Shigeki Umeda
email: shigeki@cc.musashi.ac.jp

A system maturity model for supply chain management

Keywords: Supply chain Management, Capability Maturity Model, System Maturity model, Information system management

A supply chain system is a chain of processes from the initial raw materials to the ultimate consumption of the finished product linking across supplier-user companies. Huge investment is needed to build supply chain business systems. The goals and objectives must be revised repeatedly in accordance with its growth. A methodology is needed to support system management through its system life cycle. This paper proposes a novel system maturity model for supply chain management, SCMM (Supply Chain Maturity Model).

Introduction

A supply chain system is a chain of processes from initial raw materials to ultimate consumption of the finished product linking across supplier-user companies. It provides functions within and outside a company that enable the value chain to make products and provide services to the customers.

System development needs its goals and strategies to be clarified. In addition, these goals and objectives must be revised repeatedly in accordance with its growth. Continuous improvement is, needless to say, very important in every system management. However, a repetition of improvement alone often produces fruitless investment in system management. The concept of a qualitative, stratified framework is needed to manage system growths for supply chain system.

Enterprise evolves organization capability by using re-structuring and reengineering. This principal is applied universally to every enterprise organization. Supply chain system is also the same as this. To correspond this primary rule, we need own process models considering organization maturity. SCOR (Supply Chain Operations Reference) model is a process reference model that has been developed and endorsed by the SCC (Supply Chain Council) as the cross-industry standard diagnostic tool for supply chain management [START_REF]SCOR overview[END_REF] [START_REF]Supply Chain Council, Supply Chain Operations Reference (SCOR) model[END_REF]. SCOR enables users to address, improve, and communicate supply chain management practices within and between all interested parties. However the SCOR model only ad-dresses the "optimized" process operations. It does not include a concept of "Maturity" of system that is essential to phase-based system development. SCOR model is, in a sense, description of the best status of processes in supply chains.

CMM (Capability Maturity Model) was created for the software industry as a model for judging the maturity of an organization's software processes and identifying the key practices that are required to increase the maturity of those processes. The CMM has become a de facto standard for assessing and improving software processes [3].

This paper proposes a novel model for supply chain system maturity: SCMM (Supply Chain Maturity Model). Several measurements will be discussed for system's maturity of supply chain systems. This model is an evolution model for supply chain system integration. Individual maturity level associates with supply chain management problems. First, this paper proposes the supply chain maturity model. Second, it clarifies that each maturity level responses to supply chain management problems. Finally, conclusions and perspectives of future researches are described.

Supply Chain Maturity Model: SCMM

This section proposes a maturity model for supply chain system. CMM (Capability Maturity Model) is, as previously described, a model for judging the maturity of an organization's software processes. It also identifies the key practices in those processes [START_REF]SCOR overview[END_REF]. This paper has applied the system maturity levels to a generic supply chain system. The maturity levels are defined as five levels, such as "Initial", "Repeatable", "Defined", "Managed", and "Optimized". Furthermore, supply chain management problems have been contrasted with individual system maturity levels.

Management view

(1) Process view SCOR model defines provides key process according as its process classification; such as PLAN, SOURCE. MAKE. DELIVER and RETURN. The details of these processes are, for examples, described in the SCOR process model.

(2) Resource view

Supply chain resources are classified as the following four categories; such as "physical", "human", "information", and "finance".

Physical resources are poured in the organization, where they occupy each specific position and space. Examples of physical resources are land, factory, office, equipment, machines, vessels, raw materials, products, semi-finished products, byproducts, supply of goods, and rubbish, etc. All of these resources assert their original primacy, and it demonstrates its potential power according to the verbal and written instructions, human strength in the organization, and contributions to the organization's goal.

Human resource represents enterprise organizations and its members. Every person belongs to a particular sub-organization, and owns competence according as his/her missions. A person is basically an active resource that works based on his/her mission. However, his/her activity has often been controlled by another one. In this case, he/she or a particular group works as a passive resource. Performance of this resource depends on his/her authority, knowledge, skills, and availability of other resources; however, knowledge and skills are too hard to be measured. Accordingly, it is too difficult to measure this resource quantitatively without using simplified measure such as human-months.

Information resource includes raw data (first-hand information), meta-data, data models, ontology, and hardware systems, including both fore-and back-end systems. Many of modern enterprises own data as digitalized forms, so these data form can be automatically transformed to another one. Almost information resource is passive resource except highly intelligential software. This resource is transferable to attributes of other type resource. Information resource is generally classified into three groups, such as "Infrastructure", "System enabler", and "Application".

Financial resources include assets, cash, deposits, operating funds, and investment capital, and etc.

(

3) Maturity view

Every organization evolves gradually by its growths and process improvements. Capability Maturity Model (CMM) is such a model as describing such organization growth processes (maturity). The objective of this model is to define details of business processes at each maturity stage. This model provides, however, maturity in software development organizations. Accordingly, it cannot be directly applied to supply chain organizations. However, the modeling framework would be applicable to describe maturity growth of supply chain systems.

Maturity model

The maturity model represents organization maturity levels, which are composed of the following five stages.

Repeatable

(Pre-integration) 4 Managed (Full-integration) 5 Optimized (Automated-integration)

1 Initial (Chaos)

3 Defined (Primal-integration)

Fig. 1. Supply Chain Maturity Model

Initial (Chaos): This is a pre-stage of process integrations of a firm. Almost all of business activities are operated in process-driven. Individual processes are "point solutions". In this stage, business processes are reaction-based, and often depend on specific persons. Ad hoc decisions are often done based on the past successful experiments. Accordingly, the process quality is unstable. Resources are categorized such as "physical", "human", and "information", however, the management is ad hoc and not systematic. Information systems mainly provide stand-alone applications. The data are transferred among processes and organizations, however it data exchange methods are often ad hoc. The most important item at this stage is "Vision building" for process, resource , and information management. ( 2)

Repeatable (Pre-integration): Business processes are stable but very limited visibility across the wider supply chain. The process definitions are primary documented, trained, and measured. The processes are partially supported by information system. Resources are mainly managed by category-dependent-way. Information system provides a set of stand-alone applications, and application-based data transformation. The common data transfer methods are provided in neither company-wide nor the chain-process-wide. Information system provides a set of stand-alone applications and Business process management and resource management are partially linked with the information systems. However, information system does not provide company-wide data connections. Companies should make detail investment plans to perform the robust information system infrastructure.

(3) Defined (Primal-integration): All of business process are fully documented, trained, and measured. And, individual process is fully supported by information systems. Resource data schemata are defined in enterprise's data repository. Information systems provide well-defined and standardized data transformation among chain member companies. The plans for Information systems are linked with business process improvement activity plans. Investment to the information systems are proceeded toward full standardization of process input/output and information system enablers. The process definitions in SCOR model are corresponded to this maturity level. [START_REF] Mcfland | Information technology changes the way you[END_REF] Managed (Full-integration): Business Process Management (BPM) is at full throttle, and it would proceed to quantitative process quality management and realtime monitoring and performance measurement. Every resource is managed by using resource ontology in process chains, and integrated information system would be synchronized with business process and resource management. It also provide services for consumers and customers. [START_REF] Porter | How Information gives you competitive advantage[END_REF] Optimized (Automated-integration): Level 5 is as much conceptual as it is factual. Business processes are fully formalized and authorized toward continuous improvement. Resources are managed including autonomous life-cycle management, and the information system provides full support of business process and resource management.

Investment policies would discussed toward the continuous improvement in process chains

Supply Chain Maturity

Supply Chain Growth on IS investment Business Process Mgmt

Resource Mgmt

Fig. 3. Supply Chain Maturity Model with process integration

Figure.3 represents a rough sketch of SCMM. The lengthwise represents supply chain system maturity, meanwhile, the crosswise represents the extent of process integration. The boxes in the figure show system maturity levels, which are "Initial", "Repeatable", Defined", "Managed", and "Optimized", respectively. For an example, "Level III Defined" is a stage that partner collaboration is possible, and systems are integrated in partially intra-enterprise and partially interenterprises.

The dotted slope line represents trends of system evolution magnitude. System would mature rapidly if the Business Process Management (BPM) shows high performance; on the contrary, it would do in slow pace if BPM performance would be low.

Conclusion and future research

Managing supply chain is an execution of process life cycles such as system vision building, design, implementation, practice, and maintenance. A robust methodology for information system life-cycle management is needed [START_REF] Umeda | Planning and Implementation of Information system in supply chain system[END_REF]. This is a typical PDCA (Plan, Do, Check, and Action) cycle discussed in Total Quality management (TQM).

This paper proposed a novel maturity model for supply chain system (SCMM). The model proposed here, still stays at a primitive stage; however, it would be the first step to clarify supply chain system management considering systems' maturity. Our next step of this work will be detail specifications of the proposed model, and methodologies to use the SCMM for life-cycle management for supply chain systems.

Capacity planning problems: Capacity planning is to determine the amount of capacity required to produce in the future. This function includes establishing, measuring, and adjusting limits or levels of capacity. In general, this planning includes the process of determining in detail the amount of labor and machine resources required to accomplish the tasks of production. This group includes RSCP (Rough-cut Supply chain Capacity Planning) and SCRP (Supply chain Capacity Requirement Planning).

(2)

Resource planning problems: Resource planning is capacity planning conducted at the business plan level. It is the process of establishing, measuring, and adjusting limits or levels of long-range capacity. Resource planning is normally based on long term production plans but may be driven by higher-level plans beyond the time horizon for the production plan, e.g., the business plan. It addresses those resources that take long periods of time to acquire. Resource planning decisions always require top management approval.

(3) Lead-time planning problems: Semantics of the term "Lead-time" is basically "the time between recognition of the need for an order and the receipt of goods". The definition is often used in a logistics context. Individual components of lead-time can include order preparation time, queuing time, processing time, move or transportation time, and receiving and inspection time. This problem directly impacts the inventory planning problems through the Lead-time inventory, the inventory that is carried to cover demand during the lead-time.

(4)

Production planning problems: There are two phases of production planning; the first phase is an aggregate production planning and the second phase is an production planning. An "Aggregate production plan" implies budgeted levels of finished products, inventory, production backlogs, and plans and changes in the work force to support the production strategy. Aggregate planning usually includes total sales, total production, targeted inventory, and targeted customer backlog on families of products. Operational production plan is a more detailed set of planned production targets that meet the goal of the higher level manufacturing output plan. It is based on an agreed-upon plan that comes from the aggregate (production) planning function. It is usually stated as a monthly rate for each product family (group of products, items, options, features, etc.).

(5) Supplier selection problems: One of the major issues, when a system planner designs a supply chain or a manager reviews performance of the existing supply chain is supplier selection problem. It is a significant decision as it affects the system performance for a long time. From supply chain performance viewpoint it affects all the primary problems discussed above.

(6)

Outsource planning problems: Outsource planning is one of the very important problems for modern manufacturing enterprises. This is because maintaining expertise in all the technologies and processes required for manufacturing a product is almost impossible in single company. In addition to that, a proper outsourcing of process lets a company concentrate its resources on particular core processes, allowing the company to maintain its competitive position. Again, the outsourcing decisions impact all the primary problems discussed above and thus impact the supply chain performance.

(7)

Operational strategy selection problems: This problem includes selecting the strategy to operate the supply chain. Suppose that the supply chain designer has solved the primary problems, has selected the best business partners as his/her suppliers and has decided the non-core processes to be outsourced, he/she still needs to decide how to control the flow of products through the supply chain. The problem examples are as follows:  How to choose between PUSH, PULL, and Hybrid PUSH-PULL?  How to choose the strategy such as STS, MTS, ATO, MTO, at each stage of the supply chain? The problems in the above description are typical issues in supply chain management. Each problem would be discussed according as its maturity level. (1)(2)(3) would be discussed at comparatively low maturity levels (Level I Initial, Level II Reatable), (4)(5) would be argued at middle maturity levels (Level III Defined) , and (6) [START_REF] Kettinger | Strategic information system revisited: A study in sustainability and performance[END_REF] would be considered at high maturity levels (Level IV Managed, Level V Optimized).

Supply chain management problems and investment problems on information technologies

Information Technology (IT) is one of the most imperative concerns in today's enterprise business environment. This is because information systems are one of vital pivots in modern enterprise management. Building effective investment policy in enterprise information systems is a critical matter.

Many discussions have been done on this matter since 1980's. Investment problems on Information systems are often discussed in the views of enterprise strategy building. (i.e. [5][6]). Furthermore, these discussions include cost-effectiveness problems [START_REF] Kettinger | Strategic information system revisited: A study in sustainability and performance[END_REF][8], resource-based approaches [START_REF] Bharadwaj | A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation[END_REF][10], company's competitiveness [START_REF] Umeda | Planning and Implementation of Information system in supply chain system[END_REF], and etc.

Enterprise information system is one of company resources and besides; it is also a management enabler for other resources. Further, the information systems' evolution is tightly coupled with a growth of the company. Making investment policies in enterprise information system should consider that capabilities of company organizations depend on its resources and its growth.


Dennis Schiemann

Sudharshan Santhanam

Günther Schuh

Operating Curves based Working Capital Management for Engineer to Order Manufacturers

Keywords: Working capital management, engineering to order, logistics operation curves

Working capital management is one of the key disciplines that must be prudently monitored for a firm in pursuit of profits, liquidity and growth. The focus of this paper is on the engineer-to-order manufacturers, and the objective is to analyze the correlations between the reference processes of the engineer-toorder production approach with the key postulates of working-capital management and deliver a mathematical operating curves model, whose purpose and goal is basing on the rationale, that is underlying in the parent logistic operating curves theory.

Introduction

The appropriate levels of current assets and current liabilities, the way in which the short-term requirements of the firm are financed and the composition of the short-term and long-term financing, all involves a trade-off between the profitability and risk. The conceptual evaluation of the working-capital management, the engineering to order production approach and the operating curves theory accounts this trade-off to strike a sensible balance between the profit and the risk involved.

Analysis of Key Concepts

Working Capital Management

Working capital management refers to the underlying, structured and effect-oriented analysis to make comprehensive and sensible, short-term financial decisions. Shortterm financial decisions seem relatively less complex to make, than the process for long-term and medium-term ones but are equally important and critical. This fact is put explicitly, as an organization still could fail in spite of identifying valuable investment opportunities, most optimal capital structure and most perfect dividend policies, just because it emphasized lesser on short-term planning. [START_REF] Brealey | Fundamentals of Corporate Finance: Selected Material. With Additional Material From, Fundamentals of Corporate Finance[END_REF]. The net-working capital of a firm is defined as its current assets minus its current liabilities. The net-working capital is that capital required by the firm to satisfy its short-term capital requirements. [START_REF] Brealey | Fundamentals of Corporate Finance: Selected Material. With Additional Material From, Fundamentals of Corporate Finance[END_REF] [START_REF] Preve | Working Capital Management. Financial Management Association Survey and Synthesis Series[END_REF].

The working capital accounts implicate the free-cash-flow of the firm. The direct reason and relevance comes from the cash-cycle. The cash-conversion-cycle is an important concept in the working-capital management that potentially affects the components of working-capital. The cash-conversion-cycle is mathematically summarized as Cash-Conversion-Cycle = (Inventory Period + Receivables Period) -Accounts Payable Period [START_REF] Brealey | Fundamentals of Corporate Finance: Selected Material. With Additional Material From, Fundamentals of Corporate Finance[END_REF].

Engineer to Order Production Approach

The continuously increasing demand for specific customer value orientation and the need to develop customized parts with longer lead times, expensive capital investments and technical expertise, is promoting the role of engineer-to-order manufacturers in the supply chain. The engineer-to-order (ETO) production approach enables firms to have increased agility and flexibility, positioning them ideally to respond against unexpected and rapid market shifts through their production of one-of-a-kind products. [START_REF] Douglas | Developing due dates in an engineer-to-order engineering environment[END_REF]. But given all this, there has been relatively lesser research outputs exclusively to the ETO production models. [START_REF] Schuh | Prozesse[END_REF][START_REF] Gosling | Engineer-to-Order Supply Chain Management : A literature review and research agenda[END_REF]. Given a high level of product complexity and customization, engineer-to-order companies account critical factors during their planning phase as significant uncertainty in the operating times, limited resources to respond in a situation of demand fluctuation and complexity and concurrent product developments. [START_REF] Earl | Planning Complex Engineer-to-Order Products[END_REF].

Logistic Operation Curves Theory

With difficulties in quantifying large number of variables and the limitations associated with the mathematical functions that could approximate the required primary scenario as close as possible, a complex production system cannot be absolutely reflected in a mathematical model. The activities within the production processes hence must be classified with respect to their objectives in the value chain. Production and testing, transportation, and storage and supply are defined as the primary reference processes. [START_REF] Nyhuis | Fundamentals of Production Logistics: Theory, Tools and Applications[END_REF]. Due to the embodied conflict in the objectives of the primary production reference process, it has been difficult for the practitioners to quantify the variables and interdependencies and thereby perform a targeted logistics positioning. Intuitive and experience driven decisions addressing this trade-off results in an imbalance, given the complexity of the production process. The logistics operating curves theory addresses this difficulty in quantifying the trade-off and interdependency between the objectives of the production reference process. A logistic operating curve visually represents the correlation between a specific parameter of interest (the objective or dependent variable) and an independent variable. Therefore, for every value of the independent variable that can be changed by external conditions, at least one value can be determined for the objective. [START_REF] Nyhuis | Fundamentals of Production Logistics: Theory, Tools and Applications[END_REF].

Operating Curves based Working Capital Management for Engineer to Order Manufacturers

The targeted mathematical operating curves model must adhere to the ground rule stipulated by its parent logistic operating curves model, which is targeted logistic positioning between the key, fundamental and trade-off variables in production logistics viz: work-in-process (WIP) and the maximum possible output rate. Attempting the best to adhere to this ground rule, the ideal solution is to create a targeted positioning between the key trade-off variables that encompasses into its scope, the concept of workingcapital management, ETO production approach and the operations of the ETO manufacturers. The key trade-off concept of corporate finance viz: profitability and the risk would address the challenge of encompassing the scope and are potential fundamental variables in the proposed operating curves model. The effectiveness of these potential fundamental variables in encompassing the scope could only be realized when the concepts of capital budgeting are included in the analysis. Hence the need for taking the concepts of capital budgeting becomes important and relevant.

Definition of Ideal and Calculated Operating Curves.

As While pursuing a Bottom-Up Approach from a Pure Operational Perspective to a Pure Financial Perspective although initially was felt much logical, could not contribute sufficiently and effectively towards Zero-in-On of a pair that can satisfy the Challenging Conditions and the Scenario that is being attempted to fulfill. Hence pursuing a Top-Down Approach from a Pure Financial Perspective to a Pure Operational Perspective, the Key Trade-Off Concept of Micro-Economics, thereby of Corporate Financeof which the Concept of Working-Capital Management is a part. Profitability and Risk would address the challenge of encompassing the scope. This is a solution to the scenario for which the Operating Curves Model is being attempted to be built, and are hence declared as the Potential Fundamental Variables for the Operating Curves Model. The Declaration of Potential Variables of Interests Representing Conceptual Working-Capital Management are described and the Four Distinct Operating States that Encompass the Given Scenario will be shown in Fig. 1. Operation State (OS) 1 is Organic and Non-Significant Risk Growth as well as Organic and In-Organic Profit Growth. OS 2 is Organic, Partially In-Organic and Non-Significant-to-Significant Average Risk Growth as well as Organic and Reduced (Relative to State 1) In-Organic Profit Growth. OS 3 is In-Organic and Organic Steady and Sustained Risk Growth as well as Implicated Organic Profit Growth. OS 4 Increased Organic and Significant In-Organic Risk Growth as well as Plummeting Organic Profit Growth. (1)

TC = [ R c [$] × O c [%]] + [n × AT [$]] [$] (2)
TC Changed = [[R c-Actual [$] × O c [%]] + [n × AT [$]] [$] (3)
The Opportunity Costs Foregone by the Firm from Investing in Other Attractive Opportunities, due to its decision to pay a Reduced Monthly Principal Dues. (𝑶𝑪 𝑬𝑴𝑺𝑫 )

𝑂𝐶 𝐸𝑀𝑆𝐷 = 𝑃 [$] × [𝐻𝐹𝐼𝑅 [%] -𝐹𝐼𝑅 [%]] [$] (4)
The Opportunity Cost that is Foregone by the Firm from Investing in Attractive Opportunities due to a Postponement in the Repayment Schedule of its Debt.(𝑶𝑪 𝑷𝑹𝒑𝑺𝑫 )

(𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 ) = [[𝑋[$] + 𝑋[$]. 𝐹𝐼𝑅 [%]]. 𝐹𝐼𝑅 2 [%] + [𝑌[$] + 𝑌[$].

𝐹𝐼𝑅[%]]. 𝐹𝐼𝑅[%] + [𝑍[$] + 𝑍[$]. 𝐹𝐼𝑅 [%]]] -[[𝑋[$] + 𝑋[$]. 𝐹𝐼𝑅 [%]] + [𝑌[$] + 𝑌[$]. 𝐹𝐼𝑅[%]] + [𝑍[$] + 𝑍[$]. 𝐹𝐼𝑅 [%]]].

(

The Magnitude by which the Profitability on Additional Sales-Generated due to a Relaxed Credit Standard, exceeding the Required Rate of Return on Additional Underlying Investment (𝑴 -𝑹𝑪 𝑷 𝑨𝑺 >𝑼 𝑰𝒏𝒗 )

(𝑀 -𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 ) = [𝑃𝑟𝑜𝑓𝑖𝑡 𝑇𝑆𝑉 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 -𝑇𝑆𝑉 -[[𝐴𝐼 𝑇𝑆𝑉 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 -𝐴𝐼 𝑇𝑆𝑉 ] × 𝑅[%]]] (6)
The Proportion of Receivables that is Defaulting (𝑫𝑭 𝑹𝑪𝑻 ) The Magnitude by which the Profitability of Additional Sales, after Bad-Debt Losses -Generated Due to Re-Calibrated Credit-Terms, exceeding the Required Return on Underlying Investment. (𝑴 -𝑹𝑪 𝑷 𝑨𝑺-𝑩𝑫𝑳 >𝑼 𝑰𝒏𝒗 )

(𝑀 -𝑅𝐶 𝑃 𝐴𝑆-𝐵𝐷𝐿 >𝑈 𝐼𝑛𝑣 ) = [[𝑃𝑟𝑜𝑓𝑖𝑡 𝑇𝑆𝑉 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 -𝑇𝑆𝑉 ] -[𝐶 𝐷𝐹 𝑅𝐶𝑇 ] - [[𝐴𝐼 𝑇𝑆𝑉 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 -𝐴𝐼 𝑇𝑆𝑉 ] × 𝑅[%]]] (7)
The Magnitude by which the Reduction in the Cost of Bad-Debt Losses exceeding Additional Collection Expenditures(𝑴 𝑹𝑪𝑩>𝑨𝑪𝑬 )

(𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 ) = [[𝐶 𝐷𝐹 𝑅𝐶𝑇 [$] -𝐿𝐶 𝐷𝐹 𝑐𝑝 [$]]] -[𝐶 𝐶𝑃2 [$] -𝐶 𝐶𝑃1 [$]] (8)
Configuring these Potential Fundamental Variables to be an even effective Trade-Off Variables and analogous to those in the Parent Logistic Operating Curves Model, these become Maximum Possible Profit 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑥 and the Ideal Minimum Risk 𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 (see Fig. 2).

𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐺) 𝐼𝑑𝑒𝑎𝑙 :

(𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 )[$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟(𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (15)

Underlying Procedures Involved in defining the Calculated Operating Curves.

The following equations 16 to 23 are a straight forward answer to the research questions. The Left Hand Side of the equations represent the variables that are identified from the analysis; and that which represent the efficiency of working-capital management of the firm. The Right Hand Side of the equations represent mathematical relationship that accounts the effects of operational variables, identified as potential for the engineer-to-order manufacturers. Equations 18 and 19 are a robust correlation between the extremities and the mean value of the fundamental trade-off variables.

(𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$]𝑜𝑟 [$] 2 {𝑡} = (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟 [$] 2 . (1 -√1 -𝑡 𝐶 𝐶 ) + 𝛼 1 . (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟 [$] 2 . 𝑡 (16)
𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑒𝑎𝑛 [$]{𝑡} = 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑥 [$]. (1 -√1 -𝑡 𝐶 𝐶 ) (17)
(AC)[$] {t} = ψ × (Ideal Risk Min )[$]or [$] 2 . (1 -√1 -t C C ) + α 1 . (Ideal Risk Min )[$]or [$] 2 . t- (18)
As already stated, these variables take into their scope all the constraints that have been imposed by the described problem statement. All other equations are formulated in terms of the variable representing the x-axis in the operating curves model. The operating curves model with the identified set of equations clearly is a targeted positioning of the fundamental trade-off variable, and is a mathematical correlation between the dependent and the independent variable. Hence the mathematical operating curves model takes into its scope conceptual working capital management and the ETO production approach and clearly satisfies the purpose and rules stipulated by the parent logistic operating curves theory (see Fig. 3).

Conclusion

A mathematical operating curves model, that correlates the important operations of the engineer-to-order manufacturer with the important postulates of conceptual workingcapital management, has been formulated. In doing so, the model has completely complied with the rationale in the goal of the parent logistic operating curves model. Which is target positioning of the significant and important set of trade-off variables.

Orientation to the engineer-to-order production approach, has served as a constraint both qualitatively and quantitatively. As target positioning requires being most specific, a micro-level consideration for the important operations of the engineer-to-order production approach has been done. This consideration enabled recognition of potential variables of interests that satisfy the requirements as well as conceptually correlate with the postulates of working-capital management. It is a sincere belief that the output of the research potentially could be another small expansion, amongst many major expansions to the parent logistic operating curves theory.

Fig. 1 .

 1 Fig. 1. Four Distinct Operating States that Encompass the Given Scenario

Fig. 2 .

 2 Fig. 2. Ideal Operating Curves Model 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐴) 𝐼𝑑𝑒𝑎𝑙 : 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑥 [$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 𝑜𝑟 (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (9) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐵) 𝐼𝑑𝑒𝑎𝑙 : (𝐴𝐶)[$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟(𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (10) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐶) 𝐼𝑑𝑒𝑎𝑙 : (𝑂𝐶 𝐸𝑀𝑆𝐷 )[$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟 (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (11) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐷) 𝐼𝑑𝑒𝑎𝑙 : (𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 )[$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟(𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (12) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐸) 𝐼𝑑𝑒𝑎𝑙 : (𝑀 -𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 )[$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟 (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (13)

( 1 ( 1 ( 1 (

 111 𝑂𝐶 𝐸𝑀𝑆𝐷 )[$]{𝑡} = 𝜓 𝑂𝐶 𝐸𝑀𝑆𝐷 × (Ideal Risk Min )[$]or [$] 2 . (1 -√1 -t C C ) + α 1 . (Ideal Risk Min )[$]or [$] 2 . t, Where 𝜓 𝑂𝐶 𝐸𝑀𝑆𝐷 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡, 𝜓 𝑂𝐶 𝐸𝑀𝑆𝐷 > 0 𝑎𝑛𝑑 𝐷𝑢𝑒 𝑡𝑜 (𝐻𝐹𝐼𝑅 [%] -𝐹𝐼𝑅 [%]) (19)(𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 )[$]{𝑡} = 𝜓 𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 × (Ideal Risk Min )[$]or [$] 2 . (1 -√1 -t C C ) + α 1 . (Ideal Risk Min )[$]or [$] 2 . t, Where 𝜓 𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡, 𝜓 𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 > 0 𝑎𝑛𝑑 𝐷𝑢𝑒 𝑡𝑜 (𝐹𝐼𝑅[%])(20)(𝑀 -𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 )[$]{𝑡} = 𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 ) × Ideal Risk Min )[$]or [$] 2 .(1-√ 1-t C C )+α 1 .(Ideal Risk Min )[$]or [$] 2 .t, ,Where𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 ) = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 , 𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 ) > 0, 𝐷𝑢𝑒 𝑡𝑜 (𝐹𝐶 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 [$],𝑉𝐶 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 [$])(21)(𝑀 -𝑅𝐶 𝑃 𝐴𝑆-𝐵𝐷𝐿 >𝑈 𝐼𝑛𝑣 )[$]{𝑡} = 𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆-𝐵𝐷𝐿 >𝑈 𝐼𝑛𝑣 ) × Ideal Risk Min )[$]or [$] 2 .(1-√ 1-t C C )+α 1 .(Ideal Risk Min )[$]or [$] 2 .t , 𝑊ℎ𝑒𝑟𝑒 𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆-𝐵𝐷𝐿 >𝑈 𝐼𝑛𝑣 ) = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡, 𝜓 (𝑀-𝑅𝐶 𝑃 𝐴𝑆-𝐵𝐷𝐿 >𝑈 𝐼𝑛𝑣 ) > 0, 𝐷𝑢𝑒 𝑡𝑜 (𝐷𝐹 𝑅𝐶𝑇 [%] , 𝑉𝐶 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 [$]) -(22) (𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 )[$]{𝑡} = 𝜓 ((𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 )) × Ideal Risk Min )[$]or [$] 2 .(1-√ 1-t C C )+α 1 .(Ideal Risk Min )[$]or [$] 2 .t , 𝑊ℎ𝑒𝑟𝑒 𝜓 ((𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 )) = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡, 𝜓 ((𝑀 𝑅𝐶𝐵>𝐴𝐶𝐸 )) > 0, 𝐷𝑢𝑒 𝑡𝑜(𝐷𝐹 𝑅𝐶𝑇 [%])

Fig. 3 .

 3 Fig. 3. Calculated Operating Curves Model 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐴) 𝐼𝑑𝑒𝑎𝑙 : 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑥 [$] vs. (𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$]𝑜𝑟(𝐼𝑑𝑒𝑎𝑙 𝑅𝑖𝑠𝑘 𝑀𝑖𝑛 )[$] 2 (24) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 (𝐴) 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 : 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑒𝑎𝑛 [$] 𝑣𝑠. (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] or (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] 2 (25) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 𝐵 : (𝐴𝐶)[$]𝑣𝑠. (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] or (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] 2 (26) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 𝐶: (𝑂𝐶 𝐸𝑀𝑆𝐷 )[$]𝑣𝑠. (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] or (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] 2 (27) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 𝐷: (𝑂𝐶 𝑃𝑅𝑝𝑆𝐷 )[$]𝑣𝑠. (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] or (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] 2 (28) 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑢𝑟𝑣𝑒 𝐸: (𝑀 -𝑅𝐶 𝑃 𝐴𝑆 >𝑈 𝐼𝑛𝑣 )[$]𝑣𝑠 (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] or (𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑛 )[$] 2 (29)

  Ground Rule stipulated by the Parent Logistic Operating Curves Model, to the Operating Curves Model. To answer this Ground Rule, this model should conduct a Targeted Positioning between Two Key Trade-Off Variables. While there are Many Pairs or at least more than one pair of Trade-Off Variables, on a Holistic Approach and to answer the Problem Statement in Full and Complete, the Trade-off Variable that is of Potential Interest must encompass, in its scope the Underlying Concept of Working-Capital Management, the ETO Production Approach and the Operations of ETO Manufacturers.

already stated the Operating Curves Model must at any chance not deviate the Basic and Fundamental, Yet Key and Very Important Answer that has been Delivered by the Parent Logistic Operating Curves Model-Which is the basis for this Operating Curves Model, to the Question of Conducting a Targeted Logistic Positioning.

This hence has become a


Affonso Celso

Marcos Couto

Oliveira De

Antonio Sergio Morais

Dr Oduvaldo Brejão

Dr Pedro Vendrametto

Costa Luiz De Oliveira

Neto

Managing Enterprise Resource System (ERP) and Balanced Scorecard (BSC) in Food Industry in Brazil -food and beverage products -: A Multiple Case Study

Keywords: Strategy, Systems, Information Technology, ERP, BSC

This study aims to verify, analyze, and describe the importance of managing the Enterprise Resource Planning-ERP information system and the Balanced Scorecard-BSC strategic management system in the food industry in Brazil. The synergies, benefits, problems, and difficulties between them that can influence the implementation of the strategy in the organization to improve the results are analyzed.The present study focuses on the relationship between ERP and BSC, as well as the particularities of each that can influence the efficient management of the organization. The study was based on a multiple case study in five food industries located in Brazil.The theoretical framework was based on topics related to production and operations, strategy, organizational systems, integrated management system (ERP), strategic management system (BSC) and performance indicators. The conclusion of this study highlights the importance of each of these systems, as well as the synergies, benefits, problems, and difficulties among them in the execution of the strategy in organizations.

Introduction

The Brazilian food industry (food and beverage products) has faced decades of problems with high tax burden, high raw material costs, high logistical costs and fierce market competition. With this, Brazil loses its competitive power about the quality and prices practiced by other countries [START_REF]ABIA -Associação Brasileira das Indústrias de Alimentação. Relatório Anual 2016[END_REF].

According to the Brazilian Food Industry Association (ABIA) [START_REF]ABIA -Associação Brasileira das Indústrias de Alimentação. Relatório Anual 2016[END_REF] the food industry, which includes the production of food and beverage products, represented in 2016 the amount corresponding to 12% of the Brazilian GDP. The results could be better if the strategy planned by the companies could be translated into actions and performance indicators that could involve a larger number of employees [START_REF]ABIA -Associação Brasileira das Indústrias de Alimentação. Relatório Anual 2016[END_REF].

Amongst the main challenges for the food and beverage industry that require extensive attention and continuous process improvement, it is possible to mention: stock control and storage, product's distribution, quality control and compliance with specific standards. Given the sector's challenges, it is worth emphasizing that the use of an integrated information system (ERP) and a strategic management system (BSC) is a fundamental tool for strategic, tactical and operational management [START_REF] Rifkin | The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism[END_REF]. According to [START_REF] Kaplan | The Strategy-focused Organization: How balanced Scorecard Companies Thrive in the new business environment[END_REF], for the BSC use in the Strategic Management System to be effective, it needs to be integrated with an ERP -Information Management System.

Constant monitoring of competitive pressures over the years has prompted a series of investigations, not only in the area of integrated information systems but also in the strategic management systems segment of organizations [START_REF] Neumann | Gestão de sistemas de produção e operações[END_REF]. It is interesting to note that according to [START_REF]ABIA -Associação Brasileira das Indústrias de Alimentação. Relatório Anual 2016[END_REF], 96% the food industries have been working with ERP and BSC systems for more than ten years in getting their business.

This study aimed to analyze the ERP system and the BSC system in the food industry for identifying:

 Whether there is integration between ERP and BSC and the benefits that the ERP offers to the BSC;  The problems and difficulties of the ERP to meet the BSC; and  The consequences for the organization of the problems and challenges generated by the ERP to meet the BSC.

Literature Review

A synthesis of the theory to the support of the study is provided, with the presentation of a brief history of management of production systems and operations, strategy, organizational systems, integrated management systems -ERP's, performance indicators and BSC -strategic management system.

The topics were analyzed as the theoretical basis for the understanding of each of the theories, the relations between them and for the purpose of the research that is to verify the alignment (synergies/difficulties) between the BSC and ERP in the execution of the strategy in the organizations.

The authors of the BSC -strategy management system [START_REF] Kaplan | Conceptual Foundations of the Balanced Scorecard[END_REF] conceptualize strategy as the choice of market segments and customers that business units intend to serve [START_REF] Kaplan | The balanced scorecard: measures that drive performance[END_REF].

The Information Technology (IT) is considered as one of the corporate resources that support strategy at all levels of the organization, giving elements to the business in gaining competitive advantage [START_REF] Canepa | Práticas de alinhamento estratégico: umestudo exploratório em organizações industriais e de serviços[END_REF]. The importance of IT grows at all levels of decision-making, IT improves the communication, integration, and exchange of information among all areas of the organization, enabling at all levels knowledge and operationalization of the strategy defined by the organization [START_REF] Brobeck | Configuração de um processo de seleção, aquisição e implementação de ERP considerando os Grupos Sociais envolvidos[END_REF].The quality of every decision depends greatly on the quality of information and synergies among the systems deployed in the organization [START_REF] Robins | Management[END_REF].The evolution of management information systems has brought advances in production planning and control systems, with significant cost reductions and improved quality and customer satisfaction [START_REF] Vollmann | Sistemas de planejamento e controle da produção: para o gerenciamento da cadeia de suprimentos[END_REF].

The choice of an ERP affects the stakeholders or social groups that participated in the market. ERP is the choice to determinate a critical point to ensure the obtaining of transparent and reliable information that supports the strategy and decision making in the organization. It is necessary for the information systems strategy to reflect the company's strategy [START_REF] Micheli | Executing strategy through comprehensive performance measurement systems[END_REF].In the case of organizations, the indicators enable the unfolding of the business goals in the organizational structure, ensuring that improvements achieved in each unit will contribute to the overall purposes of the organization [START_REF] Kaplan | Conceptual Foundations of the Balanced Scorecard[END_REF]. The indicators help the organization to direct its efforts towards the strategy and test the organization's progress [START_REF] Kaplan | The balanced scorecard: measures that drive performance[END_REF].

The acceptance and worldwide growth of the use of the BSC methodology are due to the IT areas, which automated the method, thus allowing its application with greater practicality [START_REF] Kaplan | The Strategy-focused Organization: How balanced Scorecard Companies Thrive in the new business environment[END_REF]. The BSC methodology is based on four perspectives on the organization's vision and strategy: Financial Perspective, Customer Perspective, Internal Processes Perspective and Innovation and Learning Perspective [START_REF] Kaplan | Translating Strategy into Action[END_REF].The benefits considered by scholars and researchers in the implementation of the Kaplan and Norton model are in the alignment of outcome indicators with trend indicators. Those are related to consideration of different interest groups, such as in the analysis and execution of the strategy; the strategy communication; it is focused on actions,and being a flexible management and monitoring tool for strategic planning.

Materials and Methods

The Research

A preliminary bibliographical survey was carried out to identify the main questions regarding the ERP system (functions, benefits, comments, and statements) and the BSC system (functions, purposes, benefits, and limitations) in current literature. Afterward, 12 pilot companies were visited to identify which companies were willing to participate in the survey. With the support of these companies, there were chosen the main questions that should be included the questionnaire to satisfy the research objective.The questionnaire had 15 questions, and it was applied to the manager of the financial area, controlling, logistics, production, information technology, human resources, administration, sales and after sales.

The research was carried out in five food industries based in Brazil with the objective of verifying and analyzing the existence of synergies and difficulties between the BSC (Balanced Scorecard) strategic management systems and the ERP (Enterprise Resource Planning), in the execution of the organization's strategy.

The Case Study

Due to the empirical research, we choose the case study method to describe and analyze the integration and synergy between BSC and ERP [START_REF] Yin | Case study research: design and methods[END_REF].In a research conducted using the case study method, dual-dimensions should be considered [START_REF] Yin | Application of Case Study Research[END_REF]:

 The number of cases that make up the study; and  The focus attributed to the unit under analysis. The purpose of the use of multiple cases is to enable the comparison between the ERP system and the BSC system in different companies that use these two management systems.Table 1 presents the characteristics of the food industries researched.

Discussion of the Case Study -Results

Analyzing the answers of the interviewees, it was possible to state that:

 The main synergies between the BSC and the ERP are in the planning, calculation, monitoring, and dissemination of performance indices together with the speed, quality and reliability of the information;  Synergy and integration between ERP and BSC brought improvements in production and operations processes, productivity gains, cost savings, improved product and service quality, increased market share and profitability;  The benefits generated by the integration and synergy between the BSC and the ERP refer to the monitoring and control of the objectives and goals of the industry, timely correction in the decision-making process, the reports made available at any time and improvements in the processes of cost rationalization;  The difficulty in integrating BSC with ERP is one that could at first be considered predictable: the ERP was not prepared to meet all the needs of the BSC; consequently, there was a need for adjustments and improvements with the generation of additional, unplanned costs.

The problems in integration and difficulties between BSC and ERP systems observed in all industries surveyed were:

 The areas that presented the greatest difficulties in this regard were the areas of logistics, production, and sales, while in the financial, accounting and controlling areas, the integration of the BSC was normal;  However, the greatest difficulty common to all the industries surveyed was the lack of understanding of those involved in the proposal that the BSC system intended to take the organization's strategy to the knowledge and participation of all. The training factor was considered the most vulnerable point in the process and was later used on a larger scale to raise awareness and minimize resistance to change.

Final Remarks

The use of information management systems (ERP) and strategic management (BSC) becomes a significant competitive advantage for the food industries in Brazil, so it is possible to understand why 96% of the food industries use ERP and BSC in the management of their businesses.

The multiple-case study method discussed in this study showed that the ERP and the BSC, as well as the synergies them, meet the needs of the management and execution of the strategy in these organizations. Both ERP and BSC systems, integrated, bring benefits in stock control and storage of the raw material and the finished product, avoiding perishable products spoil and cause damages. Both ERP and BSC integrated through synergies, contribute to the control of quality management that is determinant in the food industries, through defined processes and parameterized for each batch produced. These systems make it possible to manage the quality of products purchased and finished, issue reports of non-conformity, batch domain and mainly product traceability. ERP and BSC, individually, and primarily by their synergies facilitate compliance with the legal standards that certify the release of consumption for the product. These systems issue technical reports, printing of nutritional labels with details of product specification and its components to meet specific legal standards. It is observed that when there are synergies, integration between the ERP and the BSC these companies become more competitive and more profitable.


Günther Schuh
email: günther.schuh@fir.rwth-aachen.de

Dominik Kolz
email: dominik.kolz@fir.rwth-aachen.de

Morphology of Strategic Components for Data-Driven Industrial Services

Keywords: Industrial services, data-driven services, morphology

The design of data-driven industrial services in the context of industry 4.0 represents a major challenge for industrial service providers and manufacturing companies for investment goods. Data-driven services require technological and strategic components that most companies have not build up yet and that differ from current configurations. That is why many companies lack a systematic approach and implementation competence for the use of data in the context of industrial services and therefore face the challenge of not being able to expand their market position in an ever-growing competition for data. The present paper addresses this research deficit with the aim of describing strategic features and characteristics of data-driven industrial services by identifying the related crucial features and characteristics through a morphological approach. This will enable industrial service providers to improve strategic and operative management decisions in order to define a specific strategy and to configure data-driven services.

Introduction

Within the last 50 years, information technologies have changed the way companies conduct their business more than once: The rise of the computer in the 60s led to automation within value chains and raised awareness on the use of data. Afterwards in the 80s, the internet allowed real-time collaboration, facilitated global supply chains and boosted the generation and usage of data. The next transformation, the internet of things, has already started and impacts competition and strategy like none before. [START_REF] Porter | How smart, connected products are transforming competition[END_REF][START_REF] Keskin | Strategies in Smart Service Systems Enabled Multi-sided Markets. Business Models for the Internet of Things[END_REF] Parallel to that development, companies underwent a transformation process, developed from product manufactures to solution providers, and thus generate additional value for their customers. In practice, it can be observed that companies follow this transformation process, but the offering of operating models and output-based solutions is not established extensively: Service level agreements and output-oriented business models are offered by only 18%, respectively 13%, of global industrial companies. [START_REF] Baines | Proving the Service Continuum: Quantifying the strategic and economic impact of global service transformation[END_REF] This can be attributed to the relatively low usage of data that enables companies to learn from their products, learn from their customers and adapt their products and services to the needs of their customers. [START_REF] Husmann | Smart Services -Neue Chance für Services 'Made in Europe'-KVD-Servicestudie[END_REF][START_REF] Dijkman | Business models for the Internet of Things[END_REF] 2

Objective of developing a morphology

The use of data and big data in an industrial context is mainly driven by quality improvements and the resulting reduction in the frequency of errors and process efficiency improvements. Process engineers use enormous amounts of data to control, monitor, and optimize the performance of processes. [START_REF] Yin | Big Data for Modern Industry: Challenges and Trends Proc[END_REF] Although premium manufacturers have a high digital competitiveness and therefore the best conditions to compete in a data-driven world, they often fail to develop databased value-added services [START_REF]Recommendations for the Strategic Initiative Web-based Services for Businesses[END_REF][START_REF] Kagermann | IT-driven business models[END_REF]. This can be attributed to the following practical problems: Most industries are lagging behind the chances and potentials of digital transformation. Decision-makers focus on the realization of internal potentials first, such as cost savings through technology use or cost reduction through more standardized service processes. However, the realization of potentials driven by innovations is largely neglected. This includes the use of data-based services to increase revenues. [START_REF] Husmann | Smart Services -Neue Chance für Services 'Made in Europe'-KVD-Servicestudie[END_REF][START_REF] Marr | Big data. Using smart big data, analytics and metrics to make better decisions and improve performance[END_REF] The same applies for the scientific research gap: Previous approaches dealing with digitalization, data-based business models, and digital driven services mostly describe case studies or individual characteristics of data-based services, but lack the provision of a detailed description, based on detailed features and characteristics (Table 1). In summary, there is a lack of a scientifically based, illustrative and practical systematization of industrial, data-based services.

In order to describe data-driven services, the following definition is used in this paper: Data-based services are intelligent, internet-based services. They are either linked to physical products as data providers and in doing so complement the products, or are based on data-driven services that are detached from products. Smart services are datadriven services based on digital platforms that enable the formation of service ecosystems in which different actors are organized. [START_REF]Recommendations for the Strategic Initiative Web-based Services for Businesses[END_REF][START_REF] Herterich | The Next Wave of Service Innovation: How cyber-physical systems can be leveraged for effective industrial equipment operations and empower industrial service[END_REF][START_REF] Brownlow | Data-Driven Business Models: A Blueprint for Innovation[END_REF][START_REF] Chae | Big Data and IT-Enabled Services -Ecosystem and Coevolution[END_REF]

Theoretical Foundation

The report of the working group "Smart Service Welt -Internet-based Services for the Economy" by Acatech represents a fundamental conceptual basis for the present paper. The scope of the report lies within the linking of processes, products and services with data. This connection is seen as the most important basis for the development of new services and business models. The developed layer model schematically describes the composition of digital infrastructures for smart service delivery in four levels: smart spaces, smart products, smart data and smart services. In accordance to the layer model of digital infrastructures, the following components are utilized in order to develop a morphology for strategic components of data-driven, industrial services: collect data, data transfer and backup, analyze data and service delivery. [START_REF]Recommendations for the Strategic Initiative Web-based Services for Businesses[END_REF]

Morphology of Strategic Components for Data-Driven Industrial Services 3

Besides this fundamental work by Acatech, a literature review has been conducted. In Table 1, an extract of the central scientific approaches for the elaboration of this paper is listed. Previous research deals with technical or strategic components in the field of data-based-services, the internet of things, big data and analytics, business intelligence and business models. Regarding this research, features, classifications, morphologies and typings are developed. However, none of the previously conducted research delivers a consolidated morphology of strategic and technical features for databased-services.

Table 1. Literature review (Harvey Balls: meets the criterion/ partially meets the criterion)

Methodology

Analytical research methods, such as classification, morphological methods or typing, are used to gain insights into research objects. They contribute decisively to generating new knowledge on investigation objects. According to the aim of the paper of describing the main strategic components of data-driven industrial services, the morphological method is particularly suitable, since the construction of a morphological box allows the derivation of multi-dimensional solutions to describe real situations with features and characteristics. The following procedure, consisting of three consecutive steps was conducted in alignment with a research approach following Zwicky [START_REF] Zwicky | Entdecken, Erfinden, Forschen im Morphologischen Weltbild[END_REF] and Welter [START_REF] Welter | Die Forschungsmethode der Typisierung: Charakteristika, Einsatzbereiche und praktische Anwendung WiSt -Wirtschaftswissenschaftliches Studium 35[END_REF]:

1) Definition of the test area:

The formation of types starts with the analysis of the test area and the identification of the objects to be examined.

2) Selection of suitable features:

After the definition of the test area, the features are identified, with regard to the research objective. These features can be descriptive or type-forming. Type-forming features are constitutive and define a type, while descriptive features build up a type. Characteristics of features can be distinguished in polar or scalable. If characteristics are discrete, assuming real values (e.g. company size); they are called polar features, while gradual characteristics (e.g. temperature) that can be described by an infinite number of characteristics are named scalable characteristics.

3) Determination of meaningful characteristics: After the selection of appropriate features, the design of the corresponding characteristics is carried out. This is done by means of qualitative and quantitative descriptions of the subject of the investigation.

Morphology of strategic features and characteristics of datadriven industrial services

Below, the features of the morphology are briefly described. In order to develop and offer data-driven services, companies need to consider the following components, derived from the layer model of digital infrastructures [START_REF]Recommendations for the Strategic Initiative Web-based Services for Businesses[END_REF]: Collect data, data transfer and backup, analyze data and service delivery. Following the morphological approach, the strategic features and characteristics depicted in Figure 1 have been identified.

Fig. 1. Overview of strategic components of data-driven industrial services

Service delivery.

The Focus of service provision describes an object-oriented, value-chain-oriented or ecosystem oriented approach. In case of the object-oriented focus, own objects, foreign objects or both, own and foreign objects can be distinguished. [START_REF] Yin | Big Data for Modern Industry: Challenges and Trends Proc[END_REF] The feature Key activities consist of the virtual and physical provision of services. The virtual provision consists of the characteristics of the provision of raw data and the Morphology of Strategic Components for Data-Driven Industrial Services 5 provision of knowledge. [START_REF] Hartmann | Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-Up Firms[END_REF] An additional step lies within the virtual provision of services as the orchestration within a supply chain or ecosystem. The physical provision of services is based on the characteristics of the provision of services by the provider itself, or the provision by a third party.

The Revenue model for data-driven industrial services can be divided into three characteristics: Free add-on to an object, individual billing and performance related billing. The last characteristic can be divided into pay-per use, pay on availability and pay on production. [START_REF] Ansorge | Ordnungsrahmen für die Positionierung industrieller Dienstleister[END_REF][START_REF] Nagle | The strategy and tactics of pricing. A guide to growing more profitably[END_REF] The feature Connection and implementation is depicted through two main characteristics and describes the effort for implementation as purely administrative or physical. A purely administrative implementation occurs, when the object is already connected to the internet and a connection to the provider is established similar to plug and play. In case of a physical connection, objects and systems have to be connected to each other. This physical connection can be established by the customer itself, by the provider or by a third party.

The feature Key resources consists of physical resources such as plants and equipment, properties, machines, systems and spare parts. Intellectual resources are brands, knowledge, copyrights and the partner network. [START_REF] Hartmann | Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-Up Firms[END_REF][START_REF] Schuh | Einführung und Grundlagen des Managements industrieller Dienstleistungen[END_REF][START_REF] Kagermann | IT-driven business models[END_REF] In this case, the partner network is viewed as a separate characteristic.

The Effort for individualization describes the companies' effort, in order to provide standardized or customer-specific services. For this reason, the following characteristics are used: combination of standardized services, individually adapted services by occasion and customer-specific generation of overall services. [START_REF] Ansorge | Ordnungsrahmen für die Positionierung industrieller Dienstleister[END_REF] The Customer access and system integration is described by four main characteristics. The first characteristic describes the access to a service through manual channels (e-mail, USB-sticks, etc.), followed by the provision of services through the system of the provider. This could be a web-portal or a software that is distributed to the customer in order to deliver dashboards and visualizations. A deeper integration is achieved through the link into the customers' existing ERP or PLM system. The fourth characteristic describes platforms that can be closed (operated and organized by a single company) or open (provision of a technological basis that enables collaboration between different players) [START_REF]Recommendations for the Strategic Initiative Web-based Services for Businesses[END_REF].

The Duration of business relationship can be divided into three characteristics. Short-term (single transaction), medium-term (<2 yrs.) and long-term (>2 yrs.) [START_REF] Weber | Properties and Characteristics of Product-Service Systems -An integrated View[END_REF].

Data transfer and backup and Analyze data.

The components Data transfer and backup and Analyze data have no overall strategic importance. Following this paper, further research will be conducted for these technical components, including for example the capability for data exploitation, data query, data quality management or method of data analysis.

Collect Data.

Data sources can be divided into internal and external data. Internal data itself can be divided into several data sources: data that exists or is created in IT-systems, but that is not used and data that is, among others, generated for a certain purpose through web tracking, physical devices, crowdsourcing and social collaboration methods. External data uses acquired data that is purchased, or provided by customers. [START_REF] Hartmann | Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-Up Firms[END_REF] The Data base consists of multiple steps. At first, data of a single object is considered. This is expanded by the view of aggregated object data of a single customer. The next steps focus on aggregated object data of several customers or aggregated data of a value chain. The last characteristic is aggregated data of an ecosystem.

The developed morphology helps service providers and manufactures to position themselves strategically by combining different characteristics. The validity of the developed morphology, consisting of strategic components of data-driven industrial services has been confirmed in several company workshops and expert discussions. The development of different types of data-driven industrial services represents a downstream research work and a logical next step. A possible type formation is briefly explained below.

Morphology of strategic features and characteristics of data-driven industrial services.

Figure 2 shows the developed features and characteristics in accordance to the morphological approach.

Fig. 2. Strategic components of data-driven industrial services

Focus of service provision

Key activities

Revenue model

Connection/ implementation

Key resources

Effort for individualization

Customer access/ system integration

Duration of business relationship

Data sources

Data base

Morphology of Strategic Components for Data-Driven Industrial Services 7

Knowledge as add-on to own objects: This type focuses on own objects. However, the raw data obtained through the object is transformed to information and knowledge through data analysis methods. In this case, it is no longer merely a matter of a dashboard for visualizing raw data, but rather the individual provision of customer-specific recommendations for machine optimization or use, comparable to a remote consulting service. The vendor is able to provide this knowledge since he can access aggregated object data of many customers through the installed base, create comparability and generate specific knowledge about the use and improvement of the object. Access to the data is important; however the intellectual ability to draw conclusions from data is a key resource. The billing of the virtual service is performed individually and the service is delivered virtually. Since the vendor only supports the customer with specific services on a selective basis, the service is based on one or more individual transactions.

Conclusion and Outlook

The scientific result of this paper is a morphology for strategic components of datadriven industrial services. The morphology depicts a detailed summary of strategic components for data-driven industrial services based on data collection and service delivery. The results enable strategic and operative decision makers to align the future service portfolio and configure data-driven services for individual domains. Therefore, the scientific result of this paper closes the previously stated research gap. Further research may be conducted in order to develop technical components of data-driven industrial services. These results will be developed in the follow-up to this paper and in accordance with the developed regulatory framework, especially with regard to data transfer and backup and data analysis. In addition, the model will have to be validated by industrial users.


Ulf Bergmann

Matthias Heinicke
email: matthias.heinicke@ovgu.de

Approach for the Evaluation of Production Structures

Keywords: Production structure, suitability of system´s configuration, structural adaption, evaluation procedure

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

Introduction

Only a few analytical approaches exist evaluating the appropriateness of the production structure regarding pending customer demands [START_REF] Hildebrand | Plug + Produce -Gestaltungsstrategien für die wandlungsfähige Fabrik[END_REF]. Most planning approaches try to pre-determine a flexible system structure [START_REF] Tompkins | Facilities Planning[END_REF]. For this reason, we introduced the Structural Quality (SQ) that enables the assessment of the spatial and temporal structure [START_REF] Bergmann | Resilience of productions systems by adapting temporal or spatial organization[END_REF]. In this paper, we illustrate an SQ-based approach and its application potential.

The arrangement of the elements of a production system (spatial reference) and the planned configuration of the relations a product undergoes to its production (time reference) is called structure [START_REF] Hildebrand | Plug + Produce -Gestaltungsstrategien für die wandlungsfähige Fabrik[END_REF]. As an essential (planning or design) activity for the configuration of production systems, structuring determines the implementation costs of the production processes. Based on the specific type and number of workstations (sys-tem´s composition) and the relationship between them, a production structure defines the basic structure of a production system (system's setup) as well as its function (system's behavior) [START_REF] Costanzo | Enterprise Organization and Operation[END_REF].

Spatial system configuration, reflected as the economic arrangement of workstations, is done by optimizing the layout on the basis of relevant design criteria, such as transport performance and the communication index. Thus, it is aimed at minimizing expenses when "using" the relations during operation [START_REF] Bergmann | Resilience of productions systems by adapting temporal or spatial organization[END_REF][START_REF] Francis | Facility Layout and Location: An Analytical Approach[END_REF]. The system´s behaviorprocesses and their interactions as temporal structure -is based on the system´s predefined setup, and is influenced by the basic principle of the order processing´s flow and sequence. Thus, it is aimed at maximizing the performance of resource elements [START_REF] Nyhuis | Fundamentals of Production Logistics Theory, Tools and Applications[END_REF].

The vector SQ as key figure enables verifying the suitability of the installed system structure and a specific production program in terms of a proper function of the production system. According to Chandler's insights into the interrelations between structure and strategy, the production task (expressed by the production program) is one of the main inputs to structural design within the framework of production structure planning [START_REF] Chandler | Strategy and Structure: Chapters in the History of the American Industrial Enterprise[END_REF][START_REF] Mehl | Fiat-Auto: Struktur schlägt Strategie: Aufstieg und Fall eines autoritär geführten Unternehmens[END_REF]. Theoretically, any change in the production program per se leads to a changed structure in terms of a modified spatial-temporal organization of the production system. On the contrary, industrial production processes require a long-term stable cost function. Therefore, long-term valid structures for production systems are typically sought in production management [START_REF] Wiendahl | Changeable manufacturing: classification, design and operation[END_REF]. In practice, any established production structure is a transition between the function-oriented and the object-oriented type of structure [START_REF] Chase | Operations management for competitive advantage[END_REF]. Consequently, the definition of a structure is commonly a long-term decision of the general principle of structuring.

However, the underlying assumptions of such a defined structure are counteracted by the increasing individualization of customer requirements. Due to significant shifts in production program, there is increasing pressure to change an installed production structure. Beyond the limits of the equipment's flexibility [START_REF] Wiendahl | Changeable manufacturing: classification, design and operation[END_REF], adaptations to the inherent system-related properties are necessary, i.e. a re-configuration of the system's composition, setup, and behavior. Companies do not use every change in the production program as an opportunity to adopt the installed production structure. Typically, more often rationalization measures are introduced, such as process-specific modification or integration of production steps. Finally, there are adapted structures in the form of casespecific operation solutions. This results in huge amount of different transitional structures.

SQ reveals the potential of an adaptation of the production structure. At this point, SQ facilitates an assessment of changing the spatial and temporal structure. Both in the planning phase as well as in the operation and optimization phase, suitability of production structures for the present production task can be evaluated. If the existing production structure is a planning construct, an anticipatory structure evaluation takes place, whereas structure is actively monitored during operation.

2

Holistic Evaluation Approach

Basic Process Model

Derived from control theory, we illustrate our re-configuration model as a block diagram of a closed-loop control system (Fig. 1). The object of observation is a production system with a planned or already installed production structure as described above. Typically, key performance indicators are recorded during system operation. Such performance indicators along with sufficient input variables are necessary for an overall re-configuration or adjustment of production structures. Starting point for such a reconfiguration is a relevant change to the production program. Such changes affect variations in the type and quantity of the products to be manufactured in relation to the original design specifications of the production system. In a multi-stage procedure, the SQ-based structure evaluation is performed in the configurator. Thus, necessary adaptation measures can be derived. Nevertheless, unlike the actual "control theory", such measures refer to the change of the control system, i.e. the present structure of the production system. Thus, the following integrated configuration procedure is based on human decisions. However, proper indicators and detailed analysis of the Structural Quality of the production system support the detection of changes and the adopting of appropriate measures.

Integrated Configuration Procedure

The configurator carries out a two-step evaluation and decision-making process. At first, changes of the production program are detected by a preliminary evaluation. Moreover, the amount of value alternation facilitates decisions about further steps. After this rough assessment, a detailed analysis is based on Structural Quality [START_REF] Bergmann | Resilience of productions systems by adapting temporal or spatial organization[END_REF]. The more in-depth evaluation procedure enables an identification of significant shifts within the production program. Thereby planning and design activities are taken into account simultaneously. As a result, proper measurements in terms of structural adaptions are revealed. This allows the planner both, to evaluate the suitability of the current production structure and to identify adequate structural solutions in case the present production system is not sufficient regarding the pending tasks anymore. Thus, the integrated configuration procedure is an iterative evaluation and decision-making process.

During preliminary assessment, the planner gets first impressions about the effect of the changed production program on the already installed production structure. Thus, the planner can detect if there are certain alterations within the production program. If the limits defined by the planner in advance are adhered to, then no action is necessary. If the indicator values go beyond defined thresholds, then a detailed structure evaluation is carried out using SQ-determination. Based on this SQ-determination, the planner finally decides which specific changes have to be worked out. This refers to both, temporal and spatial aspects of the production structure.

The determination of the Structural Quality within the integrated configuration procedure is an event-driven process, triggered by a changing production program. In this case, the vector SQ serves as an important criterion for allowing the planner to weigh the need for changes in the production structure (cf. Fig. 1). Structural decisions are consequently long-term decisions regarding the general principle. For this reason, a periodic check of the functional structure also is recommended. Then, the configurator runs as a time-discrete application.

Key figures for Evaluation of Production Structures

Spatial Structure

In order to evaluate the spatial structural component, an assessment of the relevant spatial layout is required. Basically, we focus on the complexity of material flow. Minimal "complexity of the material flow" is found within the purest form of a production line. Precisely one upstream and one downstream workstation without return flow characterize such a production line. However, maximum "complexity of the material flow" is found in a fully meshed job-shop structure. In order to assess the "complexity of the material flow", we use two indicators: The degree of linearity and the standardized degree of cooperation.

The latter determines the number of material flow relations existing between the workstations [START_REF] Kühnle | Lecture Notes in Manufacturing Systems Design and Manufacturing Process Organisation[END_REF]. For the standardized degree of cooperation, the value 1 is defined as a threshold value: In the range 0 ≤ κ ≤ 1, the working stations are directly connected to one another in a sequence. Thus, structure of such arrangements tends to be a linear. If more than one workstation is connected to more than two other workstations, the val-ues´ range is κ > 1. Such arrangement tends to be a job-shop structure. The degree of linearity takes into account the number of return flows in contrast to the prevalent sequence of operations.

A change in intensity and basic orientation of the relations imply the trend of a potential structural change. Minimum "complexity of the material flow" exists when the degree of linearity assumes the value of 1 and the standardized degree of cooperation has a value of 0.

Temporal Structure

The temporal structure considers the workflow, i.e. the logical and temporal linking of the partial tasks of production [START_REF] Kühnle | Lecture Notes in Manufacturing Systems Design and Manufacturing Process Organisation[END_REF]. The sequence of relations between the system elements (workstations) expresses the temporal component of the processes running in the production system. Based on the intensities of the relations as dynamic variables, different temporal courses can be characterized. Parameters of the system-related temporal structure are mean value and variance of material flow´s intensity. These figures refer specifically to the (variable or constant) amplitude of the system load. The regularity of the intensity is determined by the loadoriented coefficient of variation. It provides reliable information on the variability of the load resulting from a changed product and / or quantity mix. If the production system is fundamentally capable of dealing with the upcoming production program, order-induced average system load as estimated value of processing times characterizes the dynamics of load situation. It combines the mix of the production task and the intervals of the specific customer demands. If both aspects are differentiated, the workload for each order and the average time span between the respective customer orders result. However, the effect on the production system is only apparent when considered aggregated.

Functional Structure

The functional production structure is based on different product characteristics (demand profiles) and thus leads to a segmentation of the production in bestseller (quantity focus) and exotics (diversity focus) [START_REF] Kühnle | Lecture Notes in Manufacturing Systems Design and Manufacturing Process Organisation[END_REF]. The distinction with regard to a product specification requires a certain diversity of the product range with divergent continuity and contrasting order volumes, i.e. the primary structuring takes place according to the type of production demand. Both the assessment of quantities as well as product changes are thus of central importance for evaluating the suitability of an existing or planned production structure.

The quantity index indicates the average number of products for each order. The average order volume combined with the corresponding coefficient of variation expresses the quantity mix of a production program. In contrast, the variance index represents the number of product variants of an organizational unit (product mix). In this case, nominally different types of orders can be combined into identical product families due to their process-related system load.

An increasing quantity index and a decreasing variation index indicate a concentration of the production program on a decreasing number of rather homogeneous products. Similarity is expressed particularly by a reduction of the coefficient of variation. On the contrary, the organizational unit tends to a broader and more heterogeneous product spectrum if the trends are reverse. By using these indicators, a decisive characterization of the production program is carried out which takes account of both the quantity and the product mix. Thus, a decision regarding the basis segmentation of the production system into organizational units is enabled.

4

Case Study

Indicator-based Check of Production Structures

In the following, we will present an overview of the practical use of SQ by means of case study data, whereby the "Basic Process Model" forms the framework. In addition to the integrated SQ-assessment for the evaluation of the spatial and temporal aspects of production structures (event-driven process), the periodic check of the functional structure of a production system (time-discrete process) is also shown using the "Integrated Configuration Procedure". According to the presented approach, the mode of operation of the configurator is described; both for the evaluation of the spatial and temporal structure components (event-driven mode) as well as the assessment of the functional structure of the given system (time-discrete mode). The installed production structure is based on the former production program shown in Fig. 2 (top). Currently, a change in the production program is apparent, as shown in Fig. 2 (bottom). If this change is significant, the need for structural adaptation is analyzed in the following sections.

Consequently, at first the preliminary assessment of relevant indicators is executed. The standardized degree of cooperation amounts 0.92 (previous: 1.29). The degree of linearity amounts 1 (previous: 0.85). Thus, after the change of the production program, a consistently forward-directed sequence of operations is present. With regard to the standardized degree of cooperation, a production line appears to be suitable. Thus, the complexity of material flow decreases. According to the change of the indicator values, the production structure gradually migrates from a job shop production towards a production line. Indicators for the temporal aspect of the production structure show an ambiguous development. The mean workload remains approximately constant. Accordingly, the capacity of the production system and thus the system composition is still sufficient. In contrast, the coefficient of variation alters from 98% to 132%. Diversity of process times increases, which is indicative for a shift towards a job shop production. This contradicts the development of the other indicators.

With regard to the functional structure, quantity index increases and variation index decreases. In contrast to the coefficient of variation, the variation index compares the similarity of production processes and their workload for every workstation. Whereas the number of products grows, their homogeneity also enlarges. There are different groups of products with nearly the same production sequence (e.g. group 1: ET-5, ET-6, and ET-7; group 2: A-100 and A-200 etc.). This underpins in particular the change of the spatial indicators. However, a further in-depth analysis seems to be necessary.

Detailed Analysis of Production Structures

Finally, detailed structure evaluation determines spatial, temporal, and functional SQ. For a detailed SQ-assessment regarding material flow, we refer back to known (ideal-typical) arrangement algorithms for minimizing transport effort. For the purpose of a comparison of future and actual structure types, a visualization in form of scaled block-layouts is helpful. Transport effort decreases from 59,023 to 51,809 transport units per year. Subsequently, the ideal spatial arrangement was used as normalization value of 100% transport effort. Based on the previous production program that indicates a job shop production, spatial component of Structural Quality improves from 1.90 to 1.67. Additionally, a simulated shift from a job-shop structure to a production line reveals a potential improvement of about 13.9%.

Derived from three logistic performance indicators: throughput time, output rate and work in progress (WIP), the quality of Temporal Structure refers to the operation point of the production systems. To sum up, all figures decrease when retaining the current control strategy: CONWIP (Constant Work-in-Progress). Output rate declines from 338.3 h per SCD to 313.2 h per SCD, WIP decreases from an average workload of 5718.8 h to 3437.5 h, and mean throughput time lowers from 17.28 days to 11.27 days. The decrease of the output rate reveals a less effective system behavior, whereas the reduction of WIP and lead-time represents an improvement. However, the simulationbased assessment of operating points shows that the existing system composition is still capable to fulfill respective production tasks. Thus, the Temporal Quality improves from 2.71 to 1.43.

The evaluation of the functional structure emphasizes that a splitting of the existing organizational unit according to product families revealed seems to be promising. However, further segmentation of the organizational unit may require other structural solutions respectively. In this case, the integrated configuration procedure and in particular the SQ-determining is conducted iteratively until an adequate segmentation including suitable production structures is found.

Conclusion and outlook

The integrated configuration procedure facilitates an easy detection of structural changes by indicators. These indicators reveal the amount and direction of change. If defined thresholds are exceeded, an in-depth analysis is conducted. This detailed analysis comprises an assessment of the suitability of current production structure by determining the Structural Quality vectors. During this procedure, planning and design alternatives are considered systematically. Thus, an iterative improvement of suitability is enabled. The evaluation of different structural variants is supported by simulation studies because only in this way dynamic behavior and performance of the altered production system become obvious.

The case study highlights the application of the multi-stage approach. If thresholds of indicators are exceeded or indicator´s development shows an ambiguous trend, a detailed analysis of Structural Quality is inevitable. Assessment of functional structure indicates a different segmentation of the considered organizational unit. With regard to proper product families in terms of homogeneous processes and workload, at least three new organizational units are reasonable. Each of these units need an appropriate production structure. In this regard, further research is necessary to extent the iterative procedure.

Fig. 1 .

 1 Fig. 1. Configuration Procedure as closed-loop system.

Fig. 2 .

 2 Fig. 2. Former production program (top) and actual production program (bottom).