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This chapter presents a new approach to teach process costing that uses worksheets to create the information necessary to account for costs. The approach employs a five-column, five-row worksheet that presents weighted-average and FIFO costs per equivalent unit simultaneously. Then, the goal of process costing, accounting for costs, is formally presented in a manner to emphasize its importance. As a result, students are better able to compare and contrast the two process-costing methods.

This chapter covers three main concepts: it provides an overview of supply chain management (SCM), introduces the concepts of procurement and what is entailed within this function, and explains how inventory is managed. In the first section, SCM is considered broadly but also in the context of hospitality. The key roles and objectives of SCM as well as the significance of supply chain risk and disruption are considered. In the second section, the concept of sourcing is discussed. Sourcing is a critical function in any organization: without this, an organization would cease to operate. The importance of supplier selection is explored, with methods to make the most appropriate selection and for subsequently managing suppliers. Finally, the third section focuses on how inventory management can be optimized. Concepts such as economic order quantity (EOQ) and ABC analysis are explored, along with alternatives to traditional inventory management methods.

Supply chains deliver goods and services between shippers and receivers, covering collection, transportation, distribution as well as their handling and storage in between. In particular, transportation services are carried out by different transport modes. In some modern supply chains, different categories of air cargo carriers – combinations, freighter-only, and/or integrators – provide critical transport services.

This chapter develops a methodology for estimating the performance of supply chains served by an air cargo carrier network. The methodology is based on indicators of infrastructure use, technical/technological level, operational factors, economic factors, and environmental performance. This proposed methodology is applied to estimate performance of supply chains served by an integrated air cargo carrier – FedEx Express – operating a single hub in the US domestic air network. Results indicate that the methodology may be useful for estimation of overall supply chain performance under the condition that relevant data are available.

Stock markets during the day are relatively centralized, while night markets, due to the dominance of electronic trading venues, are fragmented. Though electronic markets at night allow more competition for order flow, they may result in decreased order interaction and decreased transparency. Using transaction data for three exchange traded funds (ETFs), we find that bid–ask spreads are wider at night due to higher order processing costs, market maker rents, and inventory holding costs. Results show that night markets are informationally fragmented and are not able to impound information available in net order flow to the same degree as day markets.

This chapter discusses a collaborative strategy for noncompetitive small- and medium-sized enterprises (SME's) aiming to reduce their logistics costs by means of a joint replenishment of multiple items. The proposed approach is an extension of the classical joint replenishment problem, named as a Stochastic Collaborative Joint Replenishment problem (S-CJRP) because it considers stochastic demand, warehouse and transport capacity constraints, and multiple buyers and vendors. Operating this method implies three main challenges: (1) determining the frequency with which each buyer should replenish the products; (2) allocating investments and benefits between partnering buyers; and (3) deciding whether to coordinate the supply chain internally or outsource its coordination. The S-CJRP is solved through a heuristic approach, which deals with uses of the Shapley Value Function to allocate the investments and benefits, and it explores the coordination through several simulation scenarios, all of which exhibit prospective cost reductions in inventory management. Preliminary results show that third-party logistics providers could be a valuable resource in coordinating SMEs along a supply chain.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

We examine the sample self-selection and the use of LIFO or FIFO inventory method. For this purpose, we apply the Heckman-Lee’s two-stage regression to the 1973–1981 data, a period of relatively high inflation, during which the incentive to adopt the LIFO inventory valuation method was most pronounced. The predicted coefficients based on the reduced-form probit (inventory choice model) and the tax functions are used to derive predicted tax savings in the structured probit. Specifically, the predicted tax savings are computed by comparing the actual LIFO (FIFO) taxes vs. predicted FIFO (LIFO) taxes. Thereafter, we estimate the dollar amount of tax savings under different regimes. The two-stage approach enables us to address not only the managerial choice of the inventory method but also the tax effect of this decision. Previous studies do not jointly consider the inventory choice decision and the tax effect of that decision. Hence, the approach we use is a contribution to the literature. Our results show that self-selection bias is present in our sample of LIFO and FIFO firms and correcting for the self-selection bias shows that the LIFO firms, on average, had $282 million of tax savings, which explains why a large number of firms adopted the LIFO inventory method during the seventies.