Hans-Henrik Hvolby

Mihai Kenn Steger-Jensen

Mihai Neagoe

Sven Vestergaard

Paul Turner

Collaborative Exchange of Cargo Truck Loads: Approaches to Reducing Empty Trucks in Logistics Chains

Keywords: Collaborative, transport, cargo, logistics, agent-based, constraints, optimise

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

1

Introduction.

Based on data from the European Union [START_REF] Juan | Horizontal cooperation in Vehicle Routing Problems with Backhauling anf Environmental Criteria[END_REF] identified that empty backhauls represent about 25% of the road transportation activities and that loaded trucks in average use 57% of the capacity. This triggered a further investigation into how planning of truckloads is conducted as well as conditions and rules for transport companies. This research project was initiated with the aim to reduce the empty running by exchanging loads among freight carriers. The centre-point of the project is to provide real-time data and advanced decision support tools to the transport companies and thereby reduce costs as well as environmental benefits in terms of reduced pollution and congestion. The potential benefits of horizontal collaboration for logistics services providers are recognized in the literature in the form of increased efficiency and productivity gains [START_REF] Gansterer | Collaborative vehicle routing: A survey[END_REF], decreased environmental impact, and improved market presence or access [START_REF] Cruijssen | Horizontal cooperation in logistics: Opportunities and impediments[END_REF][START_REF] Cruijssen | Horizontal Cooperation in Transport and Logistics[END_REF]. From an economic perspective, modeling results of collaborative approaches reveal cost savings ranging from less than 10% [START_REF] Quintero-Araujo | Quantifying potential benefits of horizontal cooperation in urban transportation under uncertainty: A simheuristic approach[END_REF], between 20 and 30% [START_REF] Soysal | Modeling a green inventory routing problem for perishable products with horizontal collaboration[END_REF][START_REF] Montoya-Torres | On the impact of collaborative strategies for goods delivery in city logistics[END_REF][START_REF] Bailey | Models forminimizing backhaul costs through freight collaboration[END_REF] to as high as 50% [START_REF] Sanchez | Reducing the carbon footprint in a vehicle routing problem by pooling resources from different companies[END_REF]. The effectiveness of collaborative approaches can be influenced by various factors such as the geography [START_REF] Pérez-Bernabeu | Horizontal cooperation in road transportation: A case illustrating savings in distances and greenhouse gas emissions[END_REF] and not least the partners' similarity in distribution networks [START_REF] Adenso-Díaz | Analysis of the synergies of merging multi-company transportation needs[END_REF]. Only two freight carriers are participating in the research (demonstrator) project but in a later implementation, a higher number of carriers are required to gain a high efficiency and to reduce the number of non-connecting deliveries.

From an environmental perspective, the reduction of empty backhaul trips was suggested as an alternative to mitigate the transport environmental footprint [START_REF] Pérez-Bernabeu | Horizontal cooperation in road transportation: A case illustrating savings in distances and greenhouse gas emissions[END_REF]. Environmental impact reductions will likely follow the cost reductions of collaborative approaches since fuel consumption accounts for a large proportion of transport costs.

Sustainability of the industrial sector has become one of the most significant societal, political and business issues due to the fact, that the manufacturing sector has a huge impact on the environment, economy and the quality of human life. The focus on the impact of supply chain activities including logistics and transportation has captured a huge academic and industrial interest which has led to significant contributions regarding figures and measures [START_REF] Dornfeld | Green Manufacturing: Fundamentals and Applications[END_REF][START_REF] Bunse | Integrating energy efficiency performance in production managementgap analysis between industrial needs and scientific literature[END_REF], concepts and strategies and methodologies and tools such as Life-Cycle Assessment (LCA) and Corporate Social Responsibility (CSR).

While collaborative transport planning can generate a series of efficiency benefits, companies are also required to navigate challenges regarding information sharing and security with rivals and ensuring the delivery of adequate services by other companies for their own customers [START_REF] Cruijssen | Horizontal Cooperation in Transport and Logistics[END_REF]. In cases where full information disclosure is accepted by collaborating parties, decisions can be made by a central decision-maker. However, in cases where not all information is shared amongst parties, decentralized decision-making approaches are adopted [START_REF] Gansterer | Collaborative vehicle routing: A survey[END_REF]. In a decentralized decision-making approach, transporters can retain the ability to choose which routes or cargoes they are willing to exchange. The decisions by individual transporters regarding routes or cargoes can depend on the information, options available [START_REF] Ukovich | A: A Decision Support System for Cooperative Logistics[END_REF], as well the relationships between logistics service providers and their customers [START_REF] Gligor | The Role of Personal Ralationships in Facilitating Supply Chain Communications: A Qualitative Study[END_REF][START_REF] Gligor | The role of personal relationships in supply chains: An exploration of buyers and suppliers of logistics services[END_REF]. In this context, decision support systems can be useful tools to inform decision-makers of available collaboration alternatives while allowing the option for human direction of the final choice.

Today, only few companies are part of supply chains that jointly supports an overall planning body to optimize the flow. To support optimized goods flows between independent second-party logistics providers (2PLs) calls for supply chain collaboration solutions enabling increasing visibility and decision support integrated in current transport management systems. Firms are used to optimize internally, but collaborative logistics creates the need to develop a new business model that takes inter-firm relations into account.

This paper lines up the findings so far as well as conceptual solutions to support sharing of loads. Two options have been identified: an agent-based approach and an optimisation approach. The paper is organised as follows. First, the planning approach in the transport companies are presented alongside constraints in terms of policies (as well as driving regulations). Secondly, the identified options are presented followed by a discussion and conclusions.

Planning approach in case companies.

The freight carriers in focus are specialised in handling point-to-point transport freights, normally minimum 5 pallets. The direct approach has a number of advantages compared to a hub-and-spoke setup that are mainly used for smaller freights. The main advantage is less handling and faster delivery time, whereas the downside is a higher risk of empty running. The freight carriers have three types of shippers: contractual, regular and occasional customers. Contractual customers have to be served, whereas regular customers could be turned down. Occasional customers are accepted if the load fits with the planned route and loads.

Most order requests come from customers, but some requests come from other transport companies aiming to sell of transports that do not fit into their routes and capacity. The actual load per day for contractual customers are not known until a few hours before the truck starts its route and may change over the day. This setup generates some uncertainty with respect to which transport orders to accept. On the one hand, if too many orders are accepted, the transport capacity may be insufficient for the day's deliveries. On the other hand, accepting fewer orders or a more risk-averse approach may mean that transport capacity is not fully utilized.

Due to uneven load balances, trucks are rarely fully loaded on both outbound and inbound trips. The goal is to have a full outbound load and aim for as high load as possible on the inbound trips. Most freight carriers have some casual transports to increase loads when possible, e.g. empty pallets. Other options is transport portals such as Timocom and Teleroute [17,18,19] that act as a broker in between shippers and freight carriers. Uber Freight [20] is also entering the European market, but here the portal is a freight marketplace focused on full load transportation.

The transport companies investigated, plans their transports manually but use domain-specific systems to support the decision making and to share the current plan with colleagues as well as the final launch of trips. Orders from contractual customers arrive in semi-automated ways such as excel sheets or through manual access to customers information system. Regular and occasional customers communicate via email or phone. Surprisingly, very limited planning was conducted in real-time and re-filling of trucks were rarely used. The companies were aware that a further investigation of these areas could improve capacity utilisation and earnings.

The normal strategy by the route planners is to accept most requests as "it is easier to form a good plan if you have more orders". During the planning process, you may sell off requests that were accepted earlier in the planning process or contractual transports if the load either exceeds the transport capacity or is too small to fill up a truck. In the latter case, the planners contact transport colleagues by phone or email to sell of transports. Usually, the price of a transport increases during the day and therefore there is a risk element in having too many unallocated transports in the late afternoon.

Planning concepts.

As previously stated, the idea with the paper is to present planning concepts supporting the current processes in transport companies. Two different concepts have been brought forward with the aim of improving the capacity utilisation:

• an agent-based approach aiming at supporting selling and buying transports to and from other transport companies • an optimisation approach where all transports are re-distributed among the participating transport companies under a given set of constraints configured by each participant such as:

• accepted transport colleagues to undertake a transport for the given company • blocked customers and types of goods that are not to be transferred to colleagues • a matrix expressing how may extra miles of route deviation you are willing to drive in order to pick up X number of pallets (e.g. 2 miles for 2 pallets and 20 miles for 10 pallets • A threshold value expressing the minimum profit margin for accepting a request

Fig. 1. Conceptual illustration of tenders, requests, constraints and business model

The constraints are used to evaluate as to whether a request from a customer or a transport company qualifies for manual decision making. To enable this pre-qualification process, the order request must as a minimum include the following information: type of goods, number of pallets, weight and volume, order date & time, pick-up information (date & time, address, customer name), delivery information (date & time, address, customer name), and offered price.

In an agent-based setup, the route planner will receive the pre-qualified requests which match the configured settings. This ensures that only relevant requests require the attention of the planner. Further, the system should support a full business process transaction in terms of automatically enter the order specification, notify the cargo owner when the product are delivered, invoice the cargo owner, etc. These processes are quite labour intensive in the current manual based setting.

As trucks constantly move and new orders are accepted while some old orders are sold off the picture changes over time. We have tried to illustrate this in figure 2. The planning process is similar to the "Net-Change" in Enterprise Resource Planning (ERP) systems. This mean that during the day new orders are received and accepted and each time we consider how to best fit the new order in the existing plan. The alternative to this is a "Full plan" (in ERP this is done during the night or in some companies over the weekend due to time constraints) where all plans are cleared and a totally new plan is developed. The benefits of this is that the Net-Change does not find an optimal plan but a feasible plan given the already planned tasks. As an alternative to the agent-based planning concept a constrained or optimised concept could be considered. Constrained based planning is based on hard and soft (or goal) constraints [19]. Its distinguishing feature is that the objectives can be stated as minimising deviations from pre-specified goals. Hard constraints are not overruled, whereas soft constraints can be overruled, if necessary. If the number of trucks is considered as a hard constraint then capacity load, based on customer order acceptance, is considered as a soft constraint. As no plan optimisation objectives or criteria are considered, this option produces a feasible but not necessarily an optimal plan. Therefore, a hidden plan objective function is used to drive the planning and trade-off among the soft constraints. The hidden plan objective function is defined as minimizing plan cost. In addition to hard and soft constraints, it is possible to use business rules and demand priorities. Business rules are used as explicit decisions made when there are more options to choose among in the plan generation. Business rules are ranked by use of priorities of given topics and play an important role in constraint-based planning by avoiding the traditional (time-consuming) re-planning and re-scheduling after plan generation.

Optimised plans are generated based on plan objectives, penalty factors and constraints beside the hard and soft constraints [START_REF] Hooker | Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction[END_REF]. The constraint-based rules are exchanged with decision variables and penalty factors, instead of the hidden object function and business rules and demand priorities. In the optimisation, the soft constraints might be overruled if this reduces the total costs. For example, demand priority and supplier allocation ranks could be overruled to reach the best profit.

The benefits of a constrained or optimised plan is that it automates the planning process. The question is, however, whether planning automation and elimination of individual decision-making for each company is a change that transport companies are willing to instantiate. With reference to the manufacturing area, it is a long "travel" to move from manual planning to semi-automated and fully automated planning, as this change requires reliable data and fit-for-purpose decision rules.

Discussion and conclusion

The paper discuss possible planning concepts in an automated collaborative logistics system based on real-time collection and analysis of shipment and tracking data which makes it possible for large competing logistics companies to share load capacity on less-than-truckload shipments and minimize empty-running. Currently, planning and coordination is handled manually and the aim of the ongoing project is to optimize the utilization of load capacity and minimize empty running, reducing costs, traffic congestion, and truck emissions. At present only the most obvious constraints have been included in the work. Currently, we are e.g. not able to handle dependent options where one option only is relevant if another option also is fulfilled. It is also required that all trips have been initiated before the collaborative tool is able to suggest relevant loads to share (the chicken and egg challenge). Regular (returning) customers has less integrated planning routines but expect anyway that their loads are handled in spite of lacking contractual agreements. To support this, we suggest that historical data are used to forecast capacity requirements on a daily basis to make room for these customers.

Finally, a threshold value for the minimum number of empty pallets in the truck are needed if refill of trucks is considered. This value depends on the type of truck and the number of deliveries to be completed. The important issue here is to avoid unloading of many "new" pallets to enable offloading of "older" pallets. This last issue are to some degree in contrast to the initiating goal of obtaining a higher capacity load on trucks.

Fig. 2 .

 2 Fig. 2. Real-time scan for relevant tenders to accept or reject a request based on constraints such as position and time-window.

Acknowledgements.

The authors would like to express their gratitude to the Innovation Fund Denmark which the Collaborative Cargo (Directly) research project, is a part off.


Melissa Demartini
email: melissa.demartini@dime.unige.it

Federico Galluccio

Paolo Mattis

Islam Abusohyon

Raffaello Lepratti

Flavio Tonelli

Raf- Faello Lepratti

Closed-Loop Manufacturing for Aerospace Industry: An Integrated PLM-MOM Solution to Support the Wing Box Assembly Process

Keywords: Digital manufacturing, aerospace industry, closed loop manufacturing

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

Introduction

The digital transformation has already changed the way products are designed, produced and delivered and this has a profound impact on manufacturing companies [START_REF] Burger | Investigating Flexibility as a Performance Dimension of a Manufacturing Value Modeling Methodology (MVMM): A Framework for Identifying Flexibility Types in Manufacturing Systems[END_REF] [2]. In this competitive scenario, manufacturing companies perform by using up-to-date information to progressively remove delays to the management and execution of its critical business processes. Therefore, they need to monitor their value-creation processes controlling both the internal (e.g. scheduling flexibility) and external events (e.g. customization and volume flexibility) in order to maintain their competitive advantage [START_REF] Burger | Investigating Flexibility as a Performance Dimension of a Manufacturing Value Modeling Methodology (MVMM): A Framework for Identifying Flexibility Types in Manufacturing Systems[END_REF]. In the light of the above, the success of a manufacturing company depends upon effective coordination across all stages in a product's development, from design and production up to aftermarket services [START_REF] Fei | Digital twin-driven product design manufacturing and service with big data[END_REF]. Companies should define and optimize manufacturing processes, manage data and encourage collaboration between different types of engineers by incorporating both digital and product definitions. Thus, an integrated system is necessary to manage the different production stages and comprising various business functions. To this end, the aim of this paper is to introduce an innovative system to bridge the vertical integration gap between the engineering and manufacturing domains leading to an integrated production management system, which includes Product Lifecycle Management (PLM) and Manufacturing Operation Management (MOM). Thanks to the advancement of innovative technologies, such as Digital Twin and a Digital Thread, this gap could be enclosed. Through digital twin, any entity or physical system, product or process can be represented in a simulated way to respond immediately to external changes to prevent problems solving them in real time or improving performances. The digital twin is fed by information from the digital thread [START_REF] Demartini | Digitalization of manufacturing execution systems: The core technology for realizing future smart factories[END_REF], which is the chain of information that connects all the involved parties with data to design and produce products [START_REF] Mike | Smart connected digital factories: Unleaching the power of Industry 4.0. Cloud computing and services science selected papers[END_REF]. The term "thread" is used because it interweaves and brings together data from all stages of the product and production lifecycles [START_REF] Haijan | Digital twin-driven cyber physical production systems towards smart shop-floor[END_REF]. Bringing together the digital twin and thread, it is possible to create a collaborative, connected information loop, the so -called closed loop manufacturing (CLM). CLM enables companies to synchronize and optimize production across product design, production planning, manufacturing execution, automation and intelligence from consumer use in the field.

Research Goals and Questions

The purpose of this research is the design, implementation and industrial evaluation of an innovative system to bridge the vertical integration gap between the engineering and manufacturing domains. A digital twin of a wing box and its assembly process is created in PLM by building the bill of material (BOM) and bill of process (BOP). Then it is shown how in MOM system the production phase is facilitated by managing production operations and supporting the execution of the processes leading to a CLM. The literature is witnessing of the transition of the aerospace industries, which are moving from fixed to flexible production cells, requiring a higher level of vertical integration between enterprise systems and shop floor controls and managing a higher level of customization. Aerospace companies are deploying new digital technologies in manufacturing to speed up production and reduce costs. In essence, this study is conducted to answer the following research question: RQ: A solution based only on ERP system can properly manage highly complex product's BOMs? When the products are highly complex, the integration between PLM-MOM is necessary to manage BOMs that otherwise can't be managed using only the ERP. The BOM involves unique challenges for the management of information across business functions. It is initially created by engineering to meet engineering needs, but multiple departments rely on it and need to use the information it contains in different ways. Managing these different views, integrating the various engineering disciplines, and maintaining completeness and traceability become more difficult as product complexity increases. To answer this research question, this study provides a qualitative literature review to analyze the current scenario and then a proof of concept of an integrated PLM and MOM system to demonstrate its benefits. This paper is organized as follows: in Section 2 the literature review is depicted, Section 3 describes the methodology adopted for this paper. Section 4 presents the results, while in Section 5, conclusions are described.

Research Background

The literature review has been performed by selecting papers from Scopus. Authors chose this database for its ample coverage of articles in this field [START_REF] Demartini | Quality management in the industry 4.0 era[END_REF]. The Authors' strategy was to identify articles that included "Smart manufacturing", "Product lifecycle" and "Digital twin" as keywords in all fields. Additionally, the Authors considered various synonyms of each of these terms such as "smart factory" and "Digital thread". The aforementioned search technique allowed identification of 38 academic papers, which were rigorously reviewed in order to evaluate their adherence to the study. After reading all the papers, none of them talks about the importance of using the digital twin to realize the interconnection and the interaction between the PLM and MOM platforms within the manufacturing enterprises. This gap in the literature review shows that more work is needed in this area. Today's manufacturing enterprises use platforms to manage the product design as well as the production planning and the manufacturing execution. The design and implementation models are developed on a PLM platform. Modern PLM systems bring together product and process development, allowing interaction in the development process. Instead, the MOM platform covers the scheduling and execution of the work orders based on the development process. Usually PLM and MOM platforms do not have an appropriate interaction with each other and therefore the world of product and process design and that one of production and execution are not integrated for flexible, scalable production processes that maximize the responsiveness to real-time manufacturing events. Building a CLM is the solution to solve this problem. In this research, in order to achieve CLM and support the journey to Industry 4.0, PLM and MOM platforms have been made able to communicate with each other. It means they have access to, and understand the language of the others. MOM is where the virtual plan becomes the physical reality and it is also where decentralized production can be globally orchestrated. To conclude, it is possible to claim that the topic of the digital manufacturing starts to be studied deeper over the last year. Nevertheless, it must be said the fact that over 38 articles identified, only a few were interesting could mean a lack of knowledge of the importance of the CLM from the scientific literature point of view. This lack represents the starting point of this paper, that try to fill this gap by showing an application of the integration between PLM and MOM systems in the aerospace industry.

Methodology

A proof of concept software based on PLM and MOM platform has been implemented, the activity has been developed in Siemens Industry Software under the European Project AirGreen 2, an integrated research action of the REG IADP (Regional Innovative Aircraft Demonstration Platform) through the steering and coordination of LEONARDO Aircraft. The project aims to develop and demonstrate innovative concepts and methodologies enabling the realization of a wing of new generation. This wing will be characterized by an innovative structure, as a result of an improved life cycle design; a high level of adaptability, enabling load control and alleviation strategies, and enhancing the aerodynamic performance at the different flight regimes; an innovative aerodynamic design, oriented to the preservation of the natural laminar flow and for the drag reduction. Among the various work packages that have been set in order to achieve these goals, Siemens Industry Software has been involved in that one In the industrial case analyzed during this research, a wing box assembly method based on a predictive Dimensional Management (DM) strategy has been used. It involves the reverse engineering of some main wing components into a Digital Twin, representative of the actual shape of the parts within a defined level of accuracy. Intermediate parts are then defined into the Digital Twin Environment and transformed into real items through a dedicated manufacturing method developed by Leonardo Aircraft. The achievement of quality requirements is ensured adopting a Verification and Validation approach during the Product Life Cycle developmental stages. Customer requirement, Process Capability, Tooling and Methods are developed in concurrent engineering with the design within the Digital twin environment with the aim to guarantee customer needs expressed in terms of geometrical requirements at each product level (KCs), with a top down approach. Both RSS Stack-up evaluations and Montecarlo simulations are used to support variation propagation analysis and tolerance allocation in 1D and 3D; the Build-Up strategy achieved by extensive use of DM allows a set of advantages: i) readiness for the digital factory revolution since all feedback from shop floor is fully implemented; ii) allows lower fixed costs and easier design changes; iii) reduce labor costs and iv) involves less assembly variability. Despite all the benefits of Build-Up, there are several risks associated with it. Primarily, it requires much more attention to issues of variation. For this reason, greater attention shall be paid to enforce Statistical Process Control strategies including proper feedback and reporting in the Digital Twin trough the PLM applications. Therefore, in addition to the process based on the Build-Up strategy an alternative assembly method has been developed in order to mitigate potential failures discovered or predicted. It is a Back-up solution, based on failure scenarios analysis and tolerances (defective parts).Both processes have been modeled on the PLM platform but only the first one has been transferred to the MES.

Results and Discussions

In the Siemens PLM solution (Teamcenter) the BOM of the wing box as well as the BOPs related both to the machining process and to the wing box assembly have been built. They are the virtual twin of the product and virtual twin of the processes, respectively, which will be maintained in one shared manufacturing master data model and authored in one environment for production planning and production execution. BOPs have been built by creating process structure and creating operations. The link between the product structure and process is formed by associating the end item of the BOM as target for the BOP structure. Parts from the BOM are then assigned to the operations as "consumed parts". BOPs also define the data that must be collected during each operations and the electronic work instructions (EWI) that allow the communication of all the manufacturing process information between engineers and operators. The two assembly methods have been implemented in the PLM using a 150% BOP. With a 150% BOP is associated a 150% BOM, they are just other names for variant structures, or more specifically, configurable structures. Configurable BOMs and related BOPs have one or more optional or different components and/or optional or different operations, which, when properly set, define a specific variation of a product and its production. A "Configurable BOM" alias "150% BOM" alias "Variant BOM", is used by manufacturer when dealing with highly complex product such as in the aerospace domain and when there is a need to maintain a balance between configurability, time to manufacture, and preserving the cost under threshold. A configurable bill of material contains all the parts that are required to manufacture the material to a customer's specific requirements. A "Select-Condition" is then applied on this predefined structure that determines whether a part is to be included in the final BOM or not. Modern PLM tools enables manufacturing companies to better adapt to engineering practices and facilitates efficient management of these configurable BOMs using a unit effectivity method. Effectivity might be defined as a date, serial number or, in a more complex way, as a "unit". Effectivity definition originally comes from ERP environment. The most typical example is "date effectivity" which defines the available to a particular part or item. PLM originally was created without effectivity in mind. Most of engineering systems were "revision" oriented rather than "effectivity" oriented. It means PLM is used to manage different revision of objects rather than defines their effectivities and it is the ERP system that manage the BOMs and their variations. A 150% BOM haven't been built but the same BOM without any variation can be composed by different parts depending on the adopted assembly method. Custom parts based on Digital Twin configuration will therefore be different from those used in the other assembly method that involves traditionally fettled and drilled parts. Once the BOMs, the BOP and the related resources have been verified, the components of the 150% (master data like e.g. Material, Process, Operation, etc.) have been released from the PLM to the MES Siemens solution. Structures, relations and configurations are not relevant in this step. Instead, it only cares about the building blocks of the unconfigured structures. Then, imagining that a request to produce a wing box using the DA assembly method comes from an ERP system, in the MES work orders have been created specifying the effectivity needed and the download of the configured production structures (100 % BOPs) from the PLM is then automatically triggered. Therefore, in the manufacturing execution system several work orders related to the just released processes has been executed. During their execution, materials are displayed as information about what has "to be consumed" for each single operation or step with the aim to guide the operators in consuming the right parts at the right places (operation station), at the right time (when the product is in the operation station) and collect in real-time the information about their consumption. Data collection form containing all the data and measures to be collected are displayed in the operator environment as well as the supporting documents and the work instruction to perform the operations, so that in a single place operator can find what to do and what to measure. Both work instruction and data collection form directly come from PLM model as part of the BOP: this means the MES shows, operation by operation, the document version according to the most updated BOP. Once the processes have been executed on the MES, the manufacturing operations performance are analyzed. The reporting and analysis layer have the scope to provide transparency to an information layer higher than the operational, which is supported by the as build and genealogy of the product and work orders inside the MES. It is the layer where we can compare work orders and get relevant KPIs and information about products, materials, defects and equipment outside the boundaries of a specific work order. It supports decision makers in the analysis of the plant information giving them the opportunity to understand how eventually modify and improve the products or its related process. The digital model of the production and process has been continuously compared with actual production to reduce the differences between as planned and as build. A PLMderived and MES-driven BOP can ensure that every material and operation is performed as it was intended. Requirements; which has been established in the earliest phases of engineering, become features of the aircraft design, dimensioned in the 3D models, published in the BOP, send to operators in EWIs, have been measured and finally stored in the as-built thanks to the digital thread. The as-built record can be compared to the as-ordered configuration to highlight deviations on the shop floor versus customer expectations. Once published back to the Digital Twin it can be matched against the as-engineered definition. The Build-Up strategy analyzed in this research is possible since it integrates the information available from production databases and methods into the design. This means that time is not spent working on components within the final assembly where a high level of capital expenditure is then tied up in these operations and a bottleneck to production exists. Finally, to answer the RQ: An ERP system can properly manage highly complex product's BOMs? When the products are highly complex, the integration between PLM-MOM is necessary in order to manage their BOMs that otherwise can't be managed using only the ERP. PLM allows managing and maintaining both the engineering and the manufacturing aspects of a BOM in a single context during each stage of a product development process. The BOM is the result of a lot of design activity and if an ERP system, coming in at the end of the process, hold it we might fail to represent the real way in which the decisions, the logic, the engineering rules are built in to the product. ERP solutions generally do not actually optimize or have development tools for defining what the manufacturing BOM is. They just focus on executing a defined BOM. Modern PLM systems instead have the tools to make changes, analyze and optimize the BOM and therefore it makes sense to manage it through these solutions. To conclude, the problem of "siloed data" can be largely eliminated, this because there is now a bi-directional level of communication between the ERP, PLM, business systems and the shop floor, allowing for greater and immediate control of the business on the production floor.

Conclusion

Among all industries aerospace sector has been identified as an excellence case to show the benefits that Digital Twin, Digital Thread and CLM managed through PLM and MOM solutions, can bring. As demand for aircraft continues to grow, the aerospace industry has one of its largest opportunities to drive down existing backlogs, and increase competitiveness moving forward in integrating the world of product and process design and that one of production and execution into one common manufacturing model. Aerospace and systems will be built in a far more complex manner. Newer technologies and processes require new methods of manufacturing. In order to face these challenges, aerospace manufactures should introduce integrated PLM and MOM solutions into their manufacturing processes and thus obtain optimized order management, an automated advanced planning and scheduling for coordination of the supply chain flows and optimization of material synchronization, and all the benefits deriving from a CLM. In this regard, using the combined power of both PLM and MOM technology is a key part of company digitalization process. To drive innovation, it is essential to have the right technology in place in order to reduce development time and deliver high-quality solutions, with the ability to adapt to changes easily at every stage of the process.

  called "Innovative Wing Structure D&M (Design & Manufacturing)". The "Innovative Wing Structure D&M" scope is the development, verification and optimization of suitable advanced technologies and materials for the manufacturing and assembly of the wing box aimed to the reduction of the manufacturing costs and the improvement of the environmental aspects if compared to a traditional process. In this context, Siemens and Leonardo Aircraft contribute to the development of a process for the industrial case study under the European Project AirGreen 2 -task 2.1.1.15 "Wing Box Assembly Manufacturing Executing System (MES) in a Manual and Automatic assembly environment collecting Manufacturing Data and Performances KPIs.

Figure 1 :

 1 Figure 1: Architecture of the CLM

Software under the European Project AirGreen 2, an integrated research action of the REG IADP (Regional Innovative Aircraft Demonstration Platform) part of the Joint Technical Programme, the steering and coordination of LEONARDO Aircraft. The AirGreen 2 project is an Innovation Action funded by the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation programme, under Grant Agreement N°807089 REG IADP).


K.-D Decker
email: decker@uni-bremen.de

Thoben
email: thoben@uni-bremen.de

M T Alvela Nieto

E G Nabati
email: nabati@uni-bremen.de

D Bode
email: dbode@uni-bremen.de

M A Redecker
email: m.redecker@uni-bremen.de

A Decker

K.-D Thoben

Enabling Energy Efficiency in Manufacturing Environments Through Deep Learning Approaches: Lessons Learned

Keywords: Manufacturing, Energy efficiency, Deep learning, Industry 4.0

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

Introduction

Today's manufacturing is moving towards an upgrade of the currently available manufacturing practices to a more efficient and intelligent level [START_REF] Zhong | Intelligent manufacturing in the context of industry 4.0: A review[END_REF]. This upgrade incorporates advances of various fields, particularly the field of artificial intelligence, which helps in different facets of a manufacturing company such as machines, processes, facilities, software and staff [START_REF] Albus | An intelligent systems architecture for manufacturing (isam); a reference model architecture for intelligent manufacturing systems[END_REF]. Thus, sensory data will be then collected across the manufacturing company. Exchange of information and instructions in near real-time between smart machines and smart products will be a remarkable vision of manufacturing industries in the coming future [START_REF] Zhong | Intelligent manufacturing in the context of industry 4.0: A review[END_REF]. Under this setting, effective usage of data should consider different aspects of improving quality, reducing costs and energy simultaneously [START_REF] May | Energy management in manufacturing: Toward eco-factories of the future -a focus group study[END_REF]. Reducing the consumption of energy is referred to as "energy efficiency" from an engineering point of view [START_REF] Irrek | Defining energy efficiency[END_REF]. To achieve energy efficiency in manufacturing, researchers such as May et al. [START_REF] May | Energy management in manufacturing: Toward eco-factories of the future -a focus group study[END_REF] considered the challenges against efficient energy usage in manufacturing. Based on the studies of [START_REF] May | Energy management in manufacturing: Toward eco-factories of the future -a focus group study[END_REF] [START_REF] Zhong | Intelligent manufacturing in the context of industry 4.0: A review[END_REF], artificial intelligence (AI) has the potential to enable energy efficiency in manufacturing. However, as observed in the literature research, only a few papers show AI as a technology for reducing energy consumption in manufacturing. In this paper, we look at the methods of Deep Learning (DL), as DL is one of the subfields of AI. DL has been considered in this study because of its potential to support near real-time decision making in manufacturing by handling large data from different sensory components as well as their complexities. In addition, the high-volume modelling capability of DL allows automatic processing of large raw data instead of "handcrafted" data. This aspect of DL is a powerful advantage over conventional machine learning (ML) methods because the performance of ML models is limited by their ability to process high dimensional and varied input data in their raw form [START_REF] Lecun | Deep learning[END_REF]. Therefore, we investigate DL through three use cases for the assistance of energy-efficient processes in manufacturing.

Section 2 describes the challenges for achieving energy-efficient manufacturing as well as DL as a technology for enhancing efficiency in terms of energy. Section 3 introduces three case studies in the context of energy-efficient manufacturing. In each use case, DL contributes to an energy-efficient process. Numerical analysis of DL-models performance is not the focus of this article. Instead, in section 4, practical lessons learned during the implementation and deployment of DL models in manufacturing processes are described. Section 5 provides the conclusion.

Challenges for enabling energy-efficient manufacturing

Modern manufacturing has been affected by growing energy prices, environmental regulations and customer demands to meet sustainable products [START_REF] May | Energy management in manufacturing: From literature review to a conceptual framework[END_REF]. Above all, industries face several barriers while implementing energy efficiency in manufacturing. Firstly, companies produce manufacturing goods concerning quality and time efficiency. On the one hand, energy efficiency is mostly not considered due to the possible multiplicity of process configurations as manufacturers alone cannot monitor numerous, high-dependency parameters and optimize them regarding energy efficiency. On the other hand, most up-to-date datadriven methods are developed without considering how these methods can be applied in practice [START_REF] Kusiak | Smart manufacturing must embrace big data[END_REF]. Consequently, manufacturers fail the implementation of pragmatic approaches to improve their processes energetically, qualitatively and economically; so that, changes in process parameters will not negatively impact their product quality.

Secondly, the energy consumption of manufacturing machines is not static but dynamic. In some cases, it also depends on the variation in the quality of input raw materials. Moreover, machinery often uses different interfaces to their internal meters, which becomes an inconvenience while interpreting the different production data and later while linking them to the energy-related data. Thirdly, some manufacturers do not know how to treat their available production data and how to interpret them in order to efficiently improve their processes and products [START_REF] Kusiak | Smart manufacturing must embrace big data[END_REF]. Particularly, there are not many software and modelling systems to analyze data in a simple manner yet [START_REF] Kusiak | Smart manufacturing must embrace big data[END_REF]. In this regard, manufacturers require tools or settings that provide them with operational information of machines and data of energy consumption, which can then be used to evaluate and control energy-related performances [START_REF] May | Energy management in manufacturing: From literature review to a conceptual framework[END_REF].

Based on the above challenges, a better understanding of processes and settings in manufacturing as well as a better usage of available process data with less hand-engineering are required. Moreover, the use of tools or technologies that provide exact information about manufacturing operations, energy consumption and the ones that at least do not harm the quality of the end product are preferred. In this paper, we provide three relevant industrial use cases where DL is applied as a method that meets the above observations.

Energy efficiency in manufacturing with deep learning

In many energy-intensive manufacturing processes, there is no available model which supports energy efficiency when considering the fluctuations of input raw materials or other process-varying inputs such as quality features. A quality feature defines both the quality of the process and of the good, taking into account the energy efficiency of a manufacturing process. The following energy-intensive use cases were considered in this research because their raw materials (or natural resources) have inconsistent properties, i.e., the quality of raw materials themselves vary in their composition over time, which affects the process behavior. Additionally, manufacturing processes may change with regard to maintenance and operator skills, which also affects the data associated with the process. Here, DL approaches can be used as an advanced predicting tool once they have been trained on large data of a particular process. Therefore, the integration of DL forecasting models enables energy efficiency in processing.

In the first case study, according to results of Bouktif et al. [START_REF] Bouktif | Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches[END_REF], a data-driven model based on a Long Short Term Memory (LSTM) architecture to predict the energy consumption in food processing is implemented. In the second case study, inspired by the experimental results of the Single Shot Detection (SSD)-based model [START_REF] Liu | Ssd: Single shot multibox detector[END_REF] on standard datasets, image processing for identifying raw materials in a waste processing facility is applied. And in the last study, an approach of Yan et al. [START_REF] Yan | On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach[END_REF] for predictive maintenance in the animal feed industry is investigated by using an autoencoder (AE).

Use case 1: Energy efficiency in food processing

The industry of food processing involves energy-intensive processes. To ensure a high-energy efficiency and an optimal setting of parameters in a certain french fries processing line [START_REF] Behrens | Demonstrationsvorhaben Wärmerückgewinnung und Abwärmenutzung durch Kombination von zwei unterschiedlichen Prozesslinien am Beispiel von Pommes Frites und Chips: Abschlussbericht zum Vorhaben 20265[END_REF], parameters like temperature and steam pressure have to get adjusted continuously with fluctuations in the raw materials used. In this case study, streams of the raw material get scanned and measured, which generates a complex time-series of statistical histograms according to a rawmaterial criterion like shape or humidity. The generation of a data-based model for food processing in relation to the efficiency of energy and final product quality must include the quality of input raw materials and variations of the process settings.

In order to handle these complex histograms of process data, an LSTMbased data-driven model is applied. LSTM addresses sequences of varying-length statistics and captures long-term process dependencies on different time-scales data of food processing. The time-series production data and different settings of parameters in a food process have been collected for six months; so that in this case study, an LSTM-based approach learns features from these data histograms. Thereby, it projects the relationship between sensor data and quality features of the food process onto the model. Fig. 1 shows a process flowchart for implementing LSTM in order to increase energy efficiency in a french fries processing line. The model provides online forecasts from both the energy consumption and the final food quality. The forecasting is visualized and assists the plant operator, who is able to react in time to variations in process behavior rather than waiting for the end product inspection. This additional information leads to quicker reactions, reduces end product rejections caused during quality inspections and effectively enables energy efficiency in food processing.

Use case 2: Energy efficiency in waste processing

Waste management is one of the major global challenges of today. The collected municipal, commercial and industrial waste is first sorted in a waste processing facility. The sorted solid waste material is then pressed into tight bales in a certain baling line [START_REF] Gmbh | UPASMART. Die Intelligente Kanalballenpresse: Automatische Kanalballenpresse ausgestattet mit Künstlicher Intelligenz[END_REF], which are ready to be delivered for reuse or further treatment. The length of the final bale and the pressure to be applied to the waste materials depends on the type of the input raw waste to be processed in a baling line. Therefore, prior to pressing, the streams of waste material, which are transported by a conveyor belt, must be visually identified. Thereby, material-dependent parameters of a baler press must be continuously adjusted. Otherwise, a poor quality bale is rejected, causing additional logistical, financial and energy costs. Hence, a key performance indicator for a waste baling line is proper identification of the input raw materials and the energy consumed during the process of baling. In this case study, a robust identification of input materials in a waste processing line to enhance energy efficiency is considered. Fig. 2 illustrates how the integration of an SSD-model helps to automatically adjust the parameters of a baler press in a waste baling line. The SSD-model forecasting assists both, automatic adjustment of process parameters and an energy-efficient baling process.

Use case 3: Energy efficiency in feed processing

Different machinery is used for processing animal feed. Hammermill machine is used to shred and make natural raw materials such as maize, wheat to smaller pieces [START_REF] Meyer | Producing animal feed with less electricity and heat[END_REF]. The process of converting the grains (e.g., maize) to smaller particles is an essential step in the animal feed production because it affects the extent to which the animal's body can absorb the feed nutrients. After analyzing the energy consumption in a feed processing plant, it was found that the energy consumed during the shredding process of a particular compound feed product caused by hammermill failures is more compared to energy waste related to raw material or due to operator behavior. An automated Supervisory Control And Data Acquisition (SCADA) system shows the warnings and alarms related to failure and maintenance of this equipment. However, no further information has been gained for forecasting the occurrence of failures based on SCADA warnings and alarms. A predictive maintenance system offers to predict the occurrence of next possible sensory failure by using the SCADA information, which can be integrated into the maintenance schedule. The input data from the SCADA system contain the followings; the frequency of warnings and failures, their importance (risk), type of raw material, which is being shredded in the hammermill and speed of hammermill. The data from sensors has been collected for 1.5 years in the form of daily logs. Because of the existence of failure codes, high-dimensional data behaviour and their interdependence, the data has a complex behaviour; so that, an AEbased model for predictive maintenance is proposed here. Fig. 3 illustrates the model proposal based on an AE-model. An AE-model gives suggestions of the anomalous behaviors of a hammermill process based on complex high-level representations of sensory data. The results of AE are presented in the form of clustering spaces of failure types which shows the similarities among failures. As a result, predictions of next failures are added into the maintenance schedule, which in turn helps to adjust maintenance and production plan and therefore, enable an energy-efficient process.

Discussions and findings

The three case studies revealed the following findings. Application of DL approaches provide means for better analysis and comprehension of a manufacturing process and its settings, which in the case studies was exploited to automatically provide suggestions to enhance energy efficiency. However, still there are challenges associated with adaptation, implementation and deployment of DL models for energy-efficient manufacturing processes. Challenges to the development of energy-efficient manufacturing in terms of data and learning transferability are discussed here.

Challenges of annotation in datasets. As DL models become larger and more complex, they require training datasets that are bigger than those required by other ML techniques. Majority of the available manufacturing datasets are not labeled, or if labeled, there exist noisy labels which must be manually removed. Moreover, it is not easy to define labels by hand i.e. for images of waste materials. Sometimes, hand-labeling becomes very complex to differentiate among classes, i.e. when waste materials to be identified are located very close to each other or when they are overlapping and only a small portion of the material is visible to the DL model (in the input image).

Dependence on the variability of input data. A common presumption in DL is that the performance of DL algorithms has high dependencies only on the scale and quality of raw datasets. So far, DL has shown feasibility in our case studies when DL is applied to only an input data type such as images and to welldefined tasks. However, the DL-models of our use cases show that the DL still has difficulties in identifying objects when the variability within the data class is high. Specifically, material classes that appear in many colours and shapes are still tough for the deep model to classify correctly, although significant volumes of data are used.

Dependence on hardware performance. By selecting a deep model, a certain dependence on the used hardware platform is considered. The depth of the model architecture and the available data impact the DL-model performance significantly. Both of these factors demand the usage of high-performance hardware such as GPUs due to computationally intensive processes during the training of deep models.

Model transferability. Additionally, the generation of DL-models and maintenance of them require more data, which for a novel or changed-domain manufacturing processes are not always readily available. Transferability of the entirely or even parts of deep models used in our case studies to similar instances within the models are shown to be possible.

Conclusion

This paper presented an overview of the potentials of DL to achieve energy efficiency in manufacturing processes. DL does not only offer a new point of view into the manufacturing operations but also supports near real-time measurement and energy saving during manufacturing. In our case studies, the integration of DL played a significant role in production planning and therefore, in the energy efficiency of these processes. Moving towards an energy-efficient production planning requires the inclusion of energy efficiency within the goals of production design, input quality control and maintenance at all levels, together with time cost and flexibility. The reduction of the machinery idle-times through energyefficient process planning with the combination of better orders organisation,

Fig. 1 .

 1 Fig. 1. Application of a LSTM-based data-driven model in food processing

Fig. 2 .

 2 Fig. 2. Application of a SSD-model in waste processing

Fig. 3 .

 3 Fig. 3. Application of an AE-model in feed processing

Acknowledgments

The authors would like to thank the Federal Ministry for Economic Affairs and Energy (BMWi) and the Project Management Juelich (PTJ) for funding the project "AI supported platform for the assistance of production control for improving energy efficiency" -KIPro (funding code 03ET1265A).

predictive maintenance and quality controls lead to a better prediction of the workflow and therefore to better assistance in energy-efficient manufacturing.


Daniela Invernizzi
email: daniela.invernizzi@outlook.it

Paolo Gaiardelli
email: paolo.gaiardelli@unibg.it

Emrah Arica
email: emrah.arica@sintef.no

Daryl Powell
email: daryl.j.powell@ntnu.no

MES Implementation: Critical Success Factors and Organizational Readiness Model

Keywords: Manufacturing Execution System, Critical Success Factors, Organizational Skills Readiness Model

Manufacturing Execution Systems (MES) have evolved to alleviate the drawbacks of Enterprise Resource Planning (ERP) systems by providing realtime information exploitation from the shop floor. In parallel with the increasing number of companies adopting MES, MES vendors have exponentially increased over the past two decades. While companies tend to focus merely on the technological aspects of the MES implementation, the adoption of MES implies an organizational transformation process that needs to be properly addressed by companies for implementation success. This is important because the new functions, services, and operability offered by the MES needs to be aligned with existing business processes and practices. Considering the human, technological, and organizational aspects holistically, this paper outlines critical success criteria and proposes an organizational readiness model for MES implementation.

Introduction

Implementation of Information System (IS) can bring many benefits to production planning and control, including ease of management, competitive advantage, efficient use of resources, effective data exchange, and the ability to provide accurate and timely information for decisions. As such, Manufacturing Execution Systems (MES) promise to improve the production control task through the collection and analysis of data in real-time. Data and information are made available to all users involved in the production activities in order to measure current performances, analyse flow' operations and identify opportunities for improvement.

However, when a company opts to implement MES, it often has limited knowledge regarding the factors for a successful implementation. Many unsuccessful MES implementations can be due to critical errors in selection and adoption of the system. The implementation process must be aligned with the company's strategy, and a correctly planned and controlled implementation method is required. In the context of MES implementation, it is important to draft the most realistic model for the future manufacturing organization. The model can facilitate the understanding on which steps are needed and what activities are necessary to guide the manufacturing control tasks successfully.

Based on a comprehensive literature review using search terms "Manufacturing Execution System", "MES", "Enterprise Resource Planning", "ERP", "Enterprise Systems", "Information Systems"; "Critical Success Factors" and "Implementation" in Science Direct and Google Scholar, this paper outlines the critical success factors for MES implementation taking human, technological, and organizational (HTO) aspects into account. The identified factors are further consolidated into an organizational readiness model (ORM) to assess the preparedness of companies for MES implementation.

Critical Success Factors in MES Implementation

Critical Success Factors (CSFs) are defined as "the few key areas of activity in which favourable results are absolutely necessary [for a particular manager] to reach [his] goals" [START_REF] Bullen | A Primer on Critical Success Factors[END_REF]. While there is a significant amount of research on CSFs in the implementation of ERP and other enterprise systems, the literature on CSFs for MES implementation is scarce. However, as an enterprise system, MES implementation possesses many similarities with ERP systems. As such, this study also benefits from extant literature by building on CSFs for ERP. The identified CSFs were categorised into three dimensions: human, technological, and organizational (HTO). This categorisation is consistent with [START_REF] Petter | Information systems success: The quest for the independent variables[END_REF], which states that human, technological and organizational factors represent the most significant dimensions for successful implementation of information systems.

Human factors

Human skills, perception, and experience play a central role in successful MES implementation. Our literature search uncovered the following human factors: Project team. A MES implementation team should consist of a set of best and brightest individuals, to foster innovation and creativity [START_REF] Shanks | Differences in Critical Success Factors in ERP Systems Implementation in Australia and China: A Cultural Analysis[END_REF] [4] [START_REF] Nah | Critical Success Factors for Enterprise Resource Planning Implementation and Upgrade[END_REF]. Individuals with greater knowledge, reputation and influence within the organization are the preferred candidates. Building a cross-functional team is also important. Competence and knowledge of the members should complement each other [START_REF] Görtz | Basiswissen ERP -Systeme. Auswahl, Einführung & Einsatz betriebswirtschaftlicher Standardsoftware W3I[END_REF]. External consultants should be directly part of the project team, to support the implementation [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF] and help employees develop the necessary technical skills for design and implementation [START_REF] Sumner | Critical Success Factors in Enterprise Wide Information Management Systems Projects[END_REF]. In addition, as successful implementation requires trust and the willingness to cooperate, teams with high morale, motivation and awareness are more likely to achieve successful MES implementation. Communication. Communication is one of the most challenging and difficult tasks to achieve in any implementation project. Top management, project teams, employees and other stakeholders must communicate directly and clearly, sharing detailed information about the project status. A good level of communication should be obtained from the early stages of the implementation, making results and objectives easy to understand [START_REF] Bhatti | Critical success factors for the implementation of enterprise resource planning (ERP): empirical validation[END_REF]. Education and training. Employee education is a priority in the early phases of the project, as people must be aware of what will change and be guided towards acquiring new skills [START_REF] Roberts | MRPII implementation: key factors for success[END_REF]. Training should concern the software and its functions, the IT skills, the quality and accuracy of data and the responsibilities in the new processes [START_REF] Bhatti | Critical success factors for the implementation of enterprise resource planning (ERP): empirical validation[END_REF]. An appropriate training plan requires the identification of the right methodologies to adopt for each user, considering his/her individual characteristics, knowledge and skills. The knowledge of consultants or software vendors can be used to allow autonomous management of activities and problems [START_REF] Françoise | ERP Implementation by Critical Success Factor Management[END_REF]. User involvement. User participation is a key parameter for improving the quality of any enterprise system and information. The user's involvement should be foreseen in two phases: first, to define the needs to which the system will have to respond and second, to define the level of participation in the software implementation [START_REF] Bhatti | Critical success factors for the implementation of enterprise resource planning (ERP): empirical validation[END_REF]. With users' contribution, the system could be developed as a more user-friendly application. Accessing the correct data when needed improves individual performance. Moreover, employees with more job responsibilities are more productive, loyal and satisfied about their work [START_REF] Karim | Impact of job satisfaction, perceived organizational justice and employee empowerment on organizational commitment in semi-government organizations of Pakistan[END_REF] [13].

Technological factors

Technological factors affect MES adoption, especially in terms of user-friendliness and software adoption success. Literature reveals the following technological factors: MES selection process. Hardware selection is driven by choices on a software package [START_REF] Arica | Status and Future of Manufacturing Execution Systems[END_REF]. Therefore, during the selection process, it is important to identify the requirements to be met by the selected MES, plan and perform a professional analysis of the software package and ensure the fit between MES and existing enterprise system. Additionally, software stops due to errors and maintenance should be assessed [START_REF] Nah | Critical Success Factors for Enterprise Resource Planning Implementation and Upgrade[END_REF] [START_REF] Rosario | On the Leading Edge: Critical Success Factors in ERP Implementation Projects[END_REF]. Technological infrastructure. A MES implementation implies a complex transition from previous information systems and business processes. Before starting the implementation, the new enterprise system's architecture should be established. This should prevent reconfiguration at every stage [START_REF] Wee | Juggling toward ERP success: keep key success factors high[END_REF]. As the software architecture could be modified to meet the requirements of the MES, legacy systems must be evaluated, to be ready to face problems and hindrances during implementation [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. Moreover, the definition of an appropriate architecture that can support and integrate software and hardware must be considered. System integration with other IS, as well as system interoperability in terms of flexibility, accessibility, integration and efficiency, is also essential [START_REF] Modrák | Mapping Development of MES Functionalities[END_REF]. This aspect will no doubt prove to be of fundamental importance in the coming years, to better manage the new challenges created by Industry 4.0 [START_REF] Almada-Lobo | The Industry 4.0 revolution and the future of manufacturing execution systems (MES)[END_REF]. Data management. Data quality management refers to the selection of data and the level of accuracy satisfied by the implemented system [START_REF] Saade | Critical success factors in enterprise resource planning implementation: A review of case studies[END_REF]. The data analysis plan is recommended to be develop from the beginning. Data model must be compatible with data requirements to avoid implementation delays. Preventive measures can be taken by developing a plan for migration and cleaning up data. Identification of data that must be uploaded and conversion of data structures into a single consistent format is also crucial. In addition, support tools must be deployed to monitor accuracy, timeliness, completeness, consistency, accessibility and security of data [START_REF] Saenz De Ugarte | Manufacturing execution systema literature review[END_REF][START_REF] Morariu | Policy-based security for distributed manufacturing execution systems[END_REF].

Organisational factors

Organizational factors are often overlooked by companies during the MES implementation process [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. The following critical factors were identified from the organizational perspective. Top Management Support. Top managers must be fully involved and oriented to allocate and provide valuable resources to the implementation [START_REF] Holland | A critical success factors model for ERP implementation[END_REF][START_REF] Dai | Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer[END_REF]. In addition, top management should also influence the process of selecting the MES delivery partner [START_REF] Chung | An empirical study of success factors influencing the implementation of information systems outsourcing[END_REF], handling users' resistance [START_REF] Lee | Successful implementations of MES in Korean manufacturing SMEs: an empirical study[END_REF] and encouraging users' participation during the implementation. Among others, the Project Champion is the top manager devoted to support the implementation project and the reference person to which the team refer in case of problems or conflicts [START_REF] Loh | Critical Elements for a Successful Enterprise Resource Planning Implementation in Small-and Medium-Sized Enterprises[END_REF]. He/she is responsible for the identification of strategic objectives, and collaborates with the project team to check if the business perspective is successfully translated into the adopted solution. Project Management. Good project management is vital to successful MES implementation [START_REF] Yang | Critical Success Factors for MES Implementation in China[END_REF]. Project management refers to the ongoing management of the implementation plan [START_REF] Finney | ERP implementation: A compilation and analysis of critical success factors[END_REF], that involves the creation of team work to support the implementation process. Therefore, project team tasks should be defined and comprehensively documented, where inter-departmental cooperation between the involved stakeholders must be set up [START_REF] Shaul | Critical success factors in enterprise resource planning systems: Review of the last decade[END_REF]. Coordinating, scheduling and monitoring of defined activities as well as identification and management of risks ensure that the stated objectives are achieved [START_REF] Bhatti | Critical success factors for the implementation of enterprise resource planning (ERP): empirical validation[END_REF]. Moreover, realistic input is required to avoid any unnecessary delay and also up to date information [START_REF] Loh | Critical Elements for a Successful Enterprise Resource Planning Implementation in Small-and Medium-Sized Enterprises[END_REF]. Change Management. Change management strategies aim for handling the enterprise wide cultural and structural changes [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. Sharing a solid culture and corporate identity facilitates the management of this change [START_REF] Rosario | On the Leading Edge: Critical Success Factors in ERP Implementation Projects[END_REF] [START_REF] Falkowski | A Recipe for ERP Success[END_REF]. Moreover, resistance to change could hinder the transformation and delay the project. Organisations with a continuous improvement mind-set are more open to change [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. This suggests that firms that manage MES implementation as a structured improvement project may be more likely to succeed. Business Process Re-engineering. Manufacturing companies often find it difficult to adapt their traditional processes software implementation requirements. Business Process Re-engineering (BPR) might be adopted to make business and production processes more flexible [START_REF] Sjödin | Smart Factory Implementation and Process Innovation[END_REF] thus reducing errors and simplifying the integration of future MES updates. As the implementation of MES leads to changes in the management system, it is necessary to manage and control such a change according to the selected the BPR plan and implementation strategy. Implementation Strategy. A clear strategic vision of the implementation process ensures a more effective result. An initial strategic plan, including management of expectations, alignment between business and IT strategies and business change provides a macro perspective of the implementation. As users and stakeholders' involvement reduces barriers and conflicts, it is important to act positively, affecting communication and expanding knowledge about the new software [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. Acceptance Control. Measurement and evaluation of the implementation process ensures that deviations can be identified and promptly corrected [START_REF] Leyh | Critical Success Factors for ERP System Implementation Projects: An Update of Literature Reviews[END_REF]. Therefore, building performance indicators and mechanisms to monitor progress is essential. Key performance indicators will be analysed and updated throughout the project, thus contributing to achieve system acceptance and improvement.

Organizational Readiness Model

We suggest that an Organizational Readiness Model (ORM) could help organizations understand the current level of preparedness before embarking on the implementation of a MES system, for example to identify strengths, weakness, opportunities and threats related to such an implementation. As such, we categorized the previously identified HTO-factors through dialogue with a group of industrial IT professionals in Norway and Italy. The model is built upon four levels of maturity: Premature, Aware, Willing, and Ready. These levels are summarized in Table 1:

Table 1. Organizational skills readiness model for MES implementation

Readiness Level Description

1° -Premature

The organization has decided to implement MES but has very limited knowledge of all HTO-factors

2° -Aware

The organization has minimum knowledge MES and demonstrates effective mechanisms for education and learning as well as project management

3° -Willing

The organization demonstrates the required level of top management support and exhibits a grand plan for how the implementation process will be executed.

4° -Ready

The organization is aware of how to manage data, monitor implementation and is able to govern the change management process effectively.

Critical Success Factors at each Readiness Level

For each of the identified readiness levels (1-4), it is anticipated that an organization should be able to relate to the various critical success factors (Figure 1). For example, in the "Premature" phase, when the desire to implement MES is born in the organization, it is anticipated that much of the pre-emptive knowledge and characteristics required for successful MES implementation will not be exhibited by the organization. Before compiling the business case and subsequent implementation strategy, basic knowledge, education and training should be received by key parties at the organization. Selecting the correct vendor is important to create a relationship based upon mutual trust, as the collaboration that follows shall make or break the implementation.

At "Aware" level, the focus shifts to the company's project management capability, and to the understanding the required integration level within the IT architecture. When internal skills are insufficient, the support of vendors or external consultants can be necessary. The ability to construct and deploy an effective communication strategy is also a key criterion at this level.

At third level of maturity, "Willing", the roles of project manager and of the project team work become central. Members must be carefully selected as they play a primary role during the software implementation. Wrong choices of team members as well as the selection of unskilled or influential project manager, could jeopardize the whole project. The company starts a processes re-engineering phase and management changes are needed.

At "Ready" level, the focus shifts mainly to data management and change management. Critical aspects also concern technological infrastructure. However, the acceptability of the new system must be taken into account, as imposing a new way of working and convince people about the validity of the new system is not straightforward.

If an organization demonstrates all of the requirements at each level, we suggest that the likelihood of successful MES implementation is maximised.

Conclusion

This paper outlines the critical success factors (CSFs) for MES implementation, considering human, technological and organizational (HTO) aspects holistically. An organizational readiness model for assessing the preparedness of an organization in terms of each of the CSFs is further proposed. Besides these theoretical contributions, our model can be used by practitioners (i.e. managers, vendors and consultants) to gear a global overview of a MES implementation. As very often, organizations do not have the knowledge and resources necessary for the adoption of a new software, this work constitutes an opportunity to benefit from a structured approach for an effective and fruitful implementation of MES. Moreover, the achieved results can be used to create guidelines and checklists to adopt before and after the completion of each MES implementation phase, reducing the risk of failure and leading companies to perform a successful MES implementation. This paper is of course not without limitations, which require some further exploration. In particular, the proposed organizational readiness model should be tested and validated in action in order to assess its completeness and usefulness, as well as to identify necessary improvements. Moreover, in order to understand more fully whether a company is ready to proceed with a MES implementation, the model should be expanded to incorporate a skills perspective.

Fig. 1 .

 1 Fig. 1. Organizational Readiness Model: CSFs for MES implementation