Beyond forecast: Chief Technology Officer Ahmet Kayıran talks how RNV.ai manages retail in real-time

In the retail sector, forecasting is no longer just about numerical analysis - it has evolved into a strategic competitive advantage that blends speed, adaptability, and operational intelligence.

In today’s landscape, marked by volatile consumer demand, real-time market dynamics, and widespread uncertainty, powerful forecasting systems go beyond historical data: they capture environmental signals in real time, learn rapidly, and continuously reshape themselves.

With this vision, RNV.ai is redefining the boundaries of forecasting - combining data driven decision-making, continuously learning AI algorithms, and the generation of meaningful insights. Going beyond traditional systems, RNV.ai not only delivers recommendations but also clearly reveals the underlying cause and effect relationships, empowering brands with operational agility.

In the summer 2025 issue of Bi’Sektör Magazine, we discussed the most common areas where forecasting errors occur in retail, how the right system creates advantages not only in inventory management but also across customer satisfaction, sales, cost, and time - and how RNV.ai is leading a transformation in the industry.

Q: In retail, forecasting has become more than just a tech capability — it’s a competitive advantage. For you, does a good forecasting system hinge on accuracy, or on adaptability?

A: Forecasting today is no longer merely a technological capability - it’s a strategic advantage. Although technological advancements and data smart algorithms are increasingly common, there’s often a gap between the results generated and actual decision-making processes.

In companies struggling to make decisions under uncertainty, a good forecasting system should not only produce highly accurate outputs but also adapt swiftly to changing conditions. Especially in fast-paced markets like retail, being “correct” isn’t enough - what makes the difference is how quickly models can be updated and adapted to new conditions.

For us, a powerful forecasting system is one that captures environmental variables in real-time and continuously reshapes itself. Today, dozens of inputs - stock levels, customer demand, campaign effects - continuously feed new information into the model. If your system can’t handle this flow, your accuracy quickly becomes obsolete.

Thus, I’d answer: while accuracy is an important metric, adaptability ensures the sustainability of the system.

Q: What improvements do retailers typically see when working with RNV.ai? Do fewer order errors happen, does inventory turnover improve, or do only decision processes speed up?

A: Retailers working with us usually notice that they improve more than they expected — across multiple areas simultaneously. What starts as “smart inventory management” gradually extends to “enhanced customer satisfaction.” That’s because RNV.ai systems not only speed up decision-making but also make those decisions more agile and precise.

Yes, order errors decrease. Yes, inventory turnover improves. But the real impact lies here: decision quality becomes centralized, and operational processes across the board improve. KPIs such as warehouse dispatch performance, product shelf life, store replenishment effectiveness, time-to-sale for incoming stock, lost sales due to misplanning, excess inventory, and display issues - all see improvement. We provide an end-to-end system that is fully measurable and trackable.

Another key difference is that we don’t just offer a forecast - we explain the why. Instead of asking “should we send ten units there?”, users get answers like “why ten units are needed, which data supports this recommendation, and what you gain from it.” We believe this reflects the new definition of operational intelligence.

Q: “Collecting data for efficiency isn’t enough, you must translate it into the system’s language.” How do you enable this transformation for brands? How do you overcome resistance in transitioning from manual to automated systems?

A: Actually, for brands, the real challenge is not gathering data - it’s transforming data into a decision ready language. Typically, data lives outside systems - in spreadsheets, emails, field notes… when data is recorded, it’s easy to systematise, but many insights are internally processed by individuals and not formally documented.

So we begin by focusing on both recorded and informal data, then plan how to formalise that data. In this process we map data sources, note frequency, and establish a data ownership framework. Then we convert this data into a mathematical language the system can understand: normalising, labelling, building relational structures. Finally, we process it through our models and connect it with decision-makers - augmenting workflows as decision support and expert systems.

When moving from manual to automated systems, resistance often arises because users fear losing control. That’s why we design automation to assist, not replace humans. Our recommendation systems also explain the reasons behind decisions. Users can see not only what should be done but why. As trust grows, resistance fades and turns into collaborative engagement.

Q: Near-future demand forecasting is increasingly important. How do your AI enabled systems predict the immediate future? How often do they update? How do they adapt?

A: Merely looking at historical data or knowing “what’s happening today” is now insufficient. We need to anticipate tomorrow.

In our systems, near-future forecasts run not just on past data but on real-time behavioral signals, market pulse, local shifts, pricing and promotional inputs. For example, when a product’s turnover rate changes in a store, it’s interpreted not just as “low stock,” but as a “change in demand pattern” signal.

We monitor such changes daily, not weekly, because missing a week in retail means missing a season. Updates involve not just retraining but context specific shifts: models reprioritise variables, adjust feature importance.

We don’t use AI only to forecast based on historical data - we complement forecasting algorithms with optimisation tools that adapt to uncertain environments, offer scenario-based modeling, and propose solution sets satisfying all possible outcomes.

Q: Many chains still rely on regional managers’ intuition for ordering. How should efficiency and intuitive decisions be balanced? How can technology optimise this?

It’s a very real situation. Many large chains still make order decisions based on “I know that region.” But the real question is: knowing versus feeling. Experience is certainly valuable, but if it isn’t systematic, it’s not sustainable.

We don’t replace intuition - we strengthen it with data. For instance, when the system generates an order recommendation, it tells the user: “This recommendation worked previously on this specific behavior.” So decision-making isn’t just about numbers - it has context and narrative.

Technology here strikes a balance: it doesn’t exclude intuition but makes it measurable and testable. Users sometimes override the system; we record and feed those interventions back. Thus the system learns over time, enabling both efficiency and expert insight to coexist.

Q: Which KPIs do you recommend retailers track to measure the benefits from your systems? For example: stock-out time, shrinkage rate, product availability score?

A: At RNV.ai, we go beyond delivering forecasting accuracy. We also observe how forecast accuracy impacts corporate culture, operations, and profitability - crucial both for clarifying ROI and making AI’s real effect visible.

We track metrics across operational, financial, and decision-quality dimensions: stock holding time, inventory turnover, stock-out rate, product availability, etc. Plus, our self-service BI tools allow end users to create their own data sets and reports.

Q: As summer 2025 begins, which product groups see the most forecasting errors? How do demand forecasting systems adapt to such seasonal fluctuations?

A: The year 2025 has been a period when retail has been more sensitive than ever to macroeconomic factors. Consumer purchasing behavior changed significantly - decisions once made easily became delayed and scrutinised.

Special holiday promotions underperformed, and campaigns no longer drew the same reaction. It wasn’t just economic slowdown - nature driven factors also challenged retailers: for instance, a delayed summer season or regionally extended heat waves led to large deviations in seasonal launch timing.

These changes present serious problems for traditional forecasting systems, which still rely on old behaviour patterns - leading to underperformance. We address these issues with dynamic forecast adaptation. When the gap between forecasts and actual sales for certain product groups becomes meaningful, models are retrained with different feature sets.

Declines are interpreted via causality-based algorithms, and feature weightings are adjusted accordingly. As a result, I can confidently say: in this period, the most successful brands aren’t those with the highest accuracy - they are those that adapt fastest. RNV.ai systems are designed for exactly this flexibility. We read changes, recognise signals, and recalculate recommendations.