Integral Logistics Management — Operations Management and Supply Chain Management Within and Across Companies

7.6 Summary

Variant-oriented techniques are required when the market demands flexibility in meeting customer specifications. Today, this is frequently the case for the investment goods market. Some of the techniques also support production without order repetition, in particular the production types mass customization and one-of-a-kind-production. There are adaptive and generative techniques. Adaptive techniques determine a “parent version,” from which the bill of material and the routing sheet for the actual customer production order are derived. Subsequently, positions are added, modified, or deleted. Generative techniques use rules that already exist in an information system and that select the product variant, starting with data of the customer order, out of a set of possible components and operations.

The master production schedule (MPS) is best established at the level of the (customer) order penetration point (OPP). Downstream from this point, a final assembly schedule (FAS) is a possible tool to make the end items according to specific customers’ orders.

For low-variety manufacturing, there are in the simplest instance standard products with only a few options (in the dozens). This results in a tendency toward a rather high stocking level. For the demand of each variant a percentage of the total de­mand, called option percentage, is predicted. Because this is also a stochastic variable, the standard deviation of the demand for a variant is greater than that of the demand for the product family. The sum of independent demands for the variants is thus greater than the independent demand for the product family. In the more difficult case, the number of manufactured products is still much greater than the number of variants, which, however, can lie in the hundreds. This case can be handled in a manner similar to the case above. However, data redundancy in the representation of products and processes increases, and this also raises the efforts required to search and maintain master data and order data.

For high-variety manufacturing, that is, for products to customer specification or for product families with many variants, the number of variants increases to the magnitude of the demand. The use of stochastic methods would lead to high safety demand in variants and thus high inventory. Because in the best case there remains only potential repetitive production, we must move from stochastic to deterministic methods. Through almost the entire supply chain, the products are manufactured according to demand, with no stockkeeping. Inventory in raw materials and in purchased parts is replenished after use.

A product family with many variants is the typical case with mass customization. Here, the order can be produced directly, because all possible variants of the product family have already been included in product and process design. There can be millions of variants, that is, production with many variants. Each variant results in a different product. However, in characteristic areas, all product variants and also the production process are the same. Such product families are based on a concept in which the manifold variants are generated through combination of possible values of relatively few parameters. In principle, there is only one (maximal) bill of material and only one (maximal) routing sheet. To select positions for an order and to check compatibility of parameter values, knowledge-based techniques are used. Production rules then contain an if-clause, which is a logical expression that varies in the parameters.

Products according to (changing) customer specification are closely related to the engineer-to-order production environment. There are various different archetypes. In the classic case, known as Complex ETO, adaptive techniques are used. Basic ETO and Repetitive ETO are based on generative techniques (and therefore on mass customization as a production type), with adaptive techniques applied afterwards. With Basic ETO, a limited degree of automation can be achieved by defining product families with an unfinished product structure that looks like a template. The result of the configuration is often already useful for initial cost calculations and for logistical control during order execution. In the case of Repetitive ETO a fast and efficient engineer-to-order is a prerequisite. Here, a permanent enabling process is required. Additional know-how that is gained during order execution is fed back to the enabling process. The parameterization for product families, particularly for component families, must be determined carefully, thereby ensuring their commonality.

There are significant differences in cooperation between the R&D and Engineering depart­ments in companies. Factors that influence the cooperation entail the development of a portfolio with four sectors of fundamental types of cooperation in ETO firms.

Course sections and their intended learning outcomes

  • Course 7 – The Concept for Product Families and One-of-a-Kind Production

    Intended learning outcomes: Produce logistics characteristics of a product variety concept. Explain adaptive and generative techniques in detail. Describe the use of generative and adaptive techniques for engineer-to-order. Differentiate various ways of cooperation between R&D and Engineering in ETO Companies.

  • 7.1 Logistics Characteristics of a Product Variety Concept

    Intended learning outcomes: Differentiate between high-variety and low-variety manufacturing. Describe different variant-oriented techniques, and the final assembly schedule.

  • 7.2 Adaptive Techniques

    Intended learning outcomes: Explain techniques for standard products with few variants as well as techniques for product families.

  • 7.3 Generative Techniques

    Intended learning outcomes: Disclose the combinatorial aspect and the problem of redundant data. Present variants in bills of material and routing sheets as production rules of a knowledge-based system. Explain the use of production rules in order processing.

  • 7.8 Scenarios and Exercises

    Intended learning outcomes: Apply adaptive techniques for product families. Disclose the use of production rules in order processing. Elaborate the setting the parameters of a product family.

  • 7.9 References


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