I always look forward to collaborations with industry partners. My contact information can be found on the home page.
I am particularly interested in studying advanced technologies and their impact on operations and supply chain management. Below is a selection of my research work:
Zhang, Y., B. Tomlin. 2023. Renting 3D Printers: Pricing and Technical Service Offerings to Customers of Varying Expertise. Under review.
Customers with short-term, low-volume 3D printing (3DP) needs can rent a printer from a manufacturer or distributor (hereafter, OEM) or use a third-party printing service provider (hereafter, print farm). Customers differ in their 3DP expertise and will therefore differ in their cost of attaining a target quality if using a rented printer. Print-farm technicians are experts. To compete, OEMs offer technical service when renting printers. In practice, some OEMs bundle service with rental while others offer optional service. We explore the OEM’s rental and technical service design problem and the influence of customer heterogeneity on the optimal package and profit. We model an OEM facing a customer population that varies in their 3DP expertise (either a two-point or uniform distribution). Utility-maximizing customers can choose to rent a printer (and if so what package and what quality to attain) or to use a print farm (and if so what quality to attain). The OEM chooses what rental packages and technical service levels to offer (levels of service, prices, and whether to bundle or not), where technical service enhances a customer’s expertise but is costly for the OEM. We fully characterize the optimal package design and the conditions for when bundling is optimal. For a two-point population, we prove the OEM prefers a more heterogeneous population if the midpoint expertise is low but can prefer a less heterogeneous population otherwise. Our results establish that managers wishing to bundle (due to ease of execution) should be more cautious about bundling at intermediate customer heterogeneity than at the extremes when customer segments are very similar or very different.
And if print farms are competitive (i.e., offer a low price), managers should carefully monitor the print farm price because any increase will exacerbate the profit loss of bundling.
Song, J.-S. , Y. Zhang. 2023. Predictive 3D Printing of Spare Parts with IoT. Forthcoming at Management Science.
-This paper was selected for presentation at the MSOM Supply Chain Management SIG, 2022
-SSRN Top Ten download list, 2021
Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the Internet of Things (IoT) and 3D printing (3DP). IoT monitors complex systems in real-time, while 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a 3D printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. While the public perception indicates that this integration would enable on-demand printing, our research suggests this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print-to-stock, based on a threshold that depends on the system’s status. We also identify a printing mode called predictive print-on-demand that allows for minimal inventory, and find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT’s value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3D printing.
Zhang, Y., W. McCall, J.-S. Song. 2024. Case - Digitizing Spare Parts Supply Chain via 3D Printing - An Operational Cost Analysis. Articles in Advance. INFORMS Transactions on Education.
- An earlier version of this case study was selected as Finalist of 2022 INFORMS Case Competition.
Zhang, Y., W. McCall, J.-S. Song. 2024. Case Article - Digitizing Spare Parts Supply Chain via 3D Printing - An Operational Cost Analysis. Articles in Advance. INFORMS Transactions on Education.
The case presents a sourcing problem and a manufacturing problem faced by an original equipment manufacturer, seeking recommendations for sourcing a diverse range of parts for high voltage equipment, as well as making decisions on the manufacturing strategy for a component used in a water monitoring system. The case provides an opportunity to explore the qualitative and quantitative aspects of 3D printing versus traditional manufacturing, specifically in terms of operational cost. Furthermore, this case facilitates discussions on the potential impact of 3D printing on supply chains. It is suitable for use in graduate and undergraduate courses, as it introduces key concepts such as manufacturing and inventory policies, queueing theory, and lifecycle analysis. Ultimately, the case is designed to promote a deeper understanding of the challenges and opportunities that manufacturers face in today’s rapidly evolving technological landscape.
Zhang, Y., B. Westerweel, R. Basten, J.-S. Song. 2022. Distributed 3D Printing of Spare Parts via IP Licensing. Manufacturing & Service Operations Management. 24(5) 2685-2702.
-SSRN Top Ten download list, 2020
Additive manufacturing, also known as 3D printing, has the potential to shift supply chains from global networks that rely on centralized production with traditional manufacturing technologies to mainly digital networks with distributed, local 3D printing. Particularly well positioned to drive this transition are original equipment manufacturers (OEMs) who design and produce capital goods. We consider an OEM supplying a single part to multiple buyers over an infinite horizon. We study how the OEM can digitize the spare parts supply chain by leveraging 3D printing via intellectual property (IP) licensing. We first set up a benchmark model of the traditional physical supply chain with centralized production by the OEM. We then propose the OEM to act as an IP licensor by selling spare parts designs, rather than physical parts. With the license agreement, a buyer can print spare parts locally through a third-party printing service provider, enjoying a much shorter lead time and lower setup cost. Given a license, each buyer chooses whether to switch to the IP licensing channel or stay in the traditional channel. The OEM selects the license terms to maximize his total profit across both channels. We characterize the OEM's optimal license and the resulting supply chain configuration. We show that 3D printing's competency in price plays a dominant role in decentralization. Through a numerical experiment with realistic parameter settings, we demonstrate that decentralized supply chain occurs in a surprisingly large number of cases. The proposed new business model can also significantly increase the OEM's profit. Our results indicate that IP licensing by OEMs can become a major enabler in the transition to digital supply networks with distributed 3D printing, benefiting all parties involved.
Song, J.-S., Y. Zhang. 2020. Stock or Print? Impact of 3D Printing on Spare Parts Logistics. Management Science. 66(9) 3860-3878.
-This paper won the First Prize in 2019 Columbia Business Initiative/CSAMSE Best Paper Award Competition
-This research was featured on Fuqua Insights (link), 3D Printing Industry (link) and Additive Manufacturing (link).
We present a general framework to study the design of spare parts logistics in the presence of 3D printing technology. We consider multiple parts facing stochastic demands, and adopt procure/manufacture-to-stock versus print-on-demand to highlight the main difference of production modes featured in traditional manufacturing and 3D printing. A multi-class priority queue with deterministic service times is employed to capture the intrinsic heterogeneity among spare parts and reflect the operational details of 3D printing. To minimize long-run average system cost, our model determines which parts to stock and which to print. We find that the optimal 3D printer's utilization increases as the additional unit cost of printing declines and the printing speed improves. The rate of increase, however, decays, demonstrating the well-known diminishing returns effect. We also find the optimal utilization to increase in part variety and decrease in part criticality, suggesting the value of 3D technology in tolerating large part variety and the value of inventory for critical parts. By examining the percentage cost savings enabled by 3D printing, we find that, while the reduction in printing cost continuously adds to the value of 3D printing in a linear fashion, the impact of the improvement of printing speed exhibits S-shaped growth. We also derive various structural properties of the problem and devise an efficient algorithm to obtain near-optimal solutions. Finally, our numerical study shows that the 3D printer is in general lightly used under realistic parameter settings but results in significant cost savings, suggesting complementarity between stock and print in cost minimization.
Chen, L., J.-S. Song, Y. Zhang. 2017. Serial Inventory Systems with Markov-Modulated Demand: Derivative Bounds, Asymptotic Analysis, and Insights. Operations Research. 65(5) 1231-1249.
-An earlier version of this paper won the Third Prize in 2016 Columbia Business Initiative/CSAMSE Best Paper Award Competition
In this paper, we consider the inventory control problem for serial supply chains with continuous, Markov-modulated demand (MMD). Our goal is to simplify the computational complexity by resorting to certain approximation techniques, and, in doing so, to gain a deeper understanding of the problem. To this end, we analyze the problem in several new ways. We first perform a derivative analysis of the problem's optimality equations, and develop general, analytical solution bounds for the optimal policy. Based on the bound results, we derive a simple procedure for computing near-optimal heuristic solutions for the problem. These simple solutions reveal a closer relationship with the primitive model parameters. Second, we perform asymptotic analysis with long replenishment lead time and establish an MMD central limit theorem. We further show that the relative errors between our heuristics and the optimal solutions converge to zero as the lead time becomes sufficiently long, with the rate of convergence being the square root of the lead time. Our numerical results reveal that our heuristic solutions can achieve near-optimal performance even under relatively short lead times. Third, we show that, by leveraging the Laplace transformation, the optimal policy becomes computationally tractable under the gamma distribution family. This enables us to numerically compare various heuristic solutions with the optimal solution and to demonstrate that our heuristic outperforms existing heuristics in most cases. Finally, we observe that the internal fill rate and demand variability propagation in an optimally controlled supply chain under MMD exhibit behaviors different from those under stationary demand.
Cheung, K. L., J.-S. Song, Y. Zhang. 2017. Cost Reduction Through Operations Reversal. European Journal of Operational Research. 259(1) 100-112.
In some manufacturing and service processes, several stages must be performed, but there is some freedom in the ordering of stages. Operations reversal means switching the order of two stages. Several authors have studied the benefits of operations reversal, focusing on the reduction of a certain variable's variance or a related measure. This paper focuses instead on cost. We construct a model with the standard objective of minimizing the long-run average inventory-related cost. First, by using stochastic orders, we identify conditions under which operations reversal reduces cost. We find that in some cases the variability and cost objectives agree on when operations reversal is beneficial, but in other cases, they disagree. In particular, when demands are multinomially distributed, variability reduction may be accompanied by cost increase. We show that to guarantee a lower cost, we need certain properties on the aggregated demand at the choice-level (such as demands for sweaters of the same color). Finally, we examine the effects of cost parameters and lead times on operations reversal under the cost measure.
Lu, L., J. M. Swaminathan, G. Wang, Y. Zhang. 2018. Procurement Contracting under Product Recall Risk. Working Paper.
Product recall is commonly observed in various industries with production outsourcing. Managing product quality and mitigating the financial impact of product recalls pose great challenges to manufacturers due to demand uncertainty and non-contractibility of suppliers' quality effort. To understand the interdependence of supply chain quantity and quality decisions, we develop a procurement contractual framework under both demand and recall risks. We consider a model in which a manufacturer outsources to a supplier the production of a component, which is subject to potential quality failure leading to a product recall. The manufacturer acts as the Stackelberg leader offering a recall cost sharing contract to the supplier. We analyze two settings: a pull system in which the supplier makes the quantity decision and a push system in which the manufacturer makes the quantity decision. We find that the manufacturer achieves a higher production quantity and induces a higher quality effort of the supplier in the push system than in the pull system. Therefore, the manufacturer can improve quality by taking on the demand risk of the supply chain. Moreover, the presence of product recall risk decreases the production quantity in the push system but does not affect the production quantity in the pull system. Interestingly, the manufacturer can improve quality and profit by decreasing her share of the total recall cost without affecting the production quantity of the supply chain in both the push and pull systems.