Manufacturing System Design Decomposition™
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General Overview of the MSDD

The Manufacturing System Design Decomposition (MSDD)

The MSDD is a decomposition of general objectives and means for repetitive manufacturing systems. The decomposition distinguishes six general functions of manufacturing systems: quality, identifying and resolving problems, predictable output, delay reduction, operational costs, and investment. The decomposition is therefore a functional decomposition as opposed to a physical decomposition. A physical decomposition would decompose physical entities such as departments, manufacturing cells. Each physical element, however, must achieve each of the functions stated above. Applying the MSDD to a system design process forces to think differently about manufacturing systems than in physical structures, which was found to be very helpful in industrial applications. 

The following paragraphs describe the overall structure of the MSDD. A more detailed description of each branch is available by clicking on the appropriate box in Figure 1 or using the hyperlinks in the text.

The highest-level functional requirement was chosen to be “Maximize long-term return on investment.” It is important to emphasize here that the goal is to maximize return on investment (ROI) over the life cycle of a given system, not just in the immediate future. Although long-term ROI may be very difficult to predict accurately, ROI is taken here as the highest-level focus of the manufacturing function as it represents a general objective that is applicable to a wide variety of manufacturing environments and is not inherently contradictory to any accepted improvement activities.

The design parameter chosen as the means to achieve FR-1 is DP-1, “manufacturing system design.” The MSDD focuses on the shop floor aspects of manufacturing systems. Aspects such as product design, marketing are not in the scope of the MSDD, even though they certainly contribute to overall performance and ROI. The second level of the MSDD was determined based on the components of ROI as given by:


Thus, the components of ROI are revenue, cost, and investment over the life cycle of the manufacturing system. The requirements placed on these components are that life cycle cost and investment should be minimized and revenue should be maximized in order to maximize long-term ROI, as shown in Figure 1.

Maximizing customer satisfaction (DP-11) was selected as the means to maximize revenues. This DP was then further decomposed based on the key attributes of manufacturing system performance that affect customer satisfaction: conformance quality (FR-111), on-time delivery (FR-112), and minimal lead-time (FR-113). The prescribed means for achieving high quality is to ensure that production processes have minimal variation from the target (DP-111). DP-111 is focused on improving processes rather than trying to use final inspection to prevent the shipment of bad parts. The design matrix at this level (shown graphically with arrows in Figure 1) shows that achieving conformance quality (DP-111) is critical for improving customer satisfaction. Quality variation and the production of defects makes system output unpredictable, which adversely affects FR-112, “Deliver products on time,” and means that more parts will have to be produced to replace these defects, adversely affecting FR-113, “Meet customer expected lead time.” High conformance quality is a critical factor required to reduce the affect of DP-111 on the predictable delivery and lead time of a manufacturing system design.

The MSDD treats customer satisfaction as a prerequisite for the rest of the decomposition, meaning that it is a goal that must be achieved before costs and/or investment can be minimized. The MSDD interrelationships show clearly that minimizing running costs and investment at the expense of customer satisfaction is not a valid means for achieving the highest-level goals of the system design. This information is consistent with related empirical and theoretical work in the literature. Ferdows and De Meyer (1990) developed a “sand cone” model, describing that manufacturing capabilities should be built by starting with quality, then focusing on dependability, then reaction speed and flexibility, and finally focusing on cost efficiency.



On-time delivery (FR-112) and short lead-time (FR-113) are achieved by reducing the variation,, and the mean, , manufacturing throughput time (described by DP-112 and DP-113, respectively, as shown in Figure 1). Variation reduction requires the ability to respond rapidly to production disruptions when disruptions occur, which is designated by the branch R1, and the increase of the reliability of production resources, designated by the branch P1. Mean throughput time reduction is decomposed based on the various causes of delays in manufacturing systems (FR’s T1-T5). It is important to note the distinction made here between causes of variation in throughput time (addressed by DP-112) and causes of increases in mean throughput time (addressed by DP-113). The decomposition of DP-112 focuses on the elimination of factors that cause variation in the predicted system output time; decomposition of DP-113 focuses on factors that increase throughput time but that can be accurately predicted. Likewise, the process quality branch isolates factors that affect process variation in terms of the mean and variation.

The operational costs decomposition focuses on the efficient use of direct and indirect labor, and facility costs. The investment is not further decomposed yet. The path dependency of the MSDD states that operational costs and investment are influenced by decisions made on the left side of the decomposition and should not be the starting point in the system design process.


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