Knowledge that Transforms

Consider a buyer, facing uncertain demand, who sources from multiple suppliers via online procurement auctions (open descending price‐only auctions). The suppliers have heterogeneous production costs, which are private information, and the winning supplier has to invest in production capacity before the demand uncertainty is resolved. The buyer chooses to offer a push or pull contract, for which the single price and winning supplier are determined via the auction. We show that, with a pull contract, the buyer does not necessarily benefit from a larger number of suppliers participating in the auction, due to the negative effect of supplier competition on the incentive of supplier capacity investment. We thus propose an enhanced pull mechanism that mitigates this effect with a floor price. We then analyze and compare the outcomes of auctions for push and (enhanced) pull contracts, establishing when one form is preferred over the other based on the buyer's profits. We also compare our simple, price‐only push and pull contract auctions to the optimal mechanisms, benchmarking the performance of the simple mechanisms as well as establishing the relative importance of auction design and contract design in procurement auctions.

Two factors that their influence on the demand has been investigated in many papers are (i) the shelf space allocated to a product and to its complement or supplement products and (ii) the instantaneous inventory level seen by customers. Here we analyze the joint shelf space allocation and inventory decisions for multiple items with demand that depends on both factors. The traditional approach to solve inventory models with a state‐dependent demand rate uses a time domain approach. However, this approach often does not lead to closed‐form expressions for the profit rate with both dependencies. We analyze the problem in the inventory domain via level crossing theory. This approach leads to closed‐form expressions for a large set of demand rate functions exhibiting both dependencies. These closed‐form expressions substantially simplify the search for optimal solutions; thus we use them to solve the joint inventory control and shelf space allocation problem. We consider examples with two products to investigate the significance of capturing both demand dependencies. We show that in some settings it is important to capture both dependencies. We consider two heuristics, each one of them ignores one of the two dependencies. Using these heuristics it seems that ignoring the dependency on the shelf space might be less harmful than ignoring the dependency on the inventory level, which, based on computational results, can lead to profit losses of more than 6%. We demonstrate that retailers should use their operational control, e.g., reorder point, to promote higher demand products.

This paper investigates the effect of learning and forgetting on production scheduling decisions. Numerous papers have appeared on this topic in the last four decades; they show that firms are better off producing in larger batches in the presence of learning and forgetting. However, these papers fail to consider one or more of realistic features of learning and forgetting; factors such as (1) the amount forgotten increases with break length between two batches and (2) the forgetting could be slow over an initial short interval followed by fast forgetting. Our paper contributes by demonstrating that a consideration of these realistic features leads to a different conclusion—firms may be better off producing in smaller batches in the presence of learning and forgetting. This is a new insight that provides one more justification for producing in small batches.

India is an important frontier for economic growth, investments, and development. The service sector, like the manufacturing industry, in India is booming. Following the trend of their western counterparts, service organizations in India are implementing enterprise‐level information and communication technologies (ICTs) to support service processes. In this paper, we used socio‐technical systems theory to develop hypotheses about the effects of ICTs on the five job characteristics, i.e., skill variety, task identity, task significance, autonomy, and feedback, in the job characteristics model (JCM) in a service organization (a bank) in India. We also tested the entire JCM that relates job characteristics to job satisfaction and job performance via various mediators and moderators. In a 32‐month longitudinal field study of 1743 employees, we gathered one wave of data before an ICT implementation and two waves after the implementation. We found that, although the ICT enriched employees' job characteristics, employees reported significantly lower job satisfaction and job performance. To understand this puzzling finding, we conducted a qualitative study and identified four contextual forces that contribute to these results and hinder successful implementation of ICTs in the service sector in India and, possibly, other developing countries: environmental barriers, learning difficulty, culture shock, and employee valuation.

Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real‐time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base‐stock policy, in which the optimal base‐stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.

Under group buying, quantity discounts are offered based on the buyers' aggregated purchasing quantity, instead of individual quantities. As the price decreases with the total quantity, buyers receive lower prices than they otherwise would be able to obtain individually. Previous studies on group buying focus on the benefit buyers receive in reduced acquisition costs or enhanced bargaining power. In this paper, we show that buyers can instead get hurt from such cooperation. Specifically, we consider a two‐level distribution channel with a single manufacturer and two retailers who compete for end customers. We show that, under linear demand curves, group buying is always preferable for symmetric (i.e., identical) retailers. For asymmetric retailers (i.e., differing in market base and/or efficiency), group buying is beneficial to the smaller (or less efficient) player. However, it can be detrimental to the larger (or more efficient) one. Despite the lower wholesale price under group buying, the manufacturer can receive a higher revenue. Interestingly, group buying is more likely to form when retailers are competitive in different dimensions. These insights are shown to be robust under general nonlinear demand curves, except for constant elastic demand with low demand elasticity.

ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.

This paper examines the choice of supply chain structure for a proprietary component manufacturer (PCM). The PCM, who is the sole supply source of a critical component used to assemble an end product, can either provide its component to an original equipment manufacturer (OEM) in the end‐product market (component supplier structure), develop the end product exclusively under its own brand (monopoly structure), or provide the component to the OEM as well as develop the end product under its own brand (dual distributor structure). Typically, the end products of the PCM and the OEM will be differentiated, and the OEM tends to have a capability advantage (compared with the PCM) in producing the end product. Our paper studies the impact of this degree of differentiation and capability advantage on the optimal choice of distribution structure. We then investigate how investing in component branding, enhancing the value of the end product, using alternative supply contracts, and product valuation uncertainty influence the PCM's optimal choice of distribution structure.

The computer software industry is an extreme example of rapid new product introduction. However, many consumers are sophisticated enough to anticipate the availability of upgrades in the future. This creates the possibility that consumers might either postpone purchase or buy early on and never upgrade. In response, many software producers offer special upgrade pricing to old customers in order to mitigate the effects of strategic consumer behavior. We analyze the optimality of upgrade pricing by characterizing the relationship between magnitude of product improvement and the equilibrium pricing structure, particularly in the context of user upgrade costs. This upgrade cost (such as the cost of upgrading complementary hardware or drivers) is incurred by the user when she buys the new version but is not captured by the upgrade price for the software. Our approach is to formulate a game theoretic model where consumers can look ahead and anticipate prices and product qualities while the firm can offer special upgrade pricing. We classify upgrades as minor, moderate or large based on the primitive parameters. We find that at sufficiently large user costs, upgrade pricing is an effective tool for minor and large upgrades but not moderate upgrades. Thus, upgrade pricing is suboptimal for the firm for a middle range of product improvement. User upgrade costs have both direct and indirect effects on the pricing decision. The indirect effect arises because the upgrade cost is a critical factor in determining whether all old consumers would upgrade to a new product or not, and this further alters the product improvement threshold at which special upgrade pricing becomes optimal. Finally, we also analyze the impact of upgrade pricing on the total coverage of the market.

One way to coordinate workers along an assembly line that has fewer workers than work stations is to form a bucket brigade. The throughput of a bucket brigade on discrete work stations may be compromised due to blocking even if workers are sequenced from slowest to fastest. For a given work distribution on the stations we find policies that maximize the throughput of the line. When workers have very different production rates, fully cross‐training the workers and sequencing them from slowest to fastest is almost always the best policy. This policy outperforms other policies for most work distributions except for some cases in which limiting the work zones of workers produces higher throughput. In environments where the work can be adjusted across stations, we identify conditions for a line to prevent blocking.