September 18, 2023

Transition from budget-based planning towards one-year rolling horizon approach

How to increase overall competition

Transition from budget-based planning towards one-year rolling horizon approach

At a glance

Challenge

In a competitive agricultural thread market, the client adopted a mass-customization strategy, tailoring product packages for individual clients. However, the existing budgeting process was insufficient for managing such high customization. With clients' preferences changing throughout the year and no decision support system to update predictions, stock levels have increased to meet fulfillment demands.

Solution

The solution combines a forecasting algorithm and a tactical production planning model. The forecasting algorithm uses an ensemble of time-series and machine learning methods, incorporating budget and advance demand information. Monthly updates ensure proactive sales predictions. The production planning model categorizes products as Make-to-Order (MTO) or Make-to-Stock (MTS) based on factors like client concentration and demand uncertainty. A rolling optimization algorithm is then used monthly to generate the most profitable 12-month production plan.

Results

Our holistic solution reduces stock levels by 17.5% and machine utilization by 11%, while maintaining customer service levels. The yearly planning horizon improves production lot sizes. The decision support system aggregates relevant data in a flexible interface, enabling a more sophisticated, data-driven approach that allows the team to focus on higher-value activities.

Challenge

In a competitive agricultural thread market, the client adopted a mass-customization strategy, tailoring product packages for individual clients. However, the existing budgeting process was insufficient for managing such high customization. With clients' preferences changing throughout the year and no decision support system to update predictions, stock levels have increased to meet fulfillment demands.

Approach

Solution

The solution combines a forecasting algorithm and a tactical production planning model. The forecasting algorithm uses an ensemble of time-series and machine learning methods, incorporating budget and advance demand information. Monthly updates ensure proactive sales predictions. The production planning model categorizes products as Make-to-Order (MTO) or Make-to-Stock (MTS) based on factors like client concentration and demand uncertainty. A rolling optimization algorithm is then used monthly to generate the most profitable 12-month production plan.

Results

Our holistic solution reduces stock levels by 17.5% and machine utilization by 11%, while maintaining customer service levels. The yearly planning horizon improves production lot sizes. The decision support system aggregates relevant data in a flexible interface, enabling a more sophisticated, data-driven approach that allows the team to focus on higher-value activities.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

In an ever more competitive market, our client in the agricultural thread market has been following a mass-customization strategy by tailoring the package of its products for each client.

The budgeting process, previously used to define production needs, is no longer enough to address such a degree of customization. Moreover, clients’ preferences change throughout the year, and there is no decision support system to review the initial predictions. Hence, stock levels have been rising to accommodate these fulfillment challenges.

r solution encompasses a forecasting algorithm and a tactical production planning model.

The forecasting algorithm relies on an ensemble of a time-series forecast with a machine learning method capable of accounting for the budget and advance demand information.

The monthly update of the forecasts guarantees a more proactive revision of the sales predictions. The tactical production planning model works hierarchically. Information such as sales volume, client concentration, and demand uncertainty is first used to make a yearly categorization of the products as Make-to-Order (MTO) or Make-to-Stock (MTS), with further guidelines whether an MTS product should be produced to the final packaging or as a semi-finished product.

Then, a rolling optimization algorithm is monthly employed to prescribe the most profitable production plan within a 12 months’ horizon.

By adopting our holistic solution, our clients may expect a reduction of stocks by 17,5% and a decrease in machine utilization by 11% without jeopardizing customers’ service level.

The anticipation given by the yearly planning horizon leads to an increase in the production lot sizes. From a qualitative perspective, our decision support system aggregates all the relevant information in a flexible and accessible interface.
A more sophisticated and data-driven approach allows the team to allocate its time to more value-adding activities in the process.

Our AI-generated summary

Our AI-generated summary

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