We study if discounts for products at their expiry dates can reduce waste and increase profit. In a Markovian inventory model we obtain combinatorial expressions for the transition rates, but with no informative stationary distribution. In a regime where customer arrivals and order-up-to-level grow large, we obtain via Donsker's theorem expressions for waste and profit. In an MNL setting we prove that optimizing regular prices and discounts always reduces waste compared to not giving discounts.

Consumers often exhibit showrooming behaviour in which they visit a brick-and-mortar (BM) store to gather product information but complete the product purchase in the online channel. Many BM retailers carry exclusive store brand products. We examine how consumer showrooming interacts with a BM retailer's exclusive store brand strategy. Contrary to common notion, our findings reveal that the BM retailer can benefit from consumer showrooming when it carries an exclusive store brand.

We investigate whether horizontal competition drives the increase of the number of product portfolio varieties of self-interested firms that compete for demand through their product portfolio sets. We characterize the equilibrium, in both, the complete information game and the incomplete information game and prove that neither firms have the incentive to go beyond its monopolistic choice. Moreover, we show that proliferation may fail as an entry barrier when the game is played a la Stackelberg.

Many manufacturers frequently introduce new products and retire low-performing SKUs. These portfolio adjustments cause a demand shock for existing products. We study the impact of these demand shocks on the bullwhip effect for existing products. We prove that retiring products always increase the bullwhip effect for existing SKUs while introducing new products does not necessarily lead to this increase. We also study the behavior of the bullwhip effect as function of time remaining to the shock.

We propose a three-step demand forecasting framework that combines the expert's knowledge of the market with the machine learning algorithm's ability to leverage historical information to forecast seasonal demand for rapid innovation products. Using data from Canyon Bicycles, we find a 29% reduction in forecast error (measured by WMAPE) over a purely judgmental forecast.

We present our work with a publicly listed food manufacturer in the UK and a private equity firm that invests in the heavy equipment industry to improve their procurement practice. We used natural language processing and machine learning to organize their vast unstructured procurement data and to classify the suppliers and products into hierarchical categories. With our accompanying decision support tool, we identify the procurement inefficiencies and provides request-for-quote (RFQ) targets.