A simulation approach to determine the probability of demand during lead-time when demand distributed normal and lead-time distributed gamma

Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model

A Simulation Approach to Determine the Probability of Demand during Lead-Time When Demand Distributed Normal and Lead-Time Distributed Gamma

Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Besides, it is found that little attention has been given in analysing completion time at production line from previous literatures. Therefore, this paper fill the knowledge gap by predicting completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late job’s due date from its completion time. A wellknown company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market plac

An enhanced approximation mathematical model inventorying items in a multi-echelon system under a continuous review policy with probabilistic demand and lead-time

An inventory system attempts to balance between overstock and understock to reduce the total cost and achieve customer demand in a timely manner. The inventory system is like a hidden entity in a supply chain, where a large complete network synchronizes a series of interrelated processes for a manufacturer, in order to transform raw materials into final products and distribute them to customers. The optimality of inventory and allocation policies in a supply chain for a cement industry is still unknown for many types of multi-echelon inventory systems. In multi-echelon networks, complexity exists when the inventory issues appear in multiple tiers and whose performances are significantly affected by the demand and lead-time. Hence, the objective of this research is to develop an enhanced approximation mathematical model in a multi-echelon inventory system under a continuous review policy subject to probabilistic demand and lead-time. The probability distribution function of demand during lead-time is established by developing a new Simulation Model of Demand During Lead-Time (SMDDL) using simulation procedures. The model is able to forecast future demand and demand during lead-time. The obtained demand during lead-time is used to develop a Serial Multi-echelon Inventory (SMEI) model by deriving the inventory cost function to compute performance measures of the cement inventory system. Based on the performance measures, a modified distribution multi-echelon inventory (DMEI) model with the First Come First Serve (FCFS) rule (DMEI-FCFS) is derived to determine the best expected waiting time and expected number of retailers in the system based on a mean arrival rate and a mean service rate. This research established five new distribution functions for the demand during lead-time. The distribution functions improve the performance measures, which contribute in reducing the expected waiting time in the system. Overall, the approximation model provides accurate time span to overcome shortage of cement inventory, which in turn fulfil customer satisfaction

Multi-objective optimisation of dynamic short-term credit portfolio selection :the adoption of third party logistics credit for financing working capital contrained small and medium sized enterprises in supply chains

PhD ThesisMany companies, especially small and medium sized enterprises, are faced with liquidity problems. The shortage of working capital in their businesses has prevented supply chains from achieving effectiveness and efficiency in management. Although they can access short-term loans from banks and suppliers, the willingness of these credit lenders to lend short-term capital is often restricted by the fact that they cannot monitor whether or how their customers will use the loans according to the agreements. In many cases, this fact makes it difficult for capitalconstrained companies to obtain sufficient working capital from existing funding sources. A business practice called Integrated Logistics and Financial Service has been developed, which can improve banks’ monitoring of how their loans will eventually be used via the alliance of third party logistics companies and banks. The emergence of credit offered by third party logistics companies (termed as 3PLC) provides more choices for working capital constrained companies. Following on traditional bank overdrafts and trade credit, the new 3PLC became the third type of credit available to short-term working capital constrained companies. A new issue arising from this situation is how a working capital constrained company can determine a credit portfolio from multiple working capital sources. Current studies of credit portfolio management are still silent in considering 3PLC. Moreover, limited studies have integrated credit portfolio management into material flow management in supply chains. In light of the aforementioned discussions, this thesis aims to optimise dynamic credit portfolio management in supply chains to achieve the different business objectives of working capital constrained companies. To achieve the above aims, this thesis firstly applies an analytic hierarchy process and linear programming model to optimise a single objective. It applies the analytic hierarchy process to evaluate the concerns of working capital-constrained companies in selecting credit. These concerns are identified through a thorough literature review focusing on the considerations of small and medium sized enterprises’ in borrowing short-term credit. The analytic hierarchy process has been applied to determine the priority of the identified concerns and the preferences of borrowers for bank overdrafts, trade credit and 3PLC. A linear programming model has been developed based on the results obtained from the analytic hierarchy process model. It determines the maximum borrowing amount for a given period from multiple credit sources. To reflect the complexity of working capital constrained companies borrowing credit, thisthesis has extended the model from single objective optimisation to multiple objectives optimisation. Consequently, a goal-programming model has been developed. This model provides the solution of optimizing two business objectives including overall cost and backorder penalty cost minimization. Numerical examples have been conducted to test and analyse all the mathematical models. This thesis contributes the following aspects: 1) the new 3PLC together with bank overdraft and trade credit have been considered into credit portfolio management; 2) borrower’s concerns and credit preferences relating to the three types of credit have been identified and evaluated; 3) mathematical models have been developed for credit portfolio selection over multiple periods