From a tactical supply chain planning perspective: An Event-Driven Analytics for successfully managing product transitions

This project is a Ph.D research project funded by MADE FAST to cooperate with international companies (Velux and Danfoss Drives). Nowadays, in the era of Industrial 4.0, firms are transforming & upgrading their products and service by integrating digital/data-relevant attributes into their products to extend product portfolios and brand line for satisfying customers’ preference. However, new and old product portfolio are substitutable because of product attributes similarity and variant overlapping. Substitution effect, also referred to cannibalization effect, could push some customers to shift their preferences from old to new, or new to used products. Managing product cannibalization is complex. A lot of studies on cannibalization effect in the extant literature are in Marketing domain that were build upon assumptions. The marketing literature, given the attributes similarity and variants overlapping between new products and old products, suggests that cannibalization effect is significant. Nowadays, the amount of data available for analysis and information flows are growing exponentially. Appropriately taking advantage of data with advanced analytical techniques could benefit to the decision-making. In this study, we defined cannibalization/substitution effect is an event amid product transition. Cannibalization effect enlarge the difficulty of synchronizing demand and supply due to higher variability and unpredictability that are driven by complexity and ruin the forecast performance owing to lack of fact-based information. We will not work on how to predict cannibalization. However, product transition is a driver of reengineering supply chains and planning systems. we explore how data should be managed to fit in the advanced data analytics tools under a specific purpose and what data are valuable for an analysis with specific purpose, then exploring their mechanism at the tactical planning level. In this study, we will contribute to relevance and rigor. From relevance perspective, we demonstrate design artifacts on how to manage, configure and process data based on a specific business needs. From rigor perspective, we will build a a conceptual framework based on what we have
implemented and received feedback