Grocery retail isn’t for the faint of heart. From high levels of competition to razor thin margins, grocers face challenges that rarely have simple solutions. Ultra-fresh products like fruits and vegetables pose some of the largest difficulties because they’re unlike any other category in food retail, but they also comprise one of the largest revenue streams and opportunity centers. Let’s take a look at three common challenges in fruit and vegetable management — and strategies for how to turn those obstacles into opportunities.
Addressing Inventory Drift
Because fruits and vegetables are sometimes sold by weight rather than by unit, grocers must be able to account for any inventory drift that may occur. If you order three cases of crackers, you can rest assured that volume won’t change over time. Three cases of avocados, though, will lose weight and value as they approach spoilage, and carrots lose value when shoppers tear off leafy greens they don’t want to pay for. You can’t re-sell those carrot tops alone, after all.
While no single solution can eliminate it, grocers must account for drift in their inventory projections to maintain service levels. A planning solution that integrates drift data into balance projections will better position planners to place orders that improve availability.
Accounting for Extreme Variability
Such challenges get even harder given a short assortment period. Harvest unpredictability means planners often work with just a one-week horizon for fruits and vegetables. You may expect orange season to begin in late October when planning six months in advance, but plans go out the window if it starts in November — and the same applies for season’s end.
Then consider that orange shipments come from multiple vendors from multiple countries providing multiple variants of orange. Each shipment can fluctuate wildly in amount, size, quality, and price depending on what these vendors can supply — and that’s just for one fruit out of all the fruits and vegetables you stock. Grocers working with long-term order projections need a system that allows them to proactively manage short-term replenishment, ensuring availability while preventing spoilage.
For more predictable products, a time-series forecasting model is quite effective — but even the most accurate historical data is useless in the face of extreme variability. By using a combination of methods, ranging from time-series forecasting to machine learning algorithms, grocers can ensure the most accurate, up-to-date forecasts and replenishment possible. Machine learning can, for example, adjust lifecycle demand patterns to match changing harvest patterns or use localized weather data to connect the dots between warm weather and increased demand for fresh fruits and salads.
The more flexible the planning system, the more quickly planners can adjust to the most recent information available within a short assortment period. A forecasting solution should, for example, let you move a time-series forecast out by weeks if the harvest starts late, while price elasticity can model demand against sell-prices to ensure your order amounts maintain availability and prevent spoilage.
Managing the Complexity of Product Variants
Variability also means fruits and vegetables experience large demand shifts based on availability, quality, and the store’s needs. Low availability is incredibly damaging in fresh, and stores must calibrate purchasing, planogramming, replenishment, and promotions to ensure full shelves. If yellow onions run out, it makes sense to fill that space with red onions, even if it’s not ideal — that’s simple enough. But if a store has too many red onions and not enough yellow, it could promote the red variety to force cannibalization, preventing a stock-out on the low-availability item while mitigating risk of spoilage for the overstocked one.
But one of the greatest challenges — and opportunities — in fresh management might be its massive amount of master data. Few grocers stock a single variant of apple, for example. Most stock a range, such as Red Delicious, Golden Delicious, McIntosh, and Granny Smith — plus the organic variant of each. Of these eight, each item may have multiple product codes that reflect supplier, country of origin, and so on.
Only a best-in-class planning system can effectively manage the volume and complexity of this master data. If you have 20 product codes for cucumbers, the right solution can aggregate those codes and forecast on the group-level rather than forcing planners to forecast each individual product code. It will also let you filter to forecast for locally sourced cucumbers, imported cucumbers, organic cucumbers, and so on.
Transforming the Value Chain
The health of a fresh department is critical to profitability, but suboptimal processes cost grocers millions each year. Of course, no single solution can solve every problem at once, but there’s an enormous, untapped opportunity to capitalize on these challenging ultra-fresh products. To do so, grocers must systematically improve the entire value chain: master your master data, ensure space and replenishment plans can react quickly to changes in demand, improve operational efficiency in stores, and enable longer planning horizons despite short assortment periods.
This kind of value chain transformation calls for close cooperation between a wide range of stakeholders — from supply chain to assortment planners, from planogrammers to purchasing. But those who are willing to redesign and unify their approach to fresh management have an opportunity to turn old challenges into a new competitive advantage.