THE INTEGRATED SCENARIO DATA MODEL I. Gräßler, J. Pottebaum
Heinz Nixdorf Institute, Chair for Product Creation, Paderborn University, Paderborn, Germany
Abstract
Strategic planning of products and production systems is the key to create business opportunities utilizing the full potential of evolving technologies, to adapt to changing markets and to enhance time-to-market. As a prerequisite, methods and tools like Scenario Technique approaches need to be more intuitive and efficient. To increase the availability, timeliness, data security and forecast reliability, the Integrated Scenario Data Model (ISDM) has been designed as an instrument to extend evidence-based influences on strategic planning and engineering. The approach is based on a model-based integration of existing complementary data resources, formats and standards (e.g., STEP, SCOR, ISCL and ISA-95). The main contribution is based on an analysis and semantic mapping of available information resources and the required inputs for scenario based engineering. It facilitates the aggregation of trend data representing especially economic and political impact factors, data acquired from product lifecycle management tools (including, e.g., ERP, MES and PDM systems) as well as constraints, for instance determined by ethics and legislation.
While models exist in all relevant fields, there is no transparent and integrative approach for the creation of future scenarios available so far.
Thus, the ISDM increases the reliability of scenario creation and of business model development and reduces the efforts for iterative or even agile application in product development processes.
Keywords:
Scenario technique, Strategic planning, Product development, Data management, Semantic modeling
1 INTRODUCTION
Strategic planning of products and production systems nowadays means to create business opportunities utilizing the full potential of evolving technologies and interconnected, global markets [1]. New business models [2] are essential taking into account the highly dynamic innovations from all relevant disciplines [3]. Targeting an enhanced time-to-market in general, strategic planning needs to speed up significantly [4]. However, contemporary Scenario Technique approaches are characterized by manual
provision with required input information (such as environmental, social, political and technological trends) and meta-information (like probability and interdependencies of trends); trend estimation, assessment of risks and experience with previous products are collected by manual estimation and time consuming interviews, literature research and discussion with strategic planners and CEO's [5]. Depending on low quality of input information, it is rather unrealistic to forecast reliable scenarios for the future.
To increase the availability, timeliness, data security and forecast reliability, an Integrated Scenario Data Model (ISDM) has been developed and will be described in this contribution. The ISDM represents an instrument to extend evidence-based influences on strategic planning and engineering. The approach is based on a model-based integration of existing data resources, formats and standards. The ISDM maps the semantics of available information resources and the required inputs for scenario based engineering. It facilitates the aggregation of:
Trend data representing especially economic and political impact factors
Data acquired from product lifecycle management tools (including, e.g., Manufacturing Execution and Product Data Management Systems)
Constraints, for instance implied in terms of data quality or determined by ethics and legislation.
While models and tools are established in all relevant fields and partially used for commercial applications, there is no transparent and integrative approach for the creation of future scenarios available so far.
The paper is structured along this line: Chapter 2 provides an analysis of a) Scenario Technique for strategic planning and b) integrated data management approaches. The research approach presented in chapter 3 highlights key research questions addressing the requirements for the ISDM structure with regard to input data and traceability of Scenario Technique.
Chapter 4 introduces the ISDM focusing on its core elements. Finally, chapter 5 provides a summary and an outlook to future research directions.
2 STATE OF THE ART
Data integration for Scenario Technique needs to be based on a twofold approach: Demands are determined from a methodological perspective, while available data can be identified by analyzing prevailing IT systems and management tools. In the following, the state of the art will be investigated in these two areas. First, the principles of Scenario Technique are amplified and resulting information flows are deduced (chapter 2.1). Second, existing data management approaches to provide necessary input information are analyzed (chapter 2.2).
2.1 Scenario Technique
The Scenario Technique with original military roots has meanwhile found various applications in social and economic issues [6, 7, 8]. Origin of the consistency-based approach is formed by Reibnitz [9]. Reibnitz was the first a) to provide a comprehensive method and process model, which contains all the necessary steps and b) to apply a funnel with a positive and negative extreme scenario of the future. Reibnitz mentions explicitly that the real future development will only adjust between the two extreme scenarios, but not that one of the extreme scenarios will be achieved in the same manner.
Therefore the process model by Reibnitz is often cited and taken as a basis for further advancements (as examples, cp. [7, 10]). Hence, in the following it will be taken as a reference in order to analyze activities and identify relevant information flows within Scenario Technique. Thus demands for the ISDM are deduced from a methodological perspective.
The goal of future scenario analysis is set by the client and taken as input for the “task analysis“(step 1, see Figure 1). Input from other data sources are characteristics of concerned branch of industry, strategic business unit and product group (S1-Ext). This input can be retrieved from official statistics, company-internal knowledge of business unit and product group, e.g. benchmark analyses and self-evaluation. As a result of step 1, the subject of investigation and the as-is status of the company is specified (S1-Int). The as-is status is described by stakeholder (including priorities and interests), technology portfolio, strengths and weaknesses, threat of competitors and substitutes, and bargaining power of buyers and suppliers.
In the following “influence analysis” (step 2), external areas of influence affecting the company or strategic business unit are identified (e.g., procurement and buyer markets, competition, policy and legislation, technology, economy as well as the company). Corresponding influence factors are derived, evaluated and cross-linked to each other (S2-Ext).
According to their position in the system grid, influence factors can be active, passive, ambivalent or buffering. On this basis, the most relevant influence factors are selected and entitled as “key factors” (S2-Int).
Trend analyses are taken as external input for step 3 “trend projections”
(S3-Ext). Key factors (S2-Int) are formulated neutrally as “descriptors”. For example, a value-neutral formulation of the key factor ‘market growth’ would be ‘market development’. On the basis of such descriptors, their current and future statuses are described. In the course of this, unique and alternative descriptors are distincted from each other. Unique descriptors describe a future status by one value, which is often represented by a linear relationship with time. Alternative descriptors however may take different values in the future [9]. As a result of step 3, trend projection of unique (S3b-Int) and alternative descriptors (S3a-Int) is created.
Figure 1: Information flow analysis diagram.
Only trend projections of alternative descriptors are taken as input for step 4
“bundling of alternatives” (S3a-Int). The aim of this step is to review the various alternative developments that have been identified in step 3 with one another for consistency or compatibility and logic. Consistent descriptors are bundled to raw scenarios and evaluated intuitively with regard to their stability and difference. Alternative development opportunities of cross-linked descriptors can also be bundled by mathematical models, such as cluster analysis, branch-and-bound or evolutionary algorithms. Results of step 4 are consistent, stable and very different scenarios (S4-Int).
Together with trend projection of unique descriptors (S3b-Int), such selected raw scenarios (S4-Int) form input data for step 5 “scenario interpretation”.
Raw scenarios are detailed, completed and interpreted verbally.
These completed scenarios form input data for step 6 “analysis of consequences” (S5-Int). Opportunities and risks arising from the completed scenarios are identified individually for each scenario. Corresponding measures are also derived separately for each scenario. As interim result, a preliminary strategy is formed by intersection of the measures (S6-Int).
In step 7 “analysis of disruptive events”, possible external and internal fault events (S7-Ext) are collected and analyzed regarding their relevance
for the specified subject of investigation. A fault event represents an event which is unlikely to occur, but significantly in its effects for the company [9].
Relevant disruptive events are identified and their significance is evaluated.
Further, preventive and reactive actions (crisis plan) are determined (S7-Int).
Goal of step 8 “scenario transfer” is to formulate a guiding strategy on the basis of preliminary strategy (S6-Int), preventive and reactive actions against disruptive events (S7-Int). In addition, alternative strategies are defined and an environment monitoring system is established (S8-Int).
In summary, the following demands result from this analysis of activities and relevant information flows on the database for the ISDM. They form the basis for reliable and traceable scenario creation and analysis:
Links to external data sources (S1-Ext, S2-Ext, S3-Ext, S7-Ext in Figure 1) have to be provided persistently. This is a prerequisite to access such data sources automatically and facilitate traceability of created scenarios.
Internally generated interim results (S1-Int, S2-Int, S3a-Int, S3b-Int, S4-Int, S5-S4-Int, S6-S4-Int, S7-Int) as well as results (S8-Int) have to be stored.
2.2 Integrated Data Management
Scenario Technique requires reliable input information subsuming, for instance, data describing the actual situation including product programs, key technologies, competitors, results from trend analysis, needs and potentials. Data management approaches and systems are available to act as ‘external resources’. They need to be assessed with regard to relevance for different phases of the Scenario Technique, level of detail and corresponding value to influence the scenario definition and availability with regard to prevailing IT systems, communication networks and data formats.
The digitization of processes in production chains changes the role of human factors [11]. It extends the opportunity to derive relevant data based on concepts like agile manufacturing, knowledge-driven and virtual enterprise and autonomous control [12]. The conceptual information basis is built by tools along the product lifecycle and the automation pyramid [3, 13]:
Enterprise Resource Planning (ERP) systems subsume data about resources, customers, offers and orders including economic and technical information, acceptance rates and actual projects.
Product Lifecycle Management (PLM) carries information from requirements specifications to lessons learned from after sales services.
Product Data Management (PDM), often included in PLM tools and concepts, provide centralized access to all relevant technical documents based on integrated product data models.
Manufacturing Execution Systems (MES) support decision taking within production systems. MES rely on actual and real-time production data, subsuming data from manufacturing and quality management processes combining and utilizing functionality of Computer Aided Manufacturing (CAM) and Computer Aided Quality assurance (CAQ) software systems.
Figure 2: Existing data and prevailing IT systems for an Integrated Scenario Data Model (examples based on ISA-95, OSCL and [16]).
These systems support the management of data to be used especially for scenario input parameters characterized as ‘certain’ data. Certainty often correlates with objective and retrospectively retrieved data; ‘uncertain’ data which is essential to be incorporated in the foresight phase needs to be derived and incorporated utilizing specific approaches [14] and modeling probabilities [15, 16]. Additionally, aggregated views based on these systems and related tools (e.g. for requirements engineering and engineering change management [3]) help to identify relevant information for the task analysis phase.
Various sets of data are available in different data management tools (see figure 2). Boundaries between these systems are kind of vague, holistic architectures very different [13]: ERP systems often subsume PPS functionality due to their overarching functionality while companies still run specific PPS systems. Similarly, PDM is an essential part of PLM contributing the technical basis for product data management. Architectural concepts and reference models exist in standardized form, e.g., regarding processes (cp. SCOR reference model), automation level (cp. ISA-95 model) and data exchange (cp. OSLC for linked lifecycle data).
The differentiation among the different system categories is mainly driven by different perspectives instead of altering technical capabilities. Therefore common data formats and shared terminologies are needed to enable interoperability and flexibility. They define a framework to represent the semantic structure of a) specific data sets and b) their interrelationships.
The most widely used standard is the Standard for the Exchange of Product Model Data (ISO 10303 STEP). STEP is extended for various application domains. For the ISDM approach the most relevant applications are targeting Product Life Cycle Support (PCLSlib), product data management via the PDM schema and consequently "Managed model based 3D engineering" represented by the application protocol STEP AP 242 (ISO
10303-242). Besides these structural views to data integration, several approaches address the organizational level of PDM/ERP integration (cp.
[18]). Acquiring data from these tools and especially their interfacing elements is a key challenge for the scenario analysis (e.g., combining analyses on the acceptance of product configurations and the closing ratio).
Therefore it is essential that organizational aspects like global company internal networks and supply chains are taken into account [19].