Helped a Leading Plumbing Giant to Discover an Estimated $2 Million Savings in Production Costs Using a Manufacturing Demand Forecasting Solution






Demand Forecasting Solution

An improvement of upto 15% in manufacturing demand forecasting accuracies across key market categories and SKUs, when compared to previous forecasting numbers

  • Improvements to forecasting accuracies projects an estimated $2Million impact by allowing for a refined production schedule
  • The automated framework can be scaled to more SKUs and geographies in the future
  • IBF forecast error impact simulator 

The client is one of the world’s largest manufacturers of bath and kitchen fittings. In the absence of an effective manufacturing demand forecasting model, they faced problems such as less-than-desirable accuracy of forecasted orders, additional cost in Inventory management (Insufficient/ Excessive inventory), inability to meet retailer demands on time, last-minute staffing and operational changes, and expedited interplant and customer delivery.  

The two major problems they faced with their existing manufacturing demand forecasting model were:

  • The forecasting accuracy was low for some SKUs (Range of 44% to 66%) It was a black box solution, so it was unable to breakdown the forecasts to the component factors such as seasonality, promotions and market factors.  

There were also other challenges in data collection, processing and consumption such as:    

  • Data silos (promo, price, market category, etc., all isolated from each other and from different sources, with different, non-standard formats). 
  • The account managers spent a lot of time estimating and making manual adjustments to these forecasts . 

Each manager had his/her own logic to make these adjustments. Hence there was lack of consistency across accounts and there were no means to track or log these adjustments.

They realized that there was a lot of scope for improvement. They wanted a forecasting solution that not only had better accuracy but also was easy to interpret, consume and incorporate, and sought out the application of data science in manufacturing industry.   

TheMathCompany partnered with the client to create an automated framework for manufacturing demand forecasting that builds, evaluates and fine-tunes models based on an exhaustive list of model-parameters combinations.

The automated manufacturing demand forecasting model framework tries all these model-parameter combinations for each SKU and picks the combination that best captures the nature of that SKU. We included multiple forecasting techniques and an exhaustive list of parameters (~5400) across models.  

A Power BI dashboard (for consumption by account managers) is an interactive tool that is easy to interpret and consume and enables the user to visualize forecasts at multiple levels (As granular as SKU level and can be rolled up to a market category level).  

Our solution was designed in such a way that it could be deployed even in case of an expansion (From 250 SKUs in phase 1 to maybe 10000 SKUs in the future). Macroeconomic Indicators of the US Economy were encoded with automated API Feeds serving data to the model pipelines.” 

The data science and demand planning teams can use for evaluating accuracies across SKUs.

Account managers can use to visualize historic and forecast sales units and revenue, and understand the impact of forecasting error on the demand planning at SKU/category levels.

Provides a water-fall breakdown of the contributions of different factors such as promotion, price, baseline and market factors to each week’s forecasted units.

By carefully tuning the performance, we could ensure a scaling of the solution to over 100,000 SKUs. Macroeconomic Indicators of the US Economy were encoded with automated API Feeds serving data to the model pipelines.

The following forecasting techniques were applied:


  • Autoregressive Integrated Moving Average (ARIMA)
  • Unobserved Components Model (UCM)
  • Holt Winters
  • Moving Average


  • Extreme Gradient Boosting (XGBoost)
  • Unobserved Components Model (UCM)

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