Knowledge that Transforms

We are living in an era in which data is generated in huge volume with high velocity and variety. Big Data and technology are reshaping our life and business. Our research inevitably needs to catch up with these changes. In this short essay, we focus on two aspects of supply chain management, namely, demand management and manufacturing. We feel that, while rapidly growing research on these two areas is contributed by scholars in computer science and engineering, the developments made by production and operations management society have been insufficient. We believe that our field has the expertise and talent to push for advancements in the theory and practice of demand management and manufacturing (of course, among many other areas) along unique dimensions. We summarize some relevant concepts emerged with Big Data and present several prototype models to demonstrate how these concepts can lead to rethinking of our research. Our intention is to generate interests and guide directions for new research in production and operations management in the era of Big Data.

In this day, in the age of big data, consumers leave an easily traceable digital footprint whenever they visit a website online. Firms are interested in capturing the digital footprints of their consumers to understand and predict consumer behavior. This study deals with how big data analytics has been used in the domains of information systems, operations management, and healthcare . We also discuss the future potential of big data applications in these domains (especially in the areas of cloud computing, Internet of Things and smart city, predictive manufacturing and 3‐D printing, and smart healthcare) and the associated challenges. In this study, we present a framework for applications of big data in these domains with the goal of providing some interesting directions for future research.

With the rise of the Internet economy, an increasing number of firms are offering their core products through online platforms, but retail add‐ons directly to consumers. Meanwhile, many online platforms have also started adopting the agency (model) contract, where the upstream firms decide the retail prices of products while the downstream platforms take a predetermined cut from each sale. This study examines the interaction between an upstream firm's add‐on strategy and a downstream online platform's distribution contract choice. We find that such a firm prefers bundling the add‐on and the core product together under the wholesale contract, but prefers retailing the add‐on separately under the agency contract. Our research thus is the first to suggest that the distribution contract can critically affect a firm's choice between add‐on pricing and bundling. On the platform side, we show that a higher commission rate does not always result in a higher profit for the platform under the agency contract. We further identify two conditions under which the platform prefers the agency contract over the wholesale contract: The commission rate for the platform cannot be too low, and the market potential of the add‐on cannot be too large. For the overall channel, we show that the interaction between add‐on pricing and distribution contracts leads to sub‐optimal channel performance. That said, it is possible for both the firm and the platform to obtain higher profits under the agency contract than under the wholesale contract. Finally, we also demonstrate the robustness of our findings under several alternative model specifications.

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts.We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out‐of‐sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best‐performing method (random forest) yields an out‐of‐sample MAPE of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

To avoid inventory risks, manufacturers often place rush orders with suppliers only after they receive firm orders from their customers (retailers). Rush orders are costly to both parties because the supplier incurs higher production costs. We consider a situation where the supplier's production cost is reduced if the manufacturer can place some of its order in advance. In addition to the rush order contract with a pre‐established price, we examine whether the supplier should offer advance‐order discounts to encourage the manufacturer to place a portion of its order in advance, even though the manufacturer incurs some inventory risk. While the advance‐order discount contract is Pareto‐improving, our analysis shows that the discount contract cannot coordinate the supply chain. However, if the supplier imposes a pre‐specified minimum order quantity requirement as a qualifier for the manufacturer to receive the advance‐order discount, then such a combined contract can coordinate the supply chain. Furthermore, the combined contract enables the supplier to attain the first‐best solution. We also explore a delegation contract that either party could propose. Under this contract, the manufacturer delegates the ordering and salvaging activities to the supplier in return for a discounted price on all units procured. We find the delegation contract coordinates the supply chain and is Pareto‐improving. We extend our analysis to a setting where the suppliers capacity is limited for advance production but unlimited for rush orders. Our structural results obtained for the one‐supplier‐one‐manufacturer case continue to hold when we have two manufacturers.

As waste from used electronic products grows steadily, manufacturers face take‐back regulations mandating its collection and proper treatment through recycling, or remanufacturing. Environmentalists greet such regulation with enthusiasm, but its effect on remanufacturing activity and industry competition remains unclear. We research these questions, using a stylized model with an original equipment manufacturer (OEM) facing competition from an independent remanufacturer (IR). We examine the effects of regulation on three key factors: remanufacturing levels, consumer surplus, and the OEM profit. First, we find that total OEM remanufacturing actually may decrease under high collection and/or reuse targets, meaning more stringent targets do not imply more remanufacturing. Consumer surplus and the OEM profit, meanwhile, may increase when OEM‐IR competition exists in a regulated market. Finally, through a numerical study, we investigate how total welfare changes in the collection target, what happens when the cost of collection is not linear, and what happens when IR products are valued differently by consumers.

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.

Recent research indicates that consumers hold significant concerns about the quality of remanufactured products. To better understand this phenomenon, this manuscript combines surveys and experimental studies to identify the antecedents of perceived quality—in the form of perceived risk of functionality and cosmetic defects—and their significant impact on consumers' willingness to pay (wtp) for remanufactured electronics products. The study also controls for alternative explanations for wtp suggested in the literature, such as consumers' wtp for new products, environmental beliefs, disgust aversion toward used products, brand perceptions, risk aversion, and various demographic traits. Importantly, the study empirically estimates the magnitude and distribution of discount factors for remanufactured electronics products—the ratio between wtp for a remanufactured product and wtp for a corresponding new product—among consumers. Finally, the manuscript analytically compares a monopolist's decision to include remanufactured products in its portfolio under both the empirically derived discount factor distributions and the classical linear demand model, which assumes constant discount factors. Interestingly, the classical linear demand model remains reasonably robust for high‐level insights, such as the presence of cannibalization and market expansion effects. However, the analytical model that uses the empirically‐derived distributions of discount factors demonstrates significantly higher profitability than predicted by the classical linear model. This fundamental link between risk perceptions, wtp for remanufactured products, and profitability provides new insights on how to manage demand and product pricing in closed‐loop supply chains.

We consider manufacturer rebate competition in a supply chain with two competing manufacturers selling to a common retailer. We fully characterize the manufacturers’ equilibrium rebate decisions and show how they depend on parameters such as the fixed cost of a rebate program, market size, the redemption rate of rebate, the proportion of rebate‐sensitive consumers in the market and competition intensity. Interestingly, more intense competition induces a manufacturer to lower rebate value or stop offering rebate entirely. Without rebate, it is known that more intense competition hurts the manufacturers and benefits the retailer. With rebate, however, more intense competition could benefit the manufacturers and hurt the retailer. We find similar counterintuitive results when there is a change in some other parameters. We also consider the case when the retailer subsidizes the manufacturers sequentially to offer rebate programs. We fully characterize the retailer's optimal subsidy strategy, and show that subsidy always benefits the retailer but may benefit or hurt the manufacturers. When the retailer wants to induce both manufacturers to offer rebate, he always prefers to subsidize the manufacturer with a higher fixed cost first. Sometimes the other manufacturer will then voluntarily offer rebate even without subsidy.

Research on buyer–supplier relationships (BSRs) has often focused on only one side of the relationship and, thus, has tended to overlook asymmetries. Yet, a buyer (supplier) may often deal with a bigger supplier (buyer) or one that has higher levels of trust, respect, and reciprocity. Therefore, we examined how two types of asymmetries—size and relational capital—affect perceived opportunism and performance. We used dyadic data from 106 buyers and their matched suppliers gathered from a survey and an archival database. The results demonstrate that the degree and direction of both asymmetries affect the BSR. Our results also reveal that an imbalance of relational capital in a firm's favor may have the opposite effect from that intended. In other words, the firm's counterpart perceives more, rather than less, firm opportunism. The results also suggest that a buyer observes lower benefits in the presence of size asymmetry, whereas the supplier's perception of benefits is unaffected. Thus, our research represents a significant step forward in understanding BSRs and asymmetries by (i) bringing attention to two key asymmetries inherent in BSRs and (ii) showing that these asymmetries are not unidirectional in their influence on perceived opportunism and performance.