Integrating anticipative replenishment-allocation with reactive fulfillment for online retailing using robust optimization

Ministry of Education, Singapore under its Academic Research Funding Tier 1; Lee Kong Chian Fellowship; MPA Research Fellowshi

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location-allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities

An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories

We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robust optimization (RO) methods emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of improved average-case performance by overcoming the presence of endogenous outliers. This bimodal strategy is particularly valuable for balancing the tradeoff between lost sales at the store and the costs of cross-channel e-commerce fulfillment, which is at the core of our inventory optimization model. These factors are asymmetric due to the heterogenous behavior of the channels, with a bias towards the former in terms of lost-sales cost and a dependence on network effects for the latter. We provide structural insights about the BIO solution and how it can be tuned to achieve a preferred tradeoff between robustness and the average-case. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management, which are siloed by channel and location. Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance

Joint Inventory and Fulfillment Decisions for Omnichannel Retail Networks

With e-commerce growing at a rapid pace compared to traditional retail, many brick-and-mortar firms are supporting their online growth through an omnichannel approach, which integrates inventories across multiple channels. We analyze the inventory optimization of three such omnichannel fulfillment systems for a retailer facing two demand streams (online and in-store). The systems differ in the level of fulfillment integration, ranging from no integration (separate fulfillment center for online orders), to partial integration (online orders fulfilled from nearest stores) and full integration (online orders fulfilled from nearest stores, but in case of stockouts, can be fulfilled from any store). We obtain optimal order-up-to quantities for the analytical models in the two-store, single-period setting. We then extend the models to a generalized multi-store setting, which includes a network of traditional brick-and-mortar stores, omnichannel stores and online fulfillment centers. We develop a simple heuristic for the fully-integrated model, which is near optimal in an asymptotic sense for a large number of omnichannel stores, with a constant approximation factor dependent on cost parameters. We augment our analytical results with a realistic numerical study for networks embedded in the mainland US, and find that our heuristic provides significant benefits compared to policies used in practice. Our heuristic achieves reduced cost, increased efficiency and reduced inventory imbalance, all of which alleviate common problems facing omnichannel retailing firms. Finally, for the multiperiod setting under lost sales, we show that a base-stock policy is optimal for the fully-integrated model.With e-commerce growing at a rapid pace compared to traditional retail, many brick-and-mortar firms are supporting their online growth through an omnichannel approach, which integrates inventories across multiple channels. We analyze the inventory optimization of three such omnichannel fulfillment systems for a retailer facing two demand streams (online and in-store). The systems differ in the level of fulfillment integration, ranging from no integration (separate fulfillment center for online orders), to partial integration (online orders fulfilled from nearest stores) and full integration (online orders fulfilled from nearest stores, but in case of stockouts, can be fulfilled from any store). We obtain optimal order-up-to quantities for the analytical models in the two-store, single-period setting. We then extend the models to a generalized multi-store setting, which includes a network of traditional brick-and-mortar stores, omnichannel stores and online fulfillment centers. We develop a simple heuristic for the fully-integrated model, which is near optimal in an asymptotic sense for a large number of omnichannel stores, with a constant approximation factor dependent on cost parameters. We augment our analytical results with a realistic numerical study for networks embedded in the mainland US, and find that our heuristic provides significant benefits compared to policies used in practice. Our heuristic achieves reduced cost, increased efficiency and reduced inventory imbalance, all of which alleviate common problems facing omnichannel retailing firms. Finally, for the multiperiod setting under lost sales, we show that a base-stock policy is optimal for the fully-integrated model.http://deepblue.lib.umich.edu/bitstream/2027.42/136157/1/1341_Govindarajan.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136157/4/1341_Govindarajan_Apr2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136157/6/1341_Govindarajan_Jan18.pdfDescription of 1341_Govindarajan_Apr2017.pdf : April 2017 revisionDescription of 1341_Govindarajan_Jan18.pdf : January 2018 revisio

Simulating an optimal continuous review inventory policy online retail

In the online channel of grocery retailers, customers typically define a specific date and time at which they want to receive their orders. This customer order time window provides additional flexibility, which can be used to improve the policy for optimizing inventory. This improvement allows to better adjust the ordering point and the ordering quantity, leading to a decrease in the total cost, which comprises the ordering, holding and stockout costs. This study is based on a previous thesis, where the (s, Q) inventory policy explicitly accounts for the ordering window. The policy considers that the customer demand as well as the customer ordering window are stochastic. In this thesis, we extend and refine the policy by exploring the multiple on order setting, which introduces more complex and extreme scenarios to the inventory system with ordering windows. The validations were performed using simulation for a variety of parameter configurations. The simulator was implemented in Excel, using VBA, and the policy was optimized via numerical optimization using MatLab. The revised policy proved to be appropriate both for single and multiple on order scenarios. There is an avenue of future research to be explored in this online retail setting, particularly in the exploration of inventory policies that account for the ordering window

Hyperconnected fulfillment and inventory allocation and deployment models

Consumption patterns have been changed dramatically over the past decades, notably by the growth of e-commerce. With the prevalence of e-commerce and home delivery, customer expectations for a faster, punctual, and cheap delivery are increasing. In fact, many customers are expecting for same-day or x-hour deliveries now and offering fast delivery becomes more and more critical for e-retailers to survive in a fierce market competition. However, many companies are simply lacking financial, physical, and/or operational resources to increase their responsiveness. Focusing on solving the challenges in the perspective of fulfillment and inventory, we aim to find a breakthrough from a recently emerging logistics innovation movement induced by the introduction of the Physical Internet (PI). PI can potentially enable responsive yet affordable fulfillment for companies of any size through open asset utilization and multi-player operations. The key of PI innovation is transforming asset-driven logistics operations to service-driven logistics operations. This thesis provides an academic foundation for hyperconnected fulfillment to effectively satisfy the growing customer expectations on responsive deliveries. We first present a comprehensive design and evaluation of a hyperconnected fulfillment system. Then, we focus on providing inventory operations models, inventory allocation and deployment respectively, which maximally utilize the key features of hyperconnected fulfillment system: connectivity, flexibility, and decentralization. In Chapter 2, a hyperconnected fulfillment and delivery system is designed in the context of the last-mile operations in urban areas. A comprehensive system and decision architecture of the hyperconnected system is modeled. We carefully design the scenarios to show a gradual transformation from dedicated to hyperconnected system in each thread of delivery and fulfillment so as to reveal the marginal impact of each step of transformation. We conduct a scenario analysis using a simulation platform built upon the system and decision architecture where autonomous agents are optimizing their decisions and interact with the environment. The experimental results clearly demonstrate the potential benefit of hyperconnected urban fulfillment and delivery system by concurrently improving often opposing performance criteria: economic efficiency, service capability and sustainability. Chapter 3 tackles an optimal inventory allocation problem among multiple sales outlets. Specifically, we analyze a case where a dropshipper allocates availability to multiple e-retailers via availability promising e-contracts (APCs). Under the APC, the e-retailers do not observe actual availability and this information asymmetry leads them to pose a promised availability threshold (PAT). PAT is a threshold on remaining promised availability set by an e-retailer for a product of a dropshipper, below which the e-retailer unlists the product and thus does not accept any more orders from customers, until the promised availability is climbed above the threshold by the dropshipper. The dropshipper's APC problem with PAT is modeled as 2-stage stochastic program with two stochastic parameters: demand and PAT. We design and evaluate three contract policies differentiated by the allowance level for overpromising: guaranteed fulfillment, controlled fillrate, and penalty-driven fillrate policies. We also present a modeling approach to convert the endogenous demands, per-retailer-distribution of which is affected by the APCs, to exogenous demands with linear substitution constraints. The numerical results show the penalty-driven fillrate policy is the dominating strategy for dropshippers especially under a lean availability. Chapter 4 tackles an inventory deployment problem under the context of open asset utilization and responsive fulfillment. When it comes to very responsive deliveries, such as X-hour deliveries, the physical availability of inventories near the delivery locations becomes necessary, which requires a broad and dense fulfillment network. The open asset utilization and service-driven fulfillment operations of the PI can enable affordable access to such decentralized fulfillment network comprised of the open fulfillment centers. We evaluate the benefit of such decentralized fulfillment network for a responsive fulfillment and develop an appropriate inventory deployment model, which possesses a partially pooled demand and inventory structure induced by responsiveness requirements, as a variant of Newsvendor. We derive a pragmatic heuristic inventory solution, W-solution, and present an efficient binary search based solution heuristic, W-heuristic. Then, via numerical experiments over both theoretical and empirical demand distributions, we demonstrate the advantage of decentralized network and w-solution over centralized network and allocation-based inventory model, pre-allocation model, respectively. We also report rather counter-intuitive observations that the w-solution which accounts for pooling leads to more inventory than pre-allocation model which does not account for pooling under low sales margin.Ph.D

Supply chain collaboration and sustainable development goals (SDGs). Teamwork makes achieving SDGs dream work

The global push towards sustainable development has led to an upsurge in academic literature at the juncture of supply chain collaboration (SCC) and sustainability. The present paper aims to map this growing literature to understand how SCC can contribute to the achievement of broader Sustainable Development Goals (SDGs). Via a systematic review of literature (SLR), the paper maps key themes at the intersection of SCC and sustainable development. Relying on nine key themes, the study presents novel insights into the domain of SCC for sustainable development. The results of the SLR reveal that collaborative innovation, collaborative process and product development are key mechanisms driving SCC. However, the extant literature has not devoted much attention to the effectiveness of SCC mechanisms or their performance. Further, the current study posits that more effective SCC strategies can boost the sustainable operational performance of the supply chain (SC) by enhancing capacity building and resource utilisation. Based on the contingency approach, this study offers a novel framework linking SCC to SDGs. The study thus has the potential to help managers and practitioners identify strategic fields of action for achieving SDGs.publishedVersio

Supply chain collaboration and sustainable development goals (SDGs). Teamwork makes achieving SDGs dream work

The global push towards sustainable development has led to an upsurge in academic literature at the juncture of supply chain collaboration (SCC) and sustainability. The present paper aims to map this growing literature to understand how SCC can contribute to the achievement of broader Sustainable Development Goals (SDGs). Via a systematic review of literature (SLR), the paper maps key themes at the intersection of SCC and sustainable development. Relying on nine key themes, the study presents novel insights into the domain of SCC for sustainable development. The results of the SLR reveal that collaborative innovation, collaborative process and product development are key mechanisms driving SCC. However, the extant literature has not devoted much attention to the effectiveness of SCC mechanisms or their performance. Further, the current study posits that more effective SCC strategies can boost the sustainable operational performance of the supply chain (SC) by enhancing capacity building and resource utilisation. Based on the contingency approach, this study offers a novel framework linking SCC to SDGs. The study thus has the potential to help managers and practitioners identify strategic fields of action for achieving SDGs.publishedVersionPaid open acces

Essays in Vertical Agreements and Consumer Behavior

This thesis collects three independent essays and a literature review. Two of them relate to vertical agreements. The first essay explores a retailer's choice in allocating control rights over the decision of retail prices. Results show that retailers adopt a hybrid configuration as a middle ground between two extremes, where pricing decisions are delegated, for all products, either to retailer or manufacturers. The second essay investigates the make-it-or-license-it choice of a brand owner under the risk of moral hazard when licensing the extension product to a third party. Brand licensing emerges as an equilibrium choice under brand dilution (respectively, enhancement) when the consumer perceives a large (small) distance between the extension product and parent brand. The third essay explores the issue of rating bubbles within online feedback systems by means of a field experiment. The analysis found the presence of positive social influence bias, in that high ratings affect the individual rating behavior in a significant way. The last paper is accompanied by a thorough and deep review of the literature about the consequences of online user ratings on product sales/performance (economic dimension) and product adoption/rating behavior (behavioral dimension). The topic is increasingly investigated by academic researchers and industry professionals alike. This overview presents established results and insights as issues for future research