Inventory Control Strategy Research Articles

Multi-Echelon Integrated Inventory Control Strategy of Equipment Spare Parts

Inventory control strategy of spare parts is an effective way to improve equipment support efficiency and scientific and reasonable inventory policy can make spare parts availability reach the upper limit. Multi-echelon integrated inventory control strategy of equipment spare parts is studied. Three-level integrated inventory control model consisting of rear warehouse, equipment using units and spare parts supplier is established according to the architecture and control program of multi-echelon joint inventory. The algorithm of solving the proposed model is analyzed and a numerical example is provided. The proposed equipment spare parts inventory model can improve inventory quality to a certain degree and decrease the contradictions between inventory and maintenance. The correctness and effectiveness of the proposed model are verified by the example, which provides an effective model for equipment spare parts inventory.

Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches

In this paper, a two-degrees-of-freedom Internal Model Control structure is incorporated in production inventory control for a supply chain system. This scheme presents an intuitive and simple parametrization of controllers, where inventory target tracking and disturbance (demand) rejection in the inventory level problems are treated separately. Moreover, considering that the lead times are known, this scheme presents a perfect compensation of the delay making the stabilization problem easier to handle. This control structure is formulated for a serial supply chain in two ways (by using a centralized and a decentralized control approach). The behavior of these inventory control strategies is analyzed in the entire supply chain. Analytical tuning rules for bullwhip effect avoidance are developed for both strategies. The results of controller evaluations demonstrate that centralized control approach enhances the behavior with respect to the inventory target tracking, demand rejection and bullwhip effect in the supply chain systems.

基于ERP的(s,S)策略下库存优化控制决策支持系统

Because the ERP software can not dynamically obtain optimal order policy and false to effectively analyze historical data,this paper establishes optimization mathematical model under discount criteria in order to minimize inventory cost.We also analyze the data in ERP software and dynamically obtain all kinds of product's optimal order policy which are similar to(s,S) strategy.Furthermore,we design and develop inventory control strategy optimization decision support system which provides decision support for users and greatly reduce inventory cost for enterprise.

The Impact of Operational Decisions on the Design of Salesforce Incentives

When facing high levels of overstock inventories, firms often push their salesforce to work harder than usual to attract more demand, and one way to achieve that is to offer attractive incentives. However, most research on the optimal design of salesforce incentives ignores this dependency and assumes that operational decisions of production/inventory management are separable from design of salesforce incentives. We investigate this dependency in the problem of joint salesforce incentive design and inventory/production control. We develop a dynamic Principal-Agent model with both Moral Hazard and Adverse Selection in which the principal is strategic and risk-neutral but the agent is myopic and risk-averse. We find the optimal joint incentive design and inventory control strategy, and demonstrate the impact of operational decisions on the design of a compensation package. The optimal strategy is characterized by a menu of inventory-dependent salesforce compensation contracts. We show that the optimal compensation package depends highly on the operational decisions; when inventory levels are high, (a) the firm offers a more attractive contract, and (b) the contract is effective in inducing the salesforce to work harder than usual. In contrast, when inventory levels are low, the firm can offer a less attractive compensation package, but still expect the salesforce to work hard enough. In addition, we show that although the inventory/production management and the design of salesforce compensation package are highly correlated, information acquisition through contract design allows the firm to implement traditional inventory control policies: a market based state-dependent policy (with a constant base-stock level when the inventory is low) that makes use of the extracted market condition from the agent is optimal. This work appears to be the first paper on operations that addresses the important interplay between inventory/production control and salesforce compensation decisions in a dynamic nature. Our findings shed light on the effective integration of these two significant aspects for the successful operation of a firm.

Joint inventory-location problem under the risk of probabilistic facility disruptions

This paper studies a reliable joint inventory-location problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks (e.g., due to natural or man-made hazards). When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. We propose an integer programming model that minimizes the sum of facility construction costs, expected inventory holding costs and expected customer costs under normal and failure scenarios. We develop a Lagrangian relaxation solution framework for this problem, including a polynomial-time exact algorithm for the relaxed nonlinear subproblems. Numerical experiment results show that this proposed model is capable of providing a near-optimum solution within a short computation time. Managerial insights on the optimal facility deployment, inventory control strategies, and the corresponding cost constitutions are drawn.

Research on the University Inventory Control of Diet Low Maintenance Materials

The current management situation of the diet low-cost materials inventory in the university is analyzed in this paper and two solutions are given: ABC inventory analysis method and inventory control strategies. The calculation method of the ABC inventory analysis method is introduced through the management data of the low-cost materials inventory in some university, and the different control strategies are also explained according to different raw materials in the inventory control strategies. The order place and the calculation method of order quantity are given under the condition of determining the demand and lead time, and under the condition of uncertain of the demand and lead time, that provide a theoretical basis for the inventory control management.

A finite-horizon, stochastic optimal control policy for a production–inventory system with backlog-dependent lost sales

This article considers a problem of optimal production control of a one-product-type production–inventory system. The demand is a discrete-time stochastic process, while production of a limited capacity is continious in time. The dynamics of the inventory over a finite time horizon are investigated and an optimal feedback strategy for production control under a backlog-dependent lost sales effect is developed. This strategy is called a target level strategy since, contrary to the existing production–inventory control strategies, it determines an optimal target inventory level as a function of time period and the initial stock at that period. Numerical examples illustrate the applicability of the developed solutions.

Joint Production and Spare Part Inventory Control Strategy Driven by Condition Based Maintenance

Throughput of a manufacturing process depends on the effectiveness of equipment maintenance, and the availability of spare(service) parts. This paper addresses a joint production and spare part inventory control strategy driven by condition based maintenance(CBM) for a piece of manufacturing equipment. Specifically, a critical unit is continuously monitored for performance degradation during operation. The amount of degradation is utilized to initiate replacement actions in conjunction with spare part inventory control under both production lot size, and due date constraints. A degradation limit maintenance policy is combined with a base stock spare part inventory control policy to manage the manufacturing process. The objectives are to minimize the spare part inventory, and the expected total operating cost. Constrained least squares approximation, and simulation-based optimization are utilized, in a heuristic two-step approach, to determine the optimal base-stock level of spare parts, along with the preventive maintenance threshold. The resulting joint decision ascertains the allowed stockout probability for spare parts, while incurring the minimal operating cost for the required production within a fixed production duration. A case study of an automotive engine manufacturing process is provided to demonstrate the proposed decision-making methodology in practical use.

Passivity-based adaptive inventory control

A new adaptive inventory control strategy is developed by applying online adaptation in the framework of passivity-based control. By using the system model and definition of the inventory, a feedback-feedforward control structure is derived from the passivity theorem. The stability analysis and the extension of the controller to a non-passive system are also given in this paper. This control strategy is demonstrated in a transfer function example and an application to a pressure tank unit in a chemical plant.

A simulation study for a hybrid inventory control strategy with advance demand information

Choosing the appropriate production and inventory control strategy is a key factor for the success of modern enterprises. Some industries adopt a Make-To-Order (MTO) policy to improve their punctuality and flexibility, while others adopt a Make-To-Stock (MTS) policy to minimise and control the inventory. This study proposes a hybrid strategy combining MTS and MTO with prioritisation. In the proposed strategy, the pull policy is considered for regular demands while a push policy with priority is applied to customers who notify their demand needs in advance. Through a set of simulation experiments this strategy is proved to be of great effectiveness.

Multi-Scale Modeling and Inventory Control of Particle Growth Processes

In this paper, we develop a multi-scale model to describe the particle growth in a fluidized bed reactor. Population balance model is presented to describe the dynamical behavior of particle size distribution. Stability analysis is derived to determine the control configuration for the complex particulate process. Inventory control strategy is applied to control the particle size distribution.

Robust Inventory Control System

Further developments of the inventory control strategy are studied in this brief. We use high gain (sliding mode) adaptive control to handle the system uncertainties caused by modeling errors and unmeasured disturbances. It is proven that this control law makes the uncertain system globally stable. The parameters of the resulting controller are easy to tune. The simulation example and experiment studies illustrate the application of the novel method.

Joint inventory-and-pricing strategy for a stochastic continuous-review system

We analyze an infinite-horizon continuous-review stochastic inventory system in which the selling price and inventory replenishment are determined simultaneously. The demand process is Poisson with a price-dependent arrival rate. The ordering cost includes a fixed setup cost and a variable cost proportional to the order quantity. We obtain closed-form solutions for the optimal inventory control strategy and optimal pricing strategy, which provide managerial insights as well as quantitative and qualitative relationships between decision rules and system parameters. We show that the optimal price is a unimodal function of the inventory level. We also develop efficient algorithms to compute the optimal strategies and present numerical examples.

Deterministic price–inventory management for substitutable products

Revenue management involves the use of pricing and inventory control strategies to balance supply and demand in a revenue-maximising manner. Although pricing and inventory control decisions are closely related, they have historically been considered in isolation in both industry and academic work. This paper adds to the recent research stream on joint pricing and inventory control by investigating static deterministic optimisation models for firms managing multiple products sharing multiple resources. Firms must manage both sales (demand rationing) and pricing decisions in order to maximise profits. This paper shows that, under standard regularity assumptions on demand, the deterministic joint price–inventory control problem with substitutable products can be reduced to a pure pricing problem. This is not true if demand is uncertain and/or products exhibit complementary effects, in which case demand rationing can be profitable. Illustrative examples that consider linear demand with and without substitution effects are provided. In this case, the price–inventory control problem can be solved by optimising a concave quadratic objective function with linear constraints. Practical applications of the models, which have been successfully used by several industries, are presented.

Inventory control of perishables in supermarkets

In the last decade large supermarket chains have started developing and implementing automated store ordering (ASO) systems in order to improve both the quality and efficiency of the inventory replenishment decisions in their stores. These ASO systems have been developed mainly for non-perishables. The aims of this paper are 1. to better understand the product, sales and supply characteristics of perishables in supermarkets, 2. to define and operationalise relevant concepts in controlling the inventories of perishables, 3. to investigate how the intelligence in ASO systems in supermarkets can be further improved, 4. to give academics empirically based insight in the relevance of the inventory models that are currently available in the literature, insight in relevant parameter settings and insight in which models are needed. The research is carried out in cooperation with two Dutch supermarket chains and focusses on perishable items. To further improve the intelligence in the ASO systems, we will classify the items in a supermarket, based on the notion that different items need different inventory control rules. For every class of items, we describe the main logistic characteristics; moreover, for the perishable items we describe the inventory control rules which are currently being applied and we discuss the desired inventory control strategy for these items.

Coordinating inventory control and pricing strategies: The continuous review model

We analyze an infinite horizon, single product, continuous review model in which pricing and inventory decisions are made simultaneously and ordering cost includes a fixed cost. We show that there exists a stationary ( s , S ) inventory policy maximizing the expected discounted or expected average profit under general conditions.

Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process

In this paper we study a hybrid system with both manufacturing and remanufacturing. The inventory control strategy we use in the manufacturing loop is an automatic pipeline, inventory and order based production control system (APIOBPCS). In the remanufacturing loop we employ a Kanban policy to represent a typical pull system. The methodology adopted uses control theory and simulation. The aim of the research is to analyse the dynamic (as distinct from the static) performance of the specified hybrid system. Dynamics have implications on total costs in terms of inventory holding, capacity utilisation and customer service failures. We analyse the parameter settings to find preferred “nominal”, “fast” and “slow” values in terms of system dynamics performance criteria such as rise time, settling time and overshoot. Based on these parameter settings, we investigate the robustness of the system to changes in return yield and the manufacturing/remanufacturing lead time. Our results clearly show that the system is robust with respect to the system dynamics performance and the remanufacturing process can help to improve system dynamics performance. Thus, the perceived benefits of remanufacturing of products, both environmentally and economically, as quoted in the literature are found not to be detrimental to system dynamics performance when a Kanban policy is used to control the remanufacturing process.

Modeling and optimizing a joint inventory control and preventive maintenance strategy for a randomly failing production unit: Analytical and simulation approaches

This paper presents a joint optimal inventory control and preventive maintenance strategy for a randomly failing production unit which supplies an assembly line operating according to a just-in-time configuration. The production unit is submitted to a maintenance action as soon as it reaches a certain age T or at failure; whichever occurs first. In order to palliate perturbations caused by breakdowns and by planned maintenance actions both with random durations, a buffer stock is built up to ensure the supply of the assembly line at a constant rate. A mathematical model is developed to evaluate the average cost per time unit of the proposed strategy. Due to the complexity of the mathematical model and to some approximation assumptions, a second approach based on simulation and experimental design is developed. It allows us to simulate the proposed policy and to generate a second order response surface for easy computation of the optimal values of the decision variables which are the age T at which preventive maintenance must be performed, and the buffer stock level h.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s, S, p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s, S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s, S, p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions, and apply it to provide a characterization of the optimal policy.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Infinite Horizon Case

We analyze an infinite horizon, single-product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other, and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period, and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average, profit over the infinite planning horizon. We show that a stationary (s, S, p) policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical (s, S) policy, and price is determined based on the inventory position at the beginning of each period.