Methods of vendor managed inventory control in supply chain management

By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, constructs a VMI mathematics model, and analyzes the influence of VMI on inventory cost and channel profit. Finally, a special case is studied to verify that VMI is an effective supply chain strategy that can not only increase channel profit of supplier and customer but also improve full channel coordination, thereby reducing the bullwhip effect.

Distributed Order-Up-To Inventory Control in Networked Supply Systems With Delay

In this work, the dynamics of networked goods distribution systems subject to the control of a continuous-review order-up-to inventory policy are investigated. In the analytical study, as opposed to the earlier models constrained to the serial and arborescent interconnection structures, an arbitrary multiechelon topology is considered. This external, uncertain demand,following any distribution, may be imposed on all network nodes,not just conveniently selected contact points. As in the physical systems, stock relocation to refill the reserves is subject to nonnegligible delay, which poses a severe stability threat and may lead to cost-inefficient decisions. A state-space model is created and used as the framework for analyzing system properties. In particular, it is formally demonstrated that despite unpredictable demand fluctuations, a feasible(nonnegative and bounded)reserves replenishment signal is generated at all times, and the stock gathered at the nodes does not surpass a finite, precisely determined level. The theoretical content is illustrated with a case study of the Chinese oil supply system.

Inventory control model based on external and interior reverse logistics

To improve the inventory control strategy for enterprise and optimize inventory control parameters of existing external and interior reverse logistics, a multi-resource inventory control model is proposed to better simulate the logistics fact, which is aimed at periodic inventory checking and pull mode of inventory control strategy, based on the return product arrival time obeying Poisson distribution, the return product employing lotsize process and the nonzero lead time of manufaeturinge/remanufacturing. The rational cost function with multiple constraints is employed to describe the inventory model. The genetic algorithm is employed to solve the inventory cost function to obtain the optimal solution of inventory checking periods, safe inventory point, product lot-sizes and process lot-sizes of return product. An example is presented to prove the feasibility and validity of the proposed method. Moreover, the influence of manufacturing/remanufacturing lead time and reuse rate of return production on the inventory control strategy of enterprise is analyzed.

Seat inventory control methods for Chinese passenger railways

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers' historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Modeling and Simulation of Equipment Material Inventory Control Based on System Dynamics

The paper discusses how the inventory control of army equipment material runs sytematically under the two-level maintenance system,and establishes the inventory control model based on system dynamics.On the basis of modeling and simulation,the influence of different inventory upper limit on the whole system is studied,and the optimal inventory control mechanism under the model condition is foud.In addition,through the simulation of two replenishment strategies(s,S) and(T,s,S),the advantages and disadvantages and feasibility of each replenishment strategy are analyzed.

Impact of Capacity Parameters on Flexible Inventory Control Decision Model

From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to actively respond to demand fluctuations when there is a demand forecast error or a missing part of the demand information,and to avoid the risk of passive variable demand forecasting to set the immutable inventory capacity.At the same time,the game is controlled by the flexible and variable inventory control strategy and the customer’s willingness to demand.The paper mainly studies the influence of the setting of capacity parameters on the booking-limit decision and its benefits under the control of flexible space with variable total capacity.Through the two trends of capacity increase flexibility and capacity reduction flexibility in the flexible inventory control model,the mathematical performance and marginal utility methods are introduced to change the performance of the booking-limit control decision model under different scenarios.The correlation analysis between the capacity limit level and the return under the optimal Bookinglimit decision,and the above two flexibility parameters are obtained.

Unique influences of reboiler inventory control on the operation of totally reboiled reactive distillation columns

In this work,the dynamics and operation of the totally reboiled reactive distillation columns are visualized in terms of transfer function based process models.This kind of processes is found to be characterized by underdamped step responses due to the special topological configuration and the intricate interplay between the reaction operation and the separation operation involved.The under-dampness can be substantially alleviated through the tight inventory control of bottom reboiler and this presents beneficial effects to process dynamics and operation.Two totally reboiled reactive distillation columns,separating,respectively,a hypothetical synthesis reaction from reactants A and B to product C,and a real decomposition reaction from 1,4-butanediol to tetrahydrofuran and water,are employed to demonstrate these uncommon behaviors.The results obtained give full support to the above qualitative interpretation.Despite the strong influences of reaction kinetics and thermodynamic properties of the reacting mixtures,the totally reboiled reactive distillation columns are generally considered to present such unique behaviors and require tight inventory control of bottom reboiler to facilitate their control system development.

Pricing and Inventory Control Strategy for a Periodic-Review Energy Buy-Back System

Along with the rapid development of economics and enhancement of industrialization, the power demand keeps rising and frequently creates mismatch between demand and supply in electricity.This provides miscellaneous energy buy-back programs with great opportunities. Such programs, when activated, offer certain amount of financial compensations to participants for reducing their energy consumption during peak time. They aim at encouraging participants to shift their electricity usage from peak to non-peak time, and thereby release the demand pressure during peak time. This paper considers a periodic-review joint pricing and inventory decision model under an energy buy-back program over finite planning horizons, in which the compensation levels, setup cost and additive random demand function are incorporated. The objective is to maximize a manufacturer's expected total profit.By using Veinott's conditions, it is shown that the manufacturer's optimal decision is a state dependent(s, S, P) policy under a peak market condition, or partly an(s, S, A, P) policy under the normal market condition.

Three-stage decision approach of network dynamic pricing and seat inventory control

The dynamic joint pricing and seat inventory control is more practical but complicated in both formulation and solving.This paper presents a three-stage decision approach(TSDA)to attack this problem.In the first stage,the relationship between dynamic prices and their pre-sale periods is built.The game process between passengers and the airline based on maximisation of both passenger utility and airline’s network revenue is applied.Passenger’s booking and cancellation processes are simulated according to respective distributions.In the second stage,the seat allocation model for different itineraries is built based on presented unified prices.It greatly decreases computation complexity since prices,itinerary legs and pre-sale time periods are excluded from combination.In the third stage,itinerary-based revenue management model is built with embedding the nested control strategy inside.Meanwhile,some practical factors such as cancellation and no-show are considered.The experimental computation results show TSDA is effective.

Revenue management method and critical techniques of railway passenger transport

Purpose–Revenue management(RM)is a significant technique to improve revenue with limited resources.With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China,it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.Design/methodology/approach–This paper proposes the theory and framework of generalized RM of railway passenger transport(RMRPT),and the thoughts and methods of the main techniques in RMRPT,involving demand forecasting,line planning,inventory control,pricing strategies and information systems,are all studied and elaborated.The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT.The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.Findings–The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.Originality/value–The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Inventory Management of a Plant Spare Parts Supply Chain

In spare parts industries, firms are dealing with a situation which is more and more uncertain due to the supply chain structure and variable demands. This paper presents a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an （ S - 1 ; S） inventory system for controlling plant spare parts. We apply the Bayesian approach in an innovative way to specify the initial prior distributions of the failure rates, using the initial estimates and the failure history of similar items. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.

Distribution Inventory Cost Optimization Under Grey and Fuzzy Uncertainty

The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.

New bounds of (s,S) policies in periodical review inventory systems

We study the stochastic inventory problem with optimal （s,S） policies.In a finite horizon model with lost sales,we establish new lower and upper bounds of s and S.These bounds have structural implications for the optimal solutions.Consequently,when demand has a generalized phase type distribution,there are no more than a pre-determined number of minima.Similar bounds can also be found for the system where unsatisfied demand is backordered instead of lost sales.

A Kind of Backwards Thinking-fast Moving Consumer Goods (FMCG) Stock-controlling Analysis

To control the material purchase of solve the problem of purchase planning, this a fast moving consumer goods （FMCG） enterprise warehouse and paper formulates an integrated system of reverse logistics operated with a demand forecast model and an economic storage model. This is based on the supply chain and focused on the marketing terminal. By analyzing the integrated system reverse logistics and the timely information marketing terminal, the purpose is to effectively alleviate or solve the terminal stock-out which needs to and oversupply which needs to return. According to the system, it can provide a theoretical basis for the inventory cost control from the replenish enterprise

Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing Systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.

Controlling Stock and Other Inventory Issues

Inventory control is one of the most important business processes during the operation of a trading or manufacturing company as it relates to purchases,sales and logistic activities.In order to have clear inventory management,a company should not only focus on logistic management but also on sales and purchase management. Commonly,we think of the warehouse as the most important component of inventory management and the accounting department is responsible for the inventory management. However,inventory control is not only the responsibility of the accounting department and the warehouse,but also the responsibility of the entire organization.Actually,there are many departments involved in the inventory control process,such as sales,purchasing, production,logistics and accounting.All these departments must work together in or- der to achieve effective inventory controls.

LOT SIZING WITH NON-ZERO SETUP TIMES FOR REWORK

In this paper we consider a single machine multi-product lot scheduling problem in which defective items are produced in any production run of each product. In each cycle after the normal production of each product the machine is setup for the rework of the defectives of the same product and then the rework process starts. We assume that the setup time for the normal production process as well as the rework process is non-zero. Further we consider the waiting time cost of defectives for rework. This paper has two objectives. The first objective is to obtain the economic batch quantity （EBQ） for a single product. The second objective is to extend the result of the first objective to the multi-product case. Adopting the common cycle scheduling policy we obtain optimal batch sizes for each product such that the total cost of the system per unit time is minimized.

AN ALGORITHM FOR THE DETERMINATION OF THE ECONOMIC ORDER QUANTITY IN A TWO-LEVEL SUPPLY CHAIN WITH TRANSPORTATION COSTS:COMPARISON OF DECENTRALIZED WITH CENTRALIZED DECISION

This paper considers a two-level supply chain consisting of one warehouse and one retailer. In this model we determine the optimal ordering policy according to inventory and transportation costs. We assume that the demand rate by the retailer is known. Shortages are allowed neither at the retailer nor at the warehouse. We study this model in two cases; decentralized and centralized. In the decentralized case the retailer and the warehouse independently minimize their own costs; while in the centralized case the warehouse and the retailer are considered as a whole firm. We propose an algorithm to find economic order quantifies for both the retailer and the warehouse which minimize the total system cost in the centralized case. The total system cost contains the holding and ordering costs at the retailer and the warehouse as well as the transportation cost from the warehouse to the retailer. The application of this model into the pharmaceutical downstream supply chain of a public hospital allows obtaining significant savings. By numerical examples, the costs are computed in MATLAB to compare the costs in the centralized case with decentralized one and to propose a saving-sharing mechanism through quantity discount.

Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations

Airline operators face accidental damages on their fleet of aircraft as part of operational practice.Individual occurrences are hard to predict;consequently,the approach towards repairing accidental damage is reactive in aircraft maintenance practice.However,by aggregating occurrence data and predicting future occurrence rates,it is possible to predict future long-term(strategic)demand for maintenance capacity.In this paper,a novel approach for integration of reliability modelling and inventory control is presented.Here,the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity.Demand has been modelled using a superposed Non-homogeneous Poisson Process(NHPP).A case study has been performed on damage data from a fleet of Boeing 777 aircraft.The results prove the feasibility of adopting an integrated approach towards strategic capacity identification,using real-life data to predict future damage occurrence and associated maintenance slot requirements.