Inventory control for a non-stationary demand perishable product: comparing policies and solution methods

This paper summarizes our findings with respect to order policies for an inventory control problem for a perishable product with a maximum fixed shelf life in a periodic review system, where chance constraints play a role. A Stochastic Programming (SP) problem is presented which models a practical production planning problem over a finite horizon. Perishability, non-stationary demand, fixed ordering cost and a service level (chance) constraint make this problem complex. Inventory control handles this type of models with so-called order policies. We compare three different policies: a) production timing is fixed in advance combined with an order up-to level, b) production timing is fixed in advance and the production quantity takes the age distribution into account and c) the decision of the order quantity depends on the age-distribution of the items in stock. Several theoretical properties for the optimal solutions of the policies are presented. In this paper, four different solution approaches from earlier studies are used to derive parameter values for the order policies. For policy a), we use MILP approximations and alternatively the so-called Smoothed Monte Carlo method with sampled demand to optimize values. For policy b), we outline a sample based approach to determine the order quantities. The flexible policy c) is derived by SDP. All policies are compared on feasibility regarding the α-service level, computation time and ease of implementation to support management in the choice for an order policy.National project TIN2015-66680-C2-2-R, in part financed by the European Regional Development Fund (ERDF)

On order policies with pre-specified order schedules for a perishable product in retail.

This paper studies a retail inventory system for a perishable product, based on a practical setting in Dutch retail. The product has a fixed shelf life of three days upon delivery at the store and product demand has a weekly pattern, which is stationary over the weeks, but varies over the days of the week. Items of varying age occur in stock. However, in retail practice, the age-distribution is often unknown, which complicates order decisions. Depending on the type of product or the size of the supermarket, replenishment cycle lengths may vary. We study a situation where a store is replenished either three or four times a week on pre-specified days. The research aim is to find practical and efficient order policies that can deal with the lack of information about the age distribution of items in stock, considering mixed LIFO and FIFO withdrawal. Reducing potential waste goes along with cost minimization, while the retailer aims at meeting a cycle service level requirement. We present four new heuristics that do not require knowledge of the inventory age-distribution. A heuristic, based on a constant order quantity for each order moment, often generates least waste and lowest costs. However, this requires a few minutes of computation time. A new base stock policy appears second best

Multi-echelon inventory management for a fresh produce retail supply chain

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-84).Perishability presents a challenging problem in inventory management for the fresh produce industry since it can lead to higher inventory costs and lower service levels. If a supply chain has multiple echelons, that further complicates the issue since companies have an added risk of not having the right amount of product at the right location at the right time. We conduct our research on Chiquita's Fresh Express supply chain. We analyze the impact of perishability on total relevant costs. Our research focuses on determining the optimal inventory policy for the system considering inventory holding costs, shrinkage costs, lost sales costs, forecast accuracy and service levels. We test the sensitivity of the system with respect to forecast errors and the transportation lead time. We developed a discrete-event simulation model using Arena software to conduct the research. Our research demonstrates that by lowering the current target on-hand inventory levels at the distribution center and retail stores, inventory holding costs and shrinkage costs are reduced significantly. Under the optimal inventory policy, the system can save 31% in costs, improve the item fill rate at the distribution center, reduce the total shrinkage volume, and maintain high service levels of more than 95% at the retail stores. Our sensitivity analysis shows that the system is very sensitive to the forecast errors. Additionally, we recommend keeping the transportation lead time as low as possible to maximize the products' lifetime at the retail stores. Reducing the forecast errors or the transportation lead time would reduce the total relevant cost of the system while improving the item fill rates across the supply chain.by Yogeshwar D. Suryawanshi and Thomas Hsien.M.Eng.in Logistic

Grocery omnichannel perishable inventories: performance measures and influencing factors

Purpose- Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures. Design/methodology/approach- The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation. Findings- Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects. Practical implications- To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products. Originality/value- This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors

A replenishment policy for a perishable inventory system based on estimated aging and retrieval behavior

So far the literature on inventory control for perishable products has mainly focused on (near-) optimal replenishment policies for a stylized environment, assuming no leadtime, no lot-sizing, stationary demand, a first in first out retrieval policy and/or product life time equal to two periods. This literature has given fundamental insight in the behavior and the complexity of inventory systems for perishable products. In practice, many grocery retailers have recently automated the inventory replenishment for non-perishable products. They recognize they may need a different replenishment logic for perishable products, which takes into account e.g. the age of the inventory in the system. Due to new information technologies like RFID, it now also becomes more economically feasible to register this type of information. This paper suggests a replenishment policy for perishable products which takes into account the age of inventories and which requires only very simple calculations. It will be shown that in an environment, which contains important features of the real-life retail environment, this new policy leads to substantial cost reductions compared with a base policy that does not take into account the age of inventories

Perishable Items in Multi-Level Inventory Systems

This master thesis studies a two-echelon distribution system for perishable items with two non identical retailers. Each location is managed following a standard continuous (R, Q) ordering policy. The demand occurs solely at the retailers and follows independent Poisson processes. Customers are backordered when the retailer is out of stock. The items are considered as fixed lifetime perishables. Whenever an item perished, it is discarded from the stock. The model includes fix transportation time and the allocation policy at the central warehouse is a First-Come-First-Serve one. This kind of system is very complicated and therefore hard to study. In this master thesis, we focus on a simulation study of 48 different problems with both a FIFO and a LIFO issuing policy at the retailers. The goal of this study is therefore to optimize the values of R in (R, Q) ordering policies considering that the items are perishables. To do so, we try to optimize the values of the reorder points at every location. We also try to find some general behaviour of the system and we compare the FIFO and the LIFO best found solution. More than 1000 hours of computer-time were used for this study. For every problem, we conducted an optimization process to find better values of the reorder points at every location. For the FIFO case, an average cost reduction of more than 20% was found. It exists a good opportunity in term of cost savings while taking into account the perishable characteristic of the items. Another finding of our study is that the LIFO case has good performance comparing to what expected. On average, the costs increase is only 7% while considering a LIFO issuing policy instead of a FIFO one. Moreover, the values of the reorder points for the FIFO best found solution are still the same than the LIFO best found solution in 70% of the problems studied

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Approaches to assessing and minimizing blood wastage in the hospital and blood supply chain

Despite the scale of blood usage worldwide, blood remains a scarce and precious resource. As with any perishable product, careful management of inventories to minimize wastage is crucial. However, due to the nature of the supply of blood, wastage is not only an economic issue as every unit wasted, squanders the time and effort of the human donor. Blood inventory management is therefore a trade-off, ensuring 100% availability of all blood products at all times whilst minimizing wastage. Hospitals are at the front line of blood use and are the location where much blood is wasted. Inventory management practices in hospital transfusion laboratories are critical. Much of the extant literature in this area posits that good management of hospital blood inventories is due to sophisticated inventory models and algorithms. However, recent research has found that good management practices are much more important. The drivers for low wastage and good inventory management practice can be described using six key themes. Blood supply chain management is much more than managing wastage in hospitals. Proper management of the supply chain as a whole can lead to significant reductions in blood wastage. Recent research has found that methods commonly used in commercial supply chain management can lead to efficiencies in the blood supply chain context. An example of this is stock sharing or lateral transhipment of blood units close to expiry between hospitals, reducing wastage across the supply chain

A periodic inventory system of intermittent demand items with fixed lifetimes

Perishable items with a limited lifespan and intermittent/erratic consumption are found in a variety of industrial settings: dealing with such items is challenging for inventory managers. In this study, a periodic inventory control system is analysed, in which items are characterised by intermittent demand and known expiration dates. We propose a new inventory management method, considering both perishability and intermittency constraints. The new method is a modification of a method proposed in the literature, which uses a periodic order-up-to-level inventory policy and a compound Bernoulli demand. We derive the analytical expression of the fill rate and propose a computational procedure to calculate the optimal solution. A comparative numerical analysis is conducted to evaluate the performance of the proposed solution against the standard inventory control method, which does not take into account perishability. The proposed method leads to a bias that is only affected by demand size, in contrast to the standard method which is impacted by more severe biases driven by intermittence and periods before expiration