Evaluation of production control strategies for the co-ordination of work-authorisations and inventory management in lean supply chains

A decision support framework is proposed for assisting managers and executives to possibly utilise lean production control strategies to coordinate work authorisations and inventory management in supply chains. The framework allows decision makers to evaluate and compare the suitability of various strategies to their system especially when considering conflicting objectives, such as maximising customer service levels while minimising Work in Process (WIP) in a business environment distressed by variabilities and uncertainties in demand stemmed from customer power. Also, the framework provides decision guidance in selecting and testing optimal solutions of selected policies control parameters. The framework is demonstrated by application to a four-node serial supply-chain operating under three different pull-based supply chain strategies; namely CONWIP, Kanban, and Hybrid Kanban-CONWIP and exhibiting low, medium, and high variability in customer demand (i.e., coefficient of variation of 25%, 112.5%, and 200%). The framework consists of three phases; namely Modelling, Optimisation and Decision Support; and is applicable to both Simulation-Based and Metamodel-Based Optimisation. The Modelling phase includes conceptual modelling, discrete event simulation modelling and metamodels development. The Optimisation phase requires the application of multi-criteria optimisation methods to generate WIP-Service Level trade-off curves. The Curvature and Risk Analysis of the trade-off curves are utilised in the Decision Support phase to provide guidance to the decision maker in selecting and testing the best settings for the control parameters of the system. The inflection point of the curvature function indicates the point at which further increases in Service Level are only achievable by incurring an unacceptably higher cost in terms of average WIP. Risk analysis quantifies the risk associated with designing a supply chain system under specific environmental parameters. This research contributes an efficient framework that is applicable to solve real supply chain problems and better understanding of the potential impacts and expected effectiveness of different pull control mechanisms, and offers valuable insights on future research opportunities in this field to production and supply chain managers

Implementation of SPC-EPC Scheme to Lessen and Control Production Disruptions in Chlor-Alkali Industry

The quality of products in the industry can be improved by monitoring the manufacturing process and adjusting/optimizing the process input variables based on the output deviation from the target. SPC stands for statistical process control, and it is used to monitor processes in order to identify any assignable causes of variation. EPC stands for engineering process control, and it is concerned with adjusting systems inputs to keep the system output on target using different types of controllers such as integral controllers and PID controllers. Combining SPC and EPC as a unified framework proved to be effective in reducing production disruption in manufacturing industries. The main objective of this project is to redesign the production and quality control system in a chemical batch processing company, anonymous company, by developing an engineering process controller (EPC) in order to adjust the chemical process inputs to keep the output chemical concentration on best optimized target while monitoring the system using the sensitive time weighted control charts (SPC), in order to eliminate the process assignable causes of variation, improve system elements life, and reduce overall system wide costs