Companies operating under gig-contractor models are offering part-time job requiring longer work availability. This ensures quicker delivery & better service quality. Using data from an online retailer we find that workers with similar level of experience perform differently. This is explained through the role of worker availability. At similar levels of experience, workers in high-work group perform better than those in the low-work group after controlling for task and worker characteristics.

New sustainability-marketed firms have emerged in the grocery and consumer packaged goods categories responding to consumers’ rising preferences for sustainable products. Motivated by this trend in the retail industry, we study how the entry of a new firm that sells an assortment of sustainable consumer goods affects the consumers’ channel choices and the existing retailers’ market shares in two different types of product offerings -- packaged and fresh goods.

Slow-moving items constitute a large share of the retail assortment and often result in intermittent orders by the retailer. We estimate retail channel inventories based on prior orders and Point-of-Sales data to predict the timing of future orders. We demonstrate both theoretically and empirically that this an inventory modeling approach outperforms the Croston's method and thus provides a viable alternative to conventional time-series models.

We explore the effect of physical store presence on purchase decisions in omnichannel retailing. We use geolocated customer-level data from a major shoe retailer and study the differential role of physical proximity (number of stores, distance to closest store), as well as service quality (assortment breadth and service level). We find that, while proximity generally increases sales, the service quality provided by the physical store network increases offline sales but decreases online sales.

We examine transaction-level data of an Italian furniture retailer to study channel-specific effects of fulfillment lead time on demand. We find that the showroom channel makes consumers less sensitive to fulfillment lead time than both online and catalog channels. Niche products and experience goods further accentuate the difference of lead time sensitivity between showroom and non-physical channels. Our study highlights the previously-ignored lead time aspect of the physical store’s value.

An omnichannel grocery retailer can fulfill incoming orders either from the dark store or from a brick-and-mortar store. Customers are offered only those products available in the DS and the B&M store. We develop a new business model that offers customers all products available in the DS or the B&M store. We develop a new decision-making mechanism to determine the fulfilment location for each order. We computationally compare our dynamic policies with the omnisciently optimal solution.

The omnichannel retail evolution has changed the role(s) of the store. Applying a quasi-experimental design to data on store openings by an omnichannel consumer electronics retailer, we explore these new store roles and the value they provide to the retailer. We find that, in contrast to small stores, large experience-centric stores substantially benefit online-first retailers through both customer acquisition and activation mechanisms. Category-level analyses reveal the underlying mechanisms.

This paper studies a monopolistic omnichannel retailer's pricing strategies when customers are strategic in making a channel choice and a purchasing decision in the presence of product value uncertainty. We find that charging a uniform price across the sales channels and disclosing it via the online store is optimal. We study the structural properties of the optimal price and the corresponding profit. Finally, we assess the value of omnichannel retailing relative to single-channel counterparts.

We examine how consumers’ fairness concerns affect a multichannel retailer’s pricing strategy. We find that the retailer should maintain consistent price across channels only when the fraction of unfair-adversed consumers is in an intermediate range, and otherwise should charge different channel prices. Moreover, as the fraction of unfair-adversed consumers increases, the retailer may be better off by strategically enlarging the price gap.

We analyze a retailer’s interactions with a national brand manufacturer (NBM) using a setting in which the retailer makes category management and store brand (SB) sourcing decisions and the NBM strategically determines whether it should produce the retailer’s SB. Our analysis sheds light on different SB strategies observed in practice.

The majority of online sales worldwide take place in online marketplaces that connect many sellers and buyers. Online marketplaces adopt algorithmic tools to curate how the different options in an assortment are presented to customers. This paper focuses on one such tool, the Buybox, that algorithmically chooses one option to be presented prominently to customers. Our analyses show that the Buybox produces benefits for customers, sellers, and the marketplace.

Social commerce integrates user interactions and user-generated content with commercial activities in the context of social media platforms. Examples include the "shop" feature on Instagram. A social media user's on-site purchase decision involves a transformation of the mindset from "social" to "shopping" stimulated by the impulse to purchase. We propose a novel choice model to capture users' shopping behavior on social media sites and examine two strategies to sell through social media.

We consider an online retailer and characterize policies, which specify the options (selling price and delivery lead time) to be offered to customers. In the static M-option policy M options are set at the beginning of the planning horizon and the decisions on when to offer them are made dynamically. In dynamic single-option policy the retailer offers a single dynamic option. We propose algorithms to optimize the policies and evaluate their benefits.

Online customers like to receive baskets of grocery orders at a confirmed time. Online retailers increasingly offer customers a choice of leadtime, while actively backordering missing items from the baskets. This fundamentally changes strategic inventory management. We develop new allocation policies that commit to an order upon arrival rather than at the moment the order is due. We give analytical results for the performance of these policies and evaluate them with e-tailer data.

Recently, several omnichannel retailers face the growth of online sales through in-store picking. We tackle a new relevant problem where a picker picks online orders while minimizing customer encounters. The problem is modelled as a Markov decision process and solved using a Q-learning approach. Results on a real retail store suggest that retailers should scale in-store picking without jeopardizing offline customers' experience. However, choosing simplistic picking policies is not sufficient.

Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data. Future research on retail analytics can contribute to existing work by: (i) studying new decisions, (ii) using more advanced analytics, (iii) leveraging new data sources, or (iv) applying more sophisticated methods.

Apparel retailers have generally followed one of two supply chain approaches: the traditional approach (lacks quick response capabilities and produces more durable products) or the fast-fashion approach (has quick response capabilities and produces less durable products). Using an infinite horizon game theoretic model, we compare the profitability and environmental impact of the two approaches. We characterize win-win scenarios (higher profit and lower environmental impact) for both approaches.

We conduct a field experiment to examine whether algorithms should be automated or be used to augment human decision-makers. Unlike the common practice of allowing managers to override the output of algorithms, we allow retail merchants to override the inputs in order to capture the private information they possess. Our results show that the input augmentation model increases profitability by nearly 4% compared to the automation model where merchants were not involved.

Nanostore shopkeepers face complex inventory decisions how to manage their limited cash to acquire products from multiple suppliers. We conduct two studies to understand the drivers of shopkeepers’ behavior. In an empirical study, we find nanostore orders to be severely impacted by supplier visit frequency. In a laboratory experiment, we find that shopkeepers diversify their supply by deviating from optimal replenishment decisions.

Millions of nanostores sell basic items to bottom of the pyramid consumers in emerging markets. Their suppliers struggle with high operational costs because of shopkeepers’ cash constraints. We investigate the impact of trade credits on supply chain performance using difference-in-differences with matching. We find that nanostores receiving a credit transact more often, place larger orders and reject less orders. Trade credits create efficiency gains for suppliers that justify their extension.

We model the competition between online grocery retailers leading to extra emissions from home delivery fleets as a war of attrition. By analyzing the equilibrium strategies of retailers, we estimate the emissions attributable to competition. Our numerical study using data from an online grocery retailer in London shows significant potential for CO2 reduction. Our results can inform policies to reduce the negative environmental impacts of competition in the online grocery retailing sector.

We study the assortment planning problem for a single product category when a retailer faces multi-item purchasing, so-called “n-pack” consumers as introduced by Fox et al (2017). We obtain interesting properties of the product demand functions and establish that the optimal assortment is a popular set. We evaluate our model on a real-life data set and find that the demand proportions predicted by our model can be made extremely close to the actual proportion of sales.

Determining the assortment and inventory levels based on their shelf life is essential for retailers to maximize profits while avoiding food waste. Assortment and inventory decisions are interrelated by the limited shelf space. A joint approach is needed that defines the assortment size and the maximum possible inventory levels while considering product ages. We develop the first multi-period approach to integrate product shelf life and product outdating.

Motivated by the product ranking in online retail, we introduce and study the "sequential submodular maximization problem". Given an ordered list of products, a user inspects first $k$ products in the list for a $k$ drawn from a given distribution, and decides whether to purchase an item based on a choice model. The goal is to find an ordering maximizing the probability of purchase. We design near-optimal approximation algorithms for this problem, with or without group fairness constraints.