Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint ...from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.
•The forecasting literature prescribes model parameters that minimise fitting errors.•There is evidence that this does not always lead to the best inventory performance.•We propose to parametrise forecasting models directly on inventory metrics.•This is achieved using a simulation optimisation approach, matching the case at hand.•This outperforms benchmarks in terms of forecast bias and inventory performance.
Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about the product. Thus, consumer purchasing behavior ...may be impacted by the availability information. Based on a unique setting from Amazon lightning deals, which displays the percentage of inventory consumed in real time, we explore whether and how consumers learn from inventory availability information. Identifying the effect of learning on consumer decisions has been a notoriously difficult empirical question because of endogeneity concerns. We address this issue by running two randomized field experiments on Amazon in which we create exogenous shocks on the inventory availability information for a random subset of Amazon lightning deals. In addition, we track the dynamic purchasing behavior and inventory information for 23,665 lightning deals offered by Amazon and use their panel structure to further explore the relative effect of learning. We find evidence of consumers learning from inventory information: a decrease in product availability causally attracts more sales in the future; in particular, a 10% increase in past claims leads to a 2.08% increase in cart add-ins in the next hour. Moreover, we show that buyers use observable product characteristics to moderate their inferences when learning from others; a deep discount weakens the learning momentum, whereas a good product rating amplifies the learning momentum.
This paper was accepted by Serguei Netessine, operations management.
What is the link between customer‐base concentration and inventory efficiencies in the manufacturing sector? Using hand‐collected data from 10‐K Filings, we find that manufacturers with more ...concentrated customer bases hold fewer inventories for less time and are less likely to end up with excess inventories, as indicated by the lower likelihood and magnitude of inventory write‐downs and reversals. Using disaggregated inventory disclosures, we find that inventory efficiencies primarily flow through the finished goods inventory account, while raw material efficiencies are offset by higher work‐in‐process holdings and longer work‐in‐process cycles. In additional analysis, we document a valuation premium for more concentrated manufacturers after controlling for other firm characteristics, including default risk and cost of capital estimates. We conclude that investors trade off the costs and benefits of relationships with a limited number of major customers and, on balance, consider customer‐base concentration as a net positive for firm valuation. Overall, our study adds to interdisciplinary research in accounting and operations management by shedding new light on the relevance of major customer disclosures for fundamental analysis and valuation in the manufacturing sector.
The significance of inventories in business operations have never been denied. The actual role of inventories, however, is changing over time, as required by the business environment. This paper ...provides empirical background to the thesis, which says that the role of inventories in the “Golden Era” of inventory research, which was in the 1950s, was significantly different from that of today because of fundamental changes in business. This development requires new approaches in research as well.
After a summary of the antecedents, the results of a survey are analysed, and they support the above thesis. The lack of difference between the inventory performance measured by the turnover rate of those companies, whose managers accept and those who deny the birth of the new paradigm calls attention to the need for the elaboration of a more complex inventory performance measurement.
Pre‐positioning emergency inventory in selected facilities is commonly adopted to prepare for potential disaster threat. In this study, we simultaneously optimize the decisions of facility location, ...emergency inventory pre‐positioning, and relief delivery operations within a single‐commodity disaster relief network. A min‐max robust model is proposed to capture the uncertainties in both the left‐ and right‐hand‐side parameters in the constraints. The former corresponds to the proportions of the pre‐positioned inventories usable after a disaster attack, while the latter represents the demands of the inventories and the road capacities in the disaster‐affected areas. We study how to solve the robust model efficiently and analyze a special case that minimizes the deprivation cost. The application of the model is illustrated by a case study of the 2010 earthquake attack at Yushu County in Qinghai Province of PR China. The advantage of the min‐max robust model is demonstrated through comparison with the deterministic model and the two‐stage stochastic model for the same problem. Experiment variants also show that the robust model outperforms the other two approaches for instances with significantly larger scales.
Alfa Company is a medium-sized Iraqi distributor of laboratory equipment and supplies; in this study, we analyze their experience with implementing an automated inventory management system. This ...research explains the potential benefits and implementation challenges of such a system for small and medium-sized businesses. Improved inventory tracking, less storage fees, and happier consumers resulted from adopting this system. However, implementation was complex and time-consuming, requiring the expertise of outside IT consultants as well as the training of internal staff. This case study shows how beneficial it can be for small and medium-sized enterprises to work with external IT consultants and vendors to help them evaluate the costs and benefits of installing an automated inventory management system. In sum, the case study is instructive for other SMEs thinking about investing in automated inventory management systems to boost efficiency and cut costs.
In this paper we analyze the optimal joint decisions of when, how and how much to replenish customers with products of varying ages. We discuss the main features of the problem arising in the joint ...replenishment and delivery of perishable products, and we model them under general assumptions. We then solve the problem by means of an exact branch-and-cut algorithm, and we test its performance on a set of randomly generated instances. Our algorithm is capable of computing optimal solutions for instances with up to 30 customers, three periods, and a maximum age of two periods for the perishable product. For the unsolved instances the optimality gap is always small, less than 1.5% on average for instances with up to 50 customers. We also implement and compare two suboptimal selling priority policies with an optimized policy: always sell the oldest available items first to avoid spoilage, and always sell the fresher items first to increase revenue.
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