Grocery delivery app saw 10x faster time to market

Use Case

Demand forecasting

With internal data science teams fully engaged with other company projects, this app maker had difficulty building and maintaining an effective demand forecasting model. With Pecan, they transformed their raw sales and inventory data into a powerful, accurate model in just days instead of months.

Industry: E-Commerce

Company Size: Operates in 26 cities with thousands of products and over 2K employees

Solution: Forecast demand at SKU level across cities

Platform Use Case: Demand forecasting

Data Stack: BigQuery

10x

faster time-to-market

12,000

partner stores’ data included in models

80-90%

accuracy for forecasting top revenue categories

Challenge

Over 12,000 suppliers and 26 cities of data

This grocery delivery app company needed to build and maintain a demand forecast model that could accurately predict sales volume at the SKU level. In addition to needing a granular forecast for each product, the company also needed to anticipate demand for each item across more than 26 cities with over 12,000 suppliers. This complex modeling challenge made it impossible to build and deploy a scalable solution in-house.

The forecasting challenge had resulted in overstock of certain products at some stores, and understock of other products elsewhere — leading to massive inventory costs and a poor customer experience.

Solution

From raw sales and SKU data to accurate predictions

Given the scope and complexity of this client’s supply chain, the desired predictive analytics project seemed insurmountable. Yet Pecan accomplished the task in just a few days, beating the existing models created over several months in both accuracy and actionability.

The predictions of this model were 80-90% accurate, partially due to Pecan’s data enrichment with regional weather, traffic, and economic data.

Results

Unprecedented demand forecasting—and no more stockouts

With the Pecan platform, the customer deployed their models 10x faster without compromising model accuracy and usability. Since the business team could rely on Pecan to solve their long-awaited demand forecasting needs, the internal data science team could focus on custom business questions and use cases.

Pecan’s models consistently performed accurately, even with dozens of forecasts that spanned varying time horizons, configurations, business aspects, frequency, and other factors over several weeks.

Within the highest-value product categories, Pecan accurately forecast 50% of the SKUs with under 20% prediction error. Additionally, by leveraging Pecan’s proprietary data enrichment assets, the customer revealed unexpected business insights about the strongest predictors of demand for their top-priority SKUs.

Ultimately, the company saw overstock instances reduced by several orders of magnitude through using Pecan’s AI-powered predictive analytics to forecast demand. Pecan’s forecasts advanced the company’s efficacy and supply capability. Most importantly, the accurate, granular forecasts improved their customers’ experience by minimizing stockouts to historical low levels.

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