White-glove service delivery: A quantitative analysis.

White-Glove Service (WGS) is an emerging business model of last-mile service delivery with the intersection of omnichannel retailing, demand-driven supply chain, and the shared economic activity of crowdsourcing by a multiskilled workforce. Last-mile delivery (LMD) is the last leg of a journey comprising the movement of goods from a transportation hub to a final destination. Today's customers expect convenience, speed, consistency, great mobile experience, and assistance from knowledgeable, competent company employees. WGS is a recent business buzzword characterizing the response to such expectations. To provide a WGS of LMD to customers, many factors should be considered. LMD services often require multiple-expertise and flexible workforce to deliver the diverse and customized needs of the customers. The extant literature shows that academic researchers have only considered limited aspects of WGS delivery. In this paper, we present two comprehensive analytical models based on mixed integer program for WGS delivery. We show that the problems are NP-hard in the strong sense. By means of extensive computational experiment, we investigate the trade-offs of two different sourcing decisions with differing business scenarios for the large-scale problems in terms of multiskilled worker availability and last-minute changes in a customer's preference. • Present a framework of the White Glove Service as an emerging last mile home delivery. • Formulate an MIP model to accommodate several important objectives in WGS. • Provide flexible scheduling of insourcing and crowdsourcing with multi-skilled workers. scenarios. • Find workload balance directly effecting the net profit and capacity utilization efficiency. • Show robustness of proposed models to withstand different business environments. [ABSTRACT FROM AUTHOR]