Human-Machine Interaction Design for Freight Planning Systems

With the increased application of cognitive computing across the spectrum of industries, companies strive to ready their people and machines for future system change. Based on resource constraints, business needs, and the speed of change, many companies may opt for system augmentation rather than the adoption of entirely new systems. At the same time, changes in technology are increasing at paces never before realized. Against this backdrop, human actors and machines are working together interactively in new and increasing ways. Further, recent business model innovations, particularly in the retail space, have cast focus on logistics execution as a potential major competitive advantage.

In this context, we considered the conceptual question of how best to iteratively improve a logistics planning system, which is composed of both human and machine actors, to reduce transportation and labor costs and increase the ability of the organization to think and act strategically. In order to front these current technological realities – the need to stage for agent based systems and cognitive computing, the likelihood of system retrofit over rebuild, the ever increasing rate of change, and the rapid intertwining of human and machine roles – we proposed using human-machine interaction (HMI) design paradigms to retrofit an existing loosely coupled human–machine planning system. While HMI principles are normally applied to tightly coupled systems such as jet airplanes, the HMI architectural design applied novelly in this case showed significant application to an existing loosely coupled planning system. In addition to meeting the realities of today’s competitive landscape, the developed HMI framework is tailored to a retrofit situation and also meets resiliency considerations. That novel conceptual proposal of HMI frameworks to an existing loosely coupled joint cognitive planning system shows tremendous promise to address these imminent realities. With regards to the particular freight planning system considered, 71% of manual interventions were caused by the wrong sourcing facility being assigned to supply pallets to a customer. The remaining intervention causes were carrier changes 18%, customer restrictions 9%, and one change prompted by a data discrepancy. Further, at a conceptual level, the application of HMI frameworks to an existing freight planning system was effective at isolating data and alignment incongruencies, displayed lower communication costs than recurrent system rework processes, and tethered well with system resiliency factors.