Conclusiones

The developed GCM is focused on a serial multistage manufacturing process. It takes into account the constraints on inspection resources. As a result, the total manufacturing cost of a product can be reduced without affecting the quality of the product. The GCM for a serial multistage manufacturing process is based on the following assumptions which should be taken into consideration during the assessment of the case studies:

 Production configuration is assumed to be a serial multistage manufacturing process.

 There is a limited number of inspection stations (e.g. a limited budget) to be distributed in the production line.

 The aim of the objective function is to minimise the total manufacturing cost while maintaining the quality of the product. The total system cost is the sum of the total cost of processing and inspecting the parts produced in the system.

 Sample inspection is used if an inspection station is located after a workstation in the sequence.

 No more than one inspection station can be assigned after each workstation.

Table 7.1 shows the case studies which are relatively closely well-matched to the GCM. Each case study is represented by the first author‟s name followed by a two-digit publication year.

The order of case studies is organised in chronological order by the year in which the paper is published. It should be noted that (Yes) means that this characteristic was included in the case

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study and (-) means that this characteristic was not included in the case study. To simplify the selection procedure, the following criteria were used to select the appropriate case studies:

 Data availability

As can be seen from Table 7.1, there are some data which were not considered in the developed models used by the authors, such as replacement cost and external cost in case study (1). This might have occurred for the sake of simplicity in order to allow a tractable formulation model and solution. On the other hand, some data in the case studies are missing.

For example, in case study (3), some data were not given, such as the quantity of items entering the system, the number of inspection stations and the penalty cost. In case study (5), the authors included the external failure cost in their model (replacement and repair costs), but values were not given for the experiment parameters or the scrap cost in the internal failure cost. In case (6), the unit inspection cost was not provided by the authors. In case study (12), the case included only inspection and scrap costs but did not provide the input material (number of items entering the system), unit scrap cost or unit inspection cost.

 Matching to the general cost model

In this section, the purpose is to look for cases that considered similar assumptions as the developed GCM. In addition, how these assumptions were treated through the case study was evaluated, taking into consideration that some assumptions might be adapted or reconsidered.

For example, some case studies assumed that inspection was error free. In the GCM in chapter 3, both types of errors were incorporated into the GCM. This assumption was adopted in the developed GCM simply by setting parameters of inspection errors α=0 and β=1. In case study (1), the authors were interested in the reliability of each machine and the inspection time. These issues were not included in the developed GCM. In case study (2), although the case considered a limited number of inspection stations, they assumed that multiple inspection stations might be located after a workstation.

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Table 7.1: Characteristics of case studies relatively closely match with the GCM

Case

Inspection cost Internal failure cost External failure cost

Man.: manufacturing, Repl.: replacement, I and II: Type I and type II inspection errors, AOQL: average of outgoing quality level.

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On the other hand, in the GCM, it is assumed that no more than one inspection station can be assigned after each workstation. In case study (4), the case included assumptions such as the rate of processing, the rate of production and the rate of inspection. All these assumptions are not incorporated in the GCM. In case study (7), the objective function was constrained by the average outgoing quality level which is not included in the GCM. In addition, the number of items to be inspected was randomly based on a sample size selected at each workstation.

However, in the GCM, when inspection is performed, 100% of items are inspected. In case study (13) the case was interested in determining the rigor of the inspections (acceptance limits) for each inspection station. These issues are not included in the GCM.

The case studies used various optimisation methods to solve the AOIS problem. These optimisation methods included exact and approximate methods. It should be noted that most of these case studies used the total manufacturing cost and processing time to test the performance of their optimisation methods against the optimal solution. The optimal solution was obtained using the complete enumeration method (CEM). The CEM checks for all possible combinations of inspection plans in the search space. As a result, the quality of the solution obtained by a method can be measured by its closeness to the optimal solution. The running time is the processing time required to execute the computer programmes for problem-solving in a computer system. It was found that case studies (8) and (9) were nearly identical in terms of the experiment parameters used, and both of them used the heuristic method to approach the AOIS problem. Because case study (9) is more appropriate for the GCM, and also because case study (8) did not consider the external cost, case study (8) was therefore excluded from consideration.