Single-stage Supply Chain Research Articles

Conditions of reverse bullwhip effect in pricing under joint decision of replenishment and pricing

A “reverse bullwhip effect in pricing (RBP)” occurs when an amplification of price variability takes place moving from the upstream suppliers to the downstream customers in a supply chain. In this study, we investigate RBP conditions for supply chains where joint replenishment and pricing decisions are made. Commencing with a single-stage supply chain in which a retailer faces a random and price-sensitive demand, we extend the results to a multi-stage supply chain using a leader-follower game theoretical framework. We discuss RBP conditions for supply chains where newsvendor and continuous review inventory policies are employed, and present numerical examples for commonly used demand functions.

Dynamic strategies for supply disruptions in production-inventory systems

Supply chain disruptions can occur at any node if there is a vast array of triggers. A common trigger for manufacturing disruptions is supply interruptions. This paper considers a single-stage supply chain with single manufacturer sourcing from a single supplier, where supply disruptions lead to a production pause and the demand is deterministic. In view of different hitting times and durations of disruptions, this paper compares and selects proactive and reactive strategies for supply disruption management via a cost minimization model. Based on the comparison, two types of dynamic strategies are proposed to guide the mitigation approaches as the disruptions continue. One is a dynamic reactive strategy for a non-prevention system and is called passive-backup, and the other is a dynamic combination strategy that contains reactive and proactive strategies for the prevention system and is called recovery-backup. How the lead time of backup sources, disruption starting time, cost of lost sales, backup costs and backorder rate impact the dynamic strategies is also explored in this paper.

Glowworm swarm optimisation algorithm for nonlinear fixed charge transportation problem in a single stage supply chain network

In this paper, Glowworm swarm optimisation (GSO) is proposed to solve nonlinear fixed charge transportation problem (NLFCTP) in a single stage supply chain network. In a fixed charge transportation problem, fixed cost is incurred for every route, along with the variable cost that is proportional to the amount shipped. In some cases, the variable cost is associated with quadratic variables in which the cost function will be nonlinear. This problem is more difficult to solve due to presence of the fixed costs that result in discontinuities in the objective function. The objective of this paper is to determine the least cost transportation plan that minimises the total variable and fixed costs while satisfying the supply and demand requirements of each plant and customer. The performance of GSO is compared in terms of total distribution cost with a spanning tree-based genetic algorithm. The comparison reveals that GSO provides better solutions.

Cat Swarm Optimization for Single Stage Supply Chain Distribution System with Fixed Charges

ICTACT Journals are peer reviewed International Journals published quarterly. With the objective of creating Indian-based Journals of International quality and to build up local research content, ICTACT Journals exists for the purpose of providing a quality peer-reviewed platform for Scientists, Researchers, Academicians and Engineers who carry out their original research. The Journals will highlight the continued growth and new challenges in both basic research and application development.

A tri-level programming model based on Conditional Value-at-Risk for three-stage supply chain management

For the problem of supply chain management, the existing literature mainly focuses on the research of the single-stage supply chain or the two-stage supply chain that consists of a manufacturer and a retailer. To our best knowledge, little attention has been paid to the study of a more extensive supply chain that consists of a material supplier, a manufacturer and a retailer, which is a more practical and interesting case. Therefore, based on the Conditional Value-at-Risk (CVaR) measure of risk management, this paper proposes a tri-level programming model for the three-stage supply chain management. In this model, the material supplier and the manufacturer maximize their own profit while the retailer maximize his/her CVaR of expected profit. Further, we show that the proposed tri-level programming model can be transferred into a bilevel programming model, which can be solved by the existing methods. Numerical results show that the proposed model is efficient for improving the risk management of the three-stage supply chain.

Research on Production and Order Decision for Supply Chain Members of Virtual Enterprises in Uncertain Environment

As an organizational way, virtual enterprise had been the development tendency of contemporary enterprise operation. For complex market opportunities, the uncertainty of supply chain in virtual enterprises was the key of enterprise operation. This paper studied the production and order strategy for virtual enterprise members from single-stage supply chain, and then provided reference for subsequent multi-stage supply chain. This paper introduced expected loss, which was the base of making decisions for the enterprise. Through building the mathematical based on the strict mathematical derivation, the quantitative relation between the order quantity of core enterprises and production capacity of partners, and then the mathematical model was verified in the last.

Genetic Algorithm for optimisation of distribution system in a single stage supply chain network with fixed charges

This paper proposes a single stage supply chain network fixed charge transportation problem (FCTP) and a solution procedure. The objective is to find the optimum set of suppliers to be opened and determine the quantities to be supplied, so that the total cost of distribution can be minimised. The problem chosen goes beyond the traditional mathematical programming and it becomes a non-polynomial (NP) hard problem, particularly while considering the fixed charges. Genetic Algorithm (GA) approach is proposed in this paper for solving. GA is applied for optimisation of the distribution network to provide optimal or near optimal solution. To demonstrate the efficiency of the proposed method in FCTP, GA is compared with spanning tree-based GA with the aid of numerical examples. Following application and analysis of this methodology, the proposed method is identified to be efficient concerning the total distribution cost. Further, the accuracy and procedure of selecting the suitable suppliers for meeting the customer needs are found simple and systematic in approach.

A comparison of bullwhip effect in a single-stage supply chain for autocorrelated demands when using Correct, MA, and EWMA methods

In this paper, the three Correct, MA, and EWMA Methods for series demand function model (ARiMA(1, 0, 0), ARiMA(0, 0, 1), ARiMA(1, 0, 1)) have been described, formulated, and presented to examine the influence of forecast-updating methods between order quantity and actual demand in the amplifications of bullwhip effect. By using these forecast-updating methods comparison, the optimal solution of the bullwhip effect control Policies with a time-series technique under a basic one supply chain stage perspective can be obtained. Through the comparison of the more flexible bullwhip effect control Policies, a more efficient demand function model strategy of the parameter setting, using and the integrated application method in the supply chain management procedure was decided to use. In addition, a simulated procedure and systems analysis regarding these series demand uncertainty modeling parameters will be conducted to investigate the fluctuation effects on the amplifications of bullwhip effect. The proposed method permits controlling the retailer orders’ variability above the other factors in the bullwhip effect.

Conditions of reverse bullwhip effect in pricing for price-sensitive demand functions

Supply chain mechanisms that exacerbate price variation needs special attention, since price variation is one of the root causes of the bullwhip effect. In this study, we investigate conditions that create an amplification of price variation moving from the upstream suppliers to the downstream customers in a supply chain, which is referred as the “reverse bullwhip effect in pricing” (RBP). Considering initially a single-stage supply chain in which a retailer faces a random and price-sensitive demand, we derive conditions on a general demand function for which the retail price variation is higher than that of the wholesale price. The investigation is extended to a multi-stage supply chain in which the price at each stage is determined by a game theoretical framework. We illustrate the use of the conditions in identifying commonly used demand functions that induce RBP analytically and by means of several numerical examples.

A Perceptions Framework for Categorizing Inventory Policies in Single-Stage Inventory Systems

In this paper we propose a perceptions framework for categorizing a range of inventory decision making that can be employed in a single-stage supply chain. We take the existence of a wide range of inventory decision making processes, as given and strive not to model the reasons that the range persists but seek a way to categorize them via their effects on inventory levels, orders placed given the demand faced by the inventory system. Using a perspective that we consider natural and thus appealing, the categorization involves the use of conceptual perceptions of demand to underpin the link across three features of the inventory system: inventory levels, orders placed and actual demand faced. The perceptions framework is based on forecasting with Auto-Regressive Integrated Moving Average (ARIMA) time series models. The context in which we develop this perceptions framework is of a single stage stochastic inventory system with periodic review, constant leadtimes, infinite supply, full backlogging, linear holding and penalty costs and no ordering costs. Forecasting ARIMA time series requires tracking forecast errors (interpolating) and using these forecast errors and past demand realizations to predict future demand (extrapolating). So called optimal inventory policies are categorized here by perceptions of demand that align with reality. Naturally then, deviations from optimal inventory policies are characterized by allowing the perception about demand implied by the interpolations or extrapolations to be primarily different from the actual demand process. Extrapolations and interpolations being separate activities, can in addition, imply differing perceptions from each other and this can further categorize inventory decision making.

A single-stage supply chain system controlled by kanban under just-in-time philosophy

The production system using kanban was pioneered by Toyota Motor Company in Japan and subsequently it was adopted by numerous other Japanese and US companies for applying the just-in-time manufacturing principles. This research studies a single-stage supply chain system that is controlled by kanban mechanism. The supply chain system is modelled as a mixed-integer nonlinear programming (MINLP) problem. It is solved optimally by branch-and-bound method to determine the number of kanbans, batch size, number of batches, and the total quantity over one period. Meanwhile, the kanban operation between two adjacent plants is worked out considering the factors of loading and unloading time, and transport time. Coupled with plant-wide efforts for cost control and management commitment to enhance other measures of performance, a logistics system for controlling the production as well as the supply chain system is developed, which results in minimizing the total cost of the supply chain system. The results show that the improvements in reduction of inventory, wasted labour, and customer service in a supply chain are accomplished through the kanban mechanism.