Charlotta Johnsson
email: charlotta.johnsson@control.lth.se

Key

Keywords: Performance Indicators Used as Measurement Parameter for Plant-Wide Feedback Loops key performance indicators, plant-wide control, feedback loops

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

Introduction

Feedback is a fundamental concept in automatic control. Feedback loops are used extensively in industrial applications today. The feedback loops are used to assure that the output signal is controlled so that its value approaches the value of the reference signal. In a traditional production plant within the process industry, there can be as many as 1000 feedback loops, each one controlling a single parameter such as a level, a concentration or a temperature. The most frequently used controller is the PID controller. There are methods describing the procedure to create feedback loops. The first step in this method is to select the control parameters (i.e. to think what parameter that should be controlled) and how this value should be measured (i.e. what should be used as measurement signal). This paper examines how the concept of feedback loops could be used for plant wide control and how key performance indicators could be used as the equivalent to the measurement signal. The paper further gives examples of commonly used key performance indicators and it describes the work that is done within the international standard ISO 22400 [START_REF]Key performance indicators for Manufacturing operations management -Part 1: Overview, Concepts and " International Standardization Organisation (ISO) (draft)[END_REF] [START_REF]Key performance indicators for Manufacturing operations management -Part 2: Definitions and descriptions. International Standardization Organisation (ISO) (draft)[END_REF], currently under development. Further, the requirements for plant wide control are compared with the traditional concepts of automatic control and feedback loops. The first section of the paper describes traditional concepts of automatic control and single parameter feedback loops, the second section discussed plant wide control. Thereafter a presentation of commonly used key performance indicators is given, and a discussion about the similarities of plant-wide control and traditional feedback loops is given. The final section contains the conclusions.

Single parameter feedback loops

At production plants in the process industries there are many variables that need to be monitored and controlled. Feedback loops can be used to control these variables. Often, one feedback loops is used to control one variable, e.g. level, temperature or concentration. A feedback loops is shown in Figure 1. Important blocks in the feedback loops are:

• Process: The system that should be controlled, i.e. the system for which you would like to assure that the output is made equal to the reference signal. • Controller: The task of the controller is to calculate a control signal u(t) based on the control error e(t) so that the output signal y(t) approaches and eventually equals the reference signal y sp (t).

Important variables in the feedback loops are:

• Reference signal y sp (t): the reference signal is the desired value of the output signal y(t). • Control error e(t): the input to the controller is the control error e(t), i.e. the difference between the reference signal y sp (t) and the output signal y(t). • Control signal u(t): the output of the controller is the control signal u(t), also refered to as the manipulated variable. • Output signal y(t): the output signal is the measured value of the output from the process.

The PID controller is by far the most commonly used controller in industry today. There are billions of control loops [START_REF] Soltesz | On automation of the PID tuning procedure[END_REF] and the PID controller is used for more than 95% of all control loops [START_REF] Åström | Feedback Systems: An Introduction for Scientists and Engineers[END_REF]. The PID controller concept is also taught in most introductory automatic control courses, i.e. most control engineers are well aware of the basic principles of feedback. A common work-procedure used when designing traditional single parameter control loops is given in Table 1.

Step Activity

Step-1 select the control-parameter and the corresponding sensors i.e., select the variable that should be controlled (compare output signal) and select how it should be measured.

Step-2 select the manipulated-variable and the corresponding actuator, i.e. select the variable that should be used as control signal and select how it could be manipulated.

Step-3 construct the control loop, i.e. pair the manipulated-variable with the control-parameter.

Step-4 select the type of controller, tune the controller parameters and search for an optimal set-point.

Table1: Work procedure used when designing single-parameter control loops.

At production plants in the process industries it is not uncommon to have as many as 1000-10000 variables and about 100-1000 feedback loop at each site. All variables are saved in an historical database. Each feedback loop is of importance for the performance of the plant, however, it is also of importance to get indications of the plant's overall performance. A dilemma is that the single control loops do not have their focus on the overall plant performance but rather on a local part of the plant.

Plant wide control

An enterprise, having one or more production sites could be structured in a hierarchical fashion. A hierarchy used for this purpose is presented in the international standard IEC 62264 [START_REF]Enterprise-Control System Integration, Part 1 Models and terminology[END_REF] also known as [START_REF]Enterprise-Control System Integration, Part 1: Models and Terminology[END_REF], see Figure 2. The term production plant is not explicitly used in this hierarchy and is understood as either a production site or a production area. According to the role-based equipment hierarchy defined in IEC 62264 [START_REF]Enterprise-Control System Integration, Part 1 Models and terminology[END_REF], an enterprise contains one or more sites, which in turn contains one or more production areas. Each production area has one or more production units and each production unit has one or more units, see Figure 2.

The enterprise determines which products to produce and at which site they should be produced. Sites are usually geographically grouped, and are used for roughcut planning and scheduling. Areas are usually grouped by their geographic location and by their products. Each area produces one or more products, either end products for external sale or intermediates for further use by other areas at the site. Production units generally include all equipment required for a segment of continuous production. A production unit in the process industry could be e.g. a reactor or a distillation column. Units are composed of lower level elements, such as equipment modules, sensors and actuators [START_REF] Lindholm | A tool for utility disturbance management[END_REF]. At the unit level there might be several single parameter control loops, each one dedicated to control the performance of a single parameter, compare Figure 2. The single parameter control loops do not have their focus on the overall plant performance but rather on a local part of the plant.

Step 1 in the work-procedure for single parameter control loops (compare Table 1), is to select the control-parameter and its sensor. In many cases the control-parameter could be measured directly simply by placing a sensor at the correct place. When controlling a production-plant, the selection of a control-parameter might not be as trivial and it might not be possible to direclty, using a single sensor, measure the controlparameter. In many cases, the control-parameter corresponds to an indicator, i.e. a value that itself is calculated from one or many measures in the plant. These indicators are sometimes refered to as key performance indicators (KPIs).

Step 2 in the work-procedure for single parameter control loops (compare Table 1), is to select the manipulated-variable. In many cases the manipulated-variable could be modified simply by introducing an actuator at the correct place. When controlling a production-plant, the selection of a manipulated-variable might not be as trivial and it might not be possible to direclty, using a single actuator, modify the manipulatedvariable.

Step 3 in the work-procedure for single parameter control loops (compare Table 1), is to construct the control loop, i.e. pair the manipulated-variable with the controlparameter. This concept seems reasonable to use also when dealing with plant wide control. Step 4 is to select the type of controller, tune the controller parameters and search for an optimal set-point (compare Table 1). This could be a hard task, however there are known structures for controllers (e.g. PID controller) and known tuning methods (e.g. Ziegler-Nichols or Step responses). When it comes to plant-wide control, this is not as obvious. Today, the correspondance of a plant-wide controller would be the human plant manager or a similar person.

The concept of plant-wide control, could be translated to the concept of classical feedback loops by making the following comparison, the concept is illustrated in Figure 3:

• Controller = plant manager or a similar person • Process = the complete plant • Reference signal = the business objective • Control error = the difference between the business objective and the Key Perofrmance Indicator • Control-signal = the action taken by the plant manager to assure that the performance of the plant (compare output signal) is approaching and eventually becomes equal to the business objective (compare reference signal). • Output signal = the key performance indicator. A possible work-procedure for plant-wide control is shown in Table 2.

Step Activity Step-1 select the key performance indicator its corresponding definition i.e., select the indicator that should be controlled (compare output signal) and find out how it should be calculated.

Step-2

Select "manipulated-indicator" , i.e. understands what variables that should be manipulated in order to make a change in a key performance indicator in a desired direction.

Step-3 construct the control loop, i.e. pair the manipulated-indicator with the key-performance indicator.

Step-4 select the "plant-wide controller". This often corresponds to a human person. It is important to have decision support systems for this person, so that he or she can take good decisions. The decisions taken corresponds to the control-signal. The development of the international standard ISO 22400 [START_REF]Key performance indicators for Manufacturing operations management -Part 1: Overview, Concepts and " International Standardization Organisation (ISO) (draft)[END_REF] [2] could be seen as an attempt to help defining good key performance indicators (compare good mearurement-variables).

Step1: Key performance indicators

ISO 22400 is an international standard currently under development. The title is "Key Performance indicators (KPIs) for Manufacturing Operations Management". This work was started in 2009 and the intent is to develop an international standard in 4 parts. ISO22400-Part1 and ISO22400-Part2 are in its final stage and about to be released as International standards.

ISO 22400 defines a Key Performance Indicator as "Quantifiable level of achieving a critical objective" (definition 3.1.4. in ISO 22400-part1). ISO 22400-Part1 [START_REF]Key performance indicators for Manufacturing operations management -Part 1: Overview, Concepts and " International Standardization Organisation (ISO) (draft)[END_REF] also states "the KPIs are derived directly from or through an aggregation function of, physical measurements, data and/or other key performance indicators."

ISO 22400 defines a KPI by giving its content and its context.

• Content: a quantifiable element with a specific unit of measure (including the formula that should be used to derive the value of the KPI). • Context: a verifiable list of conditions that are met. ISO 22400 should be applicable in both discrete industry and continuous/process industry. The 34 KPIs defined in the standard are listed in Table 3 The KPIs defined in ISO22400 are intended to be examples of the most frequently used KPIs in industry today, i.e. a palette of KPIs from which companies can select the one that best corresponds to their business objective. It is recognized that some of the KPIs are better suited for discrete industry and others are better suited for continuous/process industry.

Example: Availability and Utilization efficiency

Two of the 24 KPIs that are defined in ISO 22400-Part2 are Availability and Utilization Efficiency [START_REF]Key performance indicators for Manufacturing operations management -Part 2: Definitions and descriptions. International Standardization Organisation (ISO) (draft)[END_REF].

Availability is defined as the Actual-Production-Time (APT) divided by the Planned-Busy-Time (PBT).

• APT: Actual Production Time shall be the time during which a work unit is producing, It includes only the value adding functions. • PBT: Planned Busy Time shall be the operating time minus the planned downtime.

Utilization Efficiency is defined as Actual-Production-Time (APT) divided by Actual-Unit-Busy-Time (AUBT).

• APT: Actual Production Time shall be the time during which a work unit is producing, It includes only the value adding functions. • AUBT: Actual Unit Busy Time shall be the actual time that a work unit is used for the execution of a production order.

Availability is thus an indicator of how much of a plant's time that is used for production whereas Utilization efficiency is an indicator of how much of a plant's capacity is utilized. The two concepts are therefore not providing an indication of the same thing.

In discrete industry a machine and/or a plant is often used to its full capacity when it is used, however, it is not unusual that a machine and/or plant is simple waiting for a new job to be assigned. The concept of Availability is therefore frequently used in discrete industries. In continuous/process industry, a unit and/or a plant is often continuously, however it is sometimes used with reduced capacity. The concept of Utilization efficiency is therefore used in continuous industries.

In order to help making the calculations of Availability and Utilization Efficiency, as well as other KPIs, in a consistent way ISO 22400 also contains time models explaining how the measures used in the formulas should be achieved.

End-Example

The definition of KPIs will help finding good control-parameters in the workprocedure for plant-wide control (Compare step 1 in table 1 and table 2). There is currently nothing in ISO 22400 that has a clear linking to the succeeding steps in the work-procedure for plant-wide control. The work of defining key performance indicators will also be an answer to the industry-wide problem of having "poor visibility into plant operations" and to start utilizing "the hidden resource that data is known to be".

Industry Relevance

MESA has identified a company as a Business-Mover if the company has improved [START_REF] Mesa | Metrics that Matters[END_REF]:

• More than 10% on one or many of the financial metrics, or • More than 1% on over half of the financial metrics.

MESA has further identified that the companies identified as Business-Movers:

• Have well identified Key Performance Indicators (KPIs),

• Have informed employees, and • Use IT-systems to get measurements, calculate KPIs and display the results. This stresses the fact that it is important for a company, interested in controlling and improving the performance of a plant, to understand what control-parameters they have access to (compare Step 1 in Table 1 andTable 2). Future research should also help identifying manipulated-indicators (compare Step-2 in table 1 and Table 2) and suitable control-actions.

Conclusion

Feedback is a fundamental concept in automatic control. Feedback loops are used extensively in industrial applications today and there are work-procedures for designing traditional single parameter control loops. In the work procedure it is important to select control-parameters, manipulated parameters and control-strategies. A similar work-procedure could also be used for designing plant-wide control feedback loops. The international standard ISO 22400 [START_REF]Key performance indicators for Manufacturing operations management -Part 1: Overview, Concepts and " International Standardization Organisation (ISO) (draft)[END_REF] [2] is helping defining commonly used Key Performance Indicators, these can be used as control-parameters. Future research also needs to focus on how the manipulated-parameters can be selected and how decision support systems can be used for selecting suitable plant wide control strategies.

Figure 1 :

 1 Figure 1: Feedback loop.

Figure 2 :

 2 Figure 2: The role based equipment hierarchy of IEC 62264 [5].

Figure 3 :

 3 Figure 3: Concept of plant-wide control.

Table 2 :

 2 Work procedure used when designing plant-wide control loops.

Table 3 :

 3 . A list of the 34 KPIs from the new international standard ISO 22400.

	Worker Efficiency	Production process ratio	Finished goods ratio
	Allocation Ratio	Actual to planned scrap	Integrated goods ratio
		ratio
	Throughput rate	First pass yield	Production loss ratio
	Allocation efficiency	Scrap ratio	Storage and transportation
			loss ratio
	Utilization efficiency	Rework ratio	Other loss ratio
	Overall equipment effec-	Fall off ratio	Equipment load ratio
	tiveness index
	Net equipment effective-	Machine capability index	Mean operating time be-
	ness index		tween failures
	Availability	Critical machine capabil-	Mean time to failure
		ity index
	Effectiveness	Process capability index	Mean time to restoration
	Quality Ratio	Critical process capability	Corrective maintenance
		index	ratio
	Setup Rate	Comprehensive energy
		consumption
	Technical efficiency	Inventory turns

Acknowledgment:

The author C. Johnsson would like to thank the Vinnova-funded research environment LCCC in which environment the author participates, and the PIC-project founded by SSF, Sweden.


Carin Rösiö
email: carin.rosio@mdh.se

Jessica Bruch
email: jessica.bruch@mdh.se

Focusing Early Phases in Production System Design

Keywords: Production system design, manufacturing industry, early phases 1

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

Introduction

There is an increasing need for efficient production system design due to the fact that manufacturing companies must be able to handle issues such as increasing individualization of customer demands, fluctuating consumption and permanent pressure on product cost and quality [START_REF] Wiendahl | Justifying Changeability: A Methodical Approach to Achieving Cost Effectiveness[END_REF]. European manufacturing companies have a high standard and a strong position in industrial engineering with innovative and customized product solutions, however, they lose market shares in mass production [START_REF] Westkämper | New Trends in Production[END_REF]. Attractive product solutions are not enough, a successful production activity is also required in order to be competitive. The potential of an effective production system design process is often not prioritized enough in manufacturing companies even though it is shown that a number of competitive advantages can be achieved such as fast ramp up, shorter time to market, robust production systems, increased market shares, higher customer acceptance, improved levels of output and increased efficiency [START_REF] Hayes | Operations, Strategy, and Technology: Pursuing the Competitive Edge[END_REF]. Although manufacturing companies have started to focus on production system design, many find it difficult to coordinate the production system design process and work in a structured and systematic way [START_REF] Bruch | Characteristics affecting management of design information in the production system design process[END_REF][START_REF] Rösiö | Säfsten: Reconfigurable Production System Design -theoretical and practical challenges[END_REF]. There are numerous explanations for the difficulties in production system design. It has been argued for example that the nature of the production system design process is not well defined, i.e. there are many different definitions and interpretations of the process and work activities involved [START_REF] Cochran | A decomposition approach for manufacturing system design[END_REF]. Part of the reason is that companies have focused on the product development because they saw it as a way to achieve competitive advantages, while the production system development process is seldom seen as a means to achieve the best possible production system [START_REF] Bellgran | Säfsten: Production Development: Design and Operation of Production Systems[END_REF]. Thus, although the term development process is well known in manufacturing companies, it is usually applied in the product development and not in the production system required to produce the products. It is, however, important to note that the design of production systems is both a technically challenging and organisationally complex activity on its own right, and the context where it operates is more multifaceted than is commonly described in the simultaneous-engineering literature [START_REF] Pisano | The Development Factory: Unlocking the Potential of Process Innovation[END_REF]. As a result, the design of production systems needs to trigger separate control and coordination of the specific set of activities required to move the project through the development process from idea to detailed design.

Overall, it can be concluded that the design of production systems is challenging but previous research provides only limited insights and guidance for managers into how those challenges can be handled. Recent studies in the area of production system design confirm that production system development generally takes place rather ad hoc and without having a long term plan [START_REF] Bruch | Management of Design Information in the Production System Design Process[END_REF][START_REF] Rösiö | Supporting the Design of Reconfigurable Production Systems[END_REF]. The potential of gaining a competitive edge by improving the way the production system is designed is hence ignored. It is a clearly shown that it is in the early phases of production system design where the most important decisions are made. If the production system is not designed in a proper way, this will eventually end up with disturbances and problems during serial production. The result is evidently low capacity utilization, high production cost and hence low profitability. The more resources that are established early in the process, the more opportunities there are to influence the production system design [START_REF] Blanchard | Systems Engineering and Analysis[END_REF]. The purpose with this paper is therefore to describe how to work and what activities to focus on in early phases of production system design by proposing a structured production system design model focusing on the early phases which can be applied by practitioners and academics. The results presented in this paper is a step towards a formalized production system design model offering improved understanding of how the design of production systems can be improved.

Frame of reference

To succeed in production system design, dedication is required as well as a shift in attention from the operations phase to the under-utilized potential of the design of production systems. Although earlier research has contributed to improved knowledge about production system design and development [e.g. 11, 12, 13-16] there is still no agreement on the approaches to use in industry. When summarizing the approaches described in literature a number of stages and activities could be distinguished, Figure 1 where the design process should be considered as an iterative, cyclic process affected in its execution by each project context [START_REF] Wu | Manufacturing Systems Design and Analysis: Context and techniques[END_REF]. In a production system design process the problem is normally defined in an initial stage, where the project is initiated and defined in terms of e.g. project leader, budget, and time plan. Thereafter, an analysis of the background including present as well as future production systems and products including market research and environmental requirements is made. Based on this, objectives for the production system are formulated. The detailed design subsequently includes first designing conceptual production system alternatives. These early phases are focused on in this paper. The alternatives are thereafter evaluated in order to choose one final solution. The chosen production system is finally designed in detail.

Figure 1. Typical activities carried out in early phases of production system design

The production system design process can be considered as a part of the new product development process. Prior research on product development best practices highlights that successful projects follow a formalized and structured cross-functional stage-gate model for the product development process [START_REF] Griffin | PDMA Research on New Product Development Practices: Updating Trends and Benchmarking Best Practices[END_REF]. A structured and systematic production system design process should tell what should be done and when, what techniques and tools will be needed at each stage, what information needs to be collected, and what the output or result of each stage would be [START_REF] Love | The design of manufacturing systems[END_REF]. A framework should include both prescriptive and explanatory elements. While the prescriptive elements assist practitioners to carry out activities in a certain way, the explanatory elements refer to how things are perceived [START_REF] Mcivor | A practical framework for understanding the outsourcing process[END_REF]. A framework useful for practitioners should consist of a number of logically sequential steps that support a structured work approach to the decision making process [START_REF] Mcivor | A practical framework for understanding the outsourcing process[END_REF]. A useful framework need also to be simple to be widely used by engineers, efficient with minimum trial-and-error actions and versatile to be applicable in different situations [START_REF] Houshmand | An extended model of design process of lean production systems by means of process variables[END_REF]. Based on the summarized phases and activities in Figure 1. these requirements are inadequately met. A study evaluating the usability of the structured production system design process proposed by Bellgran and Säfsten [START_REF] Bellgran | Säfsten: Production Development: Design and Operation of Production Systems[END_REF] showed that the usage leads to increased learnability, efficiency, effectiveness, and satisfaction [START_REF] Arnesson | Usability Evaluation of a Production System Development Framework[END_REF] and the contribution was largest in the early phases of the production system design process by putting emphasis on the planning and providing a structure to follow. Thus, focus on early phases is consequently crucial and worth to be highlighted.

Research Methodology

Data was collected by the case study methodology [START_REF] Yin | Case Study Research: Design and Methods[END_REF] where each case represented a production system design project. The cases had an embedded design where the project was studied and the embedded unit of analysis was the production system design process including both the actual process and the documented models that were used. All cases were selected based on theoretical sampling with the goal of extending the emergent theory [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF]. Six production system design projects were studied in three real-time case studies and three retrospective case studies at four different large global manufacturing companies. All interviews were recorded and transcribed. To enhance validity, real-time studies were combined with retrospective studies [START_REF] Leonard-Barton | A Dual Methodology for Case Studies: Synergistic Use of a Longitudinal Single Site with Replicated Multiple Sites[END_REF]. Through real-time studies post-rationalization was avoided, thus increasing internal validity. Data was gathered by semi-structured interviews, observations and document studies [START_REF] Yin | Case Study Research: Design and Methods[END_REF], Table 1. Before the study started the research design was carefully described and discussed with the representatives of the company. The role of the researcher varied due to the combination of real-time and retrospective studies. In case study A, E, and F one of the researchers was present at the site during one-three months to follow the production system design project. Observations were made at production system design meetings and daily contact was maintained with the production system design team. Before and after the visit at the site, contact was kept with key persons in the production system design team. As an initial activity in all the case studies, semi-structured interviews were carried out based on an interview guide which was sent to the respondents beforehand.

Respondents involved in the production system design projects were identified at different levels in the organization, such as vice president R&D, strategic operations managers, operations managers, project managers, industrial engineers, and production en-gineers. Before an interview started, the terminology used was presented to the respondents in order to avoid misunderstandings. All initial interviews were recorded and transcribed. After the interviews were conducted relevant documentation was collected, such as project management models, production system design support, support for requirement specifications, and checklists and support used by individuals, all of which were made fully available. In all cases an on-going dialogue was held with involved key persons until rich descriptions of the single cases were achieved [START_REF] Yin | Case Study Research: Design and Methods[END_REF]. Observations were carried out including attendance on project meetings and development activities in the project as well as at one meeting with the system supplier. Field notes were carefully written through the whole case study [START_REF] Yin | Case Study Research: Design and Methods[END_REF], by which all activities were documented concerning what activity was done, how it was done, when it was done, and what people were involved.

Empirical findings

The case studies showed that the production systems were designed according to structured stage-gate models including seven stages or phases including (1) initiation, (2) project definition/pre-study, (3) concept study, (4) detailed design and final development, (5) implementation/industrialization and commercialization, ( 6) followup/launch, and ( 7) disposal. The models used were mainly focused on product design but did also include a few activities related to the design of production systems. However, the process gave limited support to production system design. None of the stagegate models in the cases included all activities that needed to be carried out when designing the production system. In addition to the stage-gate models additional support was used. This support was either individual or used by whole organizations. Individual support included e.g. individual checklists that consisted of important issues to remember throughout the production system design process and were used by the production engineers. Support more widely used often included models for requirement specifications. Several of the design activities were undertaken based on the production engineers' skills. In all cases except Case A the projects were carried out by a project leader derived from product design. In Case A the project had two equal project leaders, one responsible for product design and the other one responsible for the industrialization including all activities related to production system design. In the cases were the project leader derived from product design there was a strong focus on product design related issues. In all projects a knowledge existed that production system design should be integrated with product design and the production engineers were part of the project team from the early start to have the possibility to affect the product design. However, product design engineers were not required to give feedback to the production system concept. In reality production engineering was not represented in the initial two phases, i.e. initiation and project definition for the overall new product development project. Several of the projects that were studied had a lack of in-house competence in design of production systems, i.e. there were few production engineers or industrial engineers. Further, the production system engineer's often prioritized problems occurred in the running production. The production engineer normally had the main part of the respon-sibility and did not work in a group of diverse competences as in the product development teams. This resulted in less priory on production system design activities that were not absolutely necessary to do in that point of time. To strengthen production system design competence, external equipment suppliers were often used for production system design. The external equipment suppliers were involved in the concept study or the detailed design. This often resulted in a lack of long term view, a system not clearly founded in the manufacturing strategy and a lack of knowledge among the production personnel after system implementation.

Production system design model

As described in literature a design process should tell what should be done and when, what techniques and tools will be needed at each stage, what information needs to be collected, and what the output or result of each stage would be [START_REF] Love | The design of manufacturing systems[END_REF]. Based on empirical and theoretical data a production system design model is proposed. An overview of the proposed model is described in Figure 2. In the initiation phase the project should is initiated and clearly defined by either the manufacturing engineering department (rationalization, replacement, quality, safety, environment etc.), the product development projects (new or changed products), or by the strategy board (Capacity, insourcing etc.). After the project is initiated the project scope and the organization should be defined. Projects conducted by project leaders derived from product design where production system design was not equal [START_REF] Bruch | Management of Design Information in the Production System Design Process[END_REF][START_REF] Rösiö | Supporting the Design of Reconfigurable Production Systems[END_REF] was a tendency showed in the case studies. The fact that there often was a lack of production engineers and the running production was prioritized instead of production system de-velopment [START_REF] Bruch | Management of Design Information in the Production System Design Process[END_REF][START_REF] Rösiö | Supporting the Design of Reconfigurable Production Systems[END_REF] was also obvious. To compete with production and in order to successfully design production systems it is crucial to create a project organization with all necessary competences early in the project. It was found successful to have a project leader responsible for production system design who is equal to the project leader responsible for product design to create a balance between product development and production system development. Time and resources are needed in order to focus on production system design and work in a cross functional team. In this phase commission directive should also be formulated and signed by steering committee which include scope, demand and goals of the manufacturing engineering project. The conceptual design is a crucial project phase for the production system since in the early phases the decision space is often large while the cost for changes is low [START_REF] Blanchard | Systems Engineering and Analysis[END_REF]. To put effort on the conceptual design and the requirement specification will keep the overall costs down in the long run. Based on commission directive the preliminary concept is formulated in details including e.g. time plan, layout, budget, and capacity and machine utilization. This ends up in a concept study report including all information needed to design the production system in detail.

Conclusions

The purpose with this paper was to describe how to work and what activities to focus in early phases of production system design by proposing a structured production system design model focusing the early phases which can be applied by practitioners and academics. There is a lack of focus on production system design and projects are mainly owned by as well as focused on product design. Based on a detailed production system design model activities were specified resulting in the concept study report. The design model should be used as a complement to already established product development models. The results presented in this paper was a step toward a formalized production system design model offering improved understanding of production systems design process including how it is carried out, who is involved, etc.

Figure 2 .

 2 Figure 2. Production system design model describing the earlier phases

Table 1 .

 1 Information of case studies

	Case study	Type and	Data Collection Techniques	No interviews (single/group)
		Duration		Duration [min]
	Case A	Real time	Observations, Documentations	Face to face, 10 (10/0),
			Interviews	40-90
	Case B	Retrospective	Documentation, Interviews	Face to face, 2 (1/1)
				65-76
	Case C	Retrospective	Interviews	Face to face, 3 (3/0)
				63-72
	Case D	Retrospective	Observations, Documentations	Face to face, 6 (6/0)
			Interviews	46-85
	Case E	Real time	Observations, Documentations	Face to face, 8 (8/0)
			Interviews	30-80
	Case F	Real time	Observations, Documentations	Face to face, 7 (7/0)
			Interviews	45-107, Telephone, 1 (0/1), 72


Farhad Norouzilame
email: farhad.norouzilame@mdh.se

Robert Moch
email: robert.moch@mb.tu-chemnitz.de

Ralph Riedel
email: ralph.riedel@mb.tu-chemnitz.de

Jessica Bruch
email: jessica.bruch@mdh.se

Global and Regional Production Networks: A theoretical and practical synthesis

Keywords: production network, literature review, case study

The growth of research interest in networked production has brought up numerous important concepts aimed at production networks. Still the actual research object seems to be ambiguous, making the exact application of findings insufficient. It appears that there are two main notions of the term Production Network. The first meaning is derived by a single versus a multiorganizational perspective while the second meaning comes from a regional versus global view. Obviously indistinctness exists in this field of research and clarification of terms and concept is required. The objective of this paper is to give an overview of the existing theories in accordance with the industrial practice providing a model to bridge the academic theories to industrial practice.

Introduction

The affects of globalization on manufacturing is indisputable. The fragmentation of production processes and the international dispersion of tasks and activities within them have led to the emergence of borderless production systems in shape of sequential chains or complex networks commonly referred to as global value chains [1]. Most middle sized and large companies are now creating an international market and have to build and/or manage an international network of operations either due to their organic growth or via M&A activities [START_REF] De Meyer | Key Success Factors in the Creation of Manufacturing Facilities Abroad[END_REF]. The rationale behind such action lies principally within three main categories; (1) access to low-cost production, (2) access to skills and knowledge, and (3) proximity to market [START_REF] Ferdows | Making the most of foreign factories[END_REF]. Multinational companies strive to acquire, create, and use technological assets across national borders [START_REF] Dunning | Multinational enterprises and the global economy[END_REF].

The mentioned factors and the growth of developing economies in the world have accelerated the trend of moving from stand-alone factories toward more globalized production. The management of international networks remains an under researched area [START_REF] De Meyer | Key Success Factors in the Creation of Manufacturing Facilities Abroad[END_REF], and networked production of multiple organizations is also moving into focus of research [START_REF] Hayes | Pom Forum: Operations Management's Next Source of Galvanizing Energy?[END_REF]. To improve the competiveness of firms, not only the global production network of one enterprise but also the interplay of locally linked production of buyers and suppliers gains importance. These inter-organizational relations are not only spotted by the discipline of Supply Chain Management which discusses global versus domestic concepts without specifying their view on very production-intrinsic challenges [START_REF] Harrison | Principles for the Strategic Design of Supply Chains, in The Practice of Supply Chain Management: Where Theory and Application Converge[END_REF].

This paper provides an overall understanding of the production network concept by presenting a brief summary of the related literature along with two cases as real-life implications. The paper opens up with the introduction to the topic followed by the research methodology. Later on, some concepts are elaborated in details under reference framework section. Finally, the findings and conclusions are presented.

Research method

The methodology used in this study is a combination of literature review and a multiple case study. The literature review is based on searching the keywords production, manufacturing, network, global, international and the German equivalent words along with combination of those terms on the databases Science Direct, Scopus, Elsevier, and SpringerLink via different search engines. Totally about 200 articles have been analyzed in the first phase further reduced to 18 directly related to the topic of production networks which are referred to in Table-1. The case study approach is chosen since it allowed an in-depth study of the phenomenon in its real-world context plus conformity to answer the how and why questions [START_REF] Yin | [END_REF]. Case-A is a global manufacturing company headquartered in Sweden with total number employees of approximately 1250 with eleven production sites in six different countries, four of them located in BRIC countries. The core business of the company is the production of mechanical and electromechanical solutions for commercial vehicles, construction and mining industries, and general industry. Case-B is a network of seven companies including eight plants in total located in Germany. Six of the companies are SMEs; the other is a large company. The network's focus is to produce gearboxes for energy industry. 14 semi-structured interviews have been performed in total which of 10 were conducted at Case-A and four at Case-B.

2.1

Global Production Network and Regional Production Network

Global Production Networks (GPNs) and Global Value Chains (GVCs) are two schools of Globalization studies, with their roots on Global Commodity Chains (GCCs) having some common works, as well as some differences on concepts and focus [START_REF] Sakuda | Afonso Global value chains, global production networks: towards global netChains synthesis?[END_REF]. A global production network is a manufacturing network i.e. a network consisting of wholly owned factories, i.e. belonging to the same company [START_REF] Feldmann | A strategic perspective on plants in manufacturing networks[END_REF] on a global scale.

A regional Production Network (RPN) is a cooperation of geographically close companies to produce a certain product. The RPN can be developed by an addressable strategic network or is formed through latent informal network structures of manufacturing enterprises [START_REF] Moch | Key Success Factors for Production Network Coordination[END_REF][START_REF] Müller | Production planning and operation in competence-cell-based networks[END_REF]. As long as the production cooperation is carried out, this temporary network exists. Depending on its product portfolio, an enterprise can be part of more than one RPN.

Supply Chain Management and Networked Production

Within business studies, networked value-adding processes are focused by the field of SCM which aims to establish standard measurements, functions and reference models to analyse, describe and manage networked processes. This approach is not restricted to production and manufacturing, it is also involving e.g. marketing, resellers and distributors [START_REF] Harrison | Principles for the Strategic Design of Supply Chains, in The Practice of Supply Chain Management: Where Theory and Application Converge[END_REF]. Besides Business Studies discipline, there are also approaches within the Mechanical Engineering and Production Management field. One general approach describes the formation of Production Networks as "crosscompany cooperation" and clearly differentiates between Production Networks and Supply Chains [START_REF] Wiendahl | Management of variable production networks[END_REF]. Another concept within the discipline of mechanical engineering to describe, explain and manage networked production of different enterprises is the competence-cell-based network approach. Among others [START_REF] Müller | Production planning and operation in competence-cell-based networks[END_REF] and [START_REF] Zimmermann | An approach for the quantitative consideration of soft-facts for planning and controlling networked production structures[END_REF] contribute to this concept mainly aiming SME networks. Furthermore an inter-organizational process model for networked production is the Extended Aachener PPS-Model production planning and control model of the RWTH Aachen University [START_REF] Schuh | Gestaltung der überbetrieblichen Produktionsplanung und -steuerung[END_REF]. Completing these ideas of inter-organizational production networks, the concept of Strategic Production Network is also defined as cooperation of different enterprises [START_REF] Possel-Doelken | Cooperation Between Production Companies[END_REF][START_REF] Wirth | Innovative Unternehmens-und Produktionsnetze[END_REF].

Findings

Literature Findings

To identify certain concepts and clarify their attributes, two main characteristics were ascertained based on literature study. The first characteristic is ownership of a network i.e. having one or multiple owners. The second characteristic is called geographical reach, which describes the range of the concept from regional to global scale. The summary of the findings are shown in table 1.

Table 1: classification of the literature review summary Literature Ownership Geographical reach [START_REF] Müller | Production planning and operation in competence-cell-based networks[END_REF], [START_REF] Zimmermann | An approach for the quantitative consideration of soft-facts for planning and controlling networked production structures[END_REF], [START_REF] Wirth | Innovative Unternehmens-und Produktionsnetze[END_REF], [START_REF] Baum | A Model of Collaborative Enterprise Networks[END_REF], [START_REF] Baumann | Kompetenzzellenbasierte regionale Produktionsnetze[END_REF], [START_REF] Monauni | Agility Enablers in Manufacturing Systems -Contributions of the Production Network Perspective[END_REF] multiple-owner regional/local [START_REF] Wiendahl | Management of variable production networks[END_REF], [START_REF] Schuh | Gestaltung der überbetrieblichen Produktionsplanung und -steuerung[END_REF], [START_REF] Possel-Doelken | Cooperation Between Production Companies[END_REF] multiple-owner regional/global [START_REF] Ferdows | Making the most of foreign factories[END_REF], [START_REF] Shi | International manufacturing networks-to develop global competitive capabilities[END_REF], [START_REF] Bartlett | Managing across borders: The transnational solution[END_REF], [START_REF] Vereecke | The strategic role of the plant: testing Ferdows's model[END_REF], [START_REF] Ernst | Global production networks and the changing geography of innovation systems. Implications for developing countries[END_REF], [START_REF] Maritan | Plant roles and decision autonomy in multinational plant networks[END_REF] single owner global [START_REF] Henderson | Global production networks and the analysis of economic development[END_REF] N/A regional/national/global [START_REF] Vereecke | A typology of plants in global manufacturing networks[END_REF], [START_REF] Levy | Political contestation in global production networks[END_REF] N

/A global

Based on the literature study, two major streams and interpretations of the term production network appear. One stream leans toward to the concept of GPN owned by one organisation and being led globally. The other stream refers to the RPN concept consisting of different companies with different ownership managed in a mutual manner on the basis of certain regional closeness and production dependency.

3.2

Reflections from the case studies

Networks on a global level

Global production networks are supposed as fully owned factories branding the mother company which are able to supply the customers with locally-produced products but to a global standard of quality. They provide some advantages beside the challenges mainly due to the global environment summarized in table 2. One example of such networks is demonstrated in figure-1 along with one global customer in order to depict the structure of such networks.

Figure 1-GPN of the case-A along with a major global customer's plants

Foreign-exchange rate variations affect the development of costs and consequently the global production network. From the global profitability prospective, companies have to make sure that over time, the investments pay off otherwise they might be urged to move the business to other locations.

Local cost of capital interest rates in one hand is the value of the local currency; on the other hand is the inflation and interest rate cost. This must be considered as it could affect the production costs in the respective country.

Understanding the global customers' strategy: by knowing about the end-customers' demands, production companies (especially contract manufacturers) could see the trend and the increase/decrease in the volume.

Culture: despite having a global standard to run production, the local culture will be a part of the "total". So, it must be initially defined how much of the core values of the mother company could be implemented locally. Apparently, there would always be a sort of local variants due to the local culture. Thus, it would be useful to have resources, people in specific, to carry the core values of the mother company.

Local legislations and regulations such as import/export regulations or the salary system in certain countries have great impacts on GPNs.

Custom duties This is also affecting as the duties could affect the network especially when it comes to importing equipments and machinery to the host country.

Global managers who travel around the system are required to make sure that the plants continue over time to maintain the core values of the mother company and not inventing their customized system. Åmål Networks on regional level RPNs which are comprised of SMEs, continuously struggle with their production costs due to not having the potential to decrease it through mass production. Furthermore, relevant production steps on the network are distributed among diverse partners, thus, planning, controlling and coordinating the processes becomes intricate. Beside, SMEs involved in RPNs strongly depend on network partners' quality and delivery performance. They also face too much logistic processes and the challenge of integrating IT solutions to all network partners for managing the information flow.

LEAX Group's GPN along with customer's GPN

Figure 2-RPN of case-B contained of different plants and owners

Beside the mentioned difficulties, there are advantages of SMEs within RPNs such as the capability to co-develop with the customers in a one-to-one manner because of the regional closeness and manageable number. This leads to sophisticated products, with exact fulfillment of functions and quality. Furthermore, an individual project management from the design stage to service can be realized. Additionally the production processes become flexible by adjusting the production system by means of changing the cooperating partners (Figure -2). The challenges and opportunities of the two types of production networks are summarized in table-2 resulted from the interviews performed in both case companies.

Conclusion

The article presents two main streams of interpretation out of the term Production Network which are found in literature and also represented by two cases. Figure 3 sums up both concepts in one theoretical model linking global and regional production networks with our given definitions. Regarding this model a scenario could be supposed; an enterprise in one location and an existing regional network decides to start a new production site in another country. Starting the business in a new location triggers the formation of a new regional production network to produce the desired product. Therefore, concerning the terms regional and global, two questions arise; (1) how large a "region" could be? And (2) how small "global" could be?

There should be no difference in the concept of RPN even if some suppliers are not located within a certain geographical region. Also if new locations of one company spread only over one continent, the concept of GPN would still apply. Therefore, the naming of those two concepts can be misleading and can cause confusion as well. However, the naming of those concepts which points out their actual structure will facilitate further research within this discipline. For future work the dependence of those concepts on geographical location should be reconsidered.

Defining the boundaries for the usage of the term "region" and "global" can affect the definition of those concepts. Thus, we suggest keeping the boundaries of the region and global open and not limited it to the geographical borders whether a country or a continent, but still having a directing definition: RPNs are comprised of multiple production plants in a specific geographical area with certain closeness shaped by different owners with a common product. GPNs are entirely owned production plants spread over a certain reach of distance to capture foreign markets.

Model of Global Regional Production Network

Enterprises should be aware of the usage of those concepts to apply research findings concerning their own interests and objectives. Besides, there are a lot of potentials in linking the concepts of RPN and GPN. This can support enterprises with their important decision making processes, for instance when starting a new production facility during an expansion phase to be able to start an effective production cooperation network. Also, what instruments could be applied, not only to control and compare the performance of the production sites within a GPN, but also monitor and manage the involving RPNs within GPN.

  Figure-2A global production network of a multinational contract manufacturer within automotive sector along with a major global customer's plants

Figure 3 -

 3 Figure 3-proposed model synthesizing GPN and RPN concepts

Location 2 RPN 1 Figure- 5 A

 215 Figure-5A model of a production network with 4 factory sites of one owner globally spread and with regional production networks at each location

Table 2 :

 2 The challenges and opportunities concerning RPN and GPN

			Challenges	Opportunities
	Production Network Global	(Single-owner)	Foreign-exchange rates (Currencies) Local cost of capital interest rates Understanding the global customers' strategy Culture Local legislations and regulations Custom duties (toll) Global managers (people)	Using the global redundancy for achieving volume flexibility Competence transfer reduces R&D costs on development and minimize risk Global sourcing perspective achieved by global footprint Economy of scale due to the glocal presence One production system within the whole network Local presence gives easier logistics for local market


Jacob Bossen
email: jacobbossen@gmail.com

Michael N Hansson

Ole Madsen

Kjeld Nielsen

Thomas D Brunø

An Engineer-To-Order Mass Customization Development Framework

Keywords: Mass Customization, Software Product Line Engineering, Engineer-To-Order, Software Intensive Manufacturing Systems

Developers of automated manufacturing systems are often categorised as Engineer-To-Order companies, relying on the ability to offer solutions that are tailored to the individual consumer. Managing product variety and enabling reusability between solutions becomes key concepts towards increasing competitiveness and revenue, in which Engineer-To-Order companies may benefit from adopting Mass Customization concepts. As automated manufacturing systems tends to be software intensive, it become equally important to enable reusability for physical components and for software related artefacts. In parallel to Mass Customization, Software Product Line Engineering has emerged as a way for software developers to manage variability and reusability. This paper seeks to combine the concepts of Mass Customization and Software Product Line Engineering, by introducing a development framework applicable for Engineer-To-Order companies offering automated manufacturing systems.

Introduction

According to the manifesto presented at the latest MCPC conference in Aalborg, Denmark: "the era of Mass Production is over" [START_REF] Pine | The mass customization manifesto[END_REF]. This is a widely supported statement, and Kumar [START_REF] Kumar | From mass customization to mass personalization: a strategic transformation[END_REF] even emphasises it further by stating that companies will be struggling for survival, if they do not adapt their strategy to the structural changes in markets. The major structural changes is -in this context -the demand for shorter time-to-market, increased product variety and reduced cost. With regards to manufacturing, the increasing demand for product variety will force a definitive shift in the manufacturing paradigm from Mass Production (economy of scale) towards Mass Customization (economy of scope) and Mass Personalisation (value differentiation) [START_REF] Hu | Evolving paradigms of manufacturing: From mass production to mass customization and personalization[END_REF].

Products from traditional Engineer-To-Order businesses (ETOs) are similar to products from the pre-Mass Production paradigm, where each product was tailored to the individual customer. The production methods have been developed since, but in principle they still suffer under the labour intensive work compared to other product types. For high wage countries, the ETO products are hence cost expensive to produce, since a lot of resources must be allocated to the engineering related aspects of production, and keeps the ETO businesses under a rising pressure in a global competitive market. This has triggered some of the ETOs to standardise their engineering work, and either offer less variety, or instead offer variety in a Mass Customization approach. By offering less product variety, the ETO is though compromising with their business foundation.

This paper is based on a case of a Danish manufacturing systems developer, who offer fully automated robotic solutions for surface treatment. To cope with their customers' high demand for product variety, the company offers completely tailored and high technology solutions that are cost expensive with regards to development time. The company has until now been first movers with regards to technology, but the cost expensive development creates a rising competitive environment, as competitors catch up with the technology. The issue of obtaining a more cost effective development process -while still being able to offer similar product variety as they do currently -is in many ways similar to, what moves companies in other domain towards Mass Customization and Mass Personalisation research. The research question of this paper is hence summarised to: "How can Mass Customization capabilities be utilised with regards to creating an efficient development process for ETOs with software intensive products?" The question is answered through a literature study on Mass Customization and ETO products together with a proposal of a development framework that links concepts found in the literature.

Related Work

Traditional ETO products are similar to personalised products in the sense, that the scope is to develop customer specific solutions. Talking about Mass Customization and Mass Personalisation, the scope is however also to do it at costs similar to Mass Production [START_REF] Hu | Evolving paradigms of manufacturing: From mass production to mass customization and personalization[END_REF], which bring us to the scope of the next generation ETO products. To distinguished Mass Customization and Mass Personalisation, work by Bossen and Hansson [START_REF] Bossen | Realizing mass personalization[END_REF] proposed to see a a mass personalised product as a product, where the user can design the product in collaboration with the manufacturer (co-created product) given continuous freedom within predefined boundaries (personalised modules), combined with freedom of choice (customised modules) and company decided fixed choices (common modules). In other words, Mass Personalisation is a variant of Mass Customization, and hence it is reasonable to say, that the majority of Mass Customization research is relevant for mass personalised products and modern cost efficient ETO products.

Mass Customization has over the years been defined in different ways but work from Salvador et al. [START_REF] Salvador | Cracking the code of mass customization[END_REF] summarise it to "Providing customers what they want when they want". The same authors emphasise that in practice, the implementation of a Mass Customization strategy must not be seen as the destination for the company, but rather a process that will never end. In the same work, the authors defines Mass Customization as having three common capabilities, which every mass customiser to some extent must posses. These three capabilities are defined as: "(1) the ability to identify the product attributes along which customer needs diverge, (2) the ability to reuse or recombine existing organisational and value-chain resources and (3) the ability to help customers identify or build solutions to their own needs." The capabilities are called Solution Space Development, Robust Process Design and Choice Navigation respectively. Each one of them has been proven to be essential to be a mass customiser but few companies can claim to posses them all to perfection.

The transition from mass producer to a mass customiser is discussed exhaustively in literature, but the relevancy of moving an ETO business to a Mass Customization business is somehow less debated. However, work done by Haug et al. [START_REF] Haug | From engineer-to-order to mass customization[END_REF] focus on this issue, and the conclusion is, that even though not all definitions of Mass Customization includes an ETO business -and that an ETO business will never produce end-products near Mass Customization cost -it is still reasonable to label some of them as mass customisers. The authors claim that an ETO business must divide the development process into work packages which in some cases can be fully or partly automated (separation of concerns). This creates a need for standardising the engineering work to a partly predefined solution space, and a consistent specification process that supports customer cocreation in the development process.

As the company -which this paper is based on -develops automated manufacturing systems that are highly software intensive, another important aspect is to understand how Mass Customization is applicable to software development.

Much like the producers of physical goods, software producers have been looking towards means to avoid developing software systems from scratch, and instead, developing software product lines with reusable parts that can be shared across a product line. One of the paradigms is referred to as Software Product Line Engineering (SPLE), and is based on the principles of Mass Customization [START_REF] Pohl | Software Product Line Engineering[END_REF]. An important aspect to SPLE is the separation in development process between the establishing of the platform and reusable parts, and the derivation of the individual products with the product specific parts [START_REF] Clements | Software Product Lines: Practices and Patterns[END_REF]. Pohl et al. [START_REF] Pohl | Software Product Line Engineering[END_REF] and Apel et al. [START_REF] Apel | Feature-Oriented Software Product Lines[END_REF] separates the development process even further into aspects concerning requirement establishment and realisation, and suggest a framework for implementation. The general separation of concerns principle links quite well to work on Mass Customization and ETO products by Haug et al. [START_REF] Haug | From engineer-to-order to mass customization[END_REF].

One of the key challenges with the SPLE approach and ETO products is to cope with the transition from ETO to Mass Customization. According to Krueger [START_REF] Krueger | Easing the transition to software mass customization[END_REF] there are three adoption approaches to initiate the transition; a proactive approach, an extractive approach, and a reactive approach. The proactive approach can be described as a big-bang design strategy [START_REF] Heradio | Exemplar driven development of software product lines[END_REF], where product lines are developed from scratch and follows a step-by-step waterfall approach to design and implement a product line [START_REF] Krueger | Easing the transition to software mass customization[END_REF]. The extractive approach is a refactoring strategy [START_REF] Zhang | Incremental and iterative reengineering towards software product line: An industrial case study[END_REF], where the goal is to structure the existing legacy solutions in a companies product portfolio in a more systematic way [START_REF] Krueger | Easing the transition to software mass customization[END_REF]. The reactive approach is an evolution design strategy, where an initial version of product line is designed and implemented, and gradually extending and redesigning the product line to cover more products [START_REF] Apel | Feature-Oriented Software Product Lines[END_REF].

All of the approaches have advantages and disadvantages, but for small software ETOs the extractive approach seems beneficial because the incremental adoption also yields a lower upfront investment compared to the proactive approach. This is in general because the extraction process may only concern focusing on specific parts of the product and incrementally including more parts [START_REF] Zhang | Incremental and iterative reengineering towards software product line: An industrial case study[END_REF]. The downside of the extractive approach is, that implementation of the product line is potentially limited by the structure and code practises of the legacy products, since these not necessarily was implemented in structured way. This limitation means, that the refactoring process do not result in an easy maintainable structuring of the product line [START_REF] Apel | Feature-Oriented Software Product Lines[END_REF].

Research Gap

Based on the related work identified in section 2, it can be concluded that SPLE can be used for ETO companies developing software intensive manufacturing systems. Furthermore, since SPLE originally builds on Mass Customization and basically treat the same purpose, this paper assumes that the three fundamental capabilities of Mass Customization can be linked to SPLE, and thereby enable use of research from SPLE and vice versa. One example is the performance assessment research that is essential for continues improvement of the capabilities.

We conclude that a need for a ETO Mass Customization developing framework -that links the Mass Customization capabilities and SPLE -exist.

Mass Customization Development Framework for ETO Businesses

With basis in the essential capabilities of Mass Customization and development concepts from SPLE, we propose a product development framework for ETO product developers (see figure 1). The framework is divided according to activities related to each of the Mass Customization capabilities, and the distinctive engineering aspects. This differentiation separates the concerns of developing end-products, developing the product line, and the extraction of the potential reusable legacy artefacts. These aspects are referred to as Application Engineering, Domain Engineering and Reverse Engineering respectively. The aspect of Domain and Application Engineering are basic aspects of SPLE, whereas the aspect of Reverse Engineering is included, in order to emphasise the continues evolution of the product line through legacy reuse. Hence this framework has a focus on transforming personalised/product specific parts into reusable artefacts. In order to support continuous improvement of capabilities, we havewith inspiration from Kristal et al. [START_REF] Kristal | The effect of quality management on mass customization capability[END_REF] and Nielsen [START_REF] Nielsen | Mass Customization Assessment And Measurement Framework For Industrial Applications[END_REF] -included Performance Assessment in the framework as a supporting capability. By including it as a capability of the framework, we underline the need for research addressing how to conduct Performance Assessment in this context. Furthermore we emphasise, that the proposed framework is seen from a development viewpoint, which has similarities to the non-traditional Mass Customization process view from Haug et al. [START_REF] Haug | From engineer-to-order to mass customization[END_REF]. In general we believe that Mass Customization must be seen from one or more product views and several process views. Examples of process views is for instance sales, development and commissioning, where this paper focus on the development viewpoint.

Solution Space Development

Solution Space Development is about understanding and managing the behavioural requirements for the product that are derived from customer needs. The actual process of identifying the customer needs are though not a part of Solution Space Development in the framework, since we believe this activity is allocated to a sales view-point. The focus of Solution Space Development is therefore to align customer needs to solution space and vice versa. This correlates with the original intention of Solution Space Development from section 2 besides the development of customer needs. Solution Space Development is conducted by expressing and managing communality and variability in terms which are understandable for developers but not necessarily for customers. This correlates with Kang and Lee [START_REF] Kang | Variability modeling[END_REF]. A popular SPLE approach in this context is to express needs according to features, which may be defined as a behavioural product functionality that originates from a specific customer need [START_REF] Apel | Feature-Oriented Software Product Lines[END_REF].

As in traditional SPLE, the framework includes the aspect of Application Engineering and Domain Engineering. Together these continuously cover the processes of analysing if 1) a feature already exist as a reusable artefact, 2) a new feature should be developed as a reusable artefact, or 3) a new feature should be developed as a product specific artefact [START_REF] Pohl | Software Product Line Engineering[END_REF]. For ETO companies, the entire business strategy relies on tailoring end-products with customer specific features, which in most cases will involve constantly extending the end-products with features that are not already developed. A vital aspect for ETO companies in Solution Space Development is hence, the ability to recover any product specific features from legacy products, that may resemble the possible new features derived from the Requirement Analysis in the Application Engineering aspect. This requires, that the company is able to acquire knowledge, regarding whether or not a customer specific feature already exits in the solution space. This is related to the aspect of Reverse Engineering, and the supporting capability Performance Assessment.

Robust Process Design

From a development view-point, Robust Process Design concerns the actual realisation of the features identified in the solution space. Robust Process Design is focussing on continuously improving the product related artefacts, in order to identify, if they may be reused or recombined into a stream of different endproducts. This involves consideration towards how communality and variability is implemented, but also when variability is implemented.

Both in traditional Mass Customization and SPLE, architecture-and platform development are vital processes to secure reusability and managing variability which are related to the Domain Engineering aspect [START_REF] Clements | Software Product Lines: Practices and Patterns[END_REF]. Since the demand for product specific parts are higher for ETO companies, it is important to allow the architecture to be extended with the product specific parts developed during Application Engineering. This may be handled by a static-or dynamic approach. The static approach involves clearly specifying where variation points exits in the architecture in order to interface product specific parts. The dynamic approach may be viewed as a contentiously evolution of the architecture to address the integration of product specific parts. Whether the static or dynamic approach is used, it always involves implementing variation points in the platform or in the customisable parts of the architecture. These implementations must however be monitored to justify the relevancy of the variation point in the future, and is related to Performance Assessment.

As product specific parts continuously grow in numbers -in form of legacy products -an important ability for software ETOs, is to reuse existing implementation solution if possible. This is related to the Reverse Engineering aspect. However, the intention is not to force developers to extract, and refactor product specific parts from legacy products, if the integration process is difficult. Therefore, an equally important ability in robust process design, is to access whether or not refactoring legacy products is worthwhile in the current situation, or if it will be more beneficial to develop from scratch.

Choice Navigation

In the product development view-point, choice navigation concerns the process of assisting the developer in configuring the end-product that satisfies the re-quirements of the customer. This may involve assisting tools such as a configurators or procedural specifications, to navigate the developer towards deriving an end-product. Depending on the production tools, the process of creating an endproduct may either be conducted manually, automatically, or in a combination of both.

Discussion

By introducing the Performance Assessment supporting capability, a development team has the possibility to monitor how e.g. a Mass Customization transition process is advancing. This activity is called Product Management in the framework, and is responsible for collecting, measuring and calculating Performance Assessment data. The Mass Customization performance assessment research from Nielsen et al. [START_REF] Nielsen | Mass customization measurements metrics[END_REF] is useful here, but also the linking of Mass Customization and quality management from Kristal et al. [START_REF] Kristal | The effect of quality management on mass customization capability[END_REF] and Storbjerg et al. [START_REF] Storbjerg | Mass customization and performance assessment: Overview and research directions[END_REF] is highly relevant. Possible implications by using Performance Assessment is for instance -but not limited to -the ability to measure profitability. Profitability may in this context be linked to the decision of when to include a new feature to the product line, based on the frequency of a specific personalised feature use.

By introducing view-points, it is envisioned that the Mass Customization capabilities are utilised in multiple stages of the production process besides the development view-point. For developers of automated manufacturing systems, a vital process is the actual integration of the solution in the production environment, which requires an effective commissioning process. How the Mass Customization capabilities are utilised in the different view-points, and how many process view-points that exist, must therefore be further investigated.

Conclusion

The literature study shows that SPLE research achievements are highly relevant for ETO companies developing software intensive manufacturing systems, and that SPLE and Mass Customization originally was developed with the same purpose in mind. The research gap from section 2.1 concludes, that if a framework -which links the two research areas -is developed, then contributions from both areas can be used to bring the Mass Customization research further, with regards to software intensive products. Especially the Mass Customization Performance Assessment research is relevant in order to continuously improve the process of a successful Mass Customization implementation process. This paper proposes a framework addressing how ETOs could develop software intensive products, hence the applicability of the framework will initially mainly address the software related artefacts of the product. Therefore, further research must be conducted to show if the framework has limitations with regards to more hardware intensive systems.

Fig. 1 .

 1 Fig. 1. Mass Customization development framework for ETO businesses


Andrea Cataldo
email: andrea.cataldo@itia.cnr.it

Andrea Perizzato
email: andrea.perizzato@polimi.itriccardo.scattolini@polimi.it

Riccardo Scattolini

With Andrea Cataldo

Management of a Production Cell Lubrication System with Model Predictive Control

Keywords: Model based control, Model predictive control, Hybrid optimal control, Production plant energy efficiency, Plant energy optimization

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

Introduction

Improving the energy efficiency of manufacturing production systems is nowadays a topic of huge interest. In fact, limiting the CO2 emissions [START_REF]European Commission: ICT and energy efficiency -the case for manufacturing, Recommendations of the Consultation Group[END_REF][START_REF] Oeced | OECD Key Environmental Indicators[END_REF], resizing factory energy supply infrastructures and minimizing plant energy consumptions are leading factors to save plant installation and production costs.

In particular, the reduction of energy consumption is possible through the optimization of the algorithms developed to manage their working function [START_REF] Fysikopoulos | On a generalized approach to manufacturing energy efficiency[END_REF][START_REF] Frigerio | Machine Control Policies for Energy Saving in Manufacturing[END_REF]. This paper is focused on the definition of the control algorithms for the optimization of the machines' energy efficiency. Specifically, the control policy for a production cell lubrication system has been designed, synthetized and implemented by adopting optimal model-based control techniques. These development activities consist in the modeling of the physical process to be controlled, the definition of the constraints on the process variables', and the solution of a suitable optimization problem which has then been implemented into a C++ control platform. A simulation experiment is proposed in the paper to evaluate the resulting control performances.

The paper is organized as follows. Section 2 describes the production cell and the control functional requirements. Section 3 describes the dynamic model of the operating machine lubrication system and details the MPC design, synthesis and implementation. Section 4 shows the results coming from a simulation experiment. Section 5 concludes the paper by summarizing the obtained results and the advantages of the proposed approach, besides hinting to future developments.

Description of the cell lubrication system

The considered production cell is part of the engine assembly line described in [START_REF] Cataldo | Modeling, simulation and evaluation of Energy consumption for a manufacturing production line[END_REF] whose model is sketched in Fig. 1. The production line machining operation sequence consists of Milling -Welding -Screwing -Drilling activities. However, only the machines M1, M3 and M4 (for simplicity renumbered in the following as machines 1,2,3) belong to a single production cell because they share some centralized auxiliary systems, such as the fluid lubrication cooling system.

Fig. 1. Engine assembly line layout

During its machining process, each machine produces a specific quantity of heat exchanged with the lubricant fluid, which in turn is cooled from the cooling fluid flowing into the centralized cooling system circuit. As the lubrication fluid temperature grows, the machine control system is able to open an On-Off valve that allows the cooling fluid to flow and thus the lubricant fluid to be cooled.

More specifically, the overall cooling systems operates as follows. Denote by Pcool the thermal power of the centralized cooling system, which can be modulated by the control action, and by ߛ • ܲ ௗ the thermal power exchanged with the i-th (i=1,2,3) machine lubrication fluid with temperature Ti. If all the temperatures Ti, i=1,2,3, are inside some prescribed limits, the On-Off valve is closed and the no thermal power is provided to the machines. On the contrary, if at least one of these temperatures exceeds an upper bound Thoti, (i=1,2,3), the valve opens and it is set Pcold= Pcool.

The logic behavior of the cooling system can then be defined by the following relations:

݂݅ ⋁ (ܶ ≥ ܶ ௧ ) ଷ ୀଵ → ܲ ௗ = ܲ (1)
݁ݏ݈݁ ܲ = 0

Note however that, due to the different machine characteristics and machining processes, the transferred heat to the lubricant fluids is different from machine to machine, and it is not constant during the machine working cycle.

Table 1 lists the maximum and minimum lubrication fluid temperatures for each machine, referred to a specific machining operation. In addition Table 2 presents the thermal power produced by the considered machining operations. The minimum power produced from a machine is equal to 0 that means that it is not working.

Finally, the room temperature Ta is assumed to lie in the range 10-45 [°C] while the available cooling power can be modulated between the values 0-3500 [W].

MPC design, synthesis and implementation

The control problem related to the centralized lubrication fluid cooling system consists of maintaining the three lubrication fluid temperatures in each specific range by minimizing the energy consumption of the centralized cooling system.

Standard control systems based on industrial controllers are able to manage each lubrication fluid temperature, but they cannot minimize the overall cooling system energy consumption because, due to the resulting decentralized architecture, each local controller only knows the process under its control.

A more suitable technique to solve this class of control problems is Model Predictive Control, or MPC. Nowadays, MPC represents the most widely used advanced control method in the process industry. This is due to the possibility of reformulating the control problem as an optimization one, where different and possibly conflicting goals can be taken into account, as well as to explicitly include constraints on the input and output variables. Moreover, MPC allows for the design of multivariable regulators for large scale systems, with tens or hundreds of variables, see [START_REF] Camacho | Model Predictive Control[END_REF]. Thanks to these unique advantages, MPC-based solutions are widely popular in many industrial fields characterized by continuous processes, such as the chemical, petrochemical, pulp and paper industries, power networks control and related energy market, see e.g. [START_REF] Bauer | Economic assessment of advanced process control -a survey and framework[END_REF][START_REF] Bendtsen | Stoustrup: Hierarchical Model Predictive Control for Resource Distribution[END_REF][START_REF] Negenborn | Supervisory hybrid model predictive control for voltage stability of power networks[END_REF].

MPC design

The first step for the application of MPC consists of the modelling of the process to be controlled. In view of the previous considerations, each lubrication fluid receives heat from the machining operation and transfers this energy to the cooling fluid. Therefore, the dynamics of the temperature of the i-th machine can be described by the energy balance equation [START_REF] Oeced | OECD Key Environmental Indicators[END_REF], where τ denotes the continuous time index:

ௗ் (த) ௗத = ߛ • ൫ܶ -ܶ (τ)൯ + ݇ • ܲ ௧ (τ) -݇ • ܲ ௗ (τ) (2)
where γi is the convection coefficient that models the thermal coupling between lubrication fluid and the external environment at temperature Ta; khi is the coefficient modelling the heat exchanging between the machine and the lubrication fluid; kci is the coefficient modelling the heat exchanging between the cooling fluid and the lubrication one.

MPC techniques rely on discrete-time models. Therefore, by exploiting the simple Forward Euler discretization, and denoting by ∆τ the adopted sampling time, the discretized form of ( 2) is:

T ୧ (k∆τ + ∆τ) = T ୧ (k∆τ) + ∆τൣγ ୧ • ൫T ୟ -T ୧ (kτ)൯ + k ୦୧ • P ୦୭୲୧ (kτ) -k ୡ୧ • P ୡ୭୪ୢ (kτ)൧ , ݇ = 0,1,2, … (3)
or, denoting by t the discrete-time index,

ܶ ݐ( + 1) = ܶ )ݐ( + ∆߬ • [ߛ • ൫ܶ -ܶ ()ݐ൯ + ݇ • ܲ ௧ (t) -݇ • ܲ ௗ ])ݐ( (4)
Equations ( 1), ( 4) represent the set of dynamic equations and logic statements according to which the MPC can be synthetized.

MPC synthesis

The MPC synthesis is carried out by implementing the hybrid model defining the discretized physical process (4) and the control logic propositions [START_REF]European Commission: ICT and energy efficiency -the case for manufacturing, Recommendations of the Consultation Group[END_REF], by means of the HYSDEL software tool [START_REF] Torrisi | HYSDEL-A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems[END_REF] which translates the corresponding high level structured language into a Mixed Logic Dynamical (MLD) system formalism [START_REF] Bemporad | Control of systems integrating logic, dynamics, and constraints[END_REF] of the form (5):

ݐ(ݔ + 1) = )ݐ(ݔܣ + ܤ ௨ )ݐ(ݑ + ܤ ఋ )ݐ(ߜ + ܤ ௭ )ݐ(ݖ )ݐ(ݕ = )ݐ(ݔܥ + ܦ ௨ )ݐ(ݑ + ܦ ఋ )ݐ(ߜ + ܦ ௭ )ݐ(ݖ (5) ܧ ఋ )ݐ(ߜ + ܧ ௭ )ݐ(ݖ ≤ ܧ ௨ )ݐ(ݑ + ܧ ௫ )ݐ(ݔ + ܧ
where x is the state variable representing the temperatures Ti, u is the control variable representing the cooling power Pcool, y is the output corresponding to the measured state x, while ߜ and z are vectors of logical and continuous auxiliary variables, respectively.

As previously discussed, the optimization problem is stated to minimize at any sampling time the peak of the overall cooling power Pcool along a future prediction horizon specified by the positive integer N. Therefore, the adopted performance index J is:

ܬ = min ୀ௧,…,௧ାேିଵ ܲ (݇) (6)
In order to fulfil the required bounds on the lubricant fluid temperatures, the optimization problem is subject to the following constraints:

ܶ ଵ, ≤ ܶ ଵ )ݐ( ≤ ܶ ଵ,௫ (7-a) ܶ ଶ, ≤ ܶ ଶ )ݐ( ≤ ܶ ଶ,௫ (7-b) ܶ ଷ, ≤ ܶ ଷ )ݐ( ≤ ܶ ଷ,௫ (7-c) ܲ , ≤ ܲ )ݐ( ≤ ܲ ,௫ (7-d)
At any sampling time t, once the optimal sequence ܲ (݇), ݇ = ,ݐ … , ݐ + ܰ -1 of future control variables has been computed, only its first element, i.e. ܲ ,)ݐ( is effectively applied. Then, according to a moving horizon strategy, at the new sampling time t+1 the overall procedure is repeated.

It is important to note that the optimization problem above stated may be infeasible due to the nature of the problem under study. In fact, since the cooling system is shared among different machines, it may not be possible to fulfil all the constraints at the same time. For instance, if only one machine is operating and its lubricant has to be cooled, then the others will be cooled as well, by possibly violating their lower bounds. In order to prevent infeasibility of the control solution, it is common practice to transform the hard constraints into soft constraints, in order for them to be violated only if infeasibility occurs. This feature is achieved by heavily weighing these violations in the cost function to be minimized.

In order to complete the MPC synthesis, the heating powers ܲ ௧ have been considered as disturbances acting on the processes. Two solutions are commonly exploited in order to take these disturbances into account, depending on the availability of their future behavior: a) Prediction available: whenever the future value of the disturbances is available, it should be included into the optimization problem in order to achieve the optimal solution; b) Prediction not available: if it is not available, it is practice to assume that the disturbances are constant over the whole prediction horizon, and in particular equal to the current value.

In this work the production cycles have been assumed to be known, then the solution a) has been considered.

MPC implementation

The application of the MPC law to a real lubrication system or to an equivalent dynamic simulation model, requires to run the algorithm into a control platform. Then a customized control environment based on the C++ object oriented programming language has been used, by implementing the MPC according the steps showed in Fig. 2:

Simulation results

Since it has not been possible to apply the developed control system to the real production cell, some simulation experiments have been carried out by running the MLD model of the lubrication system in the MATLAB platform [14] and the MPC in the C++ customized control platform, by interfacing them by means of synchronizing signals. The constraints on the lubricant fluid temperatures have been set accordingly to Table 1. In addition, soft constraints on upper bounds of the temperatures have been used to prevent infeasibility, as previously discussed. The must-be-cooled temperatures Thoti have been set equal to 100, 110 and 85 [°C] respectively for M1, M3 and M4 while the ambient temperature to 25 [°C] constant. Finally, the cooling power range is 0-3500 [W]. The left plots in Fig. 3 depict the selected heating power profiles, corresponding to the working cycles of M1, M3 and M4, from top to bottom. The first three plots on the right in Fig. 3 show the lubricant temperatures (in solid blue) together with their bounds (in dashed red) and the must-be-cooled temperatures (in solid-dotted magenta). It can be noted that the lower bounds are never violated because they are set as hard constraints. On the other hand, the soft upper bounds are occasionally crossed. As expected, the violations only occur when at least one temperature is close to the lower bound, which cannot be crossed, leading to the upper one crossing (e.g. see the plots at time 7-10s). The bottom-most plot on the right presents the cooling power which always lies inside the given range.

5

Concluding remarks

In this paper, a production cell based on three operating machines has been considered. In particular, the centralized lubrication cooling system energy management has represented the case study for the design, synthesis and implementation of a model predictive controller. The simulation experiment that has been carried out shows the ability of the MPC to cope with the hybrid control problem in which continuous process variables and corresponding logic constraints have been taken into account. The control action is computed using the a-priori knowledge of the bounds, resulting in better temperature profiles then those obtained with standard controllers. Moreover the small system dimensions require a low computational demand, which leads to fast computing time.

Future works should firstly focus on the deployment of the controller in a real production cell in order to compare the simulation experiment results with the real data acquired from the field. Secondly, a more detailed cooling system modelling would allow for more accurate MPC law calculation and a more realistic control action definition. Finally, an explicit formulation of the MPC, which is currently under study, will Pcool allow to replace the on-line optimization with an equivalent, and much lighter, piecewise affine controller, see [START_REF] Bemporad | Piecewise linear optimal controllers for hybrid systems[END_REF].

Fig. 2 .

 2 Fig. 2. MPC implementation into the C++ customized control environment

Fig. 3 .

 3 Fig. 3. Simulation experiment results

Table 1 .

 1 Machine lubrication systems temperature ranges [°C].

	Machines	T1min T1Max	T2min T2Max	T3min T3Max
	M1	10	140
	M2			10	250
	M3				10	125

Table 2 .

 2 Machine lubrication systems thermal power ranges [W].

	Machines	P1min P1Max	P2min P2Max	P3min P3Max
	M1	0	750
	M2			0	500
	M3				0	650

Acknowledgements. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 285363 (EMC2-Factory Eco Manufactured transportation means from Clean and Competitive Factory).