Machine learning is revolutionizing demand forecasting, driving superhuman accuracy, efficiency, and decision-making in manufacturing.
Accurate demand forecasting is increasingly important in today's cost-conscious market. But traditional methods are time-consuming and often inaccurate. Enter machine learning (ML), a breakthrough set of technologies that can transform your demand forecasting process and outcomes.
Charles Wright, Director of Data and AI at Columbus, discusses the use of ML in demand forecasting, its benefits, and how manufacturers can harness its power.
Understanding Demand Forecasts
Demand forecasting predicts future customer demand for products and services. Accurate forecasts enable businesses to maximize sales, manage inventory levels, plan production, and allocate resources efficiently.
Yet many organizations struggle with traditional forecasting methods, leading to costly errors and inefficiencies, which typically come in the form of Excel spreadsheets or expensive, limited demand planning tools.
Problems with conventional demand forecasting
Traditional demand forecasting methods present challenges that modern ML techniques can avoid. These challenges include:
- Time-consuming forecasting leaves little room for rapid adjustment
- Inaccurate forecasts lead to costly overstocks and understocks
- Forecasts that don't take into account external events and market changes reduce your ability to adapt to unexpected situations.
- High costs associated with maintaining demand planning teams and expensive forecasting tools
The superhuman advantage of machine learning
ML models take historical sales and inventory data and use it to forecast future demand numbers. This type of forecasting is very difficult for humans to do because it requires recognizing and understanding trends in complex, large amounts of data.
Machine learning regularly achieves superhuman results and can be enhanced by integrating even more data: for example, third-party data like weather patterns and holiday schedules that commonly influence purchasing behavior and therefore demand.
Benefits of cloud-based machine learning solutions
Building a customized, cloud-based ML solution offers a number of advantages over obtaining an off-the-shelf solution:
Superior accuracy
Customized ML tools are built using a variety of statistical, traditional machine learning, and deep learning based approaches. These models are regularly updated using a cloud-based ML operational framework to learn (tune and train) from the latest data.
As data changes over time, the ML model that delivers the best results may also change. Therefore, it is important to always have access to the best model to generate predictions. Off-the-shelf ML tools often offer only a few models and offer limited tuning options.
Cost-effective
Compared to expensive demand planning tools, cloud-based ML solutions are often more affordable: maintaining a custom implementation can cost less than $10,000 per year, offering significant savings compared to off-the-shelf tools that can cost hundreds of thousands of dollars.
Leverage existing data
ML used for demand forecasting leverages data that organizations already collect, such as sales and inventory data, often pulled from ERP systems. This data forms the basis for highly accurate forecasts.
Time efficiency
Because the data from ML models is already stored in your organization, data collection and processing can be automated, meaning users no longer need to manually extract data and enter it into Excel sheets, formulas, and macros.
Augmented Decision Making
ML models help users understand the relationship between the predicted numbers and the various data sets provided, allowing them to manually adjust the forecasts to make more informed predictions. Decision-making can be further enhanced by providing the model with additional data, such as macroeconomic or business-specific sales or marketing data.
Columbus Case Study
Columbus recently worked with an FMCG logistics company that was spending over £350,000 a year on demand planning, including a team of six employees, but was still losing £5 million in wasted stock.
Columbus produced an ML solution that outperformed their existing team and tools in 80% of their products, improving the accuracy of these products by up to 30%, and saving them potential costs of over £500,000 per year.
Leveraging the superhuman advantages of machine learning
Machine learning-powered demand forecasting transforms demand planning, bringing superhuman accuracy and efficiency. By leveraging existing data and integrating third-party information, organizations can achieve accurate forecasts, leading to better decision-making and resource allocation.
Ready to take your demand forecasting to the next level? Explore the possibilities of machine learning in Columbus [email protected].
Charles Wright, director of data and AI at Columbus
Charles has first-hand knowledge of leveraging AI in manufacturing, and his familiarity with the latest cloud technologies and machine learning makes him ideally suited to lead manufacturers on their journey to AI maturity.
contact address: [email protected]