Supply chain event management exploits synergies between IT and logistics and refers to the set of methods and technologies used to efficiently integrate events from all actors and processes of the supply chain. In the context of supply chain event management, we examine how events can be used to leverage situation awareness. In our approach, situation awareness is facilitated by providing the capability to detect situations, which are represented as correlations between simple events, complex events and supply chain objects (e.g., suppliers, 3PL companies, retailers and material resources). We introduce a two-phase event correlation method which first correlates simple events into complex events and then events with supply chain objects. We describe how the proposed model has been implemented in a software framework and we conduct evaluation tests to examine its situation detection capabilities.

Introduction

Market uncertainty and intense competition forces organisations to search for ways to improve their responsiveness, effectiveness and quality of products and services to customers. To acquire strategic advantage, increase corporate profit and improve market share, companies need to replace ‗standalone‘ business strategies with strategies that include their trading partners (Iansiti & Levien, 2004). Therefore, they need to collaborate closely with their suppliers or customers and try to improve processes at the level of their supply chain. A supply chain can be described as consisting of all organizations that are involved in the successive stages of design, manufacturing, distribution, marketing and retailing of a product or a service (Holland, 1995). Supply Chain Management (SCM) is one of the key factors aiming to enhance organizational effectiveness (Park et al., 2005) and operational efficiency along the supply chain, from raw materials through first and second-tier suppliers to final customers. As a term, SCM first appeared in the early 1980s and has been in widespread use ever since. It involves the efficient management of materials, processes and information along the whole supply chain. It requires control and coordination activities, as well as information flow between heterogeneous information systems (Ryoo & Kim 2015; Marra et al. 2012). As supply chain processes become highly data intensive, increasingly more events are generated and consumed by supply chain actors, systems and processes. Events can affect the process flow and execution by influencing relevant sub-processes e.g., an order submitted, a product shipped, a transportation truck wracked, raw material stock reduced, etc. The increasing association of events with supply chain processes has led developers and researchers, form both fields of logistics and IT, to coin the new term Supply Chain Event Management (SCEM) referring to the set of methods and technologies used to efficiently integrate all events involved in the planning, production and distribution of the materials and products in the supply chain so as to satisfy customers expectations (Ijioui, Emmerich & Ceyp 2007; Knickle and Kemmeter, 2002). These events may be happening across the various stages of a supply chain. Or, they may be news items, traffic reports, weather reports, or other kinds of data affecting supply chain operations. SCEM benefits from event processing methods and technologies for tracking and processing streams of data about supply chain events and deriving a conclusion from them. Complex Event Processing (CEP) is event processing that combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. The premise of CEP is to provide organizations with a new way to analyse supply chain patterns in realtime and help the business side to make decisions based on a real-time analysis of incoming data streams. Specifically, decision making is facilitated with rules that enable reaction to complex events sampled from across event streams, not just a single stream. But CEP can do more for SCEM than just reacting to streams of data about the status of a supply chain entity, towards determining the situation of a supply chain entity. Consider, for example, an airplane cruising at a certain altitude: in addition to information about its mechanical sub-systems, its situation may include information about current weather conditions, forecasted weather at destination and other significant information that potentially concern the flight and its crew. A CEP-based approach can leverage situational awareness by processing all relevant event data over a wide-enough time window and correlating them with relevant domain knowledge.