Superhuman forecasting for manufacturing business efficiencies using machine learning
Machine learning is revolutionising demand forecasting to drive superhuman accuracy, efficiency and decision-making in manufacturing businesses.
In today’s cost-conscious markets, the importance of accurate demand forecasting is increasing. However traditional methods are often slow and inaccurate. Enter machine learning (ML), a game-changing set of technologies that can transform demand forecasting processes and results.
Charles Wright, Director of Data and AI at Columbus, explores the use of ML in demand forecasting, its benefits, and how manufacturers can harness its power.
Understanding demand forecasting
Demand forecasting predicts future customer demand for products or services. Accurate forecasts enable businesses to maximise sales, manage inventory levels, plan production and allocate resources efficiently.
However, many organisations struggle by with traditional forecasting methods, leading to costly errors and inefficiencies. This typically comes in the form of Excel spreadsheets or expensive but limited Demand Planning tools.
The pain points of traditional demand forecasting
Traditional demand forecasting methods bring challenges that can be avoided with updated ML methods. These may include:
- Time-consuming forecasts, leaving little room for quick adjustments
- Inaccurate forecasts, leading to costly overstocking or understocking
- Forecasts which fail to account for external events and market changes, reducing the ability to adapt to unforeseen circumstances
- The high costs that come with maintaining a demand planning team and expensive forecasting tools
The superhuman advantage of Machine Learning
ML models take historical sales and inventory data and use it to predict future demand numbers. This type of prediction is very hard for humans as it involves recognising and understanding trends in complex and high-volume data.
Regularly machine learning achieves superhuman results and it can be enhanced by integrating even more data. For example, third-party data such as weather patterns and holiday schedules commonly impact purchasing behaviour and will therefore impact demand.
The advantages of Cloud-Based Machine Learning Solutions
Having a bespoke cloud-based ML solution built provides a variety of advantages over getting one off the shelf.
Superior accuracy
Customised ML tools are built using a range of statistical, traditional machine learning and deep learning-based approaches. These models are regularly updated using a cloud-based ML operations framework to learn from the latest data (tuning and training).
As data changes over time, the ML model that provides the best results can change, so it’s good to always have access to the best models to produce a forecast. Off the shelf ML tools will often only offer a few different models, with limited tuning options available.
Cost effectiveness
Compared to expensive demand planning tools, cloud-based ML solutions are often more affordable. Custom implementations can cost well under ten thousand per year to maintain, offering significant savings vs off the shelf tools which can cost hundreds of thousands.
Leverage existing data
ML used for demand forecasting utilises data that organisations already collect, such as sales and inventory data, often sourced from ERP systems. This data forms the foundation for highly accurate forecasts.
Time efficiency
As the data for the ML model is already stored by the organisation, data collection and processing can be automated. This means users no longer need to manually extract data and feed it into Excel sheets, formulas and macros.
Augmented decision making
ML models help users understand the relationship between the forecasted numbers and different sets of data supplied, allowing for manual adjustments to the forecast to be better informed. Decision making can be further augmented by providing the model with extra data, such as macroeconomics and business-specific sales or marketing data.
Case study from Columbus
Columbus recently collaborated with an FMCG logistics company which was spending over £350,000 annually on demand planning, including a team of 6 employees, and was still losing £5M in wasted stock.
Columbus produced a ML solution which performed better than the existing team and tooling on 80% of products. Accuracy was improved on those products by up to 30%, reducing potential costs by upwards of £500K per year.
Leverage the superhuman advantage of machine learning
Machine learning powered demand forecasting can transform demand planning, offering superhuman accuracy and efficiency. By leveraging existing data and integrating third-party information, organisations can achieve precise forecasts that drive better decision-making and resource allocation.