As supply chain operations continue to evolve rapidly, organisations continuously seek ways to leverage deep consumer insights by incorporating a wide range of variables in their demand planning process.
In the previous post, we discussed how organisations have been utilizing additional tools to maintain effectiveness in their demand planning process. In this post, we look at how adopting machine learning can enable organisations to forecast demand with high accuracy despite the increasing volatility in the supply chain.
Machine learning refers to the concept that computer programmes can make use of algorithms to automatically learn from and adapt to new data without being assisted by humans. Below are some of the capabilities that enable machine learning models to produce reliable demand forecasting results despite the volatility that is rife in the supply chain.
- Machine learning can analyze massive volumes of data: When organisations forecast demand using traditional forecasting methods, they primarily focus on historical sales data. Machine learning, on the other hand, combines historical and current data such as social media trends, marketing polls, weather forecasts and competitors’ activities to generate valuable consumer insights. Machine learning’s ability to process large datasets consisting of structured and unstructured data means that the model can take in many variables that form part of supply chain operations and use the analytics to identify relationships that influence customer demand.
- Machine learning can adapt to changes quickly: Traditional statistical forecasting generally uses historical data to make predictive analytics and the changes in demand may take time to reflect on the reports, which may result in organisations failing to respond quickly to deviations. On the other hand, machine learning algorithms are automatically updated with new data and continuously retrain their models. Therefore, they calculate demand forecasts that respond to changing market conditions and consumer behaviour in real-time.
- Machine learning is efficient: Machine learning models are autonomous, and the forecasting calculations can be done with minimal to no manual input. In contrast, traditional forecasting models require a constant manual update of data and adjustments of forecast results. Not only are these interventions time- and resource-consuming, but they also do not allow for agile responses to immediate changes in demand patterns.
Although machine learning is becoming increasingly mainstream, there are some key factors that need to be considered before machine learning can be implemented in an organization.
- Increased computer processing power: The process of training complex machine learning models requires a lot of computational power to perform efficiently. Organisations adopting machine learning models may need to increase their processing power to ensure that the programme’s learning process is effective for the model to generate valuable forecasts.
- Industry expertise: Organisations need to invest in data science and supply chain expertise when they adopt machine learning to their supply chain operations. Not only will these specialists provide guidance when determining features that need to be fed to the model, but they will also provide support in terms of software maintenance and interpreting results generated by the algorithms.
- Explainable machine learning model: The automation that the machine learning model brings to the demand planning process will certainly free up a lot of a planner’s time, but it is important to note that forecast models will never be perfect and there will be situations where a planner is needed to examine a forecast and provide feedback. To perform this task effectively, a planner needs to be able to comprehend how the algorithm obtains forecast results. However, it is important to note that machine learning models developed to forecast demand will likely be complex and consists of hundreds of parameters. Thus, organisations must consider including Explainable Artificial Intelligence (XAI) methods in their modelling to ensure that users such as planners can easily gain a well-grounded understanding of how the model works. Not only is this essential for examining the results and further improving the model, but it can also enhance the adoptability of the algorithms by increasing users’ trust in the model.
Machine learning’s capability to apply complex mathematical algorithms that can automatically detect intricate patterns and recognize complex relationships in large datasets provides much-needed value in demand forecasting. Organisations, particularly those that operate in data-rich environments, need to ensure that they adopt technologies such as machine learning throughout their operations. The accurate data analysis obtained from machine learning can help organisations generate actionable predictions that they can use to proactively respond to the changing business landscape.