Fundamentals of Production Logistics

Change is both a typical and necessary characteristic of the evolutionary process. Although companies frequently consider it a catalyst for critical situations, there is more to it than problems, risks and dangers. A company opens up new possibilities by positioning itself actively and early, consciously grasping these factors and bearing them in mind when planning its future. Doing so it distinguishes itself positively from its competitors and thus creates new potential. Working proactively during economically stable times is particularly important in this case. The risk that effective measures cannot be introduced quickly enough often originates in not being able to recognize relevant changes. Companies are ready to make alterations, especially in times of crisis. However, they often no longer have the energy or reserves, or are in a position where they need to make considerable cuts. The demand to be permanently innovative regarding products and processes is thus continuously and emphatically present ([Zahn-94], [Warn-93]). Companies need to develop strategies oriented on the future and possible solutions based not only on knowledge of their weaknesses and previous mistakes, but also in consideration of their business goals. Once established, these need to be pursued resolutely. Due to ever decreasing product lifecycles, increasing product diversity, unstable production plans, market globalization and numerous other factors, a company has to be as flexible and adaptable as the market itself.

Economic systems are generally modeled mathematically ([Prof-77], [Wöhe-90], [VDI-93]). In this chapter, we will examine some of the basic methods common to these models:

- Deductive modeling

- Empirical or experimental modeling (generally called simulation)

Due to the specific problems and application limits associated with both queuing theory and simulations, they are rarely applied in order to support the logistic oriented evaluation and design of complex production systems. In light of this and the growing demands on production logistics, a mathematical approach which could calculate Logistic Operating Curves for production processes using an approximation equation proved to be worthwhile. This equation, which in the meantime is also applied in the field (see Chap. 7), distinguishes itself from others in that it allows LOC to be calculated with less data and ensures more reliable outcomes.

In order to apply the Logistic Operating Curves Theory it is essential to sufficiently consider the model premises and to ensure that the data entered is error free. When this is not the case it has to be possible to at least evaluate the imprecision of the model’s outcomes that results from the data errors. In order to increase the reliability of such evaluations we will now extensively discuss developing and implementing calculated Logistic Operating Curves based on a concrete example (Sects. 6.1 and 6.2). In doing so we will first draw upon simulation data. This not only allows us to ensure that all of the required data is complete and of high quality, but it also makes certain that the application prerequisites are exactly adhered to. In practice the necessary data is not always complete nor error free. In particular the capacity data frequently does not correspond with the situation on the shop floor. This is also true to some degree for other data such as the work content or transport time. Nevertheless, it is generally possible to develop and utilize Logistic Operating Curves. In individual cases, it then needs to be taken into consideration that – depending on the type of data error – the possibilities for drawing conclusions as well as the accuracy of the model are more or less limited (Sects. 6.3 and 6.4). When the application prerequisites are met there is an exceedingly wide variety of possible applications for the Logistic Operating Curves. In Sect. 6.5 we will provide an overview of decision making models that are supported by them. Methods based on these models, therefore, also have LOC techniques integrated into them and as such are characterized by the fact that the logistic objectives are relevant parameters within the decision making process.

The Bottleneck Oriented Logistic Analysis (BOLA) (see Sect. 6.5.2) is a control method designed especially for logistically evaluating and improving existing production processes. This type of analysis makes it possible to describe the processes of a production area from a logistic perspective both qualitatively and quantitatively. The specific causes of the problems can thus be localized and represented as interdependencies. Furthermore, existing potential for logistic improvements as well as possible measures for developing them can be illustrated and evaluated.

While the depth of manufacturing has been significantly reduced and the demands on the production’s flexibility has increased, the costs and punctual supply of materials from outside has become one of the key corporate success factors in the manufacturing and assembly industry. Procurement and inventory management takes on the critical role of balancing the tension between the customer’s need for flexibility and the supplier’s logistic performance in order to ensure an economical, high and stable delivery of semi-finished products and additionally purchased parts for production. In doing so, properly dimensioning the amount of stock to be maintained is vital. Due to its high cost impact, the stock level is an important logistic objective in procurement. The stock has to provide a buffer for fluctuating requisitions (from customers, distributors or the manufacturer’s own production) as well as deviations in their suppliers (internal or external) delivery. This illustrates the traditional dilemma of inventory management: A balance has to be found between a high service level on the one hand, and a low stock level on the other hand.

Up to now we have concentrated our discussion on individually examining production or storage areas through the use of Logistic Operating Curves. Next, we will turn our attention to the interactions between the production and storage areas. By doing so, we can then consider entire supply chains and their logistic performance. A supply chain generally consists of various manufacturing or assembly stages partially linked with one another through storage stages, which in turn serve to decouple processes. One of the challenges in designing supply chains is that the available resources as well as both the WIP and stock have to be coordinated with each other. In the following sections, we will show which system of logistic targets exists in a supply chain (Sect. 9.1) and how the logistic objectives impact one another (Sect. 9.2). This knowledge about the interdependencies can then be used to quantify how logistic processing states impact the supply chain’s overall performance (Sect. 9.3). The example of a supply chain for a tool manufacturer will serve to illustrate this.

A company can only be successful when its production fulfils the market’s complex and quickly changing demands. Designing and controlling production are thus key functions for a company and need to be rigorously oriented on the company’s goals. Further developing the organization as well as the planning and controls is necessary and occurs on a wide scale, because many companies are strongly motivated due to disappointing logistic performance figures.