Background and Motivation

In today's highly dynamic manufacturing landscape, the relentless market shift toward extreme customer individualization that drives production down to "batch size 1" , combined with radically compressed delivery windows, has escalated the complexity of shop-floor control to unprecedented levels.

For small and medium-sized enterprises (SMEs), this market pressure exposes a critical operational gap. While these companies house massive volumes of valuable production data within their SAP systems , they are frequently unable to leverage it. Historically, the prohibitive manual effort and the highly specialized technical expertise required to implement simulation-based Digital Twins have acted as an insurmountable barrier to entry.

Locked out of advanced, data-driven forecasting, SMEs are often forced to manage volatile, highly complex production environments using rigid, simplistic priority rules like FIFO (First-In, First-Out). This systemic disconnect inevitably leads to cascading production bottlenecks, the buildup of costly excess inventory, and a significantly increased risk of missed delivery deadlines. The TeaTwin project was initiated to dismantle these exact barriers, transforming dormant SAP data into an accessible, automated engine for proactive production planning.