Narrow Results

We model the inventory decisions of a firm that maximizes its market value, namely, the expected present value of the time stream of dividends issued to its shareholders. The firm is single-product and equity-owned, it orders products from an outside supplier, its only short-term borrowing is for solvency if necessary, and it issues dividends to its shareholders while facing financial and market risks (uncertain demand). The distinguishing features of the model are the financial criterion, hence joint selection of operational and financial decisions, and non-linear replenishment costs. If the non-linearity is due to a setup cost, we show that an (s, S) replenishment policy is optimal. The analysis is not a straightforward variant of Scarf's argument. If the non-linearity is due to bilinear smoothing costs, we show that the optimal replenishment policy has the same form as in the traditional smoothing cost model. These seem to be the first instances in which the optimization of a model with a financial criterion and non-linear replenishment costs yields policies having the same forms as in the corresponding cost minimization problems.

Environmental legislation and customer expectations increasingly force manufacturers to take recovery of used products into account in their production and inventory management. One of the areas concerned is production planning with returned products remanufacturing. In this paper, we discuss the optimal decision for a joint manufacturing and remanufacturing system in a multi-period horizon, including manufacturing decision, remanufacturing and disposal decisions of the returned product. Setup costs are considered for the manufacturing and disposal activities. For an n-period model, we derive an optimal solution structure for the three activities, where the solution parameters can be computed by dynamic programming approach.

Global markets increase sales and profitability opportunities for enterprises, but more environmental uncertainty poses new challenges for operational planning. This paper attempts to introduce the idea of distributionally robust optimization into the global operation problem of a two-market stochastic inventory system, providing theoretical guidance and reference decision-making for enterprises to optimize and configure in a global market with non-overlapping geographic locations and sales seasons. We find that the demand correlation and the lack of demand information will not substantially affect the operation strategy, and the enterprise’s industrial chain and supply chain remain stable. However, reducing inter-market tariffs or logistics costs will lead to a change in strategy, and the existence of the secondary market will lead to more capacity planning in the primary market. In addition, we find that enterprises’ transshipment strategies rely significantly on exchange rate volatility. Numerical experiments were conducted to demonstrate our theoretical results.

We introduce a method to infer lead-lag networks of agents’ actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply this method to trader-resolved data in the foreign exchange market. We show that these networks are remarkably persistent, which explains why and how order flow prediction is possible from trader-resolved data. In addition, if traders’ actions depend on past prices, the evolution of the average price paid by traders may also be predictable. Using random forests, we verify that the predictability of both the sign of order flow and the direction of average transaction price is strong for retail investors at an hourly time scale, which is of great relevance to brokers and order matching engines. Finally, we argue that the existence of trader lead-lag networks explains in a self-referential way why a given trader becomes active, which is in line with the fact that most trading activity has an endogenous origin.

Efficient inventory classification is a vital activity for electronics firms that work with a large amount of inventory items. Although one of the most widely used techniques in inventory classification is ABC analysis, this technique considers only a single criterion as the annual sales volume of each item. In practice, inhomogeneity and the differences among the inventory items necessitate considering multiple criteria to obtain a reliable classification. In this study, an integrated process of the analytic hierarchy process (AHP)-The technique for order preference by similarity to the ideal solution (TOPSIS)-ABC approach is proposed and performed to solve the multiple criteria inventory classification problem in an electronics firm. The steps of the interactive approach are programmed using MATLAB. The results of the integrated interactive approach and of the traditional ABC analysis are presented. The proposed approach gives effective and implementable results for the firm.

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

Inventory management (IM) plays a decisive role in the enhancement of efficiency and competitiveness of manufacturing enterprises. Therefore, major manufacturing enterprises are following IM practices as a strategy to improve their efficiency and achieve competitiveness. However, the spread of IM culture among small and medium enterprises (SMEs) is limited due to lack of initiative and expertise as well as financial limitations in developed countries, let alone developing countries. Against this backdrop, this paper makes an attempt to ascertain the role and importance of IM practices and performance of SMEs in the machine tool industry in the city of Bangalore, India. The relationship between IM practices and inventory cost is probed based on primary data gathered from 91 SMEs. The paper brings out the fact that formal IM practices have a positive impact on the inventory cost and therefore, the IM performance of SMEs.

We study how short-term informational advantages can be monetized in a high-frequency setting, when large inventories are explicitly penalized. We find that if most of the additional information is revealed regardless of the high-frequency traders’ actions, then fast inventory management allows one to minimize positions with only second-order losses to expected returns. This is no longer possible if most of the additional information is only revealed through trades.

Health is the basis of human development. Medical and health care are closely linked to the happiness of millions of households. Information technology contributes to build better health care for the majority of resident services, reduce health care costs and save health resources. This article collects the data that based on the regional hospitals and providers of historical and applies the advanced predictive models. Auto regressive integrated moving average model (ARIMA) to predict drug demand of the area hospitals. The advanced technology to drug management for hospitals with the combination of advanced management technology and information technology, which ensure both sides of drug market benefit from the supply chain and achieve win-win situation.