Flexibly preparing offers by adapting functional resources.

Unger, Katja ; Militzer, Joerg ; Zimmermann, Matthias 等

1. INTRODUCTION

Not only for bigger companies but also for small and medium enterprises (SMEs) the attraction of new business is an essential success factor in highly competitive value-added production networks. In recent years mass customization (MC) has advanced to a key strategy to offer customers more choice (Ismail et al., 2007). The present trend to individualization means matching customer's individual needs, forces especially SME's to improve their manufacturing agility and product flexibility finally to benefit from cross-company cooperation and planning.

This paper deals first with a short delimination of the project and the methodical framework for an automated offer preparation process with short response times. To improve the needed manufacturing agility, the structure and functionality of the functional resource model is outlined in the second part.

2. RESEARCH PLACEMENT

Some publications refer to manufacturing capability for computer aided engineering (CAE) support and differentiate between manufacturing resources, processes and strategies (Molina et al., 1995). This context is similar to ours, but strategies are outlined because of the operative focus of the offer preparation process.

As one of the key components CAD/CAM integration, CAPP systems, and feature technology were already contemplated (Gao & Huang 1996). The latter is an important element for the presented functional resource model the authors are developing within a research project between the University of Applied Sciences Zwickau and the Chemnitz University of Technology.

The following drawing illustrates the tripartite focus of research (figure 1), whereas the functional resources implementation is embedded on the right side. Together with the description of a product demand on the left side ERP modules are enhanced for generating, scheduling and evaluating process variants to benefit from adapting functional resources.

[FIGURE 1 OMITTED]

2.1 Performance Request

A customer's performance request consists of general customer data, a CAD model with required product characteristics, and order-specific information, e.g. price expectations and quantity. Already done is product model design based on STEP, enabling standardized product model data (Amaitik et al., 2005). The named project uses features to limit the design engineer according to the abilities of manufacturing resources, already published by the concept of SBDCR (Teich et al., 2008a).

2.2 Resources Offer

The right pillar is the functional description of company resources, where the available operating facilities and equipment with their capabilities are represented in an innovative manner--as character-changing competence cells. An evaluation of technical and economic PFs will be provided in this paper and helps to integrate static and dynamic influences in offering.

2.3 Enhanced Planning Environment

To make a use of modeling resources and customer requests in a functional way, enhanced ERP modules are being created to present concrete offers to customers.

First module is the generation of process variants (PVP) and production plans, combined with an initial measurement by a matrix of economical requirements, done by ant colony algorithms for multi-criteria optimization problems.

The module pre-calculation calculates the costs feature-based. Each generated variant is scheduled with extracted orders from an existing ERP/SCM system (Teich et al., 2008b) to evaluate capacity restrictions of the resources.

Finally, the customer gets a representation of possible solutions as concrete offers, varying by price, date and delivery conditions as well as product implementations.

The greatest benefit for a flexible preparation of offers comes from the adaptation of PFs, because benchmarked process variants will be rescheduled after time, factor and parameter-oriented scalability. This adaptation is considered more deeply in the next paragraph.

3. ADAPTIVE RESOURCE MODEL

To explain the structure and functionality of the adaptive resource model, the authors refer to Competence Cells (CC) which is an educational term for representing abilities (Erpenbeck & Heyse 1999). These abilities have to be transferred to a manufacturing context, where PFs are used to describe the scalable behavior of CCs to flexibilize the preparation of offers.

3.1 Structural Assignment

The company resources--so called character-changing competence cells (4Cs)--have an indirect impact on the products' attributes. More precisely, the resources are bundled in Competence Cells and characterised by their parameters (general and specific ones) according to the manufacturing method or to its physical properties and conditions. These parameters with interdependences are encapsulated in so called manufacturing features. A design feature in its product-related view mainly refers only to its geometric and technological attributes, not to adaptive parameters of the production process.

As mentioned before, enhanced ERP modules generate and schedule PVPs. To break this alternative plan down, intermediate products (IPs) with Features are connected by different process categories. They are to disaggregate into manufacturing stages and those again to process elements characterized by manufacturing methods that constitute a granular operation of a work schedule (figure 2).

3.2 Functional Adaptation

Prior research of the theory of production already located the field of applicable PFs. Most relevant is the technical funding of GUTENBERG that evolves technical set values as adaptable parameters (Sonntag & Steven 2005).

Based on the structural assignment, technical PFs define each process step and cover consumption factors as input, a process step itself as throughput as and the production rate as output factor (figure 3).

Each process step is characterized by only technical efficient possibilities. However, a technical less efficient variant can be more economical. Evaluating this, it requires adapting the production rate, technical set values, or time to influence the output factors of a process step. In our model the technical PF are to transform into economic ones for re-scheduling and re-evaluating process variants.

[FIGURE 2 OMITTED]

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4. CONCLUSION

The functional resource model should enable an efficient and dynamic use of manufacturing capabilities, embedded in the framework for generating additional orders with reduced response times in a value-added production network. Present work themes include a prototypic implementation of the framework, the preparation of an UML resource model and filling a database for turning operations/ competences. For further research, the scalability of the resources model to other fields of application will be analyzed and evaluated.

5. REFERENCES

Amaitik, S.M.; Kilic, S.E. (2005). STEP-based feature modeler for computer--aided process planning. In: International Journal of Production Research, Vol. 43, No.15, Taylor & Francis Group Ltd., London, p. 3087-3101

Erpenbeck, J.; V. Heyse (1999). The biography of competences. Waxmann Publishing, New York, Munich

Gao, J. X.; Huang, X. X. (1996). Product and manufacturing capability modeling in an integrated CAD/process planning environment, In: The International Journal of Advanced Manufacturing Technology, Vol. 11, pp. 43-51

Ismail, H.; Reid, I. R.; Mooney, J.; Poolton, J.; Arokiam, I. (2007). How Small and Medium Enterprises Effectively Participate in the Mass Customization Game, In: Engineering Management, IEEE Transactions on, Vol. 54, pp. 86-97

Molina, A.; Ellis, T.; Young, R.; Bell, R. (1995). Modelling Manufacturing Capability to Support Concurrent Engineering, In: Concurrent Engineering, Vol. 3, No. 1, SAGE Publications, pp. 29-42

Sonntag, S.; Steven, M. (2005). Technical Fundings of Gutenberg's production function, In: Quantitative business management, Physica Publishing, pp. 159-183

Teich, T.; Militzer, J.; Zimmermann, M.; Unger, K. (2008a): SBDCR for Standardization of Customer Requests, In: Proceedings of the Fifteenth International Working Seminar on Production Economics, Vol. 1, pp. 495-503

Teich, T.; Militzer, J.; Unger, K.; Gaese, T.; Winkler, S. (2008b): A formalized approach for generating customer offers based on heuristic methods, In: Logistics and Supply Chain Management: Trends in Germany and Russia, Proceedings of the German-Russian Logistics Workshop, Publishing House of the Saint, pp. 272-282, Petersburg State Polytechnical University