7.3.3 The Use of Production Rules in Order Processing

Intended learning outcomes: Present an excerpt from the parameterized bill of material for the fire damper. Identify data storage complexity for the fire damper example. Disclose the use of generative techniques in connection with CAD and CAM as well as in the service industries.

Figure 7.3.3.1 shows an excerpt from the product structure of the fire damp­er in Figure 7.3.1.1. This part of the bill of material lists some attributes and if-clauses important to an understanding of production rules.

Fig. 7.3.3.1 Excerpt from the parameterized bill of material for the fire damper.

For the query, the facts — the product identifiers, order quantity, and all parameter values — have been added. Through comparison of these facts with the rules stored for the product family, program logic determines for each position the first variant of the bill of material or routing sheet for which evaluation of the rule results in the value “true.”

Try the following exercise: In Figure 7.3.3.1, what variants are selected, given the following parameter values: Type = 1, drive = left, width = 400, height = 120?

Solution: Position/variant: 130/01, 150/01, 155/01, 160/01. Also compare the exercise in Section 7.8 (Scenarios and Exercises).

Storing parameterized positions on the bill of material and routing sheet in the form of production rules has key advantages over conventional positions. Each potential position is, in one comprehensive, maximal bill of material or in one comprehensive, maximal routing sheet, listed exactly once, but it is listed together with the condition under which it will appear in a concrete order. This means that there is no longer the stored data redundancy found in the classical case without parameterizing. In terms of the combinatorial aspect, rather than having a storage problem growing multiplicatively, we now have just additive increases. For a detailed comparison of data storage complexity, see [Schö88a], p. 51 ff.

Figure 7.3.3.2 shows actual, rounded comparative numbers for the data storage necessary for the fire damper in our example.

Fig. 7.3.3.2 Comparing data storage complexity for the fire damper example.

With minimal data storage problems, any number of orders with all possible combinations of parameter values can be transposed into production orders in a simple manner. One only needs to enter the values of the parameters. All these orders contain the correct components and operations, each with correctly calculated attribute values. Moreover, all possible combinations have been defined previously and automatically. Engineering change control (ECC) is also simple. If, for example, a new component is introduced, with a typical bill of material mutation, the component identification is added as a position to the (unique) bill of material. If it is a variant, its use dependent on parameters will be given an if-clause. Quali­fied employees familiar with the design and pro­duct­ion process perform all of these tasks.

There is an advantage to the use of knowledge-based product configurators when PPC software is used in connection with CAD and CAM. With CAD, only one unique drawing is produced for all variants, but as above, it is parameterized. Within CAM, there is also only one unique, parameterized program controlling the machines. With this knowledge-based represent­ation, PPC also now keeps only one unique bill of material and routing sheet for all variants. If there is a suitable, parameter-based CAD program package, a parameterized bill of material with a drawing can be exported from CAD to the PPC software. More important, however, is the reverse direction with an order. The parameter values of the production order can be exported from the order to CAD in the bid phase (or at the latest at order release). CAD then produces an order-specific drawing. In prac­tice, this option is used in bids for products in the construction indus­try, for example. In analogous fashion, linking an order to CAM means that the same set of parameter values can serve as input to a CNC program.

And, finally, the generative technique is used successfully in the service industries, such as in the insurance branch and in banking. A family of insurance products can be seen as a product with many variants. Here, again, we find a clear case of nonrepetitive production. The setting up of a policy, or order processing, is at the same time the production of the product. The parameters are the features of the insured object as well as the types of coverage to be provided (e.g., sum insured, excess, type of compensation). The production rules of the configu­ra­tor assign the elementary products to possible contracts. Concrete entry of a set of parameters ultimately yields a concrete insurance policy and includes all calculations, particularly the premium. Here see [SöLe96]. Those readers interested in an application in banking or in uncertainty may wish to refer to [Schw96].

Exercise on rule-based configuration techniques: Determine possible configurations

Course section 7.3: Subsections and their intended learning outcomes

• 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.

• 7.3.1 The Combinatorial Aspect and the Problem of Redundant Data

Intended learning outcomes: Present setting the parameters of the fire damper. Disclose the number of possible combinations with n parameters as well as an example for number of identical bill-of-material positions.

• 7.3.2 Variants in Bills of Material and Routing Sheets: Production Rules of a Knowledge-Based System

Intended learning outcomes: Differentiate between design rules and process rules. Explain the concept of design rules or process rules.

• 7.3.3 The Use of Production Rules in Order Processing

Intended learning outcomes: Present an excerpt from the parameterized bill of material for the fire damper. Identify data storage complexity for the fire damper example. Disclose the use of generative techniques in connection with CAD and CAM as well as in the service industries.