For businesses, efficient supply chain management is crucial to ensure that the right products are available at the right time to meet their customers' requirements. That leads to predictive supply chain model. Powered by Artificial Intelligence (AI), the predictive part assists to identify potential delay factors, thus ensuring preventive measures to avoid financial losses and customer dissatisfaction.
According to the 2022 MHI Annual Report, the current usage of predictive analytics in supply chain management stands at over 20%. But this is projected to grow by over 80% in the next five years, indicating how these models are becoming imperative in ensuring robust process management. It observes large amounts of data to spot potential risk scenarios impacting supply chain. In short, it helps companies make informed decisions and optimize their operations. For example, it lets users predict customer demand, anticipate supply chain issues, optimize inventory levels, and stay ready to handle future disruptions. Several industries use predictive supply chain models, including retail, manufacturing and healthcare. But building one involves a sound understanding of analytics to create a data-driven model.
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Business benefits
With a predictive analytics-powered model, organizations can witness several benefits. They can optimize pricing, avoid stock out or overstocking, enhance warehouse management, on-time shipping and product delivery, and boost customer satisfaction.
Understanding the fundamentals
Predictive analytics uses data to predict future events. The data used in predictive analytics is often referred to as "dirty data." This is because data collected from different sources are often inconsistent, inaccurate, and duplicated. Various algorithms and statistical methods are used in predictive modeling, including regression/correlation analysis, classification/segmentation techniques, time series modeling, deep learning technologies, etc. It is crucial to select an appropriate algorithm for specific needs based on factors including interpretability, prediction time, memory requirements.
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Preparing data
Regardless of the algorithms employed, predictive analytics are only as good as the data that are used to train them. So, keeping this in mind, the next vital process is preparing the data. We call it data cleaning, it produces a quality data set that is validated, standardized, and enables your algorithms to analyze. In this context, having experienced data scientists is imperative.
Creating the model
Now it's time to create a mathematical model that, closely represents the trend an organization follows. It is always advisable to leverage a powerful model-building platform. That includes testing numerous forecasting models to identify the closest one. Here, it is vital to keep testing the model with known historical data. The next critical step is continuously adding the current data until it forecasts the desired future trends or events.
Evaluating and deploying the model
Observing how the model functions in real-world scenario is essential to get the desired outcome. It is important to ascertain the model's accuracy before deploying it.
Deploying the model into the production environment includes integrating the model with all the systems and data sources.
Regularly training the model with the most recent datasets is critical to enable the model in making correct predictions. It empowers organizations with robust supply chain collaboration and allows them to coordinate with all internal & external stakeholders.
Let an expert build the predictive supply chain model.
The idea of having a predictive supply chain model is to stay alert and mitigate risk factors that may hamper business. We also know that companies would like to focus on their core capabilities - running the day-to-day affairs at their enterprises. Deploying such a model requires a certain level of expertise, and it is practical to work with a well-versed technology partner
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About the author
Sunny Nandwani - Founder & Managing Director at Acuver Consulting Pvt. Ltd. Acuver acts as trusted partner for IT Services helping you stay in control of your Supply Chain future growth trajectories while maintaining an agile and collaborative approach to create customized solutions in the digital value chain.
