Supply chain finance for ameliorating and deteriorating products: a systematic literature review

Ameliorating and deteriorating products, or, more generally, items that change value over time, present a high sensitiveness to the surrounding environment (e.g., temperature, humidity, and light intensity). For this reason, they should be properly stored along the supply chain to guarantee the desired quality to the consumers. Specifically, ameliorating items face an increase in value if there are stored for longer periods, which can lead to higher selling price. At the same time, the costumers’ demand is sensitive to the price (i.e., the higher the selling price the lower the final demand), sensitiveness that is related to the quality of the products (i.e., lower sensitiveness for high-quality products). On the contrary, deteriorating items lose quality and value over time which result in revenue losses due to lost sales or reduced selling price. Since these products need to be properly stored (i.e., usually in temperature- and humidity-controlled warehouses) the holding costs, which comprise also the energy costs, may be particularly relevant impacting on the economic, environmental, and social sustainability of the supply chain. Furthermore, due to the recent economic crisis, companies (especially, small and medium enterprises) face payment difficulties of customers and high volatility of resources prices. This increases the risk of insolvency and on the other hand the financing needs. In this context, supply chain finance emerged as a mean for efficiency by coordinating the financial flow and providing a set of financial schemes aiming at optimizing accounts payable and receivable along the supply chain. The aim of the present study is thus to investigate through a systematic literature review the two main themes presented (i.e., inventory management models for products that change value over time, and financial techniques and strategies to support companies in inventory management) to understand if any financial technique has been studied for supporting the management of this class of products and to verify the existing literature gap

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

Optimization of Quantity Discounts Using JIT Technique under Alternate Cost Policies

In traditional economic order quantity modeling technique, as per the storage in a warehouse, the rate of demand is considered to be fixed, whereas in real world practice rate of demand may be dependent on time, price and stock. This paper studies problems based on allocation of order quantity under quantity discounts by revising mathematical models already studied in this area. For example, in a multi warehouse system like a super departmental store, the rate of demand is mostly subjective on the basis of stock demand. In industry, the maintenance of large stock of goods in warehouses has a higher probability of consumers as compared to an industry with small quantity of stock. Such procedures implied in single warehouses systems may be logical for level of stock that is dependent on demand. Hence, a good and large stock level mostly results in a higher profits and larger sales. The objective is to optimize profit under the effect of price variations in the form of quantity discounts based on an alternative cost functions, with the help of JIT inventory technique and analyzing a mathematical model based on it

An optimal inventory pricing and ordering strategy subject to demand dependent on stock level and price

This article considers the deterministic singular optimal control problem of profit maximisation for inventory replenished at a variable rate and depleted by demand which is assumed to vary with price and stock availability. Optimal policies for the product order rate and price are derived using the maximum principle. Several initial inventory regions are identified as potential inventory states for feasible profit optimisation. Bounds on the maximum price for maximising net profit or minimising loss are obtained. Numerical simulations accompanied by phase diagrams are performed to support the theoretical findings

EOQ inventory model for perishable products under uncertainty

Perishable products require accurate inventory control models as their effect on operations management can be critical. This assumption is particularly relevant in highly uncertain and dynamic markets, as for the ones generated by the pandemic era. This paper presents an inventory control model for perishable items with a demand rate variable over time, and dependent on the inventory rate. The model also considers the potential for backlogging and lost sales. Imperfect product quality is included, and deterioration is modelled as a time-dependent variable. The framework envisages the possibility to define variables affected by uncertainty in terms of probability distribution functions, which are then jointly managed via a Monte Carlo simulation. This paper is intended to provide an analytical formulation to deal with uncertainty and time-dependent inventory functions to be used for a variety of perishable products. The formulation is designed to support decision-making for the identification of the optimal order quantity. A numerical example exemplifies the outcomes of the paper and provides a cost-based sensitivity analysis to understand the role of main parameters

Two echelon inventory models with the market price, advertisement, and discount sensitive demand in the non-co-operative environment

In supply chain management manufacturers and retailers are the two most important nodes. Some decision variables are decided by the manufacturer and some are decided by the retailer to maximize their profits. In this work, market demand is considered sensitive to market price, advertising, and discount given by the manufacturer to the retailer. In this work, EOQ and advertising will be decided by the retailer and a discount will be given by the manufacturer to the retailer to motivate the retailer to generate more market demand. Manufacturers' and retailers' profit models are developed in the non-co-operative environment. Models are verified using dummy data, and sensitivity analysis is performed for all the decision variables for both manufacture and retailer Stackelberg models

An examination of the interfaces between operations and advertising strategies

This dissertation is composed of three journals examining the interfaces between the marketing variable of advertising and various aspects of the operations function of the enterprise, namely, (1) production cost [Chapter 2], (2) inventory control [Chapter 3], and (3) service cost learning [Chapter 4]. The first journal identified the optimum advertising allocation policy over time in the presence of a quadratic convex/concave production cost function when the advertising response function is concave using a modified Vidale-Wolfe model. Through analytical proofs and numerical simulations, the results indicated the potential superiority of a pulsation policy in the presence of concavity in the advertising response function only if the production cost function is convex; otherwise, the uniform policy would be optimal. The study is seen as applicable to frequently purchased products in the maturity stage of their life cycles of dominant firms in their industries practicing a zero-inventory policy in a just-in-time environment. The research objective pertaining to the second journal was to study how a firm would adapt optimum ordered quantity/production lot size and optimum advertising expenditure in response to changes in its own parameters, rival\u27s parameters, or parameters that are common to all firms in a symmetric duopoly/oligopoly market. This was accomplished by developing comparative statics (sensitivity analysis) of a symmetric competitive inventory model with advertising-dependent demand based on a market share attraction model. Both optimum advertising expenditure and ordered quantity were found to be sensitive to changes in marketing and operations parameters. The robustness of the symmetric comparative statics was assessed by using data from the brewing industry in the US that represents an asymmetric oligopoly. The empirical analysis indicated that the theoretical results obtained for a symmetric oligopoly remained valid for an oligopoly where each firm had a market share less than 50% and the market shares were further apart from one another. The study is thought to be applicable to low-priced frequently purchased consumer items in competitive mature markets. In the third journal, the original Bass model for new products was modified to incorporate advertising and customers\u27 disadoption to characterize the optimum advertising policy over time for subscriber service innovations where service cost follows a learning curve. After characterizing the optimal policy for a general diffusion model, the results pertaining to a specific diffusion model for which advertising affects the coefficient of innovation were reported. On the empirical side, four alternative diffusion models were estimated and their predictive powers, using a one-step-ahead forecasting procedure, were compared. Empirical research findings suggest that the specific diffusion model considered in this study is not only of theoretical appeal, but also of notable empirical relevance. Taken together, the analytical and empirical findings argue in favor of advertising more heavily during the early stage of the diffusion process of the new subscriber service innovation and including a related message that would predominantly target innovators. Furthermore, it might be inappropriate to model the diffusion of subscriber services as if they were durable goods. The study is thought to be applicable to service innovations that are made available to customers periodically at a subscription fee. Typical examples include, but are not limited to, cable TV, health clubs, pest control, and the internet