Although machine learning is now mainstream, the technology still puzzles many supply chain experts. According to Gartner, “while machine learning promises transformative benefits for the supply chain, current expectations for short-term readiness and benefits remain unrealistic.” Therefore, it is advisable to look beyond the hype and examine how machine learning can be used to solve business problems and value creation can be used in the supply chain. Companies are advised not to rush blindly into machine learning projects; to get the best results; you should allow enough time in advance in careful consideration and preparation.
For example, every machine learning project should begin with a clear description of the objectives. This description should include critical figures and data on the initial situation to make a clear before-and-after comparison possible. Since machine learning systems continuously learn, it can be shown how the systems get better over time and how the company benefits from the project.
Long-term, sustainable success can only be achieved if the machine learning project is built on a stable foundation. For this, it is advisable to combine machine learning technologies with an adaptive probabilistic forecast approach.
The opposite of probabilistic prediction is deterministic prediction. This method calculates a future result with exact numbers, often based on historical averages. In contrast, probabilistic prediction works with probability distributions instead of actual numbers. While a deterministic forecast is generally expressed as a series of time series of exact numbers, a probabilistic prediction is defined as a series of time series of probability distributions. In supply chain planning, advanced algorithms analyze multiple demand variables to identify the probabilities of several possible outcomes.
The benefit of the probabilistic approach is that it can differentiate between error and natural variability and between signal and noise, which is impossible with the deterministic view. This has three main consequences:
On the other hand, the probabilistic forecast provides a wealth of information for identifying risks and opportunities at every level of detail so that well-founded business decisions can be made. It also enables a perfect demarcation between things that can be controlled and improved and those beyond our control.
The combination of probabilistic forecasting and machine learning enables supply chain planners and dispatchers to create forecasts on a granular level and for different time horizons. Here, the adaptive probabilistic predictive model is first created using historical data. Once the model is up and running, machine learning algorithms are used to improve the probability of prediction, gradually adding both internal (e.g. product properties or other master data) and external data sets (including weather data, economic indicators or social media data). This step-by-step approach allows the model’s performance to be better checked and, if necessary, adjusted more easily.
Machine learning projects benefit from large amounts of data. The more significant the amount of data, the more precise the statistical significance of a machine learning model. To get started, however, more minor, traditional data sets, such as the history of a product, are often sufficient. The granularity of the data sets is also essential. In contrast to conventional analysis approaches, in which the data was aggregated to filter out the noise, machine learning analyzes precisely this noise to find correlations that train the model and make it more efficient.
As with any aspect in which data plays a fundamental role, data quality is also essential in machine learning projects. Therefore, the tools used should have governance functions to maintain the quality of information during the entire life cycle of the data.
While developing a machine learning model to handle a one-time business challenge is tempting, it is not efficient. One-off projects create “black boxes” that only the programmer understands and that business users mistrust. The results should be operationalized to achieve sustainable business benefits and get the best return on the project. Therefore, it is essential to use adaptive models that do not require constant manual adjustment, as changing business conditions can otherwise make the models unreliable.
Demand processes such as demand forecasting, demand sensing and demand shaping are particularly suitable for the application of machine learning due to their complexity and fast pace. The most popular application examples include:
Like all technologies, machine learning has its limits. Therefore, the business and process knowledge of the employees play an essential role in the coordination of the machine learning models and the evaluation of the results. Since the algorithms relieve them of tedious, repetitive tasks, the supply chain planners can concentrate on new, strategic tasks.
In a guide to developing future supply chain professionals in the digital age, Gartner cites business acumen, adaptability, political instinct, and the ability to work together as keys to improving “digital skills.” This underscores how important it is for digital supply chain organizations to focus on the human side of supply chain planning now that many processes are being automated by machine learning. Because of this, supply chain professionals who develop their skills in negotiating, business communication, and simplifying complex data are becoming increasingly valuable to organizations.
Also Read: Seven Rules For The Successful Use Of Machine Learning