General Retailers

Retailers often struggle with inaccurate, incomplete or siloed data across different systems. This lack of a unified, high-quality data foundation makes reliable forecasting impossible. These silos lead to incomplete or outdated datasets that undermine forecast accuracy. Demand volatility (referring to sudden changes in consumer preferences, seasonal spikes or disruptions) makes historical data unreliable and difficult to model accurately without real-time inputs.

However, building and maintaining predictive models requires advanced data science skills and infrastructure that many retailers lack. These models must adapt across regions and product types while handling thousands of SKUs. High implementation costs and resistance to replacing traditional planning processes often delay adoption, limiting the full value of predictive analytics.

Shoppers now expect a consistent retail experience whether they engage online, in-store or via mobile. Discrepancies among channels erode trust. For example, when an item is in stock online but unavailable at the local store. Failing to provide this unified experience, such as showing inconsistent pricing or stock levels across channels, leads to customer frustration and loss of loyalty.

On the fulfillment side, offerings like in-store pickups of online purchases, same-day or next-day delivery, and hassle-free returns add operational complexity. Using physical stores as fulfillment centers also risks disrupting the in-store experience if not managed carefully. This adds complexity to real-time inventory visibility across stores, warehouses and digital platforms.

General retailers face significant challenges when it comes to sustainability because the vast majority of their carbon footprint stems from Scope 3 emissions (emissions that occur primarily in the extended supply chain and outside their direct control). The global and complex nature of these networks makes it challenging to achieve traceability and verify eco-efficient practices such as the ethical sourcing of materials or the use of renewable energy.

At the same time, the higher procurement cost of sustainable materials and limited availability of green infrastructure — such as electric vehicle (EV) charging networks for last-mile delivery — can create a competitive disadvantage if not strategically managed. Investing in green technology (such as EVs, renewable energy and more efficient warehouses) requires higher capital. Regulatory requirements differ by country or region, and there’s often no standardization for sustainability metrics.

Challenges in workforce planning and scheduling stem from ever-shifting demand, legal constraints and workforce expectations. For example, demand variability makes it hard to predict precisely how many staff are needed. Overstaffing will waste labor costs while understaffing risks harming customer service and sales. Retailers also struggle with manual scheduling tools, which can’t dynamically adapt when foot traffic or sales spike unexpectedly.

Complications related to employee availability, turnover and regulatory compliance add more layers of complexity. Retailers usually rely heavily on part-time, student or seasonal workers, each with varying scheduling constraints. High turnover means frequent hiring and training, disrupting scheduling continuity. They must also deal with regulations around work hours, overtime, breaks and fair work weeks, which add legal risk. Unpredictable last-minute absences force managers to scramble for replacements or accept coverage gaps.