Machine Learning Within Demand Planning: How Manufacturers Can Improve Forecasts
Demand planning is one of the key applications for manufacturers, with demand solutions accounting for just under a third of a $2 billion plus market. Manufacturers now have vast supplies of data at their disposal that should, in theory, make forecasting easier. However, demand planning still remains to some extent a game of estimations. Outdated software and historical information makes it hard to plan with precision, while the explosion of data presents a huge challenge for those not equipped to deal with its diversity and volume.
Cue the power of machine learning. Although utilised by manufacturers for some time now, the race is now on to leverage its capabilities for demand planning in order to improve the customer experience, and produce forecasts much more precise than possible with traditional techniques. Many manufacturers are realising the huge potential to optimise inventory management, automate processes, and increase agility when adapting to supply chain changes.
Solving complex scenarios
Machine learning is a key component when it comes to forecasting and scheduling, especially effective in difficult scenarios. It allows planners to do a much better job of forecasting complex situations. By leveraging the skills, experience, and knowledge of planning experts in a highly efficient way across a wide spectrum of data, it can improve forecasts in an iterative, ongoing manner. And since demand planning is all about the visibility of future demand, machine learning makes for the perfect technology partnership; enabling planners to consider existing orders, future needs, and minimise stock outages.
Taking more data into consideration
Capable of creating a data warehouse relevant to your needs, machine learning makes historical analysis easy. It allows you to query, analyse, and use this information in order to predict future demand. Traditional forecasting methods project impending sales from previous sales peaks, but often overlook other considerations such as brand value, features, and information on sales channels. Using machine learning enables more data to be fused for the purposes of forecasting. By including history evaluation, rebates, and other issues under administrative control, predictions can be augmented at the level of any specific product.
Data fit for predictive analysis
Manufacturers are increasingly looking to predictive analytics as a way to differentiate themselves. They are typically very good at producing data, but that data can contain large numbers of anomalies. Analytics maturity is an ongoing journey, one that is first and foremost, best supported by real-time accurate metrics – before advancing onto predictive analytics. Machine learning fills the data gaps, cleansing and normalising information to provide an accurate data set on which to perform predictive analysis. By stripping out the inaccuracies, you get a more reliable, stable dataset from which you can make predictions.
Machine learning within Microsoft Dynamics 365
Microsoft Dynamics 365 offers machine learning as standard, available through master planning functionality. Demand forecasting experiments, example data sets which include a range of forecasting algorithms from standard linear regression to more complex one, are automatically integrated with Dynamics 365 making enabling straight forward use and removing the need for any coding. Able to meet business requirements, be published as a web service on Azure, and used to generate demand forecasts, experiments are available for download if a Dynamics 365 subscription has been purchased for a production planner at enterprise level user.
The deployment of intelligent automation is rapidly boosting the mental power of today’s manufacturing workforce. Machine learning within demand planning has the ability to elevate planners to a more strategic role, enabling them to synthesise vast amounts of information and respond quickly to complex queries. This in turn will give manufacturers the opportunity to work smarter, be more adaptive to change, ingesting and analysing larger amounts of forecast information for super-fast forecast results.