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The globalisation of the food industry increases the complexity and the difficulty in enhancing efficiency and solving issues in food supply chains. Blockchain is a promising decentralised information technology that could benefit food supply chains by reducing transaction cost and time, increasing process transparency, security, and efficiency, as well as building trust among participants. In this paper, we introduce the major blockchain platforms currently used in food supply chains and conduct a synthesis analysis to explore the benefits and challenges of blockchain technology in the food industry. We demonstrate that blockchain enables unprecedented visibility at each step of the food supply chain, helps increase transaction transparency, food safety, and quality, and also reduces food fraud and waste. Furthermore, it serves as a digital solution for reducing operational costs and improving efficiency in food supply chains. [ABSTRACT FROM AUTHOR]

In underwater laser beam machining (ULBM), water provides a cooling effect by reducing the influence of the laser heat source, which makes ULBM more suitable for marking, cutting, and postprocessing than laser beam machining (LBM). Because the laser heat source not only affects the substrate temperature, but also heats the water, this study analyzes how the cooling effect occurs when water is heated. In this study, the heat-transformed zones in ULBM and heated underwater laser beam machining (HULBM) were improved by approximately 33% and 24%, respectively, compared to LBM at 400 W. In addition, the heat-affected zones in ULBM and HULBM improved by approximately 15% and 9%, respectively, compared to LBM. The hardness of ULBM and HULBM was higher than that of LBM. Based on these results, it was confirmed that water can reduce the effect of the laser heat source and improve the mechanical properties. Experiments will be conducted on the underwater laser beam machining of various substrates, such as Inconel718 and Ti-6Al-4V, in a future study. In addition, experiments will be conducted on the underwater laser beam machining of various substrates using a cooling system that can lower the temperature of water. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.10.024 Byline: Jun-Yeon Lee (a), Leroy B. Schwarz (b) Keywords: Inventory; Leadtime management; State-dependent base-stock policy Abstract: We examine a single-item, periodic-review inventory system with stochastic leadtimes, in which a replenishment order is delivered immediately or one period later, depending probabilistically on costly effort. The objective is to determine a joint inventory policy and effort-choice strategy that minimizes the expected total costs. Our analytical and computational analysis suggests that (i) a state-dependent base-stock policy is optimal, (ii) the optimal effort strategy is such that the marginal cost of effort is equal to the value of immediate delivery, and (iii) the cost impact of leadtime reduction can be very large. We also provide some counter-intuitive results, compared with the traditional multi-period newsvendor model. Author Affiliation: (a) School of Business Administration, University of Houston-Victoria, 3007 N. Ben Wilson St, Victoria, TX 77901, USA (b) Krannert Graduate School of Management, Purdue University, West Lafayette, IN 47906, USA Article History: Received 20 March 2008; Accepted 27 October 2008

We examine a periodic-review stochastic inventory model with quantity discounts from the buyer's perspective. In each period the buyer places an order to meet stochastic demands and receives a price discount on purchases in excess of a price break point. Our analytical and computational results suggest that the buyer's optimal order-up-to inventory levels depend on initial inventory and the price break point. We also provide some counter-intuitive results, compared with the traditional multi-period newsvendor model. Key Words: Quantity discounts, Stochastic inventory model, Periodic-Review, 1. INTRODUCTION This paper considers a periodic-review stochastic inventory model with quantity discounts from the buyer's perspective. In each period the buyer places an order with the supplier to meet [...]

Investment in carbon emissions reduction is an important decision for many firms due to climate change and growing pressures from various stakeholders. In this paper, we examine the problem of jointly determining optimal order quantity and investment in carbon emissions reduction in the EOQ model with a cap-and-price regulation policy, in which the firm is penalised (rewarded) if its carbon footprint is larger (smaller) than a threshold. Cap-and-trade, carbon tax, cap-and-offset, and carbon cap can be viewed as special cases of the cap-and-price policy. The investment reduces the carbons emitted per replenishment and per unit produced. We characterise the optimal solutions and compare different regulations in terms of the firm's annual total cost and carbon footprint. [ABSTRACT FROM AUTHOR]

We examine vendor‐managed inventory contracts in a (Q, r) inventory system between a supplier and a retailer, in which a stockout penalty is charged to the supplier based on the length of the time period during which stockouts occur at the retailer. Linear and quadratic forms of the time‐dependent stockout penalty are considered. For the deterministic demand case, we find that the quadratic form of time‐dependent stockout penalty is equivalent to a proportional stockout penalty per unit short per unit time. For the stochastic demand case, we provide the exact cost expressions for the supplier and the retailer with a linear time‐dependent stockout penalty. We also discuss how the stochastic model can be extended to the case with a quadratic time‐dependent stockout penalty when there is at most one outstanding replenishment order at any point of time. We provide several interesting computational results. [ABSTRACT FROM AUTHOR]