Superhuman Forecasting for Business Efficiencies with Machine Learning

In today’s cost-conscious markets, the importance of accurate forecasting is increasing. Demand forecasting is a critical requirement for businesses to accurately forecast performance and where precision can drive confidence and success.

However, traditional demand forecasting methods are often slow and inaccurate. Enter machine learning, a game-changing set of technologies that can transform demand forecasting processes and results.

In this article, we’ll explore how machine learning revolutionizes demand forecasting, its benefits, and how your organization 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 organizations 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 which effectively function as glorified Excel sheets themselves.

The Pain Points of Traditional Demand Forecasting

Long Forecasting Times

Traditional demand forecasting can be time-consuming, leaving little room for quick adjustments. The long lead times hinder the ability to respond to market and supplier changes promptly, resulting in increased go-to-market times.

Inaccurate Forecasts

Inaccurate forecasts can lead to overstocking or understocking, both of which are costly. Excess inventory ties up capital and leads to waste, while insufficient stock results in missed sales and dissatisfied customers.

External Events and Market Changes

Traditional methods often fail to account for external events and market changes. This limitation reduces the ability to adapt to unforeseen circumstances, affecting overall business performance.

High Costs

Maintaining a demand planning team and expensive forecasting tools can be a significant financial burden. These costs add up, especially when the results are not as accurate as needed.

The Superhuman Advantage of Machine Learning

Machine learning algorithms take historical sales and inventory data and use it to predict future demand numbers. This type of prediction is very hard for humans to do accurately as it involves recognising and understanding trends in complex data.

Machine learning covers a wide range of different algorithms and approaches used to address problems like demand forecasting. By processing the historical data and trialling different algorithms, we can often find a selection which provide significant accuracy improvements over a human led approach.

To further enhance model accuracy, third-party data such as weather patterns and holiday schedules can also be integrated. Data sources like these commonly impact purchasing behaviours and will therefore impact demand. This extra data drives even more complexity into producing a forecast, and unlike people, machine learning thrives on more data.

Cloud-Based Machine Learning Solutions

Building a cloud based machine learning solution provides a variety of advantages over buying off the shelf:

Superior Accuracy via Customised Machine Learning

Off the shelf tools will often only give options to use a few different models, with limited tuning options available.

In a typical implementation we will implement a range of statistical, traditional machine learning and deep learning based approaches to find the best fit. Trialling a range of different approaches allows us to benchmark each one against each other and to pick a smaller range of models to implement later on.

These models are regularly updated to learn from the most up to date data (tuning and training). As data changes over time the model that provides the best results can change and we want to have a selection of the best models ready to produce a forecast.

Cost-Effective Solutions

Compared to expensive demand planning tools, cloud-based ML solutions are often more affordable. Custom implementations can cost under ten thousand per year to maintain, offering significant savings vs off the shelf tools which can cost hundreds of thousands.

Leverage Existing Data

Machine learning used for demand forecasting utilizes data that organizations already collect, such as sales and inventory data, often sourced from ERP systems. This data forms the foundation for highly accurate forecasts. As the data sits in a business application we can automate the data collection and processing using data pipelines (ELT tools).

Time Efficient Forecasting

With data pipelines automatically pulling data from source systems and processing it, you no longer need time to manually extract data, feed it into Excel sheets, formulas and macros.

Putting an MLOps framework in place can enable you to largely automate the machine learning as well, making maintenance and updates easier, and more importantly that the forecast is ready whenever you need it.

The MLOps frameworks and pipelines also provide auditability, explainability and other essential components of machine learning solutions.

Augmented Decision Making

Machine learning models help you understand the relationship between the forecasted numbers and different sets of data supplied, allowing for manual adjustments to the forecast to be better informed. Certain models can do this directly by producing feature importance maps and weightings for each piece of data used by the model.

We can further augment abilities to make decisions from the forecasted numbers by providing other data which impacts purchasing behaviour, both specific to the business and its products, and with macroeconomic data. This data is not likely to be included directly in the models forecast, but can be used to adjust the results. For example, launching a marketing campaigns should positively impact sales, however the data you collect on a campaign is unlikely to fit into the model itself. On the macroeconomic side as inflation comes under control customers purchasing behaviour should increase.

Case Study: Columbus and FMCG Logistics

Columbus recently collaborated with a logistics company in the FMCG sector, 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 machine learning 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.

The production solution costs came in at only £600 per month using Microsoft Azure.

Conclusion

Machine learning-powered demand forecasting can transform demand planning, offering superhuman accuracy and efficiency. By leveraging existing data and integrating third-party information, organizations can achieve precise forecasts that drive better decision-making and resource allocation.

Ready to take your demand forecasting to the next level? Explore the potential of machine learning with Columbus by reaching out to charles.wright@columbusglobal.com.