The increase in e-commerce and omnichannel commerce is having a significant impact on the supply chain sector and its warehouses. Fluctuations in demand and priorities, the requirement for value-added service, government regulations and other factors put pressure on the operational decision makers on the warehouse floor and the systems that support them. The increasing complexity of daily warehouse operations means that decision support systems will need to become more sophisticated and intelligent to assist decision makers in real-time. The aim of this literature review is to investigate how decision support in warehousing and distribution operations is examined in the research literature. The objective of this review is to understand how this decision support research can assist operational decision makers to manage and complete the daily volume of work through the warehouse. Fifty-one articles were obtained by the literature search. Articles were categorized by type of warehouse, decision support target, operational task and problem type, research article methodology, architecture and technology. Decision support is examined in almost all areas of warehousing operations with the use of a variety of methods and technologies within the research literature. Most “daily warehouse operational” decision support deals with expertise transfer and reacting to real-time events. This paper highlights the lack of research into human–machine collaboration in adaptive decision support systems to assist warehouse operational decision makers.

Warehouses are being impacted by increasing e-commerce and omni-channel commerce. The design of current WMSs (Warehouse Management Systems) may not be suitable to this mode of operation. The golden rule of material handling is smooth product flow, but there are day-to-day operational issues that occur in the warehouse that can impact this and order fulfilment, resulting in disruptions. Standard operational process is paramount to warehouse operational control but may preclude a dynamic response to real-time operational constraints. The growth of IoT (Internet of Things) sensor and data analytics technology provide new opportunities for designing warehouse management systems that detect and reorganise around real-time constraints to mitigate the impact of day-to-day warehouse operational issues. This paper presents the design and development stage of a design science methodology of an intelligent agent framework for basic warehouse management systems. This framework is distributed, is structured around operational constraints and includes the human operator at operational and decision support levels. An agent based simulation was built to demonstrate the viability of the framework.