Gd
G.T. de Meester
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Strategic positioning of MOOS in the retail technology ecosystem
An ecosystem-based diagnosis of actionable shelf insight and its role between shelf reality and store action
This thesis examines how MOOS, a shelf-sensing component provider, can position itself in the retail technology ecosystem around actionable shelfinsight. The study starts from the observation that shelf-level problems such as out-of-stocks and theft are not only visibility problems. Shelf information becomes valuable only when it is reliable, timely, connected to a workflow, owned by a decision-maker, and able to trigger store action. The research uses ecosystem-as-structure as the main theoretical lens. This lens helps analyse the activities, actors, positions, links, and alignment conditions required to turn shelf-condition information into action. Co-innovation risk, adoption-chain risk, and internal readiness are used as supporting concepts. The empirical research combines 41 store visits with a ranking exercise and eight semi-structured expert interviews. The findings show that store teams mainly need reliable signals, timely actionability, and low additional workload. At ecosystem level, shelf sensing depends on fragile links between store teams, IT, loss prevention, point-of-sale systems, workflow systems, partners, and ownership structures. Theft-related use cases are mainly shaped by urgency and response risk, while on-shelf availability use cases depend more strongly on integration and backend closure. The design phase translates this diagnosis into a positioning direction and roadmap. The thesis concludes that MOOS can most credibly be understood as a bounded, partner-ready shelf intelligence and trigger layer between shelf reality and store action. The revised roadmap should be used as a strategic decision-support tool, not as a fixed rollout plan or proof of market success.
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This thesis examines how MOOS, a shelf-sensing component provider, can position itself in the retail technology ecosystem around actionable shelfinsight. The study starts from the observation that shelf-level problems such as out-of-stocks and theft are not only visibility problems. Shelf information becomes valuable only when it is reliable, timely, connected to a workflow, owned by a decision-maker, and able to trigger store action. The research uses ecosystem-as-structure as the main theoretical lens. This lens helps analyse the activities, actors, positions, links, and alignment conditions required to turn shelf-condition information into action. Co-innovation risk, adoption-chain risk, and internal readiness are used as supporting concepts. The empirical research combines 41 store visits with a ranking exercise and eight semi-structured expert interviews. The findings show that store teams mainly need reliable signals, timely actionability, and low additional workload. At ecosystem level, shelf sensing depends on fragile links between store teams, IT, loss prevention, point-of-sale systems, workflow systems, partners, and ownership structures. Theft-related use cases are mainly shaped by urgency and response risk, while on-shelf availability use cases depend more strongly on integration and backend closure. The design phase translates this diagnosis into a positioning direction and roadmap. The thesis concludes that MOOS can most credibly be understood as a bounded, partner-ready shelf intelligence and trigger layer between shelf reality and store action. The revised roadmap should be used as a strategic decision-support tool, not as a fixed rollout plan or proof of market success.