An optimal EOQ model for perishable products with varying demand pattern

The demand pattern for most perishable products varies during their life cycle in the market. These variations must be properly reflected in inventory management in order to prevent unnecessary stock-out or excess inventory with associated increase in cost. In this paper, a multi-period economic order quantity (EOQ) model for managing the inventory of perishable items having varying demand pattern is presented. The model was formulated using a general ramp-type demand function that allows three-phase variation in demand pattern. These phases represent the growth, the steady and the decline phases commonly experienced by the demand for most products during their life cycle in the market. The model generates replenishment policies that guarantees optimal inventory cost for all the phases. Numerical experiments and sensitivity analysis were carried out to demonstrate the suitability of the model for a wide range of seasonal products. Result of the experiments revealed that the points at which demand pattern changes are critical points in managing inventory of products with ramp type demand

Optimization of a perishable inventory system: A simulation study in a Ho.Re.Ca. company

The main goal of this paper is to describe the optimization of the inventory management process in a real context of perishable food products. The study involves one of the largest Italian HO.RE.CA. companies, located in the north of Italy and operating as a provider of the catering, commercial and welfare services. A simulation model was set up with the purpose of adapting three traditional reordering policies (i.e. Re-Order Point, Re-Order Cycle, and (s,S)) to a set of products belonging to company's assortment and evaluating the resulting economic outcomes. To this end, each policy was modelled on Microsoft ExcelTM, so as to compute the total cost of inventory management and determine of the minimum cost strategy. A comparison with the current company's performance and that achievable with the optimized policy is also proposed

An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

Minimizing food waste in grocery store operations: literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Modelling and Determining Inventory Decisions for Improved Sustainability in Perishable Food Supply Chains

Since the introduction of sustainable development, industries have witnessed significant sustainability challenges. Literature shows that the food industry is concerned about its need for efficient and effective management practices in dealing with perishability and the requirements for conditioned storage and transport of food products that effect the environment. Hence, the environmental part of sustainability demonstrates its significance in this industrial sector. Despite this, there has been little research into environmentally sustainable inventory management of deteriorating items. This thesis presents mathematical modelling based research for production inventory systems in perishable food supply chains. In this study, multi-objective mixed-integer linear programming models are developed to determine economically and environmentally optimal production and inventory decisions for a two-echelon supply chain. The supply chain consists of single sourcing suppliers for raw materials and a producer who operates under a make-to-stock or make-to-order strategy. The demand facing the producer is non-stationary stochastic in nature and has requirements in terms of service level and the remaining shelf life of the marketed products. Using data from the literature, numerical examples are given in order to test and analyse these models. The computational experiments show that operational adjustments in cases where emission and cost parameters were not strongly correlated with supply chain collaboration (where suppliers and a producer operate under centralised control), emissions are effectively reduced without a significant increase in cost. The findings show that assigning a high disposal cost, limit or high weight of importance to perished goods leads to appropriate reduction of expected waste in the supply chain with no major cost increase. The research has made contributions to the literature on sustainable production and inventory management; providing formal models that can be used as an aid to understanding and as a tool for planning and improving sustainable production and inventory control in supply chains involving deteriorating items, in particular with perishable food supply chains.the Ministry of Science and Technology, the Royal Thai Government

A single period inventory model of a deteriorating item sold from two shops with shortage via genetic algorithm

Inventory of differential units of a deteriorating item purchased in a lot and sold separately from two shops under a single management is considered. Here deterioration increases with time and demands are time- and price-dependent for fresh and deteriorated units respectively. For the fresh units, shortages are allowed and later partially-backlogged. For the deteriorated units, there are two scenarios depending upon whether initial rate of replenishment of deteriorated units is less or more than the demand of these items. Under each scenario, five sub-scenarios are depicted depending upon the time periods of the two-shops. For each sub scenarios, profit maximization problem has been formulated and solved for optimum order quantity and corresponding time period using genetic Algorithm (GA) with Roulette wheel selection, arithmetic crossover and uniform mutation and Generalized Reduced Gradient method (GRG). All sub-scenarios are illustrated numerically and results from two methods are compared.

An Examination of Commercial Motor Vehicle Hours of Service Safety Regulation

The Federal Motor Carrier Safety Administration (FMSCA) stipulates Hours of Service (HOS) regulations in order to minimize or eliminate fatigued truck driving, a major cause of truck-related accidents. This research examines the efficacy of HOS with three unique research approaches: 1) a typology classification model to indicate differences in HOS regulation from other safety regulations, 2) an exhaustive enumeration method utilizing the theoretical foundation of perishable inventory theory, and 3) a statistical analysis on accidents caused by or involving truck drivers. This research provides insight into similar Air Force Instructions regarding flight-time restrictions for pilots, which attempt to prevent flying while fatigued. This research demonstrates that these types of restrictions do not directly measure fatigue and that they are subject to various unintended consequences