Integrating Artificial Intelligence into Sales and Operations Planning A Case Study for Operational Efficiency at Ascom

Abstract

Artificial Intelligence is increasingly discussed to improve supply chain planning, but its value depends on how it is integrated into existing organizational processes. This thesis investigates how AI can support operational efficiency and quality performance in Sales and Operations Planning at Ascom Global Supply Chain. The study is based on a qualitative case study, where interviews, document analysis, observations and literature review were used to analyze the current S&OP process and its conditions for AI integration. The findings show that Ascom’s S&OP process has a formal monthly structure, but that execution is constrained by fragmented information flows, manual consolidation, spreadsheet dependency, forecast uncertainty and uneven cross-functional coordination. These challenges reduce operational efficiency through recurring rework and manual interpretation. They also weaken quality performance by making planning inputs and outputs less reliable and less fit for use. The main conclusion is that AI should not be understood as a standalone solution to S&OP maturity problems. Instead, AI could create value when used as human- supervised decision support within clearly defined planning routines. Its most relevant contribution is to improve the usability, comparability, validation and traceability of planning information before decisions are made. AI can support activities such as input validation, forecast review, deviation learning and scenario evaluation. However, it cannot by itself resolve weak ownership, unclear handoffs, inconsistent regional routines or conflicting functional incentives. The thesis concludes that successful AI integration in S&OP depends on organizational and process-related conditions as much as on technological capability. Seven enabling conditions are identified, including governance, process alignment, data quality, performance evaluation, implementation capability, trust and long-term adaptability. For Ascom, the most realistic path is therefore to introduce AI gradually through targeted use cases that strengthen the existing S&OP process, while keeping final judgment and accountability with human planners and established decision forums.