There are researches that involve decision making in process improvement planning.
For instance, a framework for improvement project selection and evaluation was formulated by Aqlan et al. (2017) by using simulation and optimization techniques to solve MCDM problem. Previous frameworks with respect to Lean Six Sigma project selection were reviewed and important considerations in decision making were highlighted. The framework can be generalized into two main parts, which are
optimization and simulation. A multi-objective optimization mathematical model was constructed to evaluate projects and filter out those undesirable proposed improvement projects. Subsequently, DES simulation was used to perform scenario experiments for selected project. It is found out that out of 10 projects, 7 of them were tested applicable after considering resource availability, time and cost aspects.
Chan and Spedding (2003) had also composed an integrated multi-dimensional process improvement framework that encompassed productivity, quality and cost dimensions. It was found out that there was absence of systematic methodology that synthesizes productivity optimization, quality control and cost minimization. The framework was graphically outlined and various types of decision support tools such as DES simulation and neural network metamodel were proposed. To demonstrate the adaptivity of the framework, the framework was applied to two case studies with different objectives, which are control chart system design and quality optimization problem. Resultantly, combination of different control charts was determined for precise process control for the former, whereas the best configuration of system was obtained for the latter to achieve optimal quality and productivity at lowest cost.
In addition, AlDurgham et al. (2008) developed a Simulation Application Framework for Manufacturing (SAFM) that aids in decision making and can be adapted to wide range of simulation software. Through literature review, it was discovered that computer simulation is a renowned approach used to design from scratch, test or modify lean system. SAFM and checklists were developed to act as guidelines for simulation-based decision making in decision areas such as material handling system, layout, scheduling and manufacturing strategy. A general framework and major steps for each decision area were explicitly demonstrated in flowchart, and also validated by conducting case study at a real system. In case study, transferring semi-automated bottleneck to full automation contributes the most benefits if compared to current and other proposed models because of its higher throughput and labour productivity based on simulation result.
To enhance quality of decision making, Kibira et al. (2015) had established a scheme that coupled data analytics and simulation methods to support decision making in manufacturing. By reviewing recent works, standards and methods of data mining,
simulation and optimization were studied. The procedures for decision analysis that integrated data analytic and simulation method were presented in a flowchart. Data analytic method was performed to determine the attributes that have substantial effect on the system and the attributes were inputs of the simulation model. Simulation modelling was conducted using software to experiment different scenarios by varying the input data to obtain a set of input values that achieves optimization. Other than data and alternative analysis, the steps such as problem formulation, data collection and conceptual model design were described in the proposed decision-making scheme.
Moreover, Sachidananda et al. (2015) used DES modelling as an investment decision support tool in biopharmaceutical manufacturing. Literature about computer- aided modelling methods are reviewed and it was proposed that DES is the most suitable instrument for this research work due to its model’s flexibility and capability to visualize dynamic behaviour of the system. A step-by-step DES model construction methodology was developed and illustrated in a simple flowchart. WITNESS 13 was used to model the system and estimate performances of existing and proposed manufacturing processes. The simulation result indicates that the proposed model is worth to be invested as it has better performance than the current model. This is because by implementing the proposed model, the production time is estimated to be reduced by 50 minutes, number of operators required can be reduced by 1 and the operator utilization can be increased from 60 % to 85 %.
A study was also conducted by Subsomboon and Vajasuvimon (2016) to increase utilization and production rate of a job shop manufacturing system with the lowest cost. It was explored that computer simulation features useful statistical analysis that can aid in problem identification and comparison of performance data, especially in optimization problem. Therefore, simulation was used in this research to evaluate the proposed alternative strategies and select the most gainful one. Among the three proposed alternatives, the strategy that comprises adding one worker to operate idle machine was chosen to be implemented in real system. This is due to the strategy’s practicability and desirable simulation outcome that shows lower operating cost and higher labour utilization as well as throughput if compared with other alternatives.
Besides, Aqlan and Al-Fandi (2018) proposed a framework to prioritize, evaluate and select process improvement initiatives. Previous frameworks regarding Lean and Six Sigma methodologies were reviewed and it was found out that there was lack of consideration for several important factors during project improvement, such as prioritization of workplace areas and type of problem solving. Therefore, a framework is developed and it consists of three phases. The first phase is identifying workplace areas for improvement prioritization by assigning weights and using mathematical models. The second phase is selecting proper problem-solving methodology according to the type and criteria of problem faced with the guidance of a flowchart. Yet, the last phase is choosing the most preferable improvement projects by using mathematical model as the multi-objective decision analysis approach.
Furthermore, Jurczyk-Bunkowska (2020) had also conducted a case study that employed DES software to plan productivity improvement of a small batch size manufacturing system. By reviewing literature in the context of lean manufacturing, the researcher had highlighted strategies that can be implemented to elevate production’s productivity. Computer simulation was used to evaluate the performance of proposed configurations on the virtual system through simulation-generated statistics. Besides, a structured framework regarding process improvement using computer simulation as decision support tool was illustrated in flowchart. Out of 6 proposed model variants, it was found out that 1st and 2nd variants should be quickly implemented, whereas 3rd and 6th variants should be adopted after their implementation enablers are attempted. Ultimately, it is advocated that computer simulation is a time and cost-efficient tool in supporting such decision-making process due to provision of detailed analysis for diverse alternatives.