Teknosa

Teknosa, Turkey's prominent consumer electronics retailer, operates 205 stores, manages 16,000+ SKUs, and runs 15 distribution centers serving millions of customers across physical, web, and mobile channels. In consumer electronics — a category defined by rapid product cycles, volatile demand, and compressed margins — inventory errors carry immediate revenue consequences. Manual planning processes could not keep pace with demand fluctuations at this scale. Forecasting relied on human judgment rather than data models, producing stock imbalances: excess inventory in slow-moving locations while high-traffic stores suffered lost sales. Localizing assortments to individual store preferences was practically impossible without automation, leaving measurable revenue and customer satisfaction unrealized.

After evaluating multiple technology providers, Teknosa selected invent.ai for its deep retail inventory expertise and margin-driven AI approach. The rollout covered three integrated modules built on machine learning and predictive analytics: Replenishment, which determines optimal restocking quantities and timing across all locations; Transfers, which identifies real-time inventory imbalances and routes stock from slower stores to higher-demand ones to maximize sell-through; and Assortment Planning, which applies predictive models to tailor product selections to each store's local customer profile. The platform replaced spreadsheet-driven manual decisions with continuously updated, system-generated recommendations. Models self-adjust to shifting demand patterns without manual reconfiguration, and performance metrics are tracked in real time — enabling the planning team to transition from operational data entry to strategic oversight.

The invent.ai deployment produced measurable improvements across Teknosa's supply chain and commercial operations:

• Reduced lost sales across physical and digital channels as replenishment accuracy improved
• Higher product availability and inventory turnover across the 205-store network
• Increased overall revenue, driven by better stock positioning and localized assortments
• Streamlined daily planning workload, freeing the team for higher-value strategic work
• Optimized stock transfers that maximized sales potential at minimal operational cost

Technosa's transformation was recognized externally with the IDC Turkey Analytics Transformation of Business Award in the Big Data & Analytics category — a signal that the initiative reshaped not just operations but the company's analytical capabilities.

• Multi-store, multi-DC retailers at scale cannot manage inventory imbalances through manual processes — AI-driven replenishment is a prerequisite for maintaining availability without excess stock.
• Inter-store transfers are an underutilized margin lever: optimizing redistribution of existing inventory can recover lost sales without additional procurement spend.
• Store-level assortment localization requires machine learning infrastructure; manual planning cannot deliver this capability at meaningful scale.
• Vendor partnership model matters alongside technology capability — Teknosa's leadership explicitly credited invent.ai's collaborative implementation approach as a factor in achieving lasting operational change.