This manufacturing client struggled to make supply chain decisions based on data. The majority of their products include electronic components for various industrial applications that are purchased in large quantities by consumers and distributors. The large order sizes, lack of seasonality and long lead times added to the challenge of forecasting.
A manufacturer wanted to make better supply chain decisions but didn’t have accurate manufacturing demand forecasting data.
Ollion built an advanced manufacturing demand forecasting model using machine learning to forecast demand by week and month.
The models and process change enabled an 8% increase in accuracy for weekly sales demands and a 12% increase in monthly sales demand accuracy.
An international manufacturer was struggling to make supply chain decisions due to inaccurate manufacturing demand forecasting, the variability of their product demand and unknowns in their supply chain. A large majority of their products include electronic components for various industrial applications that are purchased in large quantities by both consumers and distributors. The nature of large order sizes, lack of any recognizable seasonality, and long lead times added to the challenge of forecasting.
Prior to this engagement, forecasting was done at an individual SKU level based on an average of the prior day, week, month, quarter and year sales quantities. Numbers that didn’t reflect any other influences or nuances resulted in forecasts that could not be trusted, lengthy manual decision-making processes and lost profits.
Ollion built advanced manufacturing demand forecasting using machine learning models to forecast demand by week and month for the client’s largest and most volatile products. Ollion introduced a process that utilized statistical packages and machine learning methods in R to drive more effective forecasts. We underwent a process of feature selection, model analysis and outlier analysis to develop a set of time series models that decomposed changing trends and volatility of the client’s past sales demand.
Our process and models enabled an 8% increase in accuracy for weekly sales to demand and a 12% increase in monthly sales demand accuracy for the largest of the client’s product offerings. The impact of these resulting baseline metrics alone was such that the client has undergone an initiative to implement similar measures across all of their divisions and the components of their supply chain.