Case studies

Demand Forecasting to Improve Service Delivery

Combined forecasts from different algorithms were done to improve demand forecasting accuracy for a leading home appliances and repair services provider. Demand forecasting accuracy went up to 92% from 65%, reducing lead time by 20%. A 4% increase in First Time Completes resulted in incremental revenue of $5 million per annum due to reduced cancellations.

The Problem
Inaccurate demand plans result in poor service levels, which was at a low 45%.

The Before State
Rudimentary forecasting process with each function operating with a different view of overall demand resulted in inefficient service delivery process.

LatentView Solution
Combined forecasts from different algorithms (ARIMA, ETS, TBATS or Holt-Winters) to boost accuracy. Forecasts were evaluated monthly and recalibrated as appropriate.

The After State
Demand forecasting accuracy went up to 92% from 65% reducing lead time by 20%. 4% increase in First Time Completes resulting in an incremental revenue of $5 million per annum due to reduced cancellations.

Download the case study

demand forecasting to improve service delivery 1
Share on linkedin
Share on twitter
Share on facebook