In this following section positions the developed method within the applied sciences and establishes the scientific assumptions, which form the basis for this work. Based on the established premises, the author develops a fitting research process for this explorative journey in Section 1.5.2. Throughout the iterative process, the author seeks to refine the reference framework of this thesis. The starting point for this iterative process is established in Section 1.5.3.
1.5.1 Scientific Positioning
Science encompasses both the formal sciences, which are concerned with the study of formal systems, and the physical sciences, concerned with the study of real systems. Formal sciences attempt to characterize abstract structures through constructing sign systems, including logic, mathematics, statistics, and philosophy. The formal sciences, unlike physical sciences, bear no relation to reality, only their logical truth can be proven (Ulrich et al. 1976, pp. 305).
In contrast physical sciences strive to describe, explain, and control phenomenon as an empirically observable section of reality, as shown in Figure 5 (Ulrich et al. 1976, pp. 305). Due to their strong relevance to reality,
the physical sciences are subject to an additional criteria in their testing:
factual truth (Ulrich et al. 1976, pp. 306).
Figure 5: Systematic of the sciences (Ulrich et al. 1976, pp. 305)
Physical sciences can be further divided into basic sciences and applied sciences. Basic sciences seek to explain phenomena and therefore the formation of explanatory models takes the foreground. In contrast applied sciences aspire to analyze alternative courses of action for the design of social and technical systems, yielding decision models and decision processes (Ulrich et al. 1976, pp. 305).
Ulrich identifies the following distinguishing elements of the applied sciences from basic research, as shown in Table 2. While in the basic sciences, the researched problems stem from e.g. discrepancies in theory, the researched problems of the applied sciences stem from practical application. Scientists of the basic sciences define research problems with the objective of explaining phenomenon in an existing reality, making the current reality the subject of their studies, while scientists of the applied sciences seek rules and models to
create new realities, using the current reality merely as a starting point to explore new realities. The basic sciences test their hypotheses using empirical methods, while in the applied sciences these serve to generate the relevant problems of the practice and test the developed design models. Therefore Ulrich deems the practice as constitutive to the applied sciences, while it is merely accessorial for the basic sciences (Ulrich 1981, pp. 10).
Table 2: Traits of the applied sciences (Ulrich 1981, pp. 10) Basic sciences Applied sciences Origin of problem
descriptions Theory Practice
Objective Explanation of phenomena in the existing reality
Rules and models for the creation of new realities
Relation to current
reality Subject of investigation Starting point for investigating other realities
Significance of
empiricism Means of testing hypotheses Means of surveying problems and testing design models Relation to practice Accessorial Constitutive
This thesis interprets manufacturing organizations as part of the complex, open, social system following the principle of Punch and Saunders et al., which are subject to a multitude of transformations (Punch 2005, pp. 25;
Saunders et al. 2009, pp. 136). Organizations cannot be assumed completely controllable based on the considerations of Ulrich & Krieg (Ulrich et al. 1974, pp. 13). Therefore, the field of business management, which strives to investigate effects of human courses of action, is understood as an applied science. The engineering sciences are also considered applied sciences. This thesis bridges both business management and the engineering sciences to investigate the ability to reduce material waste within operations management (OM). The problem formulation is identified in the industrial practice.
1.5.2 Research Process
Ulrich states that knowledge generation is inductive in applied research, while deductive in basic research (Ulrich 1995, pp. 165). The minimization of aggregate material consumption of manufacturing systems stakes out a complex and up until now inadequately addressed problem formulation derived from the practice, for which there is no suitable approach. For that reason a purely inductive approach based on empirical observations would be insufficient for knowledge generation. On the other hand, there is no theoretical foundation for minimizing aggregate material waste, rendering a purely deductive approach also inadequate.
Therefore an inductive-deductive approach is combined with a model-oriented simulation approach to support the validation of the developed method and support the discovery of interdependencies and principles.
In accordance with Kubicek and Tomczak, an iterative inductive-deductive research process is derived, with the goal of refining the reference framework of this thesis, or more specifically an understanding of the interworking of aggregate material efficiency and other factory cost and market goals. The first loop of the research process starts with the build-up of knowledge through secondary research, then deriving questions on the formed reality. To investigate the defined questions, data from expert interviews, case studies, and direct experience is collected and interpreted. In turn, through induction a stronger and more comprehensive theory is formed with every loop. In later loops, data is also generated through experiments in simulation models. At the point of publication of this work, the iterative process is frozen.
Figure 6: Research process, in accordance with (Kubicek 1976; Tomczak 1992)
1.5.3 Reference Framework
As described in the last section, a rough reference framework serves as the starting point for the iterative research process. This work investigates the interaction between operative decision-making and the occurrence of material waste forms, forming the first two elements of the reference framework of this thesis (see Figure 7). After gaining knowledge on the interdependencies of material waste forms and operative decision-making, the author constructs a model to demonstrate the mechanisms within the authority of operative decision-making to reduce aggregate material waste, represented in Figure 7 as material efficiency. The subsequently developed simulation-based method provides a structured procedure for selectively investigating effects of modified operative decision-making on aggregate material waste and the fulfilment of market and cost goals.
Figure 7: Reference framework