Inventory Control In Supply Chain Research Articles

Optimal control of inventory level for perishable goods with uncertain decay factor and uncertain forecast information: a new robust MPC approach

This paper deals with the inventory control in supply chains under the following assumptions: (1) perishable goods with uncertain deteriorating factor, (2) a future uncertain customer demand that, over a limited prediction horizon, belongs to a known compact set. The problem is to define a smooth control policy maximising the fulfilled customer demand and minimising the inventory level. This problem is here solved through a new Robust Model Predictive Control (RMPC) approach. This implies solving a min–max optimisation problem with hard constraints on the control effort (i.e. the sequence of replenishment orders). To drastically reduce the numerical complexity of this problem, the control signal is sought in the space of B-spline functions, which are known to be universal approximators admitting a parsimonious parametric representation. This allows us: (1) to reduce the number of both decision variables and constraints involved in the optimisation procedure, (2) to reformulate the numerically demanding minimisation of the worst case cost functional as a simpler Weighted Constrained Robust Least Squares (WCRLS) estimation problem. The WCRLS algorithm can be efficiently solved using interior point methods. A rigorous analysis of stability and feasibility conditions is provided.

Inventory Control in Supply Chain: a Model-Free Approach*

Inventory control in the frame of supply chain systems represents a challenging problem due to their dynamical behavior, ranging from fast changing demand, transport and delivery delays and production phenomena like bullwhip effect. Nevertheless, modeling such systems in order to capture all its components and to design a robust and dynamic management, remains an open problem. In this paper, a model-free control which permits to design several control strategies without need to any exact model, is proposed. Several numerical simulations as well as the conducted comparative studies show the relevance of the proposed approach. In addition, the paper considers the case of a poor knowledge of the supply chain model and introduce an exact and fast deterministic method which is of a algebraic flavor in order to estimate the parameters of the model.

A Review of Multi-Echelon Inventory Control in Supply Chain

Multi-echelon inventory control is the core issue in supply chain. Firstly, we summarize the current research status of multi-echelon inventory control by using Citispace which is the scientific knowledge map visualization analysis software. Second, two strategies of multi-echelon inventory optimization and control are analyzed. Then, multi-echelon inventory control problem is divided into three strategies, namely multi-echelon multi-node inventory control strategy, multi-echelon single-node inventory control strategy, and transshipment-based multi-echelon multi-node inventory control strategy, and comparative analysis. Finally, we point out future development trend.

Incentives through inventory control in supply chains

The paper shows that taking inventory control out of the hands of competitive or exclusive retailers and assigning it to a manufacturer increases the value of a supply chain especially for goods whose demand is highly volatile. This is because doing so solves incentive distortions that arise when retailers have to allocate inventory across sales periods, and thus allows for better intertemporal price discrimination. Assigning inventory control to a manufacturer is also shown to have effects on total inventory and social welfare.

BULLWHIP EFFECT IN SUPPLY CHAINS REDUCING BY DECENTRALIZED INVENTORY CONTROL BASED ON INVARIANT ELLIPSOIDS METHOD

Context. The urgent task of the decentralized inventory control synthesis of material resources in supply chains with uncertainty of external demand and availability of operational constraints has been solved. Objective is a development of the method of optimal stabilization of local supply chain nodes using a linear dynamic feedback from deviation between available and safety levels of resource stocks in the chain nodes, providing suppression of «bullwhip effect» and the development of the stability analysis method of the decentralized inventory control system in supply chains. Method. An approach based on the invariant ellipsoids method, which provides a complete and timely response of the «unknown, but bounded» external demand for resources, the stabilization of the closed local subsystems under predefined constraints on the values of local states and control actions, as well as the suppression of «bullwhip effect» in the supply chains has been invited. The stability conditions for decentralized inventory control system in supply chains using the vector Lyapunov functions method and the comparison method has been determined. Results. The problem of control synthesis using the technique of linear matrix inequalities was reduced to a sequence of semidefinite programming problems, which are solved numerically in real time. Conclusions. The method of suboptimal structural and parametric synthesis of the decentralized constrained inventory control in supply chains was further developed through the development of the invariant ellipsoids method, thus the costs associated with transportation and storage of material resources were reduced due to suppression of «bullwhip effect»; the degree of suppression of mentioned above effect for different levels of the supply chains is determined by the choice of weighting matrices of local quality criteria. The developed models and algorithms of the decentralized inventory control under uncertainty of demand and availability of operational constraints were introduced at the industrial enterprises and organizations which deal with the production, storage and distribution of material resources, such as the «Kharkivvodokanal» – one of the largest enterprises in Ukraine providing water services and the Ltd. Company «SV» (Kharkiv) – the leading manufacturer of household chemical goods in Ukraine.

Incentives Through Inventory Control in Supply Chains

The paper shows that taking inventory control out of the hands of competitive or exclusive retailers and assigning it to a manufacturer increases the value of a supply chain especially for goods whose demand is highly volatile. This is because doing so solves incentive distortions that arise when retailers have to allocate inventory across sales periods, and thus allows for better intertemporal price discrimination. Assigning inventory control to a manufacturer is also shown to have effects on total inventory and social welfare.

Supply planning for multi-levels assembly system under random lead times

Production costs and inventory control in supply chain and are very important elements for companies. Finished products have to be produced at lowest costs. For this reason, planners must reduce average stock levels and determine optimal safety lead times. In this paper we deal with a model of a multi-levels assembly system. Several types of component and one type of finished products are considered. The demand in each period is known in advance and procurement lead times are independent random discrete variables with known probability distributions. In this study we try to determine optimal order release dates for components at last level of BOM to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product. A simulation model is proposed and a genetic algorithm is used to analyze and optimize the planned lead times.

Inventory Control and Intermediation in Global Supply Chains

Inventory Control and Intermediation in Global Supply Chains

Optimization Control of the Bullwhip Effect in Supply Chain Inventory

The bullwhip effect was caused by fuzzy demand among the enterprises. In order to reduce this effect, control theory was applied to solve the inventory in supply chain. Firstly, inventory control in supply chain and the bullwhip effect was researched. Secondly, a kind of proportional integral differential (PID) controller was developed for inventory control in a three-level supply chain, and the mathematical model of the PID controller for inventory control was presented. Finally, the results show that the PID controller can evidently alleviate the bullwhip effect and inventory fluctuations under the suitable combination of control gain.

A fuzzy reinforcement learning algorithm for inventory control in supply chains

In the real world, applications with very large state and action spaces and unknown state transition probability, classical reinforcement learning algorithms usually show poor performance. One way to address the performance problem is to approximate the policy or value function. Fuzzy rule-based systems are amongst the well-known function approximators. This paper presents a Flexible Fuzzy Reinforcement Learning algorithm, in which value function is approximated by a fuzzy rule-based system. The proposed algorithm has a separate module for tuning the structure of fuzzy rules. Moreover, the parameters of the system are tuned during the learning phase. Next, the proposed algorithm is applied to the problem of inventory control in supply chains. In this problem, a fuzzy agent (supplier) should determine the amount of orders for each retailer based on their utility for supplier, by considering its limited supply capacity. Finally, a simulation is performed to show the capability of the proposed algorithm.

Inventory control in supply chains: Alternative approaches to a two-stage lot-sizing problem

The principal challenge of inventory control in supply chains is that the interacting autonomous enterprises have to plan their production and logistics under information asymmetry, driven by different, often conflicting objectives. In this paper, four different computational approaches are investigated to cope with this challenge: decomposition, integration, coordination, and bilevel programming. The four approaches are applied to solving the same two-stage economic lot-sizing problem, and compared in computational experiments. The prerequisites of the approaches are analyzed, and it is shown that the profits realized and the costs incurred at the different parties largely depend on the solution approach applied. This research also resulted in a novel coordination mechanism, as well as a new algorithm for the bilevel optimization approach to the investigated lot-sizing problem. A specific goal of this study is to highlight the so far less recognized application potential of the coordination and the bilevel optimization approaches for controlling inventories in a supply chain. © 2012 Elsevier B.V. All rights reserved.

Optimal portfolios under a value-at-risk constraint with applications to inventory control in supply chains

The optimal portfolio problem under a VaR (value at risk) constraint is reviewed. Two different formulations, namely with and without consumption, are illustrated. This problem can be formulated as a constrained stochastic optimal control problem. The optimality conditions can be derived using the dynamic programming technique and the method of Lagrange multiplier can be applied to handle the VaR constraint. The method is extended for inventory management. Different from traditional inventory models of minimizing overall cost, the cashflow dynamic of a manufacturer is derived by considering a portfolio of inventory of raw materials together with income and consumption. The VaR of the portfolio of assets is derived and imposed as a constraint. Furthermore, shortage cost and holding cost can also be formulated as probabilistic constraints. Under this formulation, we find that holdings in high risk inventory are optimally reduced by the imposed value-at-risk constraint.

Multi Objective Optimization Model for Collaborative Multi{Echelon Inventory Control in Supply Chain

The use of single objective model in which only the cost is considered in multi-echelon inventory systems does not suffice to raise the total performance of a supply chain. In this paper, we propose an optimization model in which the cost, elapsing time and the demand satisfaction rate are considered in a combined way. For the case where the capacity and supply are limited under the condition of multi classes, random demand and complex link relationships, a two-level solution algorithm is presented. This algorithm enables us to find out the optimal strategy of sites and at the same time deal with the logistic structure using a evolutionary multi objective optimization method and simulation approach. Numerical examples were designed to demonstrate the validity of the algorithm, and the results show that the multi objective optimization model can significantly improve the total performance of supply chains.

Large Deviations-Based Asymptotics for Inventory Control in Supply Chains

We consider a model of a capacitated single-class supply chain consisting of production facilities (stages) in tandem. External demand is met from the available finished goods inventory maintained in front of the most downstream facility; unsatisfied demand is backlogged. Every stage orders from its upstream facility, thus production is constrained by the local production capacity and the availability of upstream inventory. We propose production policies in two separate cases: (1) when each facility has information about its local inventory only, and (2) when each facility has knowledge of the total downstream inventory. In case (1) the proposed policy guarantees that stockout probabilities at each stage stay bounded below given constants (service level constraints). In case (2) the proposed policy minimizes total expected inventory cost subject to desirable service-level constraints. In both cases the parameters of the proposed policies are obtained analytically based on large deviations asymptotics, which leads to drastic computational savings compared to simulation. An important feature of our model is that it accommodates autocorrelated demand and service processes, both critical features of modern failure-prone manufacturing systems. We demonstrate that detailed distributional information on demand and service processes, which is incorporated into large deviations asymptotics, is critical in inventory control decisions. We discuss extensions to a multiclass setting and to a model where unsatisfied demand is lost instead of backordered.