Home delivery at its smartest

Increased efficiency with artificial intelligence

Customer

MENY

Case

The preference for home delivery of groceries over physical shopping is increasing among Norwegians. MENY's new machine learning system has revolutionized the distribution of resources, providing precision and efficiency.

  • Year

    2022–ongoing

    Area

    • Technology

Challenge

The preference for home delivery of groceries over physical shopping is increasingamong Norwegians. Consumers today prioritize personalization and expect fast and efficient delivery straight to their doors.

MENY, in collaboration with Forte Digital, aimed to optimize their logistics and streamline their delivery and picking processes to meet these demands.

However, MENY faced challenges with capacity planning for their online order processing and delivery operations. Inefficient resource allocation and frequent over- or under-staffing resulted from the difficulty in predicting the expected number of orders accurately.

To overcome these challenges, the objective was to extract maximum value from historical data and provide decision-makers with valuable insights for improving operational efficiency.

Solution

Forte Digital collaborated with MENY to create a prediction system using machine learning. The system forecasts the number of orders by analyzing historical data, campaigns, national holidays, and other relevant factors. Advanced algorithms are used to analyze past order patterns and identify trends and patterns that optimize resource allocation.

The operations team utilizes these forecasts to plan the capacity for each order processing window. This new insight into capacity planning allows MENY to optimize their operations efficiently.

The graph depicts predictions for a "picking window" between 1400-1600 in a MENY store. The predictions align remarkably well with the actual number of orders, demonstrating high precision in the forecasting model.

Results

MENY's machine learning system has revolutionized the distribution of resources, providing precision and efficiency. The system aids MENY in planning the number of employees needed to process each order, reducing the occurrence of over- or under-staffing.

This can now be accomplished up to 20 weeks in advance, providing predictability for employees and third-party suppliers. Customers benefit from enhanced flexibility and efficiency when ordering goods, ultimately resulting in a more seamless shopping experience.

Moreover, MENY can optimize delivery operations by ordering an appropriate number of cars, leading to cost savings and improved operational efficiency. Since its launch, the machine learning system has boosted MENY's sales figures significantly, and customer satisfaction surveys indicate that users are content with the flexibility and efficiency created by the system.

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