The Bullwhip effect in complex supply chains

This paper reviews the various methods of modelling the dynamics of supply chains. We then present recently documented causes of the Bullwhip effect in production supply chains, and the methodologies used to describe and measure the importance of these causes. We examine the limitations of these methodologies and suggest a combined approach discrete event-continuous simulation modelling approach to further study this phenomenon in complex production supply chains

A single-item inventory model for a non-stationary demand process

Cover title.Includes bibliographical references (p. 21-22).Supported in part by the MIT Leaders for Manufacturing Program, a partnership between MIT and major US manufacturing firms.Stephen C. Graves

Analysis of a forecasting-production-inventory system with stationary demand

Cover title. "April 1999."Includes bibliographical references (leaves 24-27).L. Beril Toktay, Lawrence M. Wein

Strategic and operational decisions in supply chains

In this work, we develop models that supply chains can employ to satisfy periodic and stochastic demand effectively. These models combine the strategic and operational decisions to cost function. Strategic decisions are represented by the size of capacity acquired, while operational decisions are represented by the parameters of the inventory policy employed.;In addition to combining the strategic and operational decisions, the developed models study three alternatives that can reduce the cost of satisfying the demand by relying on external production to outsource or subcontract some of the demand. Outsourcing is studied where a fixed amount of the product is delivered to the supply chain each time period regardless of the demand and inventory status.;Subcontracting is employed if the inventory status reaches a certain level that justifies going to a third party. Collaboration is the last alternative considered where two supply chains cooperate to satisfy the demand they are subject to. Two models of collaboration are studied; in the first model, collaboration takes place by exchanging unused capacity while in the second model, collaboration happens by exchanging finished products.;A simple stochastic approximation method is used to find the optimum parameters that minimize the cost functions considered. The costs are evaluated using simulation, while the gradient of the cost with respect to the different parameters is found using infinitesimal perturbation analysis (IPA). The validity of the suggested optimization method is checked graphically for models having three parameters to optimize.;Different experiments are conducted to study the response of the supply chain to its working environment and parameters for both single stage supply chains and multi-echelon supply chains. The working environment; is changed, by changing the variability of the demand that the supply chain is trying to satisfy and by changing the cost of the units satisfied from external sources. Holding cost, capacity cost and backordering cost are the supply chain parameters changed in these studies

A multi-echelon supply chain model for strategic inventory assessment through the deployment of kanbans

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 100-102).As global competition in the manufacturing space grows, so do corporations' needs for sophisticated and optimized management systems to enable continuous flows of information and materials across the many tiers within their supply chains. With the complexities introduced by the variability in the demand for finished goods as well as by the variability in lead-time of transportation, procurement, production and administrative activities, corporations have turned to quantitative modeling of their supply chains to address these issues. Based on the data of a heavy machinery manufacturer headquartered in the US, this research introduces a robust model for the deployment of strategic inventory buffers across a multi-echelon manufacturing system. Specifically, this study establishes a replenishment policy for inventory using a multiple bin, or Kanban, system for each part number in the assembly of products from our sponsors tractor line. We employ a numerical simulation to evaluate and optimize the various inventory deployment scenarios. Utilizing several thousand runs of the simulation, we derive a generalized treatment for each part number based on an econometric function of the parameters associated with lead-time, order frequency, inventory value and order costing. The pilot for the simulation focuses on the parts data for three earthmoving products across eight echelons, but scales to n products across m echelons. Our results show that this approach predicted the optimal quantities of Kanbans for 95% of parts to a level of accuracy +/- 3 bins.by Philip J. Hodge and Joshua D. Lemaitre.M.Eng.in Logistic

Construction supply chain modeling : a research review and interdisciplinary research agenda

Supply chain management has emerged as a popular and useful concept in the construction industry and research community since the mid 1990s. Research in construction supply chain management draws from a broad range of disciplines, notably: (1) Industrial organization economics to better understand market structure and forces and their effect on firm and supply chain behavior and (2) Analytic modeling of supply chains to improve supply chain performance along metrics such as speed, cost, reliability, quality, etc. Both industrial organization and analytic modeling provide useful but ultimately incomplete perspectives and prescriptions for construction supply chain management. As such, this paper proposes development of an interdisciplinary research agenda that draws from both fields. Towards that agenda, a review of research is presented to introduce the main ideas, relevant literature, and theory and methods in each of the two areas. From these independent reviews, applications that could benefit from a combined perspective are identified and used as a basis for development of an interdisciplinary research agenda.<br /

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Forecasting and stock control play an important role in the electric companies because outstanding forecasting and stock control increase service level obviously and decrease stock cost effectively. However, the majority of the electric materials are intermittent demand, resulting in poor forecasting and stock control performance. Therefore, exploring the reasons that affect forecasting performance and stock control is necessary. This paper explores the effects of the correlation of intermittent electric materials on forecasting and stock control. First, we divide the correlation into three categories: autocorrelation in demand sizes, autocorrelation in intervals and cross-correlation between demand size and interval. Forecasting by SBA approach and using periodic dynamic inventory strategy (T, S) to control stock, exploring the effects of these three correlations on forecast accuracy, stock cost and service level. The data shows that correlations of electric materials affect their forecasting and stock control, which will help company find more accurate forecast approach and lower the cost of stock in the future

Optimal pricing and seat allocation in the airline industry under the market competition

The current practice of revenue management is either quantity based or price based. A quantity based revenue management is most commonly observed in the airline industry; whereas a price based revenue management is practiced in retail enterprises. Recent improvement of information technology has not only increased the market size, but also has increased market competition. In a competitive environment customers choose among substitutable products depending on several rationalities, however a paramount factor in most selections is price. This thesis investigates pricing issue in revenue management and makes three contributions. First, price based revenue management is studied in the airline industry in a competitive market. Airlines compete for customers using their fare pricing strategies while having fixed capacity allocated in each fare class. The demand for each fare class of an airline is dependent on its fare price and the fare price offered by rival airline(s). A game theoretic approach is used to address the problem assuming both the deterministic and stochastic price sensitive customer demand for each fare class. The existence and uniqueness of Nash equilibrium for the game is shown for both deterministic and stochastic demands. A sensitivity analysis is carried out to determine fare pricing in each fare class considering various situations in the case of deterministic demand. The analysis is further extended to stochastic price sensitive demand, and a sensitivity analysis of the fare prices for each fare class is also reported. Second, an integrated approach to price and quantity based revenue management with an application to the airline industry is presented. The models proposed enable joint control of fare pricing and seat allocation in a duopoly competitive market. Both non cooperative and cooperative bargaining games are studied. Numerical experimentation is performed to study both competitive and cooperative fare pricing along with seat inventory control assuming a nested control on booking limits. In the case of a non cooperative game, Nash equilibrium for the competing airlines is determined assuming both symmetric and asymmetric market competition. A sensitivity analysis based on a statistical design of experiments is also presented to study the behavior of the game. Statistical evidence is established which shows that cooperation improves the revenue to the competing airlines. Lastly, a distribution free approach for pricing in revenue management is explored. The approach assumes the worst possible demand distribution and optimizes the lower bound estimate on revenue, while jointly controlling the price and capacity. The approach is first addressed to revenue management's most commonly observed standard newsvendor problem. Extensions to the problem are identified which can be applied to airline industry. Later the analysis is extended to consider the following cases: a shortage cost penalty; a holding and shortage cost; a recourse cost, with a second purchasing opportunity; and the case of random yields. An application of the approach is also suggested to capacity constrained industries facing restrictions such as limited budget. A numerical study reveals that the approach results in a near optimal estimate on revenue. Using a statistical comparison it is also shown that the outcomes of the standard newsvendor problem are significantly different than its extension

Inventory systems in the presence of an electronic market place

Ph.DDOCTOR OF PHILOSOPH