We study capacity flexibility via an innovative business practice: On-Demand Warehousing. In this emerging application, a platform connects independent warehouse providers, who are willing to sell excess capacity, with a firm that requires on-demand capacity. On-demand warehousing does not require long-term commitments, but rather provides flexible warehouse capacity, on-demand. Our results highlight how on-demand warehousing allows a firm to absorb demand fluctuations better.

Enabled through recent advances in technology, coupled with the advent of new system providers and decreased price points, automated and robotic order picking solutions evolved as a surging market. As implementation projects and the variety of solutions are rising, managers face the decision which ones to select for their specific business case. We contribute by proposing a mathematical optimization approach that assigns each stock keeping unit the most suitable solution under space constraints.

Online pickers encounter customer interference while picking orders, affecting productivity due to store traffic and queues, and service quality due to picking errors. Within the day, there are less-busy periods when stores resemble a warehouse. We match similar orders picked during peak vs non-peak periods to establish the value of picking during non-busy hours. Our research has implications for online grocers willing to be productive without incurring additional cost of maintaining dark stores