a rectangle icon, same as in FPC. If assembled bricks have not passed the inspection, they are discarded. Otherwise, packaging of assembled bricks is required. This should be done by doing two activities in parallel. One activity is to hold assembled bricks and another one is to bring a box beneath them. The first activity should not be finished before the message arrives that the box is brought under assembled bricks. This message flow is presented by a dashed arrow between these two process steps. After the message arrives, assembled bricks need to be placed in the box.

At the lower level of abstraction, all concepts from the higher level of abstraction exist, but the language also has additional concepts like resources, specific storages and new process step representations. Orchestrator uses these concepts to fill existing ML models with production logistics and smart resources. At this level, process steps can also represent activities like transportation, configuration, i.e. calibration of machines, or delay, i.e. necessary waiting. A specific storage must be defined for every input product that must be gathered from a storage and for every output product that must be placed in a storage. Process steps additionally have a resource that will execute a capability on input products. A resource can be a human worker or a machine. This is important information especially for code generation. For every process step human-readable or machine-readable instructions will be generated, depending on the provided information. Using all presented concepts, production process models are ready for code generation and consequently for an execution.

In the LEGO use case, we present in detail only one "pick" and one "assemble" process step of the DL model. Other process steps are expanded in a similar way. One assembly process step is assigned to a human worker, and another is assigned to a mobile robot. Both human worker and mobile robot must execute additional transportation process steps in order to pick a brick from the smart shelf and place it on the assembly table. In contrast to the human worker, the mobile robot in this use case needs to execute additional configuration process steps after transportation. The mobile robot is not equipped with the machine vision modules and therefore it must be calibrated after each movement to determine its position. Transportation and configuration process step notations are presented by an arrow, same as in FPC, and a gear wheel icon respectively. These process steps only include a capability, without products. The input product of the "pick" process step has the specific storage, i.e. the smart shelf, added by Orchestrator. Its output product is the same picked product. The input product of the "assemble" process step is the output product from the "pick" process step, which means it does not have to be gathered from a storage. Its output product is the brick, which will be assembled at the specific storage, i.e. the assembly table.

Using the DL model, it is possible to generate instructions to both mobile robot and human worker. The executor sends instructions to the mobile robot or the human worker and waits for their response. Once a resource completes an activity, the next one is sent. The process is finished after the execution reaches the end process step.

Conclusions and Future Work

In this paper we presented an application of the developed DSML for modeling production processes in an assembly use case and described its basic concepts. This language is used as one of the main elements of the MDSD approach that aims to improve the flexibility and the automation level of production. The DSML can be used to (i) make faster and more precise process designs, (ii) make less faults during process design, (iii) enable faster changes of production process models in the era of Industry 4.0 and (iv) model a human-machine interaction. Also, the DSML models are used by Orchestrator to manage the production, as it is important to plan process activities and integrate processes within the industrial system [START_REF] Stevanov | An Application of Period Batch Control Principles and Computational Independent Models for Supporting the Overhaul Process of the Railway Braking Devices[END_REF].

The language has been evaluated by process designers on the shop floor within a small-scale industrial production setup. We plan to further evaluate the language in additional industrial use cases and also by independent researches and process designers in order to improve the domain concept coverage and the tooling stability. Also, we plan to further investigate related research and provide a systematic literature review on production process modeling and execution.

Although a lot of concepts identified in [START_REF] Vještica | Towards a formal description and automatic execution of production processes[END_REF] were already implemented in the presented DSML, there are additional concepts that can be added in order to improve the language domain coverage, like: (i) unordered sets of process steps, (ii) quality assurance with completion and acceptance criteria, (iii) error handling flows, (iv) process variations and (v) sub-processes. Also, we plan to implement a new modeling tool feature to monitor execution of every process step and thus enable detection of delays or badly modeled process steps. Finally, ML and DL models could be generated from existing product specification formats, like Computer Aided Design (CAD) models.

Acknowledgment

The research in this paper is supported by KEBA AG Linz.


Imane Essaadi
email: imane.essaadi@emine.um6p

Richard De Neufville

De-risking investments in industrial systems using Real Options Analysis: Case of phosphates fertilizer firm

Keywords: Managerial Flexibility, Real Option Analysis, Uncertainties, Risks, Real options

Many industrial firms adopted a deterministic irreversible investment plans without any pro-active risk management strategy. The existing literature of the Supply Chain Design does not value the managerial flexibility as enabled by the Real Option (RO) theory. However, this latter considers only single project and not adapted to supply chain investments. This opens a new area of research. Thus, this paper is concerned with providing a practical and novel framework for de-risking future investments of large-scale, capital-intensive interdependent industrial projects, in an uncertain and risky environment. The suggested framework is based on real option analysis and aims to integrate managerial flexibility in terms of "real options" to efficiently adjust the investment plans to the evolution of uncertainties over time, considering interdependencies between projects, while reducing downside risks and increasing potential opportunities. This framework has been tested to assess potential risks, define and value flexible strategies to deal with the construction delays and the cost overruns in the investment planning of phosphates fertilizer firm. The insights indicate that flexibility lead to higher expected performance while reducing downside risks and raising potential upside gains.

Introduction

Complex industrial systems strategic planning involves capitalistic investments expenditures and depends upon the future evolution of the global market which is highly risky. Many firms adopt a deterministic irreversible investment plan without any proactive risk management strategy. This could lead the firm to considerable economic losses. This paper is concerned with providing a practical framework for de-risking future investments of complex industrial systems in an uncertain and risky environment, especially cost overruns and time construction delays. This problem may be significantly critical when investing in a commodity-based integrated chemical supply chain such as oil and gas or phosphates-based mining and chemical industry. These industries invest in capital intensive projects whose value chains are usually vertically integrated from mining to distribution and include complex logistic infrastructure and multi-products chemical transformation facilities and infrastructures that are hieratically dependent.

The most common approaches to deal with uncertainty in the strategic investment planning are the Stochastic Programming and Robust Optimization (RO). RO provides reliable but too conservative solutions [START_REF] Kirilyuk | Risk Measures in Stochastic Programming and Robust Optimization Problems[END_REF] since it either maximizes the profit of either the worst-case scenario or the profit of the scenario with highest deviation. SP assumes a risk-neutral decision-maker and focuses only on finding the best design that performs best on average; by optimizing the expected economic value. Thus, the optimal stochastic design could be over-sized and generates losses or under-sized and miss opportunities when the future departs significantly from expectations. This is because, the value of the overall distribution of uncertainties is different than the value associated to the average [START_REF] De Neufville | Flexibility in Engineering Design[END_REF]. Some studies [START_REF] Dal-Mas | Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty[END_REF][START_REF] Kostin | Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty[END_REF][START_REF] Gebreslassie | Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk[END_REF][START_REF] Ribas | Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming[END_REF][START_REF] Santibañez-Aguilar | Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty[END_REF] have integrated risks measures into their design, but these concern mainly the minimization of downside risks. In short, they are reactive to risks and do not provide proactive measures to increase the flexibility of the system for future to deal with unexpected events. However, good strategic investment planning will not only recognize the distribution of possible futures but also proactively take steps to de-risk and future-proof the investments. It will take steps to reduce the possible downsides, investing in ways to deal effectively with eventualities and to unlock future market opportunities. The design should integrate managerial flexibility into the system in terms of "real options" to efficiently adjust the supply chain to the evolution of uncertainties/risks over time. Real options provide the ''right, but not the obligation, to change the system easily in the face of uncertainty, such : option to abandon, to defer, to relocate and to expand/ contract the capacity.

Despite the flexibility in engineering systems being promising approach to deal with uncertainty [START_REF] De Neufville | Flexibility in Engineering Design[END_REF], its effective deployment into the system can be a challenge. It depends highly on the choice of the future alternatives actions (real options); the timing and the location of flexibility in the system. The most common forms of Real Option Analysis (ROA) used in the literature are based on analytical methods (based on dynamic programming) or on lattices (binomial or multinomial). These are typically used to analyze and price financial options. But they do not apply to physical systems for which there is no ready market, in which arbitrage-enforced pricing does not apply, and for which managerial independence invalidates potential assumptions about path independence. To cope with these challenges in valuing flexibility, novel approaches based on decision rules, linking the observations of uncertainty data to decisions, were recently proposed, as by [START_REF] De Neufville | Flexibility in Engineering Design[END_REF]. However, many applications are limited, for example to one type of flexibility (single real options) [START_REF] Cardin | An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming[END_REF] or to a single project/supply chain component [START_REF] Sharma | Options analysis for long-term capacity design and operation of a lignocellulosic biomass refinery[END_REF][START_REF] Zhang | Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming[END_REF] which cannot be applied to multiple interdependent projects/components.

Considering the background above, the main contribution of this research paper is to provide a practical approach to flexibility analysis that effectively integrates real options in large-scale complex systems involving interdependent projects. Its novelty lies in the scope and its approach:

As for its scope: we particularly interested in de-risking investment in dependent industrial projects, operating in a commodity based global market, characterized by the need for large capital resources, and where the impact of uncertainties on one potential project will consequently impact the operational functioning of her projects that depend on it.

As for its approach: to make the best use of managerial flexibility on proactively dealing with uncertainties in the chemical supply chain investments, we first model uncertainties considering their correlation, then we quantitatively evaluate their impact including financial risks underlying an investment plan. Based on this evaluation, we explore the options on investments that could be susceptible to increase the expected value from the industrial investments and reduce downside risks. The remainder of this paper is organized as follows: Section 2 provides the details of the proposed approach. In Section 3, illustrates the use of the suggested approach to the case of an integrated chemical company. The final section summarizes the major findings by providing conclusions and future research perspectives.

Proposed Framework: Real compound option analysis

The proposed framework demonstrates how to adapt investment plans to future uncertainties based on proactive and flexible strategies. It aims to avoid future downside risks and take advantage of new opportunities. It involves 5 steps as shown in fig. 1:

Step 1: Meta-Model construction

As many investment planning models are complex in nature, the evaluation of comprehensive flexibility to incorporate into the planning requires the use of meta-models. They could be developed in three ways [START_REF] De Neufville | Flexibility in Engineering Design[END_REF]: Bottom-up approach: the metamodel is built upon the aggregation of the detailed description of the model by reducing the inputs and simplifying the formulation of the model without omitting interesting details. Simulator models: mimic detailed descriptions of the system or the model responses. It considers the model as a black box, does not consider its technical details. Top-down approach: it consists in defining conceptual representations of the overall pattern of the system behavior, such as systems dynamics.

Steps 2 and 4 : Uncertainty analysis

These steps model potential input uncertainties and investigate the consequent uncertainty of the performance of the investment plan. They enable us to assess the risk-reward of different investment strategies (initial and flexible investment). The risk of loss could be evaluated by the Value at Risk -indicating the 5 or 10% probability of low returns or the complementary Value at Gain , the possible gain for given level of probability.

Step 3: Flexibility analysis

This step explores proactive strategies and ways to reduce risks (assessed in step 2) and increase opportunities given of the investment plan. This can be done by responding systematically to the questions: where, when and how the flexibility should be incorporated into the design? What flexibility? The answer to the "where" comes by analyzing the most interdependent projects in the alternative design, this comes with the deep knowledge of the supply chain/value chain. The answer to "when" is related to the timing of the risk occurrence. For example, if the potential risk is the project exe -cution delay, then we could distinguish two delay periods: either in the design or in construction phase of the critical project. The answer to the "how" comes by defining the combination of flexible options and the decision rules ("If…Then") that will structure the use of these flexibilities. These rules both state the conditions under which designers would choose to act and implement the decisions in the meta-model/ the initial model to change the design. According to [START_REF] Kirilyuk | Risk Measures in Stochastic Programming and Robust Optimization Problems[END_REF], these rules should mimic what management would do if they had to deal with a changing situation.

Step 5: Options comparison

Finally, we have to assess the value of flexibility underlined with these compound options and appreciate the risk-reward trade-off between candidate options. One measure of the option value is the difference between the overall design value with and without compound options., as calculated in Step 2, as Equation 1indicates:

Flexibility Value=max (0 , ENPV ¿¿ Best Flexible design-ENP V Optimum¿ design¿)¿

(1)

Case Study

The presented framework has been tested in the case of our industrial partner (OCP.SA) leader in the phosphate mining and chemical production industry in Morocco. Its logistics chain is spread over three independent axes "northern axis", "central axis" and "southern axis". Each axis is vertically integrated: from the extraction of phosphate ore through the chemical valorization (phosphoric acid and fertilizers). The Group has embarked on an ambitious investment program for the period 2008 to 2030. The first wave of investments focused on the extension of mining and chemical capacities located on its northern axis. The second wave of investments concerns the reconfiguration of the central axis by implanting: a new port in Safi, new acid and fertilizer plants and an acid pipeline that will link acid plants in Youssoufia with fertilizer plants in Safi. For confidentiality reasons, we will consider a fictious investment plan and normalize real data by taking the expected performance of the initial investment plan (Table 1). For the space limit, we do not present the meta-model.

3.1.Step 1: Uncertainty analysis under fixed design

Among potential uncertainties that may lead to significant losses if proactive actions are not considered, are: prices of fertilizer products and raw materials inputs (Ammonia and Sulfur), the acid pipeline construction delays and cost overruns (Table 2). By simultaneously varying different uncertain parameters within their possible range of variation (Table 3), we obtain the distribution of the possibilities of the investment plan performance (Fig. 2). This curve represents the cumulative chance of obtaining a result below any specific target value, according to ascending manner. We notice that there is 10% chance of losing respectively about 2092 MMAD or more, that is of missing around -2092 MMAD Target. This investment plan has about 40% chance of breaking (investment value =0) and could realize 1000 MMAD on average.

Step 2: Flexibility analysis

To counteract the risk of the acid pipeline construction delay, a distinction must be made between two cases depending, on the location of the delay in the progress of pipeline development (Fig. 3). Similarly, a distinction will be made between ongoing projects (P1), those whose arrival date coincides with that of the pipeline, projects under study (P2), those whose arrival date is later than the initial arrival date of the pipeline (without delay) and the new projects (P3), those whose arrival date is later than the new arrival date of the pipeline (with delay) (Fig. 3). If the delay is identified at the design phase of the acid pipeline, in our example in 2020, the firm will have disbursed part of the CAPEX for projects in phase with the pipeline, in particular the two complex site 1 and 2. Due to space limit, we will investigate only two options to limit the delay experienced at the design phase: Option 1: This option is based on extending the construction of existing projects (P1), deferring the construction of projects under study (P2),and maintaining the ar-rival dates of the new projects (P3). Extending the construction of ongoing projects results in extending the CAPEX disbursement associated with these projects and postponing the activation of these projects, while postponing the construction of new projects results in postponing the CAPEX disbursements, as information on the pipeline arrival delay is identified prior to their construction start date. The deferral of the activation of projects results in the deferral of revenues and costs.

Option 2 : This option carries forward the same rules applied in Option 1 except for the maintenance of new projects. In this second option, we will postpone their activation date, in order to lighten the financial commitments in CAPEX.

Step 4: Options comparison

Table 4 resumes the statistical metrics of each investment plan. We can conclude that the integration of the flexibility in investment planning provides better reward while reducing downside losses (Fig. 2, 4, 5). Indeed, both options lead to greater expected value. They lower VAR and and raise VAG. More remarkably, maintaining future projects dates is more interesting than postponing them, thus option 1 outperforms option 2 at all levels. Finally, the implementation of option 1 depends upon the trade-off between the option value and the cost underlying the postponement of projects type 1 and 2. This insight will support the industrial development department during its negotiation with its contractor, considering potential delays.

Conclusion and research perspectives

Major long-term investments are inherently risky, because future market, economic, and political conditions are highly uncertain. However, the literature has focused on finding the best design optimizing the expected performance and risks without inte-grating proactive measures to increase the flexibility of the system for future to deal with unexpected events. In order to address this problem, we suggested a proactive practical framework that integrates and value flexibility -real options-in large-scale complex systems involving interdependent projects. Given the complexity of the studied systems, we considered compound options, each option was applied on a specific project following a specific decision rule. Overall insight from our case study confirms that flexibility is an effective way to improve the expected performance of systems in uncertain environments. In our future work, we will provide guidance for supply chain decision makers on how and when to exercise flexibility to mitigate pandemic crisis.

Fig. 1 .

 1 Fig. 1. Real compound options analysis framework

Fig. 2 .Fig. 3 .

 23 Fig. 2. Cumulative distribution function of the investment plan value (without option)

Fig. 4 .Fig. 5 .

 45 Fig. 4. Cumulative distribution function of the investment plan value (option 1)

Table 1 .

 1 Potential new projects

	Asset	Opening	CAPEX
		date	(MMAD)
	Complex site 1	2023	358
	Complex Site 2	2023	358
	Complex Site 3	2024	358
	Complex Site 4	2025	358
	Complex Site 5	2026	358
	Acid Pipeline	2023	116
	New Port	2023	268

Table 2 .

 2 Delays and costs overruns

	Year of	Cost
	delays	overruns
	1 year	25%
	2 years	50%
	3 years	70%
	4 years	85%

Table 3 .

 3 Statistical distribution of potential uncertainties

	Commodity Price	Statistical Distribution 1	Parameters
	Sulfur price ($)	Triangular	(33, 83, 806)
	Ammonia Price ($)	Triangular	(125, 705,884)
	Fertilizer Price ($)	Triangular	(200, 263, 1280)
	Acid pipeline delays	Uniform	(0,4)

Table 4 .

 4 Statistical distribution of potential uncertainties

		Without option	Option 1	Option 2
	Expected performance	1000	1223	1055
	(MMAD)
	Option Value (MMAD)		216	55
	10% VAR (MMAD)	-2092	-1848	-1953
	10% VAG (MMAD)	4612	4831	4666

The observation of the historical data shows that the annual price, for a given commodity, is an independent variable that follow a statistical non-skewed distribution.


Maggie Bresler
email: maggie.bresler@ntnu.no

Anita Romsdal

Jan Ola Strandhagen

Olumide E Oluyisola

Principles and Research Agenda for Sustainable, Data-Driven Food Production Planning and Control

Keywords: Food industry, Production planning and control, Sustainability

This paper investigates the topics of data, sustainability, and production planning and control in food supply chains from the perspective of industrial food producers. To stay competitive in an industry with low profit margins, strong competition, and sustainability concerns, food producers need new solutions. The capture, digitization, and use of producer and downstream supply chain data enable opportunities for using data in new ways to address the existing challenges. This study proposes some principles for sustainable, data-driven production planning and control (PPC) such as capturing real-time data and tacit knowledge for use in PPC. It then investigates how these principles can impact the sustainability for food producers and the overall supply chain, by giving benefits such as reduced food waste, lower inventory levels, and reduced planning time and effort. Future research topics should address topics such as data availability, use of data in PPC, potential value of data, sustainability trade-offs, and the applications of digital technology in PPC.

Introduction

Previous studies have highlighted a need to increase sustainability and reduce food waste in food supply chains [START_REF] Papargyropoulou | The food waste hierarchy as a framework for the management of food surplus and food waste[END_REF][START_REF] Gustavsson | Global food losses and food waste[END_REF], and companies need to investigate novel ways of increasing sustainability. A major contributor to food waste is an imbalance in supply and demand [START_REF] Mena | The causes of food waste in the supplier-retailer interface: Evidences from the UK and Spain[END_REF][START_REF] Parfitt | Food waste within food supply chains: quantification and potential for change to 2050[END_REF], where for instance unsold inventories of perishable products produced in advance of customer orders are scrapped in large amounts in all stages of the supply chain [START_REF] Priefer | Food waste prevention in Europe -A causedriven approach to identify the most relevant leverage points for action[END_REF]. Data and information sharing in supply chains has long been widely heralded for the potential to better match supply and demand [START_REF] Lee | The Value of Information Sharing in a Two-Level Supply Chain[END_REF][START_REF] Zhou | Supply chain practice and information sharing[END_REF][START_REF] Wang | The bullwhip effect: Progress, trends and directions[END_REF]-leading many companies to invest heavily in information technologies to manage data and information [START_REF] Fawcett | Information sharing and supply chain performance: the role of connectivity and willingness[END_REF][START_REF] Freitas | A theoretical framework to adopt collaborative initiatives in supply chains[END_REF]. Further, advanced concepts and models for supply chain collaboration and information sharing have been developed, such as efficient consumer response, and collaborative planning, forecasting and replenishment [START_REF] Fliedner | CPFR: an emerging supply chain tool[END_REF][START_REF] Wood | Efficient Consumer Response[END_REF]. Yet, after over 30 years of research and development, few companies actually share information and proliferation of both technologies and models for supply chain information sharing remains limited [START_REF] Panahifar | A framework for Collaborative Planning, Forecasting and Replenishment (CPFR): State of the Art[END_REF][START_REF] Kembro | Information sharing in supply chains, myth or reality? A critical analysis of empirical literature[END_REF]. Simultaneously, the rise of digital technologies has a potential to transform the way data is used, both to improve current operations and innovate how processes in supply chains are carried out [START_REF] Ustundag | Industry 4.0: managing the digital transformation[END_REF][START_REF] Oluyisola | Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications[END_REF]. This paper investigates data sharing in food supply chains from the perspective of industrial food producers. Based on the assumption that data from downstream actors is available, the purpose of the paper is to propose a set of principles for how such external data can be used and combined with internal data to change the way food production is planned and controlled with an ultimate aim of reducing food waste and increasing sustainability. The result is a research agenda that outlines issues and ideas for how to achieve sustainable, data-driven food production planning and control.

2

Theoretical Background

Sustainability and Food Supply Chains

In addition to the overriding necessity to be economically viable, society now expect firms to balance their financial, environmental, and social goals [START_REF] Purvis | Three pillars of sustainability: in search of conceptual origins[END_REF]. For environmental aspects, since planning and control includes managing resources, the consumption of resources such as water, energy, and materials are relevant. Also, along with food waste, other wastes and pollution can be created during manufacturing such as greenhouse gas emissions, waste-water, or other non-food production waste including hazardous waste, food losses, and packaging material waste [START_REF] Zarte | Indicator framework for sustainable production planning and controlling[END_REF]. Social metrics that relate directly to PPC are more difficult to identify, but PPC utilizes human personnel, so job satisfaction is as an important aspect to consider [START_REF] Fernández-Macías | Automation, digitalisation and platforms: implications for work and employment[END_REF]. Furthermore, the food industry has distinguishing characteristics that set it apart from other industrial sectors. Food is perishable, so there are product-dependent constraints on inventory holding time as well as handling and storage environment requirements. Industrial food production seeks maximize efficiency and minimize long change-over and setup times. Production is sequence dependent, with certain colors, flavors, and allergens produced after others to shorten the changeovers as well. Demand of some food products is dependent on seasons, holidays, and promotions. Food supply chains consist of many actors, including raw material suppliers, producers, wholesalers, retailers, and consumers [START_REF] Romsdal | Differentiated production planning and control in food supply chains[END_REF].

Planning and Control of Production and Supply Chains

PPC can follow a variety of strategies, from engineer-to-order to make-to-stock (MTS) strategies; the difference is how much planning and production is based on customer orders or forecasts. For MTS, production is based on forecasts which are made using methods that include moving average, exponential smoothing, and algorithms accounting for trend and seasonality. After forecasting, production planning is done periodically at different aggregation levels (by time or groups of products) and must also be integrated with the inventory distribution needs at various stages of the supply chain. Plans are typically created using enterprise resource planning (ERP) and advanced planning and scheduling (APS) software systems, but these plans are not integrated and optimized across supply chain actors [START_REF] Chopra | Supply chain management : strategy, planning, and operation[END_REF]. However, it is becoming increasing necessary for manufacturing firms compete not just individually with other firms, but as a supply chains since a lot of the profitability is derived through proper planning and control of the activities of the extended supply chain [START_REF] Vollmann | Manufacturing planning and control for supply chain management[END_REF]. Supply chain planning and control takes into account the needs of multiple supply chain actors together. This is important as many benefits, such as reduced bullwhip, can be gained by thinking about how to optimize the whole system and not just the processes of one actor. Strandhagen, Dreyer and Romsdal [START_REF] Strandhagen | Control model for intelligent and demanddriven supply chains[END_REF] presented the Principles for Intelligent, Demand-Driven Control as a step towards realizing more demand-driven supply chain relationships. These principles include capturing and using real-time data for decision-making and performance measurement, sharing data across the supply chain, and moving the customer-order-decoupling-point (CODP) toward less-forecast based and more order-based approaches such as make-to-order (MTO).

Digital Technology

Traditionally, planning and control was carried out through the use of ERP and APS systems for planning, using input static and historical data and optimization parameters to plan production. However, these systems still heavily rely on human input for updating parameters and providing accurate and complete data. To make production "smarter", there is a growing trend to use Industry 4.0 methods such as artificial intelligence (AI) to address a wide variety of industrial challenges. AI is a broad category of learning-based algorithms that have the potential to find patterns in data that humans cannot and use these underlying patterns to make suggestions in, for example, the planning and control of food production. There is research being done to apply AI to PPC, with application areas such as product family and layout optimization, identifying the right control strategy, and predicting supply chain disruptions [START_REF] Garetti | Neural networks in production planning and control[END_REF][START_REF] Brintrup | Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing[END_REF].In order to use digital technology, the data must be collected and digitized. RFID and networked sensors can collect and transmit data in real-time to give a picture of the current state various points in the supply chain, from production to inventory [START_REF] Strandhagen | Logistics 4.0 and emerging sustainable business models[END_REF]. While there is not yet a consensus among practitioners about how or whether all data has value and what specific data has value, it is believed that real-time processmonitoring data is useful in forecasting and production situations [START_REF] Oluyisola | Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications[END_REF], and data such as remaining shelf life has been shown to improve alignment of supply and demand [START_REF] Kiil | Aligning supply and demand in grocery retailing[END_REF].

Method

This is a conceptual paper that explores the use of data to improve PPC. In the theoretical background, we outlined key concepts and trends that are expected to have an impact on PPC in the food sector. Through the illustrative example of a food producer, we highlight current practices and challenges to show the industry need and desire for data-driven PPC. These are then used in the following chapter to develop a set of principles for sustainable, data-driven PPC by considering the challenges of current food PPC, what different types of data are useful to PPC, what new data and technology are now available, and how the data should be used within existing or new processes. We then discuss their potential impact on sustainability because, while the principles do have specific elements of sustainable practice, the sustainability implica-tions of all the principles are not immediately obvious. Finally, the literature, principles and discussions are synthesized into an agenda for further research.

Brynild -An Illustration of Food PPC Challenges

Brynild Gruppen is a medium-sized Norwegian food producer with nearly 200 employees and an annual revenue of EUR 70 mill. The company's factory produces 46 variants of sugar confectionery products, 44 chocolate variants and 50 nut variants.

The Norwegian market for confectionery and snack products is dominated by large international actors and Brynild Gruppen has a market share of approx. 14 %. Their main customers are the four Norwegian wholesaler -retailer dyads that control 100 % of the retail market, with wholesalers requiring a 98 % service level. Consumer demand for snacks and confectionery products is highly seasonal and affected by promotional activities and product introductions, making demand forecasting difficult. Brynild Gruppen's production strategy is mainly MTS for standard products, with build-up of inventory of seasonal products months in advance. The products typically have a shelf-life of 6-18 months, and products that approach or pass their industrystandard sell-by date are sold at reduced prices through alternative sales channels or scrapped. Brynild Gruppen has limited access to data from the supply chain beyond orders from wholesalers, an annual joint planning with retailers regarding timing and product variants for promotional activities and product launches, and the opportunity to buy aggregated sales data from a national grocery database. The company's seasonal products are sold on buy-back agreements, where customers in some cases have been credited up to 35 % for unsold goods at season end. Thus, Brynild Gruppen carries a large portion of the risk associated with seasonal products, while having little or no insight into actual consumer demand or influence over inventory levels in retail stores. This complicates demand forecasting and production planning and limits the company's ability to quickly respond to changes in demand. The consequences include both lost income and scrapping of unsold products in several supply chain stages. To improve sustainability and supply chain performance, Brynild Gruppen has initiated an innovation research project together with their largest customer, one other food producer, two ICT system providers and three research institutions. One of the purposes is to explore how data from downstream actors in the supply chain can be used in PPC -and thereby create a smart, transparent, sustainable food supply chain.

5

Principles for Sustainable, Data-Driven PPC

In this section, we firstly adapt the existing principles for intelligent, demand-driven supply chains [START_REF] Strandhagen | Control model for intelligent and demanddriven supply chains[END_REF] to the PPC context (see Table 1). Secondly, we discuss how the application of the proposed principles can impact on sustainability in a food context, outlining effects for both food producers and for downstream supply chain actors.

P1

Promotional plans from producer/brand owner and downstream actors should be transformed into data to be used for PPC P2 Historic demand data on seasonal products from the producer/brand owner and downstream actors should be transformed into data to be used for PPC P3

Real-time data on demand and inventory levels from retailers and wholesalers should be transformed into data to be used for PPC P4

Real-time data on finished goods and WIP inventory levels should be captured at the producer and used for PPC P5

Tacit planning knowledge and experience on production planning should be captured and transformed into data to be used for PPC P6

Tacit knowledge of production processes and machines/production lines should be captured and used for PPC P7

Real-time performance data (including sustainability performance) should be captured, measured, and used for performance visualization and PPC P8

Plans should be adjusted based on updated data from the producer and downstream supply chain P9 AI and learning-based analytics should be applied to gain new PPC insights P10 The proportion of automated PPC processes should be increased to minimize planning time and reduce repetitive tasks for human planners, freeing up human planners for more complex PPC tasks P11 The CODP should be continuously evaluated, exploring potential for more order-based or hybrid production strategies (both across product range and over time/seasons) P12 Sustainability trade-offs should be considered in PPC and visualized to support human decision-making

The principles presented above are aimed at improving PPC and subsequently sustainability. In the following paragraphs, we discuss the potential impacts the principles can have in a food supply chain context, both for producers and downstream stages. The use of data on promotional plans (P1) and seasonal demand (P2) in PPC will enable better alignment of supply and demand in the supply chain -subsequently decreasing food waste from overproduction and reducing lost sales due to stockouts. Any reduction of food waste will also reduce the expenditure of raw materials and other resources. Using real-time data in PPC (P3, P4, P7, P8) will also better align supply and demand, as well as increase food producers' responsiveness and flexibility to changes in the supply chain. This can allow for lower inventory levels, which in turn can increase inventory turnover and increase the days of remaining shelf life available in the downstream stages of the supply chain. This again reduces the risk of food waste of expired products in wholesale, retail and consumer households.

In particular, real-time data on demand and inventory levels (P3) of new products would allow a producer to adjust production volumes to early sales data, giving them time to ramp up production to avoid lost sales of a popular item or halt production to avoid food waste of a failed launch. Additionally, more real-time monitoring of performance (P7) on parameters such as inventory levels and product quality (e.g. tem-perature, humidity and remaining shelf life) through RFID and networked sensors can reduce food waste from quality degradation. However, while adjusting plans based on real-time data (P8) will improve the quality of the plans, it also requires an increase of planning cost through the increase of time and resources needed for the adjustments. Applying AI and learning-based analytics (P9) on PPC-relevant data can provide new and valuable insights, thereby reducing planning effort, time and cost. Automation of planning processes (P10) will also reduce the amount of repetitive planning tasks, thus leading to higher levels of job satisfaction for planners. The transformation of tacit knowledge to digital data (P5, P6) has the potential to improve PPC. Standardized knowledge can be used to improve and planning processes -increasing speed, quality, efficiency, flexibility, etc. However, it is unclear how these principles could affect social sustainability. Digitizing tacit knowledge may make training easier but could also make planners feel less valuable and contribute to loss of deep experiential knowledge once algorithms replace human decision making. Any reconsideration of CODP towards more order-based production (P11) could reduce the risk of food waste due to overproduction. However, more order-based strategies such as MTO could be applicable for non-standard products (such as planned promotions) as the customer lead time expectations for standard products is typically considerably shorter than production lead time. Thus, hybrid approaches could be considered to ensure the high service level requirements are still met.

Considering all the above principles are expected to have impacts on sustainability, some indicators should be used to measure positive and negative effects (P12). Although food waste is an important indicator, it can be hard to measure and therefore other indicators should be included, such as inventory levels and remaining shelf life. It is not clear how different types of data can be used in PPC to achieve the potential benefits, nor is it clear if there are trade-offs, e.g. between financial and environmental sustainability, that should be considered in the application of the principles.

Towards a Research Agenda

In order to achieve the principles for sustainable, data-driven PPC discussed in the previous chapter in the food supply chain context, research is needed in some key areas. The topic of data availability is an underlying premise. Studies are needed to investigate which data would be useful for PPC, which data is currently available, which data is not captured, and how to capture and share data. In addition, how to use such data in PCC, including the development of logic and algorithms. Further, investigating how data can be utilized to improve existing processes and also how processes should be changed to better align supply and demand and thereby realize potential benefits. The full potential value of data also warrants further investigation. While food supply chain actors are interested in implementing new methods and technologies, wholesalers and retailers are likely to be hesitant to share data if all the benefits go to the producer. Therefore, studies into how the shared data can improve performance and identifying the possible benefits for producers and their downstream supply chain partners is needed. Key data mentioned in the principles also includes real-time data and tacit knowledge. Neither of these data types has been extensively studied in relation to PPC, so the capture and use of these data types in PPC should be investigated, both conceptually and empirically. In addition, while potential sustainability implications of the principles were outlined in chapter 5, several key questions remain. One relevant topic is how sustainability trade-offs can be accounted for in PPC, both in automated and human decision-making. Finally, in order to utilize new methods and technology to exploit the data, there is need for further study in how digital technology can be used in PPC.

Conclusion

This paper has illustrated how food producers can increase supply chain sustainability through data-driven PPC. Our proposed set of principles can be used by actors in the food supply chain to guide future collaboration and information sharing initiatives. However, the research agenda shows that more studies are needed before the principles can be implemented and the sustainability effects achieved. We believe there are great possibilities for increasing the application of data and technology in PPC to increase sustainability. While this study has limitations including the lack of validation of the principles and generalizability (which could be remedied through more rigorous methodology such as a structured literature review, multiple case studies, or a survey), this research has shown that there are great possibilities for increasing the application of data and technology in PPC to increase sustainability.

Table 1 .

 1 Principles for sustainable, data-driven PPC

Acknowledgement:

The research presented in this paper was conducted in collaboration with the DigiMat project, with financial support from NTNU, the participating companies and the Research Council of Norway.


Alessia Napoleone

Lorenzo Bruno Prataviera

Reconfigurable Manufacturing: Lesson Learnt from the COVID-19 Outbreak

Keywords: Reconfigurable Manufacturing, Scalability, Convertibility, COVID-19

To compete in the current volatile and unpredictable context, manufacturing firms increasingly need reconfigurability, i.e. the capability to adapt the production capacities and functionalities of their manufacturing systems according to evolving product families. To be attractive for practitioners, reconfigurability should require a reasonably low effort. In 2020, the COVID-19 outbreak has quickly twisted market requirements: in an unexpected market context, many firms have been reconfiguring their plants and networks to satisfy, with low efforts, the surge in the demand for very specific products. This paper analyses the reaction upheld by specific Italian manufacturing firms to the outbreak, to derive practical insights on possible ways to achieve reconfigurability in manufacturing. As for results, four insights are provided, regarding: the pre-existing know how held by firms; their network configuration; the modularity of products; and the use of smart and digital technologies. Additionally, remarking the relevance of collaboration between different firms, this paper sows the seeds for linking the reconfigurability theory with the dynamic capabilities theory.

Introduction

Manufacturing companies are nowadays dealing with volatile and unpredictable market requirements and, to keep their competitiveness over time, they need to develop the reconfigurability capability [START_REF] Shaik | Development of modular manufacturing systemsa review[END_REF][START_REF] Setchi | Reconfigurability and Reconfigurable Manufacturing Systems -State-of-the-art Review[END_REF]. Reconfigurability -referred to as changeability at plant and firm levels in several papers -is the capability of a manufacturing firm to repeatedly change or rearrange its manufacturing systems with a reasonable effort in order to produce evolving product families [START_REF] Azab | Mathematical Modeling for Reconfigurable Process Planning[END_REF][START_REF] Rösiö | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF]. Indeed, reconfigurability allows to provide the exact production capacities and functionalities needed, when needed [5].

To make reconfigurability attractive for practitioners, reconfigurations should require reasonably low efforts, in terms of reconfiguration time, cost and ramp-up time [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF][START_REF] Goyal | Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS[END_REF].

The year 2020 will be certainly remembered for the COVID-19 pandemic. This virus affects the respiratory system and, in the frequent event of complications of the infection, people need support of breathing and intensive care. The virus has spread rapidly from China to all over the world, putting health systems of many countries into a crisis: the rapid contagion of many people determines the overcrowding of hospitals. For this reason, many governments have been imposing partial or total lockdowns. As an added precaution, people have been required to wear masks when leaving their homes and to sanitize frequently their hands. In this landscape, a huge demand for masks and sanitizing gels from common people and ventilators from hospitals has been rising and has been replacing the demand for manufacturing items such as clothing and vehicles.

In this new and unexpected scenario, some firms have been reconfiguring their plants and networks to satisfy, in a very short time period and at affordable costs, the high request for masks, sanitizing gels and ventilators. Indeed, given the emergency situation, these firms have kept the reconfiguration effort as low as possible. For this reason, such crisis can add interesting insights on how to achieve reconfigurability in manufacturing. Thus, the research question addressed in this paper is: "What reconfigurabilityrelated insights are manufacturing firms reacting to the COVID-19 outbreak providing to the academic and business communities?" Specifically, this research captures concrete insights from the reactions -during the period from February to April 2020 -to the outbreak of a group of Italian manufacturing firms serving the Italian market. To this end, section 2 provides a theoretical frame to the research, section 3 describes the adopted methodology, section 4 carries out the analysis and outlines the results of the investigation and section 5 provides the conclusions and feedback for further research.

Theoretical frame

According to literature, the characteristics of reconfigurability are: modularity, integrability, diagnosability, scalability, convertibility and customization. Modularity and integrability mean the functionalities of the system and its components being separated into units with standard interfaces that can be easily combined and changed [START_REF] Andersen | Tailored Reconfigurability: A Comparative Study of Eight Industrial Cases with Reconfigurability as a Key to Manufacturing Competitiveness[END_REF]. Modularity and integrability are closely related [START_REF] Shaik | Development of modular manufacturing systemsa review[END_REF][START_REF] Mehrabi | Trends and perspectives in flexible and reconfigurable manufacturing systems[END_REF]. Diagnosability allows quick identification of the sources of quality and reliability problems [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF] and quick correction of operational problems [START_REF] Gumasta | Developing a reconfigurability index using multi-attribute utility theory[END_REF] within the manufacturing system. Scalability allows incremental changes of capacity, rapidly and economically [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF]. Convertibility 'allows quick changeover between existing products and quick system adaptability for future products' [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF]. Customization allows adaptation of system configuration for producing the required product families [START_REF] Rösiö | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF][START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF]. These characteristics allow for reducing the reconfiguration effort, thus making reconfigurability attractive for practitioners. Among the 6 characteristics, scalability and convertibility are those directly related to manufacturing systems' responsiveness to sudden changes: scalability to changes in demand and convertibility to changes in product mix [START_REF] Maganha | Understanding reconfigurability of manufacturing systems: An empirical analysis[END_REF]. Indeed, scalability and convertibility directly contribute to the goal of the reconfigurable systems, which is providing exactly the capacity and functionality needed when needed [5,[START_REF] Napoleone | Reconfigurable Manufacturing: A Classification of Elements Enabling Convertibility and Scalability[END_REF].

Several authors have worked at the identification of enablers of scalability and convertibility (see for example [START_REF] Andersen | Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation[END_REF][START_REF] Napoleone | Reconfigurable Manufacturing: A Classification of Elements Enabling Convertibility and Scalability[END_REF][START_REF] Beauville | Identification of reconfigurability enablers and weighting of reconfigurability characteristics based on a case study[END_REF][START_REF] Rösiö | Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry[END_REF]. Specifically, this paper relies on the observation that, being "reconfiguration" characteristics (i.e. directly related to systems responsiveness to changes), scalability and convertibility are supported by "configuration" enablers such as the modularity and integrability characteristics [START_REF] Napoleone | A framework to manage reconfigurability in manufacturing[END_REF][START_REF] Singh | Reconfigurable manufacturing systems : journey and the road ahead[END_REF].

Research design

This study examined implemented actions in terms of changes in the production capacity and/or functionality of production systems and identified the enablers of such actions; thus, providing insights on enablers of scalability and convertibility. Multiple cases were analyzed to increase external validity and to reduce risks to misjudge singular events [START_REF] Voss | Case research in operations management[END_REF]. As units of analysis, Italian manufacturing firms of different sizes, serving the Italian market and reacting to the COVID-19 outbreak from February to April 2020 were considered, as Italy emerged as one of the countries that was hardest hit by the epidemic. Given the dramatic situation, it was not possible to directly interview firms' representatives. Consequently, the main sources of information for the analysis were firms' websites and national business magazines. To corroborate the evidences and to strengthen a formal chain of evidence, a formal database was established [START_REF] Yin | Case Study Research[END_REF].

To support study's construct validity, multiple data sources were triangulated and additional materials available online, such as industry reports or public documents, were included in the database [START_REF] Voss | Case research in operations management[END_REF]. To increase study's internal validity the collected information was contextualized into the available theory on reconfigurability.

Sample selection was based on a theoretical sampling, driven by the opportunity to gain accessibility to the type of phenomenon of interests [START_REF] Yin | Case Study Research[END_REF]. The sample of investigation is represented in the following table (Table 1). Overall, 21 cases were selected. Although this convenience sample may have determined a possible bias [START_REF] Yin | Case Study Research[END_REF], this was considered coherent with the aim of this investigation, i.e. to observe general trends in the existing scenario and to provide prompt insights for further research. Table 1 provides general information about the cases, including the industry in which they operate. Firms' names are not provided for confidentiality reasons. In the last two columns, Table 1 specifies the outcome of the production process (i.e. the final product) and the production stage in which each firm contributed. Indeed, some firms were involved in all production stages, while some others contributed in specific stages of the manufacturing process of the final product. As shown in Table 1, cases were divided into 5 groups according to the presence of sectorial commonalities or due to the collaboration among firms with respect to the final product. This classification supported the interpretation of the results of the analysis as clarified in the following section.

Analysis of the cases and results

The great majority of the analyzed firms (except F.3.1, operating in the pharmaceutical industry, and F.4 and F.5.1, producing medical devices) would have had their plants closed down. This was due to both (i) the absence of demand for their products (e.g. the luxury clothing, the cosmetics and the cars) and (ii) the fact that the Italian Government ordered the temporary suspension of non-critical production.

Analysis of the cases

To support the analysis and the interpretation of results, the 21 cases have been sorted into five groups (see Table 1).

The first group includes two firms from different sectors, which both cooperated with F.1.3 -a consulting company operating in the field of the industrial and mechanical engineering -in order to provide ventilators to Italian hospitals. Specifically, F.1.3 patented a 3D printed valve to turn snorkeling masks into ventilators. Thanks to F.1.3's idea, low cost ventilators could be manufactured and made available in a very short time period. Consequently, F.1.1, manufacturer of snorkeling masks, started collaborating with F.1.3 and realized a mold that allowed the industrialization of the valve, thus furtherly increasing the production of ventilators. In addition, F.1.2, a producer of weapons, also cooperated with F.1.3 by using its 3D prototyping printers for the production of the valve. To quickly identify suppliers for the required raw materials, F.1.2's Chief Executive Officer exploited social media virtual networks. In this way, the firm found suppliers of raw materials from both Italy and Germany.

The second group includes 11 firms from the textile and clothing industry (except F.2.11, which produces customized luxury packaging). They all supplied masks to hospitals and pharmacies. F.2.1 quickly introduced a new treatment at one of its printing plants to make water-repellent fabric (involving around 50 people). Moreover, it involved its distribution partners all over the Italian territory (around 500 people) for the packaging and distribution of masks. Overall, it already had the required know-how and technology, but needed to widely increase the production capacity at the fabric printing plant. F.2.2 completely revolutionized its operations, with tailors working from home to produce and pack masks. Every day, staff people brought the non-woven fabrics, rubber bands and underwire to the tailors, while collecting ready-for-distribution masks. F.2.4 acquired special machines to realize a semi-automatic line to produce masks and medical scrubs, and exploited digital technologies to train tailors. F.2.8 and F.2.10 reconverted their production plants in record time to provide masks. Finally, F.2.11 exploited its operations and expertise to use the material normally used to pack jewelry items to produce masks.

The third group includes three firms operating in the chemicals industry, including the pharmaceutical and the cosmetics ones, which produced sanitizing gel. For example, F.3.1 dedicated one of its plants (already dedicated to the production of medication in gel) to the sole production of the sanitizing gel.

F.4 is the smallest (and the only Italian) firm in the world producing extracorporeal membrane oxygenators. These devices are used in hospitals for patients in extreme situations, where also ventilators are not enough to support the breathing activity of people. It quadrupled its production capacity in a few weeks by moving 130 people out of 220 total employees on the production systems.

The last group of firms includes F.5.1, a firm producing medical devices, and 2 firms within the automotive industry, which have been grouped due to their collaboration to produce ventilators. F.5.1 is the only Italian firm producing ventilators and experienced a sudden increase in the demand for such devices. Thus, it collaborated with F.5.2 and F.5.3, who manufactured some components and supported some assembly phases for the ventilators production.

The automotive industry deserves further exploration and international firms operating out of Italy can add relevant insights to the results of this research. Being characterized by high technology investments, this industry can provide a variety of different product families, such as masks, protective visors, and even ventilators. In Brazil, a firm used 3D printing to produce masks; in Spain, another firm used 3D printers to manufacture visors; in Germany, two others used 3D printers to manufacture ventilators; finally, two further firms in China assembled protective suits and masks.

Results of the investigation

Overall, the results of this analysis in terms of reconfigurability can be synthesized in four main points, which have been also represented in Figure 1: 1. Current know how held by firms. As stated in Section 2, reconfigurability should allow a firm to change or rearrange its manufacturing systems so to be able to produce any kind of product family. The epidemic has shown that to ensure reasonable efforts, firms rearranging their operations should be already provided with the required know how. For example, firms in the textile and clothing industry supplied masks, while firms in the chemical sector supplied sanitizing gel. Conversely, the automotive appears as the most reconfigurable industry, as these firms were capable to provide a variety of different product families thanks to the exploitation of up to date technologies.

2. Configuration of the network of firms involved in the value chain of the end products. The many examples of successful collaboration not only confirmed the importance of inter-firm linkages to create value, but also demonstrated that capabilities can be synergistically combined or exploited by supply chain partners [START_REF] Zacharia | The emerging role of the third-party logistics provider (3PL) as an orchestrator[END_REF].

3. Modularity of products. According to literature, it enables the modularity and integrability characteristics of manufacturing processes [START_REF] Brunoe | Integrated product-process modelling for platform-based co-development[END_REF][START_REF] Sorensen | Classification coding of production systems for identification of platform candidates[END_REF][START_REF] Joergensen | Designing modular manufacturing systems using mass customisation theories and methods[END_REF]. To this end, the modularity of the product given by the combination of the snorkeling mask with the recently patented valve is very representative. On the one hand, it allowed F.1.1 to clearly identify the specific processes needing reconfiguration, i.e. manufacturing a mold to produce the new valve instead of the traditional valve needed by conventional snorkeling masks. Moreover, regarding F.1.2, the modularity of the end product allowed the firm to clearly identify its role in the overall value chain, i.e. converting its prototyping department to produce the valves and supply them to F.1.1.

4. Smart and digital technologies. The possibility to remotely train employees (in this specific situation, to avoid the spread of the virus) is an interesting enabler of reconfigurability. Combined with augmented reality technologies, remote training is a powerful tool to support the "conversion" of operators' skills within plants. Also, the use of social media to share information and potentially build new network collaborations (as per F1.2) provides some food for thoughts. Finally, 3D printing is a very powerful technology to achieve conversion of specific functionalities, as brightly shown in the many examples in the automotive industry. Lastly, collaboration between firms along products' value chains supported the reduction of the reconfiguration effort, as it allowed to distribute roles along the value chain based on the available plants and know-how of each entity. For this reason, further research could broaden the perspective from a firm-level to a supply chain-level, by exploring the link between the reconfigurability theory and other well-established theories, e.g. the dynamic capabilities theory. Dynamic capabilities are defined as the firm's abilities "to integrate, build, and reconfigure internal and external competences to address rapidly changing environments" [START_REF] Teece | Dynamic Capabilities and Strategic Management Authors[END_REF], and have been progressively expanded from a resource-based view approach (i.e. how firms create competitive advantage and value) to address the relationships a firm has with other firms, as managing inter-organizational relationships is a key to success [START_REF] Wang | Dynamic Capabilities: A Review and Research Agenda[END_REF]. Some researchers argued that the dynamic capabilities theory helps address how to respond to the business changing environment, but may fail to describe exactly the capabilities to be operationalized [START_REF] Wang | Dynamic Capabilities: A Review and Research Agenda[END_REF]. By linking the dynamic capabilities theory with the reconfigurability theory, it might be possible to bridge the organization's capacity to efficiently and responsively change operations and develop its resources [START_REF] Teece | Dynamic Capabilities and Strategic Management Authors[END_REF], with the actions and efforts required at different manufacturing levels, from the workstation to the plant [START_REF] Rösiö | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF][START_REF] Napoleone | A framework to manage reconfigurability in manufacturing[END_REF].

Conclusions

In order to face the COVID-19 outbreak, many firms have been reconfiguring their plants and networks to satisfy, with low reconfiguration efforts, the surge in the demand for specific products such as masks, sanitizing gels and ventilators. Based on the analysis of reactions of selected manufacturing firms to this crisis, this paper provides some practical insights on enablers of the scalability and convertibility characteristics, thus on possible ways to achieve reconfigurability in manufacturing.

The main limitation of this work is that, given the dramatic situation, it was not possible to conduct in-depth analysis of the cases. Indeed, in future research, the authors aim at focusing on a subset of the cases analyzed in this work in order to provide a detailed analysis of the specific manufacturing systems and subsystems that were the subject of the changes, the related enablers and the managerial challenges rising from the reconfiguration process. On the other hand, focusing on multiple cases, this investigation aimed at providing prompt and as broad as possible food for thought for both practitioners and academics. Indeed, production managers interested in reconfigurability can acquire insights about the benefits of investing in -among the others -product modularity and 3D printing. Moreover, the analysis has brought into light two aspects offering high potentialities to the reconfigurability theory and thus deserving further research. These are: (i) smart and digital technologies, which support the characteristics of scalability and convertibility and (ii) collaboration along value chains, which promises to reduce reconfiguration efforts. Regarding the latter, this paper sows the seeds for linking the reconfigurability theory with the dynamic capabilities theory.

Fig. 1 .

 1 Fig. 1. Results of the investigation (adaptation of part of the framework provided by [17])

Table 1 .

 1 Sample of investigation

	Firm (id)	Sector	Size 1	Product	Production stages
	F.1.1	rubber and plastics	L	ventilators	all stages
	F.1.2	Weapons	L	ventilators	manufacturing of a component
	F.1.3	manufacturing consultancy	S	ventilators	manufacturing of a component
	F.2.1	textile and clothing (luxury)	L	masks	all stages
	F.2.2	textile and clothing (luxury)	M	masks	all stages
	F.2.3	textile and clothing (luxury)	L	masks and medical scrubs	all stages
	F.2.4	textile and clothing	L	masks and medical scrubs	all stages
	F.2.5	textile and clothing (luxury)	L	masks and medical scrubs	all stages
	F.2.6	textile and clothing	S	masks	all stages
	F.2.7	textile	S	masks	manufacturing of fabrics
	F.2.8	textile	S	masks	all stages
	F.2.9	textile and clothing	S	masks	all stages
	F.2.10	textile	L	Masks	all stages
	F.2.11	luxury packaging	S	Masks	all stages
	F.3.1	pharmaceutical	L	sanitizing gel	all stages
	F.3.2	chemicals (hair products)	L	sanitizing gel	all stages
	F.3.3	chemicals (cosmetics)	M	sanitizing gel	all stages
	F.4	medical devices	M	membrane oxygenator	all stages
	F.5.1	medical devices	S	Ventilators	all stages
	F.5.2	Automotive	L	ventilators	some stages
	F.5.3	Automotive	L	Ventilators	some stages

According to the European Commission, classification in Large (L), Medium (M) and Small (S) firms (based on number of employees)