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We consider an inventory system under continuous review with two demand classes that are different in terms of service level required (or penalty cost incurred for backordering of demand). Prior literature has proposed the critical level rationing ( CLR) policy under which the demand from the lower priority class is backordered once inventory falls below the critical level. While this reduces the penalty cost for the higher demand class, the fill rate achieved for the lower priority demand class gets compromised. In this study, we propose a new class of two-bin (2B) policy for the problem. The proposed 2B policy assigns separate bins of inventory for the two demand classes. The demand for each class is fulfilled from its assigned bin. However, when the bin intended for the higher demand class is empty, the demand from the higher class can still be fulfilled with the inventory from the other bin. The advantage of the 2B policy is that better fill rates are achieved, especially for the lower demand class. Computational results show that the proposed policy is able to provide a much higher service level for the lower priority class demand without increasing the total cost too much and without affecting the service level for the higher priority class. When a service level constrained optimization problem is considered, the 2B policy dominates the CLR policy when the service level difference for the two classes is not too high or the service levels required for both the classes are relatively lower. [ABSTRACT FROM AUTHOR]

We compare the performance of top-down (TD) and bottom-up (BU) strategies for forecasting the demand of an item that belongs to a product family. The TD strategy forecasts the sum of the item demands and distributes it to the individual item based upon the historical demand proportion of each item in the family. The BU strategy forecasts each item demand individually using the historical demand data for the particular item. All the item demands, which may be correlated with each other, are assumed to follow a first-order univariate moving average process. As is common in a production-planning environment. the forecasting under both strategies is carried out using the exponential smoothing technique. We show that the performance of the two forecasting strategies is nearly identical, regardless of the coefficient of correlation between the item demands, the items' proportion in the family and the coefficient of the serial correlation term of the demand process. We further investigate the relative performance of the two strategies when a fixed (rather than the optimal) smoothing constant is used for forecasting the demand under both strategies. [ABSTRACT FROM AUTHOR]

We consider the problem of co-ordinating supply chain inventories through the use of multiple common replenishment epochs (MCREs), or time periods. A single-vendor, multiple-buyer supply chain for a single product is considered, where the vendor specifies two alternative common replenishment epochs. Buyers who replenish at integer multiples of one of the two common replenishment epochs receive a discount. Buyers do so if the discount is sufficient enough to make the switch from their optimal replenishment scheme economical. Our study shows that this voluntary MCRE strategy can enhance the vendor's profit considerably, compared with strategies that have previously been proposed. [ABSTRACT FROM AUTHOR]

This study proposes an enhanced multiobjective assembly inventory routing model to assess the impact of sustainability practices on supply network performance goals. The model considers supplier incentives, supply risk, carbon emission penalty, heterogeneous fleet configuration, and demand uncertainty. Specifically, the model has economic (e.g., minimizing network cost), environmental (e.g., minimizing emission penalty), and social (e.g., maximizing supplier incentives) goals. The model incorporates a supply risk reduction policy. The novelty of this study lies in its simultaneous consideration of diverse factors in an inventory-routing context. Data sets from the existing literature are used to validate the model across various problem sizes. A modified hybrid non-dominated sorting genetic algorithm-II (HNSGA-II) is proposed to determine Pareto solutions and compare them with those derived from a speed-constrained multiobjective particle swarm optimization algorithm (SMPSO). HNSGA-II outperforms SMPSO in several critical performance metrics. The study further explores the impact of incentive schemes, low-risk supplier prioritization, and fleet configurations on sustainability performance. This study demonstrates a factorial experimentation-based sensitivity analysis on four objectives. The findings reveal that a heterogeneous fleet configuration can reduce emission penalties. However, this can result in increased network costs. A combination of low- and medium-duty vehicles is also recommended to attain economic and environmental efficiency. Service-level-based supplier incentives are found to enhance supply reliability and reduce shortages. However, this can elevate network costs and emissions. In scenarios of high demand variability, supplier incentives can ensure reliability. Conversely, cost and emission reduction can be prioritized over maximizing supplier incentives in high-supply risk scenarios. [ABSTRACT FROM AUTHOR]