Stock Policy Research Articles

The effect of visibility on forecast and inventory management performance during the COVID-19 pandemic

During the COVID-19 pandemic, healthcare facilities faced significant shortages of critical supplies like personal protective equipment, with dire repercussions. This study evaluates the potential role of decreased data visibility on these shortages, analysing different forecasting methods integrated with a periodic review inventory system (i.e. a base stock policy) on semi-simulated data encompassing several visibility issues. The forecasting methods chosen pertain to different data types: Holt and naïve methods are used as demand-based predictors, while a modified epidemiological model utilises pandemic data for demand forecasting. We scrutinise three prevalent data visibility issues through specifically crafted scenarios examining the effects of delayed, temporally aggregated, and erroneous data on system performance. Generally, our research illustrates that data visibility issues have a detrimental impact on the healthcare supply chain's efficacy. Interestingly, the system performance sees an uptick when these issues spur significantly oversized over-forecasts, e.g. when employing an epidemiological compartmental model for predictions.

Forecasting and stock control for fast-moving humanitarian aid supplies

In Indonesia, effective disaster management is imperative due to the region's susceptibility to a wide array of natural and man-made disasters. The establishment of the National Disaster Management Agency (BNPB) underscores the significance of this issue. This study investigates the improvement of disaster logistics, with a particular emphasis on the challenges associated with fast-moving humanitarian supplies. The primary obstacle in disaster logistics lies in the unpredictability of demand after the disaster occurred, coupled with the potential for supply disruptions stemming from inadequate inventory in warehouses. A critical focus of this research is placed on fast-moving humanitarian aid supplies, which are essential for immediate relief efforts, even when lead times are short. The study's primary objective is the development of a safety stock level policy for BNPB's national warehouse, adjusted through the analysis of demand forecasts and lead times. Notably, the research results for the year 2022 reveal minimal errors in demand forecasts. This outcome highlights the effectiveness of the safety stock policy in proactively addressing spikes in demand and mitigating uncertainties related to supply timelines. The research underscores the critical role of accurate inventory management, particularly in the case of fast-moving humanitarian supplies, within the context of disaster relief efforts. It emphasizes the importance of forward-thinking planning to meet urgent needs, ultimately contributing to more efficient disaster response efforts and ensuring the timely delivery of vital relief items to affected populations.

A Bi-Objective Inventory Model for Strategic Items in a Stochastic Environment with Partial Shortage: A Base-Stock Approach

This paper suggests an ordering policy for strategic items. Based on the base stock policy, we modeled a bi-objective, multi-product inventory control system with partial back-ordering. The model aims to minimize total costs and maximize the average service level, considering stochastic constraints such as storage space, maximum allowable shortage, and maximum allowable lost sales to match real-world conditions. We used three Multi-Objective Decision-Making methods, namely Individual optimization, Desirability function, and Goal attainment, to solve the problem. TOPSIS method showed the Goal attainment method’s superiority over the others. The sensitivity analysis revealed that the mean of storage space had the most significant impact on objective functions. Also, increasing the back-order percentage can reduce the system’s total costs.

The Proactive Base Stock Policy for Condition Based Maintenance

This paper introduces the proactive base stock policy (ProBSP) that incorporates degradation data into spare parts decision-making processes. The proposed policy aims to anticipate the demand for spare parts by proactively ordering them, instead of ordering each time one is needed. ProBSP involves two decision variables: the initial stock level and the degradation order threshold. To determine the optimal values for these variables, a simulation-based optimization method is developed. More precisely, a discrete event simulation is constructed to evaluate a single point in the parameter space, and an intelligent algorithm is employed to search for the optimal solution by efficiently exploring the solution space, thereby exploiting structural properties of ProBSP. Finally, numerical experiments are conducted to gain insights into the performance of ProBSP. These experiments reveal the effectiveness of ProBSP: the average spare parts stock level is reduced by 68%, with reductions exceeding 90% in specific scenarios.

Single-Product Assemble-to-Order Systems with Exogenous Lead Times

Efficient Algorithm for Base Stock Policy Optimization in Single-Product Assemble-to-Order Systems with Stochastic Lead Times Single-product assemble-to-order systems can be commonly observed in industrial settings. For example, firms targeting a niche market often choose to offer a single product; moreover, sole-product rollout is usually a preferable business strategy for manufacturers promoting a brand-new product or trying to penetrate a new market. However, maintaining an effective inventory management for those systems is a notoriously difficult problem, especially when the order lead times exhibit stochastic patterns. In “Single-Product Assemble-to-Order Systems with Exogenous Lead Times,” Muharremoglu, Yang, and Geng investigate the case with a special type of stochastic lead time and utilize a certain analysis technique to develop an efficient algorithm for the performance evaluation of base stock policies. In addition, efficiently computable upper and lower bounds are proved based on the key idea of the algorithm. Extensive numerical studies show that the heuristics and the approximation methods developed from those bounds perform well in base stock optimization.

VENDOR MANAGED INVENTORY POLICY FOR PERISHABLE PRODUCTS UNDER A COMBINATION OF FIFO-LIFO STOCK CONTROL

For retailing systems, arranging the perishable products has always been a challenge. Although more profit is made when products are sold with a FIFO policy, now customers taste in a competitive market which requires new products to be offered to them has changed the game. This paper considers a hybrid of FIFO-LIFO strategies for controlling a retailer's stock and its impact on the profits of the vendor managed inventory contracts. The problem is formulated as a multi-period, multi-store integer programming model which aims to maximize the total profit. The proposed model considers different selling prices and different holding costs for perishable products according to their remaining shelf life. A dynamic programming with a backward strategy is proposed to find the optimal order quantity for each store. The numerical experiments confirm the applicability and efficiency of the model illustrating impact of the crucial parameters of the model on the stocking policy.

Joint Discount and Replenishment Parametric Policies for Perishable Products

We consider a joint discount and replenishment problem in a discrete periodic review fashion for the sale of a perishable product, characterized by limited deterministic shelf life, replenishment lead times, and stochastic demand. Customers decide what to buy according to a linear discrete choice model, balancing price and perceived quality, uniquely determined by the residual shelf life. The decisions we consider are: How many new items to order, the age of the items to be discounted, and how much discount to offer. In this context, we compare a set of policies mixing the constant order policy and the base stock one with some easy discounting policies, optimizing their parameters using a simulation-based optimization framework. To evaluate their performance in terms of revenue and quantity of scraped items, we consider four realistic instances for a grocery retailer characterized by products of different shelf life and variance of demand. Experiments show that best results are achieved by a base stock policy that discounts products of different ages based on a threshold: If the quantity of the inventory of a given age is greater than a threshold it applies a discount, otherwise no discount is proposed. In the presented configurations, this policy increases the average reward compared to policies that do not discount.

Optimizing retailer prepayment installment in inventory systems with link-to-order discounts and power demand patterns of price and time: An effective decision support framework

Installment in the prepayment mechanism has emerged as a significant decision for many companies due to rising capital costs and increasing pressures from suppliers for various products. To motivate the companies about the prepayment mechanism, suppliers allow the frequency of installment of advance payment as a decision variable for the company and the per unit acquisition cost as a step-down function in exchange for an order-based prepayment regulation allowing the stepwise decreasing prepayment portion and duration as well. This paper investigates the optimal joint decision-making of a company’s prepayment, pricing, and stocking policies to maximize profits in the context of an order-based prepayment and discount regulation. A realistic approach is adopted in the study by considering the clients’ demand as a multiplicatively separable power function that accounts for both the price and the storage time of products. Theoretical expressions are derived to check when multiple or single installments are more profitable for the company. To achieve the maximum profit, a solution approach is introduced based on all the derived theoretical outcomes. The results of three numerical studies are documented and then the effects of order-based prepayment and discount regulation on optimal policies and maximum profit are further studied. The results of the study indicate that the profitability of the company can be enhanced through an increase in the frequency of installments, particularly in situations where the rate of interest charged is high. Finally, to draw up effective guidelines, a sensitivity investigation is conducted for all cost parameters.

A viable supply chain by considering vendor-managed-inventory with a consignment stock policy and learning approach

Inventory management challenges are optimizing inventory levels, minimizing stockouts, and streamlining the supply chain process for the vendor and the customer. The study tries to improve these challenges by proposing a new Viable Supply Chain (VSC) incorporating Vendor-Managed-Inventory (VMI) with a Consignment Stock (SC) policy for managing inventory between SC components. These strategies access the inventory level of the customer in the vendor, and if the items are sold, the customer pays the vendor for the inventory that was sold. The Robust Stochastic Optimization (RSO) with Conditional Value at Risk (CVaR) as risk criteria is utilized to solve the model. The objective function of this model combines the expected value, maximum, and CVaR of cost. To promote viability against disruption, sustainability requirements such as CO2 emissions and energy consumption are considered. An agile approach that includes a learning factor is suggested. In addition, a resiliency strategy by addressing order disruption requirements is proposed. The case study of this research is a small healthcare industry that prepares drug products. It is one of the largest industries with high sales and added value. The results show that the inclusion of VMI and CS in the main problem-solving approach resulted in a 14.8 % cost reduction compared to the scenario where VMI and CS were not considered. Finally, sensitivity analysis is performed on CVaR confidence, conservatism coefficient, learning rate, agility rate and scale of the main problem.

Enhanced environmental and economic sustainability of VMI-CS agreement-based closed-loop supply chain for deteriorating products

The present study aims to enhance the environmental and economic sustainability of any supply chain, where a single vendor collaborates with multiple buyers following a vendor-managed inventory-based consignment stock policy for deteriorating products. In contrast to the prevailing literature, a distinctive feature of this research is that each and every chain partner invests in green equipment initially, in compliance with the carbon cap-and-trade regulation of the Paris Accord. Incorporating sequential remanufacturing of used products transforms the supply chain into a closed-loop system, significantly diminishing the reliance on fresh raw materials. Albeit traditional, this study classically determines the optimal values of six major decisions that minimize the total cost of the proposed model. In terms of results, the vendor’s sequential production process of new and remanufactured items substantially reduces total cost and raises green investments for all chain partners. This study pleads to employ advanced demand forecasting tools that shall mitigate the impact of fluctuating demand. Also, this study proposes to uphold equitable partnerships between vendors and buyers in any sustainability endeavors, including emissions control. Investing upfront in the efficient collection of used items is a crucial element of sustainability.

Demand forecasting under lost sales stock policies

Demand forecasting is a crucial task within supply chain management. Stock control policies are directly affected by the precision of probabilistic demand forecasts. For instance, safety stocks and reorder points are based on those forecasts. However, forecasting and replenishment policies have typically been studied separately. In this work, we explore the influence of inventory assumptions on the selection of the forecasting model. In particular, we consider when the stock policy follows a lost sales context and the demand is estimated by means of sales data. In that case, forecasting models should use censored demand estimations. Unfortunately, the literature about censored demand forecasting remains very limited, without an accepted general solution for this problem. In this work, we bridge that gap by proposing the Tobit Kalman filter (TKF). To the best of our knowledge, this is the first time that the TKF has been applied to supply chain demand forecasting, and this approach may represent a general solution for lost sales contexts. The TKF is compared with a previous ad hoc censored demand forecasting solution that is based on single exponential smoothing. In addition, we show the performance of the TKF when dealing with trends where ad hoc approaches are not available for use as benchmarks. To express the potential benefits of the proposed approach in terms of costs and the service level, a newsvendor stock policy is employed. Simulated demand data and a case study are used to illustrate the significant advantages of the proposed tool.

A periodic review inventory model facing different disruption profiles

We study a periodic review inventory system with alternations of periods with supply availability and unavailability. The system is controlled by a periodic review base stock policy (S,T). While periodic review inventory models have been studied assuming supply disruption, they mainly invoke the limiting disruption probabilities overlooking that these probabilities may correspond to different disruption profiles (ranging from rare-long disruptions to frequent-short ones). In this context, we use a continuous-time Markov chain (CTMC), in order to formulate a periodic review inventory model that can distinguish the different disruption profiles. Through theoretical results, we determine the policy variables (order-up-to level and review interval) that minimize the long-run average cost. We show that when the disruption profile is considered, the optimal order-up-to level, although Newsvendor-styled form, is not always linear in the review interval. We present analytical properties demonstrating the impact of the different disruption profiles (under the same limiting disruption probability) and the impact of the limiting probability on the optimal policy. Furthermore, we propose an approximation to obtain a closed-form (near-)optimal solution for the review interval. Finally, numerical comparisons highlight the impact on system's cost performance of deploying approaches that either use the above mentioned closed-form solution for the review interval or the limiting disruption probabilities instead of the time dependent ones in the cost formulation.

A multi-agent-based real-time truck scheduling model for cross-docking problems with single inbound and outbound doors

Cross-docking is a logistics methodology employed in warehouses to gain a competitive advantage by consolidating and transferring freights directly from an inbound supplier to an outbound client with no or restricted storage. Real-time data processing is required for fast synchronisation of inflows and outflows. This study develops a real-time multi-agent truck scheduling model for single inbound-single outbound cross-docking for fast synchronisation of inflows and outflows. The proposed model exploits the autonomous, reactive, and distributed responsibility characteristics of the multi-agent systems to realise shared computation and respond flexible responses to dynamic events. This type of model is novel in the cross-docking literature for scheduling of both inbound and outbound trucks. The responsiveness of the proposed model is evaluated by employing a combination of different traffic levels based on truck arrival times. Furthermore, various truck-to-door assignment strategies are implemented to achieve the best performance based on key performance indicators such as the average stock level, the number of late pallets, the pallet delay and the outbound truck fill rate. To validate the experimental results, ANOVA (analysis of variance) is performed. The analysis demonstrates that the stock policy (SP) outperforms all the others by sustaining low stock levels and high on-time deliveries and truck fill rates across all traffic levels, while the time-related strategies are adequate for cases where outbound traffic is more elevated than inbound traffic.

Simulation-Based Approach for Multi-Echelon Inventory System Selection: Case of Distribution Systems

Due to the current complexity of the supply chain, multi-echelon inventory management has become challenging while also being an interesting field of research as it allows efficient control of supply chain interdependencies. It became clear to many researchers that analytical models are no longer effective for addressing the multi-echelon inventory management problem. Simulation can be used to assess and quantify the impact of each inventory strategy on a supply chain performance. Our paper aims to provide a simulation-based approach to guide decision makers select and validate a multi-echelon distribution inventory system. The proposed approach is composed of four major steps that involve characterization of the current supply chain, conceptual modeling of the multi-echelon inventory system alternatives, and finally, simulation modeling using appropriate simulation software to compare and test different options. The approach was also tested and validated through an application to the case of the Moroccan pharmaceutical products supply chain in the public sector. The results of the simulation demonstrated that adopting an installation stock policy at all levels of the supply chain with an allocation of safety stocks in the most downstream stages is the best and most appropriate alternative for the pharmaceutical supply chain under study.

New products or remanufactured products: Which is consumer-friendly under a closed-loop multi-level supply chain?

If the remanufactured and fresh products are in the market at the same price and quality, then it is difficult for the retailer to sell them all together simultaneously. There is differentiability in the market by the consumers even though they are of similar quality and price. These remanufactured products are the effect of a closed-loop supply chain. The closed-loop type supply chain problems have pulled much attention to tackling environmental and social issues for these remanufactured products. Based on the concern, a closed-loop formation of the supply chain is formulated with four players. Two types of quality are assigned for the manufactured and remanufactured products as remanufactured products maintain less quality than the newly manufactured products. The production system is imperfect, then all products are not perfect. A mathematical model with a multi-cycle closed-loop supply chain is discussed here, where the reworking process happens within the same cycle. An acceptance quality level influences the regaining rate of consumed products. The non-integrated phenomenon uses the inventory management policy operated by the vendor and the consignment-type stock policy. This mathematical problem is solved by employing algorithms, developed to achieve the local optimum (non-integrated situations) and global optimum (integrated situations). A numerical example and sensitivity analysis are provided to establish the mathematical model numerically. Results obtain that the retailer can tackle the situation in a proper way such that the closed-loop supply chain always is in profit. The profit is globally maximized, and the best results are obtained from different situations.

Global Grains Dilemma Specifically Focusing on Wheat

Global wheat crisis continues to be experienced, something that seems not to end any time soon with the causing factors remaining constant. The report highlights some of the factors that continue to influence wheat crisis across the world. Some of these factors discussed include the increased oil prices which continue to put more pressure on the production cost. There are also major producers holding stock policies that continue to influence the global wheat supply. Another factor discussed is geopolitical factor, specifically the continuing war in Ukraine, one of the biggest producers of wheat around the world. The report further focuses on government interventions, such as tariffs which come with a lot of restrictions that can influence wheat supply. Lastly, the report has focused on demand related to wheat, highlighting factors such as increasing population, growth of income especially in developing countries and values for the United States dollar.

Fixed-interval order-up-to policies and myopic optimal warehouse stock allocation for one-warehouse multiple-retailer systems

We consider a centralized two-level distribution system that consists of one warehouse and multiple retailers. The retailers are non-identical and face independent Poisson demand. The system adopts a fixed-interval order-up-to policy, in which the warehouse and retailers each order in a fixed interval to raise the echelon inventory position to a fixed base stock level. Shortages in the system are fully backlogged but transshipments between retailers are not allowed. The fixed-interval order-up-to policy has been studied in the literature under virtual allocation that reserves a unit of warehouse stock immediately after a unit of demand occurs. We consider an inventory control system that is different in two aspects. First, the warehouse implements myopic optimal allocation that allocates stock to retailers only at points of delivery to minimize system cost. Second, the warehouse adopts a non-stationary base stock policy to order stock for retailers according to their replenishment schedules. We develop methods to exactly evaluate and fully optimize this inventory control system. In a numerical study, we demonstrate that myopic optimal allocation reduces system cost by an average of 5.89% with a range from 1.45% to 10.13% as compared to virtual allocation. In addition, we develop an iterative procedure to alleviate the problem of dimensionality for myopic optimal warehouse stock allocation and a close-to-optimal heuristic solution that can significantly reduce computation.

The Implementation of Analytical Hierarchy Process to Choose the Best Solution to Increase Mould Maker Profit

Mould Maker is a company that buy and sell machine for their main business activity. Mould maker have long time machine waiting time to be sold and have low profit margin. The longer the time taken by a machine to be sold, the higher the maintenance cost for the machine. Besides, Mould Maker always purchase machines for their stock in every offer that comes to Mould Maker, so they have overstocked machine in their workshop. With many and vary type of machines that they have, Mould Maker can utilize the machines to increase their profit margin. Mould Maker have good business environment from the external factor analysis. As for internal analysis, Mould Maker still have room for improvement, some of them are the number of their stock and the number for decision maker. Caused by their low profit margin, Mould Maker cannot hire high value employee to be one of the decision makers. The problem that Mould Maker face is how to improve their profit by utilizing the machines that they have. Further analysis needed to solve this problem. The SWOT analysis used to find some strategies and need to be fit with the problem faced by Mould maker. The strategies that able to be implemented are rent their machine, accepting manufacture order, improving stock policy, boost marketing strategy, and purchasing car to help Mould Maker to sell their product. From the strategy alternatives, AHP method is used to choose the best strategy. With AHP method, the best solution can be chosen by considering the value from each consideration criteria. For this research, the best solution proposed by AHP method is to accept manufacture order.

Joint optimisation of the maintenance and buffer stock policies considering back orders

Inventory control and maintenance systems are two main processes in production systems that play a vital role in the efficiency of production resources. This study aims to propose a joint optimisation procedure for maintenance and inventory policies in a single-machine production system considering back orders. The failure rate is random and the minimum level of accessibility required for preventive maintenance is considered. To close the problem at hand to the real-world condition, demand is considered as uncertain and shortage is inevitable. In addition, the unsatisfied demand is considered as back order. The problem is described and formulated via a mathematical model and all the possible scenarios corresponding to the different possible cases of the problem are described. Then a numerical iterative procedure is introduced to solve the problem in a reasonable time. The proposed procedure does not explore all solution space, but it behaves like a numerical optimisation procedure and provides a near optimal solution. The performance of the proposed method is evaluated using a numerical instance based on a real case study. In addition, some sensitivity analyses are performed to identify the most important effective parameters.

Stability of queueing-inventory systems with customers of different priorities

We study a production-inventory system with two customer classes with different priorities which are admitted to the system following a flexible admission control scheme. The inventory management is according to a base stock policy and arriving demand which finds the inventory depleted is lost (lost sales). We analyse the global balance equations of the associated Markov process and derive structural properties of the steady state distribution which provide insights into the equilibrium behaviour of the system. We derive a sufficient condition for ergodicity using the Foster-Lyapunov stability criterion. For a special case we show that the condition is necessary as well.