Title: 3D printing for mass customization: reselling or direct selling
Speaker: Dr. Ganesh Balasubramanian
Date and time: 28 February 2025 (Friday), 10:30 – 11:30 a.m.
Venue: IEOR Seminar Room
Abstract: 3D printing allows manufacturers to produce and sell customized products that fit the exact customer specifications (biometric profile). Ensuring fit requires capturing each customer's dimensions, which can be achieved by delegating the process to retailers. This often involves on-site production of the fitted product, which implies ceding pricing power to the retailer. Alternatively, the manufacturer can involve customers directly, usually via a mobile application. By doing so, the manufacturer retains the pricing power but subjects herself to issues stemming from data imperfections, and consequently, lack of fit of the product. Hence, the manufacturer faces the following challenge: should she delegate data collection and 3D printing costs to the retailer or should she bear the 3D printing cost while engaging with the customer directly to carry out data collection? This study captures the loss in the product specification data fidelity in the direct channel, a reality that has been overlooked by the extant literature. We use a stylized game theoretic model to investigate the manufacturer's optimal channel choice for selling 3D-printed custom products.
Our analysis reveals the following key insights. First, we show, as expected, that the manufacturer benefits from selling 3D-printed custom products, in addition to its standard product, only when the printing cost is sufficiently low. Interestingly, the manufacturer might choose not to introduce the customization option even when this option helps to expand its market. Second, we show that the degree of product customization critically influences the optimal channel choice decision more than the loss in data fidelity. For low to moderate degrees of customization, the manufacturer prefers the direct channel even when the loss in data fidelity is significant. However, for high degrees of customization, the manufacturer prefers the retail channel when the loss in data fidelity in the direct channel is high.
These results imply that manufacturers operating in product categories such as apparels, medical equipment, and sporting goods, where the degree of customization is relatively high, should refrain from switching to a direct channel for their custom-fitted products until they have a technology that is portable, user-friendly, and highly reliable for customers’ biometric data collection. On the other hand, for products that are not amenable to a high degree of customization, switching to a direct channel can offer significant benefit to manufacturers even in the absence of such a portable, user-friendly, and reliable technology for customers’ biometric data collection. We check the robustness of our results considering a generalized cost function for capturing the 3D printing cost.
In this talk, I plan to present the research problem, model, key results, and their implications. This is joint work with Benny Mantin (University of Luxembourg) and Sachin Jayaswal (IIM Ahmedabad).
Bio: Ganesh Balasubramanian currently serves as an assistant professor at the T A Pai Management Institute (TAPMI), Manipal. He holds a Ph.D. in operations and supply chain management from IIM Ahmedabad. Ganesh is a visiting researcher at the Luxembourg Centre for Logistics & Supply Chain Management, University of Luxembourg. His research interests lie in the interface of operations and marketing. Specifically, he uses analytical models to generate managerial insights on inventory, pricing, and product distribution decisions. In his doctoral dissertation, he studied the role of inventory and technology adoption in decentralized supply chains. His dissertation won the Emerging Economies Doctoral Student Award for the Asia Pacific region by POMS (Production and Operations Management Society, USA). He has published his research work in prominent journals such as Naval Research Logistics, and International Journal of Production Economics.