Reorder Point System Research Articles

Multi-objective continuous review inventory policy using MOPSO and TOPSIS methods

In the dynamic business environment of today, decision-making involves considering various conflicting aspects. Inventory planning problems aim to determine how much and when to order products to satisfy customer demand at the lowest possible cost while maintaining a desirable service level. These problems can be formulated as a MOPSO algorithm, which is used to handle multiple objectives in a continuous review stochastic inventory control system (r, Q). Unfortunately, most multi-objective inventory models have been solved by aggregating objectives using specific weights or by optimizing only one objective and treating the others as constraints. Considering the complexity of real-world inventory control problems, which involves conflicting objectives such as minimizing cost and maximizing service level, the need arises to employ more precise optimizers that can generate better and more diverse non-dominated solutions of reorder point and order size system. In this paper multi-criteria decision-making framework that combines MOPSO algorithm and TOPSIS method to generate a Pareto front of non-dominated solutions and rank them based on decision makers' preferences. Initially, the original MOPSO is applied to the multi-objective inventory control problem, and then the mutation operator is integrated into the MOPSO to maintain diversity in the swarm and explore the entire search space. Next, the leader selection strategy called the geographically-based system (Grids) is replaced by the crowding distance factor to choose the global optimal particle as a leader. Additionally, the ε-dominance concept is employed to limit the archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. In conclusion, this work not only pioneers a cutting-edge approach to multi objective inventory control but also underscores its practical value. By facilitating the generation of superior solutions that cater to diverse decision-maker preferences, our methodology resonates deeply with real-world challenges and sets a new benchmark for effective inventory planning.

Determination of the optimal backorder level for a lot-size reorder point inventory system with backorder parameter

This paper attempts to investigate the existence of an optimal backorder level that will minimize the expected total inventory cost of a regular (R, Q) lot-size reorder point system that is associated with backorder effect and establish the allowable backorder levels. To achieve this, the expected order cycle cost function of the lot-size reorder point system with backorder effect is optimized to determine the optimal backorder parameter. Numerical results presented showed that an optimal backorder level of 8 units exist where the expected order cycle cost is minimum with a value ₦ 19,036,000. Furthermore between the backorder level of 0 and 8 units, the order cycle cost decreases from ₦ 19,105,000 to ₦ 19,036,000. Thereby defining the range of the allowable backorder levels. Keywords: Backordered Level, Backorder Effect, Cycle Stock, Stock-Out and Backordered Demand

Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer

The vision of a well-integrated supply chain (SC) was developed as early as 1958 by Forrester, who addressed what would eventually be called the Bullwhip Effect (BWE). The Flowcasting concept, originally called Retail Resource Planning, was proposed to connect all SC upper tiers to the storefront through fulfillment logic based on the Distribution Resource Planning (DRP) system. This method can therefore be understood as fully or SC-wide integrated DRP with a focus on the role of retailers instead of that of vendors or distributors. We studied a Canadian retailer that implemented Flowcasting in order to gain insight into the benefits and operational logic of this system. Based on the data obtained from the company, we simulate Flowcasting operations across a 3-tier SC compared to the Reorder Point (ROP) system, which was previously used at the firm, as well as a combination of ROP and DRP (ROP/DRP or partially integrated DRP), which are some of the most common implementations in use. The simulation is configured based on the company's settings, including historical average demand, demand estimates, lead time, etc. Then, multivariate regression is deployed to statistically compare the efficacy of these methods in SC management using various assessment criteria, including BWE measures. The results show that the requirement calculation logic used in an SC-wide integrated DRP system (Flowcasting) generally outperforms the other two approaches, and its benefits in curtailing the BWE become more noticeable in the upper tiers of the SC. This paper indicates the enormous potential of SC-wide integrated DRP logic in rule-based replenishment planning systems.

Cover-Time Planning/Takt Planning: A technique for materials requirement and production planning

Cover-Time Planning, or Takt Planning, is presented. It is a system for calculating material requirements and start of purchases and production. Requested production rates of sales items, or alternative prefabricated modules in stock, are “broken down”, exploded, to create the need for components, for all underlying items (articles) in the Bill of Material. Inventory and already ordered replenishments are compared with the item’s desired production rate. How long already made actions are expected to cover the desired expected sales and production rates is compared with the item’s lead time; if a forward future shortage is likely the article is signalled for a refill. With examples is described how make-to-order production is done easily. The method is a type of reorder point system, but with time instead of quantity as decision variable. Unlike a traditional reorder point system increases and decreases of production can be planned. Future work load in various production sections can be estimated. It is described how an “Available-to-promise”-system should be designed and used. Cover-Time Planning (CTP) is a complete alternative to Materials Requirements Planning (MRP). CTP responds faster than MRP, since MRP for each structural level uses a batch size to “break down” and to magnify the need at the underlying level. In the end a large material acquisition needs to be ordered maybe just to build a single end item. CTP uses the end item requirement rates on all structural levels shifted with the lead times, when customer demand changes, the entire production chain react simultaneously.

Continuous Review Inventory Systems: Practice Problems

A problem set to review the concepts of continuous review inventory systems. These problems can be assigned for additional practice in calculating order quantities, inventories, and relevant costs. This can be used with the notes Managing Inventories: Determining Order Quantity (UVA-OM-1395) and Managing Inventories: The Reorder Point System (UVA-OM-1394). Excerpt UVA-OM-1489 Rev. Apr. 4, 2014 CONTINUOUS REVIEW INVENTORY SYSTEMS: PRACTICE PROBLEMS 1. Demand for an item is 1,661 units per year. The ordering cost is $ 20 per order. The carrying cost per year (c ´ k) is $ 5.40 per item. On average, how many orders are placed each year, if the economic order quantity (EOQ) is ordered? 2. An office orders stationery from a supplier. The cost of paper is $ 20.51 per ream. The ordering cost is $ 33 per order. The annual cost of carrying the inventory is 0.22 (22%) of the unit cost per ream. The annual demand is 22,013 reams of paper. What is the annual carrying cost of inventory, assuming that the EOQ is ordered? 3. Demand for an item is 9,434 units per year. The ordering cost is $ 21 per order. The carrying cost per year (c ´ k) is $ 18 per item. What is the optimal ordering quantity? . . .

Forecasting the forecastability quotient for inventory management

This research develops and empirically tests a model for estimating the economic advantage of using a time phased order point system (TPOP) with time series forecasting rather than a simple reorder point system in an independent demand inventory management context. We define the forecastability quotient (Q) to support this economic analysis. We implement TPOP in our empirical analysis via double exponential smoothing with a damped trend, and implement ROP through a simple moving average.Our empirical study of a large dataset of time series from a Fortune 100 firm found that Q in the holdout sample can be predicted using just three variables from the estimation sample. Surprisingly, many highly touted time series metrics (e.g., the coefficient of variation and approximate entropy) and forecast accuracy metrics (e.g., the mean absolute percentage error) were not good predictors of Q. We then validated this model on four additional datasets. This research contributes both to the research literature and to managers who need to decide whether an independent demand item should be managed with a TPOP or reorder point system.

Dynamic reorder point replenishment strategies for a capacitated supply chain with seasonal demand

Managing inventory and service levels in a capacitated supply chain environment with seasonal demand requires appropriate selection and readjustment of replenishment decision variables. This study focuses on the dynamic adjustment of decision variables within supply chains using continuous-review reorder point (ROP) replenishment. A framework is proposed to adjust reorder points and lot sizes based on optimal settings within different regions of a seasonal demand cycle. This framework also includes the optimal timing of adjustments defining these regions. A discrete-event simulation model of a simple, capacity-constrained supply chain is developed and simulation–optimization experiments are performed, the objective being to minimize the total supply chain inventory subject to a target delivery service level. The performance of ROP systems with optimal static and optimal dynamic decision variable settings are compared using two different seasonal demand patterns. The results confirm that performance with dynamic decision variable adjustment is better. For a given delivery service level, average work-in-process inventory levels are almost the same for both systems. However average finished goods inventory levels decrease significantly and are more stable under dynamic adjustment. The practical implication is that both finished goods holding costs and maximum storage capacity requirements are reduced.

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.

Measuring the performance sensitivity of replenishment systems using tradeoff curves

There are a number of studies in the literature where replenishment systems have been compared on the basis of mean performance. However, little attention has focused on comparing systems on the basis of sensitivity to changes in the supply chain environment. This study compares the performance sensitivity of reorder point (ROP) and Kanban replenishment systems in a capacitated supply chain using an optimum-seeking simulation approach. Changes in the supply chain environment include transit time variability, transporter frequency, demand rates and lot setup times. Performance tradeoff curves, showing the interaction of inventory and delivery performance, are generated and an index based on the areas under the tradeoff curves is proposed to quantify the performance sensitivity. It is found that the Kanban system is generally less sensitive, in part because it operates optimally at a lower utilisation level. It is also observed that performance sensitivity depends on the environmental factor that is perturbed.

The Ordering Challenge: An Online Game to Introduce Independent Demand Inventory Concepts

ABSTRACTStudents are put in the role of a manager who watches inventory levels decrease and must order at the right time and in the right quantity to minimize costs. This interactive game requires the students to race against time and has levels of increasing difficulty. It introduces the students to the concepts of holding cost, ordering cost, backlog cost, the sawtooth curve, reorder point, order quantity, ABC categorization, and continuous review systems. The game is simple and is available free online. An accompanying assignment is designed to guide the student's reflection process while playing the game.

Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system

This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.

Managing Inventories: The Reorder Point System

Inventory replenishment concepts and tools are described. Excerpt UVA-OM-1394 Rev. Dec 1, 2010 MANAGING INVENTORIES: THE REORDER POINT SYSTEM Inventories account for a significant percentage of the current assets of companies in industries ranging from hospitals and restaurants to manufacturing, wholesaling, and retailing firms. So, managing inventories is often the key aspect of improving a firm's working capital position and its return on assets. An inventory-management system is a set of procedures for managing the items in inventory. The focus in this note is on one such system—the reorder point system. Other inventory management systems and concepts have been addressed in separate technical notes. The selection of a system for an item or group of items depends, in part, on the type of demand experienced by the item. There are two basic types of demand: forecast demand and scheduled requirements demand. Forecast Demand . . .

Managing Inventories—Reorder Point Systems

This note introduces students to the replenishment-inventory management framework, concepts, and tools. Excerpt UVA-OM-0936 MANAGING INVENTORIES—REORDER POINT SYSTEMS Inventories account for a significant percentage of the current assets of companies in industries ranging from hospitals and restaurants to manufacturing, wholesaling and retailing firms. So, managing inventories is often the key aspect of improving a firm's working capital position and its return on assets. An inventory-management system is a set of procedures for managing the items in inventory. The focus in this note is on one such system—the Reorder Point System. Other inventory management systems and concepts have been addressed in separate technical notes. The selection of a system for an item or group of items depends, in part, on the type of demand experienced by the item. There are two basic types of demand, forecast demand and scheduled requirements demand. Forecast Demand Orders may arrive at unpredictable intervals and call for quantities whose magnitude cannot be precisely predicted ahead of time. Examples of such orders include service parts orders, distributor orders placed upon a factory warehouse, or customer demand at a retail facility. When the arrival time and quantity required for each order are unknown, stocks must be held in anticipation of demand. The principal management concern is to maintain the proper quantity of each item held—given that too much stock absorbs a disproportionate share of the firm's assets, and too little stock reduces customer service. Forecasts are thus necessary and must be based on historical order rates and anticipated changes in future usage. . . .

Multi-objective inventory planning using MOPSO and TOPSIS

One of the main characteristics of today's business tends to vary often. Under such environment, many decisions should be carefully pondered over from relevant aspects which are usually conflicting. Hence, inventory planning problems, which address how much and when to order what customers need at the least relevant cost while maintaining a desirable service level expected by customers, could be recast into a multi-objective optimization problem (MOOP). In a MOOP there are normally infinite numbers of optimal solutions in the Pareto front due to the conflicts among objectives. Unfortunately, most multi-objective inventory models have been solved by aggregation methods through a linear combination of specific weights or only one objective was optimized and the others were turned into constraints. Therefore, the challenges decision makers face are not only modeling the problem in a multi-objective context, but also the effort dedicated to build the Pareto front of MOOPs. This paper first employs the multi-objective particle swarm optimization (MOPSO) algorithm to generate the non-dominated solutions of a reorder point and order size system. A ranking method called technique for order preference by similarity to ideal solution (TOPSIS) is then used to sort the non-dominated solutions by the preference of decision makers. That is, a two-stage multi-criteria decision framework which consists of MOPSO and TOPSIS is presented to find out a compromise solution for decision makers. By varying the weights of various criteria, including minimization of the annual expected total relevant cost, minimization of the annual expected frequency of stock-out occasions, and minimization of the annual expected number of stock-outs, managers can determine the order size and safety stock simultaneously which fits their preference under different situations.

Comparison of supply chain replenishment strategies in a non-capacitated distribution system

This research considers inventory replenishment in a stochastic, non-capacitated, multi-echelon distribution system. Simulation is used to model Distribution Requirements Planning (DRP), reorder point and Kanban replenishment strategies. Additional experimental factors include demand seasonality, lot sizes, demand uncertainty and transit time uncertainty. Trade-off curves between inventory and delivery performance are generated. Statistical techniques, including ANOVA and regression, are then used to determine the relative dominance of the replenishment strategies by comparing the areas under the trade-off curves. Results show the methodology developed for analysis of the results works well in identifying both main and interaction effects. DRP performance is found to be the most robust, followed by that of reorder point and Kanban systems, respectively.

Master Production Scheduling and a comparison of Material Requirements Planning and cover-time planning

For a company's long-term profitability, the most important processes are the way it starts parts of the manufacturing process before the customer order arrives and the way it determines and promises delivery quantities and times for customer orders. In practical computer applications Material Requirement Planning (MRP) and/or Reorder point systems are the base techniques mostly used. This paper presents Cover-Time Planning (CTP), a variant of a reorder point system, which is developed with a forward-looking forecasted demand rate and the decision variable is ‘time’, instead of ‘quantity’ for an ordinary reorder-point system. MRP and CTP are introduced and compared through a numerical example. MRP and CTP must treat a practical ‘make-to-order’; therefore, this paper discusses available-to-promise, planning bills and other help systems for practical applications of Master Production Scheduling. The paper presents how and why, in practice, a Master Production Scheduling system with an available-to-promise function should be used and how this system should be designed. It is also argued that a fully Enterprise Resource Planning (ERP) system cannot only be created by MRP, but also by CTP.

Supply chain simulation: generic models and application examples

This paper proposes a simulation system to support designing supply chain management operations. The most remarkable characteristic of this simulation is to represent business process activities in supply chain management. The organisation-based models are described in this paper. Activities of these models include supply-chain management operations, such as ‘demands predictions’, ‘manufacturing planning’, ‘material purchasing’, ‘manufacturing and transportation ordering’, and ‘products shipping’. These models also represent activities in manufacturing and transportation operations. This paper further demonstrates simulations of the typical forms of supply chain systems: these are ‘centre-controlled ordering system’, ‘vendor reorder-point system’, and ‘pull-operational system’. Finally, these simulation results are compared in view of supply chain performance.

Managing Inventories: The Periodic Review System

This note addresses inventory-planning and -control activities using periodic-review systems. The reorder point system is addressed in a separate technical note (UVA-OM-0936).

The application of analytic hierarchy process in classification of material planning and control systems

Many organisations keep thousands of items in their warehouses, which may worth more than 50% of all companies' expenses (Neely and Byrne, 1992). Controlling of these items by one Material Planning and Control System (MPCS) (push and pull systems) may not be rational or economic. Every methodology has different cost and service level in different environments. Therefore, the tool required to categorise the material based upon the suitability of MPCS. This paper presents a multicriteria approach within Analytic Hierarchy Process (AHP), which is capable of classifying the materials and components for different MPCS, that is, Kanban, MRP, hybrid and Re-Order Point (ROP) Systems.

Managing Inventories—Reorder Point Systems

Managing Inventories—Reorder Point Systems