Aldo Duchi
email: aduchi@ethz.ch

Paul Schönsleben
email: pschoensleben@ethz.ch

A three steps methodological approach to assess the engineer-to-order operations environment

Keywords: Engineer-to-order, Assessment Approach, Engineering-design

For many companies, "customized" means products that even the most sophisticated of product configuration tools cannot define in advance. Such products need an engineer-to-order operations environment (ETO). This study focuses on the engineering activities of companies adopting an ETO strategy. We observed that these environments are significantly heterogeneous and that this is likely to limit any potential generic applicability of strategies and practices. In this paper, therefore, a three steps methodological approach to support companies in developing a better understanding of their ETO environment is presented: Assessment of product variety and customization, modelling of the ETO process, and measurement of operational performance. Findings from the application of the approach to three industrial partners are then summarized.

Introduction

Product-variety management in today's market is, most of the time no longer an orderwinning choice, but an order qualifier [START_REF] Hendry | Product customisation: an empirical study of competitive advantage and repeat business[END_REF]. This means that in turbulent market conditions, customers seek out companies that offer shorter delivery times within a customers' price tolerance. In this context, engineer-to-order (ETO) is defined as an operations strategy that is oriented around a high degree of product customization and associated engineering processes for quickly fulfilling customer specifications. The focus of this study lies in the design and engineering activities of companies adopting an ETO strategy. This encompasses product development processes (PD)usually managed by the Research and Development (R&D) department -and orderspecific engineering processes (OSE) -usually managed by the Engineering department [START_REF] Schönsleben | Different types of cooperation between the R&D and Engineering departments in companies with a design-to-order production environment[END_REF]. These processes have a significant impact on the value chain's downstream material processes, like manufacturing, and therefore significantly impact the company's overall performance.

While working with the three industrial partners of the FastETO research project, in accordance with a literature review, we observed that ETO environments are fundamentally heterogeneous [START_REF] Willner | Exploring the archetypes of engineer-to-order: an empirical analysis[END_REF]. This is likely to limit any potentially generic applicability of strategies and practices in such a diverse range of ETO environments. Additionally, it also appeared that our industrial partners did not always have a clear understanding of their operations environment. These limitations, therefore, call for an assessment of the ETO firms with respect to engineering-design considerations.

In this study, we present the methodological approach we used to support companies in developing a better understanding of their ETO environment. A situation analysis, allowing a better understanding of the phenomenon, is indeed propaedeutic to the formulation of any company-improvement project.

A three-steps assessment approach

We propose an assessment approach for companies operating in an ETO environment based on three consecutive steps according to Figure 1: First, assessment of the product variety and customization offered; Second, assessment of the ETO process by means of process modeling; Third, assessment of the company's operational performance.

Product variety and customization assessment

i) Which kind of ETO product does the company offer?

iii) How do the company's orders differ?

ii) How do the different stakeholders see product variety and customization?

ETO process assessment

i) Process mapping by BPMN 2.0 in organizational-oriented models ii) Process and activities description (key phases, inputs, outputs, resources and elements employed)

iii) Elements-Activity-matrix to allocate the elements to the activities in which they are involved

Operational performance assessment

ii) KPI generation matrix i) Performance dimensions

Figure 1

Assessment approach for companies operating in an ETO environment.

Assessment of product variety and customization

The first step of the assessment approach, the product-variety assessment, consists of guiding the company through answering the three following questions:

Which kind of ETO product does the company offer? This first question adopts a high level and strategic point of view in the assessment of the ETO product. The aim is to be able to classify the company and the product being offered within one of the classifications of manufacturing and engineering strategy proposed in the literature. The definition of the ETO product indeed encompasses a broad spectrum of products that calls for the use of different strategic approaches. The three variables considered are: (i) Sales volume or the number of orders [START_REF] Willner | Exploring the archetypes of engineer-to-order: an empirical analysis[END_REF]. (ii) Level of engineering complexity operationalized with a time-/effort-based criterion and defined as the average number of engineering hours per order [START_REF] Willner | Exploring the archetypes of engineer-to-order: an empirical analysis[END_REF]. For this variable, we also recommend distinguishing between component eng. complexity and architecture eng. complexity, (i.e., respectively, commitment to adapting components, or developing new ones and the commitment in the combination of (main) components in the product). (iii) Degree of customization: Different definitions have been given for this. In defining it, we adopt the company's perspective: this means that a high degree of customization involves developing new components, rather than just adapting existing ones.

How do the different stakeholders of the value chain view the product? The aim of this question is to align these different standpoints on product structure and data, and emphasizes the importance of defining and managing the relationships between different department's points of view.

Product variants are sometimes designed within a traditional new PD project, but, more likely in an ETO company, have been realized through the specification and delivery of customized products to meet customer requirements. In this way, product variety is generated progressively over several years. When customizing products, the entire product realization process, also referred to the value chain, is affected [START_REF] Jiao | Process platform planning for variety coordination from design to production in mass customization manufacturing[END_REF]. Such a process, for instance, can be described based on Suh's domain framework [START_REF] Suh | Axiomatic design theory for systems[END_REF]. Within the various departments of a company, therefore, there are several ways of representing product variety and product customization. Indeed, in each phase of the value chain, one or more product structures are used that are pertinent for the overall product description [START_REF] Hvam | Product customization[END_REF]. Therefore, the company needs to conduct an assessment in which all pertinent departments collaborate in creating a general representation of the product variety range. In our approach, we consider it valuable to use three different views, briefly: customer, engineering and production view.

When necessary, the company has to refer to the concept of generic product structure (GPS) and work on a product model representing the product variety range. The GPS models the generic modules of the product and their organization in a part-of hierarchy. Concepts such as parametric, optional and alternative and components are normally encompassed. The problem of selecting parts for a product with high variety can be addressed by the GPS, with the use specialization and generalization semantics. These are typically in the form of type-of relationships between the variants (instances) and the generic modules. Briefly, the GPS aims at describing and modelling product variety without dealing with the product structure of each product variant. For examples of applications of GPS, the reader can refer to the Generic bill of material approach (GBOM) [START_REF] Hegge | Generic bill-of-material: a new product model[END_REF]; for an ETO-specific application, please see the Adaptive generic product structure (AGPS) [START_REF] Brière-Côté | Adaptive generic product structure modelling for design reuse in engineer-to-order products[END_REF]. A useful technique for supporting product modeling is the Product variant master method (PVM) [START_REF] Hvam | Product customization[END_REF].

How do the company's orders differ?

The third question aims to raise a company's awareness of the heterogeneity of customer requirements, as of the non-linear consequences of those on the degree of customization and the operations of downstream processes. The goal for the company is to be able to identify, during the sales process phase and depending on the customer requirements, the typology of project/order required. Thanks to this order (or project, product) categorization, the company can then approach customers' requests with differentiated processes (e.g. by advancing engineering knowledge in the pre-award phase) and pay special attention to budgetary and planning activities (e.g. lead-time estimation). Indeed, the company has to be aware of the technical risk and challenges involved when agreeing to satisfy a customer requirement so as not to compromise its profitability.

A basic differentiation the company could make is between "standard product (or order)" and "non-standard product". Alternatively, it could define different classes of order complexity. However, we observed in our companies that even if this variable is usually understood and perceived by the Engineering department, that understanding is not valid for the Sales department. It is not always trivial during the offering phase to understand which order requires the development of a new module, the development or specification of a new variant or whether a standard module (or one developed in a previous order) can be re-used. The company should work on the definition of criteria (and, when necessary, of procedures) for identifying the degree of customization and therefore the category of the classification of the product offered.

ETO process assessment

The second step of the assessment approach is to map out the as-is process and identify the process activities and resources, particularly focusing on where product information is being processed and where product features and order specifications are produced.

In turn, the ETO process model can be analyzed by identifying the most critical problems associated with the process. These provide insight into which problems need to be addressed by the engineering of new and improved processes, and implementation of new technical tools and organizational practices [START_REF] Schönsleben | Methods and tools that support a fast and efficient design-to-order process for parameterized product families[END_REF].

As-is process mapping. At this stage, the purpose is getting an overview of the as-is process by applying a widespread, easy-to-use and intuitive mapping tool. In this study, the result generated by the various means of data collection and analysis enabled the development of a process map for each of the companies investigated. Process mapping can be carried out using numerous process-mapping formats. The BPMN 2.0 standard is deemed suitable for the task at hand. The format used can include inputs and outputs as well as process parameters and the elements/resources (e.g., IT tools) used in this activity. Also, a characteristic of the ETO process is its cross-department and cross-functional nature. The use of an organizational-oriented process map, to illustrate the relationship between the functional units and the activities for which they are responsible is strongly recommended [START_REF] Schönsleben | Integral logistics management: Operations and supply chain management within and across companies[END_REF]. Another important characteristic of the ETO process is the variability of the process depending on the product variant of the order. To fulfill specific customer requirements, different levels of customization might be required, and consequently, different activities may or may not be triggered. Based on the results from the first step of the assessment approach, the company should be able to map when and how different activities are launched for the different product/order categories.

Element-activity matrices. Process modeling, as mentioned previously, should not focus exclusively on the activities performed, i.e. on the process side, but also take into account the resources required. Having completed the description of any process mapping and activities, a useful technique is the diagramming of elements/resources to activities in bi-dimensional element-activity matrices. Activities should be listed by type (i.e. PD, specification activities, planning and control activities) and by chronological order, to favor the subsequent qualitative evaluations. Thus, the perspective of element/resource involvement and the requirements of activities are covered with this technique. The diagramming should differentiate between the different types of elements/resources (i.e. departments, tools and technologies, product knowledge, order specifications, customer and suppliers).

ETO operational performance assessment

The third step of the assessment is to understand which performance requirements are to be put on the process if the company has to meet its overall targets (e.g. profitability, product variety strategy, etc.). This then supports the formulation of the detailed objectives of the improvement project (as for instance the FastETO project).

To provide a framework that enables ETO companies to appropriately measure operational (PD and OSE) process performance, we describe a generic performance system (PMS) for ETO operations called the ETO PMS. Companies, besides using the framework to support the third step of the ETO assessment, can use it to develop and revise their current PMS, used for diagnostic and monitoring purposes. Primarily, the relevant performance dimensions and a guideline for developing firmspecific indicators for performance measurement in all dimensions are presented.

Performance dimensions.

The following performance dimensions were adopted: price and cost, further divided in the variable and fixed cost; quality; delivery, both in terms of responsiveness and reliability; flexibility; and learning and innovation. Similar performance dimensions have been postulated for manufacturing operations in literature, such as [START_REF] Neely | Performance measurement system design[END_REF] and [START_REF] Schönsleben | Integral logistics management: Operations and supply chain management within and across companies[END_REF]. Also, the innovation and learning dimension is directly considered. This is due to its crucial importance to ETO companies that must profit from the knowledge generated during the extension of the product-solution space, a requirement that pertains to most customer orders. These performance dimensions also influence each other, and a resolution of conflicting objectives is necessary most of the time. The relative importance of each of these performance dimensions is of course determined by the strategic motivation to offer ETO products in the first place (e.g. customization as order qualifier, customization as order winner). KPI Generation Matrix: structure and hierarchy. This matrix entails the classification of all performance indicators according to three different categories. First, each indicator can be allocated to one or several performance dimensions as above outlined. As is clear, companies can measure each performance dimension by combining input, output and process indicators [START_REF] Chiesa | Performance measurement in R&D: Exploring the interplay between measurement objectives, dimensions of performance and contextual factors[END_REF]. Second, The ETO process model provides another classification, namely, the element or activity to which a specific indicator belongs. At this stage, the company should have obtained the list of elements and activities as an outcome of the process mapping and description. General indicators are also possible. Third, the PMS is characterized by two hierarchical levels. The higher level, Level 1, contains indicators that aggregate the performance of indicators at the lower level by, for example, providing the average of an indicator across an element type (e.g. a product family). More strategic and general indicators are also attributed to this level. The lower of the two levels, Level 2, details these indicators and breaks them down into components (that describe e.g. a single order performance). More specific, operational and disaggregated indicators are generally associated with level 2. This framework forms the basis for companies to define their own PMS and allows for a greater variety of perspectives than traditionally used.

Findings from the case studies

This assessment approach has been validated by applying it to three industrial partners of the FastETO research project, manufacturers, respectively, of high-rise elevators, industrial steam turbines, and co-generation plants (refer to [START_REF] Schönsleben | Different types of cooperation between the R&D and Engineering departments in companies with a design-to-order production environment[END_REF] for companies' descriptions). The benefits of each of the assessment steps are below summarized.

Assessment of product variety and customization

In summary, it emerges from completion of the first step of the assessment that the first question helps the company to familiarize itself with the objective differences that are present in the ETO environment and consequently in the manufacturing strategies. This also helps the company to familiarize itself with the terminology that can support internal communication, but also in benchmarking with other companies.

The second question helps to diagnose issues in the product variety range, particularly regarding the value created through variety and the understanding of the complexity of product variety. On the one hand, the company can visualize by mean of the product variant master the share of variants that is actually creating value. Indeed, any variant of a product function that does not relate to the customer view does not generate any customer value. On the other hand, the degree of complexity of the product range can be analyzed by observing the relationships between the various views. The higher the number of relations between views, the higher the complexity. Product changes indeed propagate through the value chain to downstream processes, generating tasks and introducing a certain degree of operational performance risk. Therefore, whenever a change is required in the product, the effort generated by the consequent engineering and manufacturing tasks, is proportional to the number of sub-systems that are affected by the change and in turn to the number of relations between views.

The third question highlighted how companies uniformly approached market opportunities and customer orders without considering the heterogeneity of the requirements and therefore the various degrees of customization required. The assessment approach instead raises the companies' awareness of the differences between non-standard and standard solutions, which are already part of the product solution space.

ETO process assessment

Companies' order-specification processes were not always mapped, visible and understood by all stakeholders. However, some process maps were present in all industrial cases: process modeling was not virgin territory. That said, process maps were mostly owned and developed by the quality department to fulfill the requirements for obtaining standard quality certifications. Existing maps and models were not developed within the scope of an improvement project by the other departments. Product-and order-activity matrices are defined by replacing the generic element type of the element-activity-matrix with the type of product knowledge used as input, and with the type or order specification that is the in-or output of each activity. The process visualization enabled by the organization-oriented process map and by the product-and order-activity matrices supported fruitful discussion about cross-department coordination and cooperation issues to be solved [START_REF] Schönsleben | Different types of cooperation between the R&D and Engineering departments in companies with a design-to-order production environment[END_REF]. Indeed, the relevance of stakeholder activity on the rest of the value chain, the interdependencies between downstream and upstream activities, as well as the criticality of the strong concurrency of the activities between different departments, were often unclear (e.g. handover from Sales to Engineering once a project has been awarded). Consequently, many improvement ideas arose, from clarifying and formalizing expected input and output to the definition of states and requirement for handover of order specifications. The combined use of the process model and the PMS presented also influenced the definition of new KPIs dedicated to monitoring the delivery performance to internal customers (e.g. technical quality of the specifications).

Attention has also been paid to identifying those activities where useful information and knowledge are generated and whether these are properly acquired and organized. In turn, the analysis focused on those activities where information and knowledge from previous projects are or could be distributed and re-used (e.g. a database of all engineering changes to support standard product-maintenance activities).

IT Tools-Activity matrices supported the investigation of the use of different IT tools and databases in each of the activities. Consequently, the company could often visualize the current fragmentation of product knowledge and order-specification information in several stand-alone databases and, in turn, the time wasted in data collection and transcription tasks, the redundancies of data in various systems, and the potential information discrepancies and specification quality issues.

In a nutshell, the second step of the proposed assessment approach allows companies to detect the above-mentioned criticalities of the current PD and order-specification processes. The PMS, at the third step, allows for generating KPIs to measure and quantify the monetary cost of these criticalities.

ETO operational performance assessment

The KPI generation matrix, including the relevant performance dimensions, were based on the process maps and description obtained with the second step of the assessment approach. They provided structural support during the KPI-generation phase. In turn, these indicators supported the definition of the detailed objectives of the FastETO project at each company. A general finding regarding performance measurement is that the Engineering department was treated as a pure cost center and challenged on cost and efficiency in most cases. As part of the proposed assessment approach, implementation of an extended PMS that considers five performance dimensions enables a more realistic assessment of the benefits of engineering on the overall value chain.

Conclusions

From a practical perspective, a methodological approach to assess the ETO environment is presented. This innovative approach is a combination of and adaptation to the ETO environment of existing managerial techniques. In contrast to traditional methodological approaches, we emphasize the need to sequentially incorporate in three steps, the assessment of the peculiarities of the ETO product, together with the modeling of the processes, and the measure of the operational performance over five identified dimensions. The three steps of the assessment are all needed and in that specific order. Indeed, with the first step, it is possible to cluster orders into "standard" and "nonstandard" categories. These imply different processes that can then be captured in the process-modeling step. Identified process activities and process elements are consequently used, during the third step, in the KPI generation matrix.

The approach supports managers of ETO companies in reaching greater transparency and a clearer overview, and thus a better understanding of their operational environment. The assessment approach has been validated through its application to three companies, differing in size, volume, maturity and product characteristics, and has been shown to be a fundamental step in the early phase of the FastETO improvement project in assessing and analyzing the initial situation of the enterprise before formulating objectives and searching for potential solutions.


Christoph Roser
email: christoph.roser@hochschule-karlsruhe.de

Hauke Meier

Masaru Nakano

Relationship Between Variants and Inventory Under Consideration of the Replenishment Time

Keywords: finished goods inventory, customer takt, replenishment time, variant reduction

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

Introduction

In many companies, the idea of inventory reduction and hence reduction of the associated costs is well received [1], [2], [START_REF] Demeter | The impact of lean practices on inventory turnover[END_REF]. Unfortunately, the implementation is often lacking [START_REF] Richter | Cargo Cult Lean[END_REF], [START_REF] Ignizio | Optimizing Factory Performance: Cost-Effective Ways to Achieve Significant and Sustainable Improvement (Kindle Edition)[END_REF]. In particular, inventory reduction is often a simple reduction of the target inventory, without the understanding of the underlying relationships. Hence, the relationship of inventory to production has been studied intensively, for example [START_REF] Akçalı | Quantitative models for inventory and production planning closed-loop supply chains[END_REF], [START_REF] Venkateswaran | Effect of information update frequency on the stability of production-inventory control systems[END_REF], [START_REF] Aggelogiannaki | An adaptive model predictive control configuration for production-inventory systems[END_REF]. This paper aims to dig deeper into this relationship, especially the relationship between customer takt and inventory, as well as the related relationship of the number of variants and the inventory.

Relevant Concepts

To understand the following theoretical and practical analysis, a few concepts from lean manufacturing have to be described briefly.

Customer Takt

The customer takt (or takt time) is one of the fundamentals for determining the speed of a production system. It represents the average demand of the customer during a time period. To calculate it, you divide the available working time by the customer demand during that time, as shown in equation (1) [START_REF] Badiru | Handbook of Industrial Engineering Equations, Formulas, and Calculations[END_REF]:

= (1)
where TT is the customer takt, AT is the available work time, and CD is the total customer demand across all product variants. The calculation hence results in the average time between a customer demand, and therefore is also the target speed a system must achieve to satisfy this demand in the long term. The customer takt can be calculated for the total of all products, as well as for an individual product variant.

Kanban-based Pull System

The analyzed system is based on a pull system using a kanban-based pull system. This is a common approach in lean manufacturing [START_REF] Sugimori | Toyota production system and system Materialization of just-in-time and respect-for-human system[END_REF]. Pull production has many advantages over conventional push production, most significantly a consistent and stable management of inventory, requiring less inventory than a push system and hence avoiding the associated costs (see above) [START_REF] Lee | A Comparative Study of the Push and Pull Production Systems[END_REF].

Replenishment Time

The replenishment time is the time needed to replenish a part. This does not mean when the next part comes down the line, but instead how long it takes for a work order to come back with a part. Fig. 1 illustrates this, for an example, with three processes.

Theoretical Approach

First we start with a theoretical approach. For simplification we assume that our system has no variation or fluctuation. This of course is a widely optimistic assumption for practical use. However, it is useful to determine the theoretical best-case scenario. We also assume that there is only one finished good inventory from which the

P1 P2 P3

Replenishment Time customer demand is satisfied. Multiple inventories for different customer groups would of course increase the total inventory.

Customer Takt Larger than Replenishment Time

Assume the system produces one product and the customer takt is significantly larger than the replenishment time. In order to be able to deliver to the customer without delay, we need to have at least one of these products in stock. If the customer wants a product, he receives the single piece in stock, and then we reproduce it to increase stock again to one piece. Hence, the behavior of the inventory over time may look like Fig. 2. Overall, we require only a single kanban card to manage production. Hence we have at most an inventory of one.

Fig. 2 Theoretical behavior if customer takt is significantly larger than the replenishment time.

Source: Author

Customer Takt Smaller than Replenishment Time

In a second theoretical example, we assume that the customer takt is smaller than the replenishment time. In other words, the customer orders faster than the time it takes to reproduce one part. This is easily resolved by adding multiple kanban to the system. The number of kanban needed is shown in equation (2), where K is the number of kanban.

=

(2)

Please note that this formula is valid regardless of the relationship of replenishment time and customer takt, but is still assuming no fluctuations. If the customer takt is much larger than the replenishment time as in the previous theoretical assumption, we need only a single kanban as shown in 3.1.

Kanban and Inventory over Customer Takt

Now it is possible to calculate the number of kanban for any customer takt. Please note again that the assumed lack of fluctuations prevents the use of these simple Time Inventory 0 1 equations for practical applications, but it is to serve a best-case illustration. The resulting general behavior is shown in Fig. 3. The time where the customer takt equals the replenishment time is indicated with a vertical gray line.

Fig. 3 Behavior of the number of kanban and inventory in relationship to the customer takt.

Units are intentionally missing, as this is a thought experiment. Source: Author

Kanban and Inventory over Variants

Using the previous equations, it is also possible to make a theoretical model of the kanban in relationship to the number of variants. In industry, additional variants are introduced with the hope of selling more products overall, although the actual outcomes vary. To allow easier comparison of different number of variants, we assume that the total quantity of products sold remains constant. In a second simplification, we assume that the total quantity produced is always split evenly across all available variants. Naturally, in industry applications this is rarely the case. Yet, this assumption allows for easy comparison of different production quantities with different variants. Similar results should hold true also if the quantity for the different variants vary. As we will see later, it all depends on whether the customer takt for a variant is faster or slower than the replenishment time. The customer demand for one variant CDi is as follows:

= ∈ 1, (3)
Substituting gives us the equation for the takt time and the number of kanban for product i.

= • ∈ 1, (4)
= • (5)
From this we can easily calculate for all variants n of the total number of kanban K as shown in ( 6) and [START_REF] Venkateswaran | Effect of information update frequency on the stability of production-inventory control systems[END_REF], which are the same equations with different substitutions.

Kanban (not rounded up) Kanban (rounded up)

Customer Takt Inventory / Kanban

RT = TT = • (6) = • • (7)
Fig. 4 shows the results for keeping the total customer demand constant and varying only the number of variants. Again, please note that this is an idealized case with no fluctuation.

Fig. 4 Behavior of the number of kanban and inventory in relationship to the number of variants. Units are intentionally missing, as this is a thought experiment. Source: Author

The time where the customer takt derived from the number of variants equals the replenishment time is indicated with a vertical gray line. Of interest again is the number kanban that are not rounded up. If the number of variants is below the threshold where the customer takt equals the replenishment time, then the total number of kanban (not rounded up) does not change regardless of the number of variants.

On the right of the threshold, the minimum number of kanban cards increases with the number of variants. Hence, while the right side supports the initial theory that the inventory increases with the number of variants, the model to the left of the threshold does not hold true to this theory. We will discuss this highly interesting result later in the conclusions. However, these theoretical results have the limiting assumption of no fluctuations or variations in the customer takt and replenishment time. Before we analyze these interesting results in more detail, we will investigate the same questions using an experimental approach.

Experimental Approach

To test these results analytically, we used a commercially available simulation tool Simul8. This analysis was conducted as part of a master thesis by one of the authors [START_REF] Meier | Analyse des Zusammenhangs zwischen Variantenvielfalt, Lagerbeständen und Lieferbereitschaft[END_REF]. Please refer to [START_REF] Meier | Analyse des Zusammenhangs zwischen Variantenvielfalt, Lagerbeständen und Lieferbereitschaft[END_REF] for details on the simulation set up.

Number of Variants

Inventory / Kanban RT = TT i Kanban (not rounded up) Kanban (rounded up) 4.1. Kanban and Inventory over Customer Takt Fig. 5 shows the resulting experimental relationship between the inventory and the customer takt. For clarity we also added the theoretical model with the not-roundedup number of kanban. Please note that due to the large numbers involved, both axes of Fig. 5 are on a logarithmic scale, resulting in straight lines rather than the curves seen in Fig. 3. The data is shown for different delivery performances, representing the percentage of customer demands that could be fulfilled immediately. The actual replenishment time of the simulation is also added as a dotted line where the replenishment time equals the customer takt.

Kanban and Inventory over Variants

Fig. 6 shows the resulting experimental relationship between the inventory and the number of variants. For clarity we also added the theoretical model with the notrounded-up number of kanban. Please note that again due to the large numbers involved, both axes of Fig. 6 are on a logarithmic scale. Again, the actual replenishment time of the simulation is also added as a dotted line where the replenishment time equals the customer takt for a given number of variants. Overall, the simulated results overlap very nicely with the theoretical assumptions, therefore verifying the theoretical model. The full data is available through [START_REF] Meier | Analyse des Zusammenhangs zwischen Variantenvielfalt, Lagerbeständen und Lieferbereitschaft[END_REF].

Conclusions

Our research shows that the number of kanban increases as the customer takt decreases as long as the takt is smaller than the replenishment time as shown in Fig. 5 and Fig. 3. Surprisingly, however, if the customer takt is larger than the replenishment time, it has no influence on the total number of kanban and the inventory.

The situation becomes even more interesting for the number of variants as shown in Fig. 6 and Fig. 4. On the right-hand side, you get the results expected by common wisdom on the shop floor. Your inventory increases as the number of variants increases. The most interesting result of this analysis, however, is on the left side. The inventory remains constant in theory and nearly so in practice. This means that as long as both the old and the new variants have a customer takt smaller than the replenishment time, you can add additional variants without incurring the penalty of additional inventory. The common wisdom on the shop floor is not true as long as you sell enough products.

[1] C. Hofer, C. Eroglu, and A. Rossiter Hofer, "The effect of lean production on financial performance: The mediating role of inventory leanness," Int. J. Prod.

Fig. 1

 1 Fig. 1 Illustration of replenishment time using value stream mapping notation. Source: Author

Fig. 5

 5 Fig. 5 Simulated behavior of the inventory in relationship to the customer takt. Source: Author

Fig. 6

 6 Fig. 6 Simulated behavior of the inventory in relationship to the number of variants. Source: Author

  Econ., vol. 138, no. 2, pp. 242-253, Aug. 2012. [2] C. Eroglu and C. Hofer, "Lean, leaner, too lean? The inventory-performance link revisited," J. Oper. Manag., vol. 29, no. 4, pp. 356-369, May 2011.

	100000		RT = TT i
	1 10 100 1000 10000 Total Inventory	1	10 Number of Variants 100 1000 10000 100000 (rounded up) Kanban 97% 99% 99,99% Delivery Performance


J W Veeningen
email: j.w.veeningen@student.rug.nl

N B Szirbik
email: n.b.szirbik@rug.nl

M P Blokhuis
email: m.p.blokhuis@student.rug.nl

A Generic Architecture for Quickly-deployable, Flexible, Scenario-oriented Serious Games

Keywords: serious gaming, scenario discovery, generic design knowledge

Serious gaming can be used in system engineering design processes, in the pre-inception phase, where investment scenarios are explored in game sessions with stakeholders. However, scant knowledge exist in the literature about how to design this kind of games. Based on the experience of three consecutive phases of game development for infrastructure projects in biogas, local wind energy and LNG refuelling infrastructures, a generic architecture was developed and made explicit as design knowledge. This paper outlines the architecture, offers insights about its application, and explains how the validation of this knowledge takes place.

Introduction

Serious games are typically used for educational and training purposes. Some games help various stakeholders (owners, users, customers) to understand better the working of complex systems. However, there are scant research results for using serious gaming as a tool to discover potential development scenarios in the design of complex scalable systems. The generic architecture presented in this paper is potentially useful knowledge for stakeholders who are willing use games in the design and development of complex systems. Such a kind of games has shown the potential to discover unforeseen scenarios based on the decisions that the players made in the game.

The architecture proposes a board game, partially supported by software -to allow players (namely the stakeholders) make calculations and decisions, and to allow the game master to give players an idea about the quantitative aspects on the board. The software's role is also to monitor and record the the decisions of the players. The board allows stakeholders to have an intuitive visual feedback on the status of the game, which changes over time as the players make decisions, mimicking the growth of their target system. The decisions made in the game entail investments into physical elements of the system to be developed. On the board, physical playing pieces are placed to represent these investments, recorded by the software.

Results with games of this kind show that solutions not thought of before the game sessions started can be found by stakeholders -providing them with a stronger incentive to commit for development of the system to be. For example, in a setting of an energy infrastructure where producers and consumers of energy were operating individually, they did not realize that they can work together to gain a more optimal energy price for both parties. Through serious game sessions, those stakeholders can come together and play the game to discover what they can accomplish when they cooperate and understand each other better. Similar early findings were presented in the work of Tan [START_REF] Tan | Negotiation and Design for the Self-Organizing City: Gaming as a Method for Urban Design[END_REF], which investigates multistakeholder games used for urban development.

The paper is structured as follows. In section 2 the research goal and the methodology used are described. Next, in chapter 3 the generic architecture is presented and what is the novelty about this architecture is outlined. Section 4 shows how the architecture can be applied for new contexts and validated further. Section 5 concludes the paper and presents shortly the immediate next steps for future research.

The Research Goal and the Methodology

There is gap in the literature about serious games -that is, there are not yet any development frameworks for games that can be deployed very quickly. Moreover, the games are not flexible and adaptable from one gaming session to the other (or even within the same session). Finally, there is no knowledge to develop games that are scenario-discovery oriented. By studying the development of specific games for scenario discovery, the authors (who were sometimes involved directly in the development) gathered knowledge with the intention to build a framework (a generic architecture with its guidelines for application) for future game development that has as a main purpose the scenario discovery.

As a consequence of this goal, the methodology chosen in this research is design-science research [START_REF] Hevner | Design science in information systems research[END_REF]. This research method indicates that an artifact needed in practice is designed within the process, it is validated (in this case by successfully using the game), and generalizable contributions are made for future designs. Here, the artifacts are a series of serious games where consumers of energy, producers of energy and investors can together discover future potential scenarios for their energy infrastructure (e.g. biogas grids, locally distributed win-produced electricity, or an LNG-based infrastructure for road freight). This artifact is specifically designed to solve the real problem of mismatch between goals and perspectives on doing investments in energy infrastructure.

The extant design knowledge used to design the initial versions of the aforementioned games is mostly taken from the game design professional literature [START_REF] Schell | The art of game design[END_REF]. Next to this body of knowledge, the research framework (FASD) proposed by Velthuizen [START_REF] Velthuizen | Introducing FASD: A Functional Architecture for Serious game Development[END_REF] after the development of the first game (named GasBoard) was used to position the next games and evaluate their design process. In the end, after developing three playable games, in order to contribute to literature, it has been decided that a generic architecture (accompanied by guidelines of how to use it) should be extracted from the lessons learned during each iteration of game design, development testing, and game validation.

3 The generic structure and process flow of scenario-centric games

The novelty of these games is that they are explicitly deployed to explore investment scenarios in a complex socio-technical context of an early system development. The specific games developed over the years were related to environments where energy producers and consumers were solely dependent on a large monopolistic grid operator (for gas, or electricity, or LNG imports). The serious games let the players (which were playing the role of the stakeholders in these systems) to engage themselves with investment decisions in alternative and local energy infrastructures. They can make investment decisions and long term commitments, erase the slate, start again, discover alternatives for investment and commitments, negotiate, argue, and try to find win-win situation in the longer term. At the end of multiple sessions of game playing, the experience can offer insights to the players (i.e. the stakeholders) how to develop a decentralized energy infrastructure that is more profitable and efficient from their local points of view. As an outcome of the game, the investment decisions and commitments that the players make can be transformed into potential future scenarios for infrastructure development. These future scenarios help the design and development of the complex system envisaged (in these particular situations, the energy infrastructure, but it can be any kind of strategic infrastructure where the stakeholders are the local communities, the local economy, the local authorities, and potential investors and system developers).

Irrespective of the nature of the system to be, over the iterations made to develop these games it has been observed repeatedly that a game of this kind needs always to consist of the following structural elements:

1. Game Roles: These are place-holders in the game for the participants who play the game. For example, such a role can be "Investor", and to each role, we can assign a set of possible actions (e.g. "invest" for the Investor role). Some roles can be easily identified by matching the roles of the stakeholders of the system, but other roles (like Investor) can be added. One player can play multiple roles, and in a more advanced setting some roles (those can have simple and predictable behaviour, easy to capture in mathematical models) can be played by soft-bots specially implemented for the task. 2. Game Components: these are mimicking the physical components of the system. For example, in a game that explores the scaling up of a LNG infrastructure, the components could be: refuelling stations, pipes, storage facilities, supply means, etc. The game starts typically with a few elements, and the number of these is increased during the game. A price is attached to each kind of components.

3. Game Board: the components appear in a "geography" context". To mimic this, a map-like support will be used to place the elements on specific locations. 4. Game Rounds: to mimic the passage of time, the game playing will be separated in rounds, each consisting of two parts: first, a mimicking of accelerated passage of time (for a given period like 1 month or 1 year, depending on the nature of the system to be); second, a period of discussion and negotiations between the players, and decision making. 5. Game Rules: because the players can interact informally during the rounds, it is necessary to establish rules for their interaction that constrain what they can do, and what they can produce. 6. Game outcomes: these can be established formally at the beginning, but such a game should be able to discover outcomes (like a new kind of contract or agreement) during a game playing. 7. Scenario: the (time ordered) set of outcomes 8. Means to record the state: besides the elements placed on the map, there should be some sort of recording the quantitative nature of states and outcomes (e.g. how much was invested at a given moment in elements). 9. Means to record the scenario: a more detailed recording mechanism for the outcomes. 10. A realism checking mechanism: an algorithm or set of algorithms that can infer the change of the state (physical and financial attributes) of the system, showing how the system grows from one round to the other. 11. Game Observers: non-player humans that have role to identify the growing scenario (this is not a task of the players normally, the system developers fill this non-player role). 12. Game master: a human who applies the rules, directs the rounds, mediates, and helps the players during a game.

Another part of the architecture is represented by the description of the process-related elements (activities) of playing the game. It has been repeatedly observed that the flow of activities is always similar to the following sequence of steps:

1. The players are instructed in the game playing and they are choosing their roles. 2. The game starts with a given situation of the board (some elements are set up mimicking the small-scale state of the system). 3. A round starts: first, the players must interact: discuss, negotiate, and make decisions (like contractual agreements, investments, etc. according with the rules). These are observed and recorded. 4. The rounds will end by applying the decisions on the board and in the realism checking mechanism, making an accelerated "jum" in time. The state of the system (in scale and output) changes accordingly. This step can be considered also the first part of the next round. 5. The players are informed about the new status, and a new round of negotiations starts from step 3.

6. The game master decides when to stop the game, after several rounds most probably when a scenario has been found, and/or the system to be mimicked in the game is reaching a sustainable magnitude. 7. Post-mortem analysis: the scenario is discussed and refined, and agreements for an eventual new game session are made.

The game master is in the control of the game, its software, and it is responsible to inform the players about the status and evolution of the game from round to round. He is checking the time, forcing rounds to end, and players should ask the game master permission for actions that are not specified a priori, and he should be able to detect easily illegal actions (which violate the rules).

Besides and after the post-mortem analysis (which is centred on the scenario), at the very end of each gaming session, a formal discussion takes place on how players experienced the game and what they thought was positive and what could be improved in the game itself. The observers can record and analyse the results of these discussions, and for the next gaming session new roles, game components, rules, or outcomes can be implemented in a new version of the game -which can involve also changes in the realism checking mechanism and the software tool. It is important for a game development team to constantly have in mind that playing the game multiple times is mainly the way to gather valuable feedback about how the game could be refined and improved. For example, in a situation when the game was played successively, and the game itself evolved, the following feedback was recurring:

-About inter-player communication: communication about the deals were not supposed to be intercepted by other players, as this might contain valuable information. Therefore, it is wise change the layout of the game space, in order to allow pairs of players to negotiate "securely", in spaces (like separate cubicles or even rooms). -About the tools used during the game: the tools were initially to difficult to use or did not allow for all the moves that were allowed by the existing rules of the game. A simple solution is to have a training round in the beginning of the game. -About the number of players: it was always argued during the first sessions that adding more players can increase the number of interesting scenarios. A simple solution is to start with the highest number of players possible. -About player roles and rules: some roles appeared to expand during the games, the investors for the example, in the initial rounds were only allowed to finance investments, but soon turned out to become asset owners and operators of elements in the infrastructure.

How to use the generic architecture and validate it further via new games

The most important insight related to this kind of games is that they should be focused on stakeholders' human behaviour. It was noted ( [START_REF] Courtney | Strategy Under Uncertainty[END_REF] and [START_REF] Weijnen | Designing Complex Systems -A Contradiction in Terms[END_REF]) that in the domain of networked infrastructures, the complex, multi-stakeholder, large-scale system design is perceived as a strategic process. However, this is not its main characteristic. It is neither predominantly financial, neither technological, albeit it has aspects related to these dimensions of analysis. In the end, what all complex system designs have in common is a dominant human dimension, given by the behaviour of various stakeholders, all competing to resolve their (sometimes opposite) goals, understand each other's different backgrounds, biases and prejudices. This is obvious for any large-scale and long term infrastructure project in energy, communication, transportation, defence, landscape transformation, or space exploration. Hence, the study of complex system design is also the study of stakeholders' social interaction and behaviours. All complex system designs (like the Delta project in The Netherlands for example) are part to particular contexts, to the societies and economies which source the resources to implement them, and are sometimes emotionally related to the technologies that are well-established and sustained in those societies (for example, currently nuclear technology is not supported by society in Sweden or Germany). A study of the process of complex system design, albeit it has to be technologically grounded, has to study the process on a behavioural, deeply human dimension. In such a project, each stakeholder tends to project its own beliefs about what the other stakeholders think, how they behave, and how they act toward a decision. Most of the time, exactly because of this projection tendency, the process to find a design and especially to find a plan to realize the design ends in failure.

Guidelines

In the following list of activities to implement a game based on the previously proposed architecture, the fourth activity in the list is the most important and should take most of the time of the game development and refinement. The proposed order of activities for implementations is:

1. Identify the physical elements of the game. Normally, these are the main physical components (modules, sub-systems) of the system to be. The elements are implemented as game pieces that can be placed on the board. 2. Identify the roles in the game. Roles may own or influence elements. For example, if similar elements are owned by two different players playing the same kind of role (e.g. an owner of an LNG refuelling station), different colors should be used for similar kinds of elements, to show different ownership (like in most of the board games). 3. Establish the board. Initially, it is advisable to start with a generic map, composed for example of a raster of equal hexagons. Physical attributes like distances can be inferred from the number of raster parts used for a certain placement of elements. Later, real life maps can be used, in conjunction with geographical elements (like rivers) and infrastructure (like cities and roads). 4. Establish a list of regulated interactions between the roles. For each kind of interaction, establish certain rules of conduct, and potential outcomes (like contracts, long term agreements, new regulations to be enacted, if the player is a regulating body).

5. Find ways to record the quantitative aspects of the state (magnitude) of the system to be at a given moment. 6. Find ways to implement the time advancing algorithms and the realism checking mechanism; initially these should not be too realistic, and should focus mostly on the financial aspects (revenue, losses and profit). Later, more physical aspects can be included in the realism enactment. It is recommended that each player should have a separate view about its own status, and everybody should have some sort of general idea of the evolution of the system to be -this sheet can be projected on a board visible for all players to see. 7. Find ways to capture the outcomes and the overall scenario that emerges.

The game should always exhibit the advancement of the game situation via the decision making of the players and not via stochastic mechanisms like dice or randomly extracted cards -for some degree of uncertainty, these can be used, but with parsimony. The outcome should be clearly a result of human interaction and not a mixture of chance and player skill like in entertainment games. There is no single winner in such games, but ultimately, what is sought after is a satisfying long term win-win outcome for all stakeholders.

Validation of the generic knowledge

In a design science approach, the validation of a generic architecture for a specific class of artifacts in is a gradual process. Basically, each successful implementation of an artifact in a given context that is using the knowledge embodied in the generic architecture is a validation step. More successful implementations in different contexts means a stronger degree of acceptance of the generic architecture and a growing perception in the professional community using it that this generic architecture is validated design knowledge.

In our years-long process of developing these games, the first game (Gas-Board) was implemented by using extant knowledge about board games, and the game was gradually adapted to the requirements of the stakeholders from the biogas industry who were involved in the development project. When the game was mature, and validated itself as a successful artifact, the developers considered that the knowledge acquired about the development process of the game should be captured in some form. This is why the FASD [START_REF] Velthuizen | Introducing FASD: A Functional Architecture for Serious game Development[END_REF] was created at that time. However, this did not represent an explicit form of a generic architecture, albeit the developers had at the time a clear implicit idea about such an architecture.

The next game (FromEnergy2Synergy, [START_REF] Veeningen | Design Science research issues in developing and applying serious games in the uncertain world of long term energy investments[END_REF]), has used FASD as input, was successfully validated by playing it many times, and added to the design knowledge by adding guidelines for implementation and deployment of the game. However, only after the development of the third game (investments in the LNG-refueling infrastructure), the developers decided to explicitly build a generic architecture, which is to be validated separately. The first step towards validation was to enact a validation workshop [START_REF] De Freitas | How can exploratory learning with games and simulations within the curriculum be most effectively evaluated? Computers & Education[END_REF], where the explicit architecture was discussed by the various developers of the three games. The net result was that the generic architecture was refined and better structured.

Finally, the generic architecture was recently (May-June 2017) used in an experiment with students, who developed games in a project. Three development teams were provided with this generic architecture (in the form of a manual) and three teams were not provided. Currently, the results of this experiment are analysed and will be published in a subsequent paper. Preliminary results show a clear advantage for the teams who used the generic architecture. For example, one of the teams, who was late in the development, managed to implement a playable, very immersive and engaging game in only three days. These developers, in a discussion after the last gaming session, considered the generic architecture document to be a crucial element in the speed of the development.

Conclusions

To our knowledge, this is the first attempt to build a generic architecture for scenario-centric games. These games have to be quickly enacted for the stakeholders in complex system projects. The game playing takes place very early in the development process (pre-inception), and the games have to be highly adaptable to changing requirements.

The next step is implement more of this kind of games that are using this generic architecture as design knowledge, and based on the success of these implementations, validate and refine further the architecture. The games can be either developed by students, for research projects, or by industrial partners interested in local infrastructural developments where "chicken and egg" situations appeared. These findings have to be reported and the generic architecture should be made available as open source for everyone who wants to develop such games. Any new successful implementations should be described along with the open source, as it validates further the architecture.


Rita Lavikka

Antero Hirvensalo

Riitta Smeds

Miia Jaatinen

Transforming a Supply Chain Towards a Digital Business Ecosystem

Keywords: Digital transformation, Developmental intervention, Business ecosystem, Peat production, Supply Chain

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

Introduction

The digitalization of data and business processes opens up new avenues for improving existing business and for creating new business. Companies often approach digitalization as a technical exercise -the right technical tools are thought to digitalize the business. However, especially in a supply chain, the biggest challenges hindering digitalization usually relate to the differing interests, working cultures, and economic models of the members [START_REF] Russo-Spena | Five Co-s" in innovating: A practice-based view[END_REF].

Digitalization changes the interdependencies between existing supply chain members, and can open it to new members, so that the supply chain can start transforming towards a business ecosystem. The transformation threatens the members' existing roles and "micro-specialized competences" in the existing value network [START_REF] Lusch | Service, value networks and learning[END_REF]. On the other hand, digitalization of a supply chain necessitates collaboration between the current and future potential members of the forming digital business ecosystem. Any single member alone cannot grasp the potential value of the forming business ecosystem as a whole [START_REF] Russo-Spena | Collaborative Practices in Innovation Ecosystems[END_REF], and thus the integration of the members' competences into collective value creation should be a shared responsibility. More research [START_REF] Russo-Spena | Collaborative Practices in Innovation Ecosystems[END_REF] is called for to understand the dynamics of ecosystem transformation. Our study addresses this need by analyzing the institutional characteristics of an ecosystem, and specifically, how these characteristics are co-created in an emerging ecosystem in a process of negotiation.

Our study focuses on the transformation of an established institutional value chain in the Finnish energy sector, namely the peat supply chain. We conducted a developmental intervention in this supply chain to facilitate its transformation towards a digital ecosystem and to study the transformation itself [START_REF] Smeds | Interventions for the Co-creation of Inter-organizational Business Process Change[END_REF]. We aim at contributing to the emerging theory on creating digital business ecosystems that builds on organizational ecosystem and co-creation literatures.

Theoretical Background

Organizational Ecosystems and Negotiated Order

Thomas and Autio define an ecosystem as an organizational field that encompasses all participants that focus on collective value co-creation [START_REF] Thomas | The fifth facet: The ecosystem as an organizational field[END_REF]. The interdependent participants of an ecosystem are bound together through three characteristics [5:12] 1. A network of participants, each of them providing a particular, complementary input to the system. Through their cumulative interaction, the participants add value. The inputs of the participants need to co-evolve. 2. A governance structure coordinates the participants' interaction for collective value creation. It consists of an authority structure for decision-making, conflict resolution, membership control for handling ecosystem openness, and coordination. 3. A shared logic glues the participants of the ecosystem cognitively and socially together in understanding their interdependency: a sense of legitimacy, trust, and mutual awareness about being involved in a shared enterprise of the ecosystem.

The institutional approach on organizational ecosystems offers also theoretical insight into supply chain transformation. Negotiated order theory holds that organizations can collaborate in constructing their organizational field by agreeing on the rules for their interactions through negotiation [5:23]. In practice-based theoretical research, negotiation is considered an important relationship between knowledge producing communities [START_REF] Brown | Knowledge and organization: A social-practice perspective[END_REF]. Negotiation is profoundly different from conventional organizational coordination: it is capable of creating dynamism in the organizational structure, and highlights the significance of balancing individual interests in inter-organizational collaboration [START_REF] Hardy | Discourse and collaboration: The role of conversations and collective identity[END_REF]. Negotiation is also needed to deal with conflict in organizational transformation. Power asymmetries can result in a reduced likelihood of co-evolutionary change in the ecosystem [START_REF] Thomas | The fifth facet: The ecosystem as an organizational field[END_REF].

Co-creation of Knowledge for Business Ecosystem Creation

The creation of a business innovation ecosystem proceeds in principle in three main steps [START_REF] Russo-Spena | Collaborative Practices in Innovation Ecosystems[END_REF]: first, the collaborators need to connect and define their relationships. Second, they negotiate shared objectives and define a common identity. Third, they define the actions needed to achieve the objectives.

When an existing supply chain begins transforming towards a digital ecosystem, it starts with the current members and their interdependencies, motives, and value adding logic. Transformation is a challenge for the current and new members of the emerging ecosystem. They have to collaboratively redefine their value adding interdependencies [START_REF] Smeds | Interventions for the Co-creation of Inter-organizational Business Process Change[END_REF]. This requires sharing and co-creating knowledge [START_REF] Russo-Spena | Five Co-s" in innovating: A practice-based view[END_REF]. Modelling the knowledge into visual boundary-objects supports knowledge sharing, collaborative ideation, and co-creation [START_REF] Star | Institutional ecology, "translations" and boundary objects: Amateurs and professionals in Berkeley's museum of vertebrata zoology, 1907-39[END_REF]. External facilitators help ecosystem members make sense of the situation, share, negotiate, collaborate, and design their changing interdependencies [START_REF] Russo-Spena | Collaborative Practices in Innovation Ecosystems[END_REF].

Research Approach and Methods

This study is based on an action research based intervention where the researchers have the role of network facilitators [START_REF] Hardy | Discourse and collaboration: The role of conversations and collective identity[END_REF]. Action research allows both to increase scientific knowledge in the area and to initiate change in the studied organizations [START_REF] Star | Institutional ecology, "translations" and boundary objects: Amateurs and professionals in Berkeley's museum of vertebrata zoology, 1907-39[END_REF][START_REF] Yin | Case Study Research: Design and Methods[END_REF]. Action research gave us the unique access into the case network for researching the transformation of the supply chain.

Developmental Intervention for Digitalizing Peat Production

We organized a developmental intervention to help in digitalizing a peat production supply chain for energy production. Fig. 1 depicts the members of the peat production supply chain: sub-contractors, a hub company, and a power plant client. In addition to these members, we invited representatives from a peat harvesting machine producer and from software providers as co-creators of ideas for digitalization, and university professors, researchers, and students. Public environmental organizations and regulative bodies were not represented but are part of the forming business ecosystem.

Data Collection and Analysis

The intervention was conducted during December 2016 -March 2017. Researchers and students from Aalto University designed and realized the intervention and collected the research data that included interview transcriptions, process and value network models, notes from four successive workshops, participant observation, and video recordings. Data triangulation increased the validity of research [START_REF] Yin | Case Study Research: Design and Methods[END_REF].

In the first workshop, a preliminary understanding of the process was co-created. Then, eighteen participants were thematically interviewed. They were members of peat production supply chains in two locations in Finland. The interviews dealt with information flows and work practices in peat production, but also with the basic assumptions and motivations of the different participants towards digital transformation. Three researchers designed and analyzed the next three workshop based on progressive problem identification and solution finding; the goal of each workshop was set based on the findings of the earlier workshops. (Table 1)

In the workshops, the participants were brought into facilitated dialogue about their forming digital business ecosystem, across their different views and goals. They discussed their networked operations and the possibilities of producing and exploiting digital data throughout their peat production process. Based on the research data, the researchers designed peat production process models and value network models that were used in the workshops as visual boundary objects [START_REF] Star | Institutional ecology, "translations" and boundary objects: Amateurs and professionals in Berkeley's museum of vertebrata zoology, 1907-39[END_REF] to support knowledge co-creation. Understand the challenges in managing peat product quality data.

Develop solutions for tackling the challenges.

Develop a concept model for the digitalized peat production.

Output

An initial process model of peat production.

A value network model.

Detailed peat production process and value network models.

A concept model for digitalizing peat production.

In data analysis, we followed the logic of abductive scientific reasoning [START_REF] Dubois | Systematic combining: An abductive approach to case research[END_REF][START_REF] Ketokivi | Two strategies for inductive reasoning in organizational research[END_REF]. Abductive reasoning is well suited for interpretive action research, where analysis progresses as a continuous dialogue between theoretical knowledge and the data that was collected at different points of time. [START_REF] Mantere | Reasoning in organization science[END_REF] 4 Findings and Discussion

The Current Supply Chain and its Transforming Characteristics

The governance of the current peat production supply chain is decided by the hub company -subcontractor relationship based on competitive bidding. The hub sets the objectives for subcontractors and controls the supply chain. The production responsibility is largely transferred to the subcontractors, who work independently according to their contracts.

The peat production process is well established as an operational process, but the product data concerning the quality of the peat is estimated by the members of the supply chain through subjective and manual practices, and communicated through various means in the different phases of the production process. The members of the peat production would benefit from the digitalization of peat data and its communication because up-to-date product data is critical for the energy efficiency of the clients' power plants, and it determines the payments to the hub and the subcontractors.

At the start of the intervention, the members of the supply chain had some ideas about digital sensors and data management systems for peat quality data. However, digitalization motivated mainly the hub company that would potentially gain the most business benefit from it. While the subcontractors in general welcomed new quality measurement technologies, they did not see how they could get any added value. With digitalization of the members' data input, their interdependencies in collective value creation could change, and the benefit that each member would gain from it was uncertain. There was no shared logic of being "in the same business ecosystem of digitalized peat production".

According to the interviews, the members had their own development agendas for digital solutions, and sub-optimization was felt a challenge. The companies did not yet perceive digitalization as a supply-chain wide effort. The powerful hub company was carrying out internal technology projects, and the sub-contractors were not part of the development work. The harvesting equipment manufacturers were only seen as providers of production machines for the current production process, not as partners in the forming ecosystem. The hub company's greater power in the present supply chain seemed to dampen the other members' motivation for transformation.

In the facilitated workshops, the supply chain members felt inspired by the idea of a wider peat production ecosystem that they could jointly start creating, supported by visual boundary objects. The competitors, technology providers, government bodies, and national policies (peat is classified as a fossil fuel in Finland and its use is considered as non-sustainable in the long run) create threats but on the other hand also offer resources and novel possibilities for digitalization. Digital transformation clearly expands the existing boundaries towards a wider ecosystem. Our results suggest that the digital transformation of the existing supply chain should be understood and facilitated from a wider ecosystem perspective. (Fig. 1)

Knowledge Co-creation and Negotiation for Transformation

During the intervention, for the first time a substantial amount of knowledge concerning the data management of the current peat production supply chain was collected and shared among the participants. In the workshops, the participants clarified their tasks in peat production. They shared knowledge, views and, motives while discussing the challenges in data management, and jointly created digital solutions for tackling them. Further, they could now grasp the importance of digital peat quality data: the whole ecosystem is partly financially compensated based on the quality of peat. A shared logic of the forming ecosystem started to germinate among the participants as they jointly created meanings of social, financial, and use value of digital data.

At this point, we have not yet collected follow-up data on the effects of the intervention on ecosystem creation. However, we can already observe some early evidence. In the facilitated workshops, the participants could collaboratively negotiate and select issues that they considered most significant for the digital transformation of the peat production supply chain. The representatives of the hub company, the sub-contractors, and the wider ecosystem members all participated actively in the negotiations that resulted in focused project proposals for furthering the digital transformation, and in ideas for the implementation of those projects. The intervention thus contributed importantly to the governance of supply chain transformation. Summarizing these effects, the intervention provided a temporary governance structure and a shared logic for ecosystem creation (Fig. 2).

Conclusions

We facilitated the transformation of a supply chain towards a digital business ecosystem systematically through a developmental intervention that included four participative workshops. In these workshops, the participants discussed, developed, negotiated, and agreed upon collaborative actions to digitalize their operations and to transform towards a digitalized business ecosystem. We contribute to the emerging literature on creating digital business ecosystems by showing how the members of the forming ecosystem can find com-Fig. 2. The intervention created a "temporary governance structure" for digital transformation mon goals, discuss the value of digitalization, and make collaboratively decisions about implementation of digital solutions through facilitated knowledge co-creation and collaborative negotiation. Our results suggest that digital transformation of an existing supply chain should be facilitated from a wider ecosystem perspective which helps the supply chain members to overcome the initial motivational challenges and create a shared logic.

Based on our results, we create a hypothesis that a developmental intervention provides a temporary governance structure for the participants to collaboratively make decisions about the transformation of a supply chain towards a digital business ecosystem. The hypothesis should be tested in other supply chain contexts. Also, to what extent the temporary governance structure carries over to the real life ecosystem creation should be studied.

Fig. 1 .

 1 Fig. 1. The participants of the peat production ecosystem during digital transformation.

Table 1 .

 1 Goals and outputs of the developmental workshops.

	Workshop I	Workshop II	Workshop III	Workshop IV
	21.12.2016	3.3.2017	17.3.2017	31.3.2017
	Goal Understand current
	data flows in the
	peat production
	process.