It is exciting how modern video streaming platforms know exactly what their subscribers want to watch. Social media platforms are also getting better every day by recommending people who we should follow or add to our friend’s list.

Big data and Machine Learning brought about these new experiences. As the supply chain needs of businesses keep changing day by day, it becomes difficult for practitioners to address them accordingly. Procurement professionals are also finding it difficult to properly select suppliers for their companies. Thanks to big data and machine learning technology, these businesses now have something to smile about.

Big Data and Machine Learning in the Supply Chain

When we introduce artificial intelligence (AI) to systems that perform our day-to-day tasks, it is possible to achieve operational efficiency. Machine learning entails the changes that occur in these AI-powered systems. Machine learning makes it possible for system tasks such as predictions, robot control, planning, diagnosis and recognition to occur. It also plays a huge role in applications such as image recognition, expert systems, natural language processing and data mining.

Today, supply chains across the globe focus on gathering huge amounts of data as part of their day-to-day operations. Professionals working with the supply chains are also realizing the benefits that data collection and inspection bring to them. As the amount of data increases, they need technologies that will help them get the most out of the data. Big data and machine learning technologies enable them to operationalize on properly-formulated strategies for efficiency in their activities as discussed below.

Early Spring 2015, Bakkersland turned to GoDataDriven asking if it was possible to predict demand for bread products at supermarkets.

The Transformation of Traditional Supply Chain Management Tools

Over the last two decades, lots of advances came into play as far as supply chain management goes. The visions of supply chain businesses changed as these advances came into play. These businesses also began going digital to effectively meet their clients’ demands. The need to reshape supply chains across the globe increased as management processes become complex. Furthermore, the professionals in those institutions focus on enhancing sustainability and transparency in their operations.

Traditional supply chain management tools are constantly changing due to the advent of big data and machine learning technologies. These tools can now give forecasts and predictions on risks and uncertainties related to supply chains. They also amplify processes such as supplier relationship management, inventory management, warehouse management, transportation, and logistics. The forecasts and predictions help logistics professionals to adopt effective strategies and take appropriate actions for the betterment of their businesses. They also allow for the closer inspection of data gathered by supply management tools.

Big data and machine learning innovations make it possible for supply chains to learn from their past activities. It enables them to identify the best relationships based on how their previous activities transpired. Instead of forecasting them and optimizing their activities, they are in a position to make rational decisions.

Consumer Behavior Analysis

Though consumer behavior analysis doesn’t seem vital to supply chains when compared to other disruptive technologies, it has a huge impact on supply chain businesses. E-commerce businesses are likely to benefit from consumer behavior analysis. It entails the collection of large amounts of consumer, product, company and industry information from the worldwide web. It also entails the process of organizing and visualizing this information using diverse web mining tools and methods.  

Consumer behavior analysis, as a process, relies on the big data and machine learning disruptions. Web analytics tools such as Google analytics can help supply chains to draw inferences from their consumer information. They can also monitor consumer activities and learn more about their customers’ purchasing decisions.

With big data and machine learning disruptions, supply chains get seasonal forecasts on their customers’ needs. These forecasts enable then to make adequate resourcing and inventory plans for their growth. The data is extremely beneficial to any business operating in the e-commerce space. The ability to source data from numerous and diverse sources proves to create more economic opportunities for supply chains and e-commerce retailers.

Supplier Relationship Management

It is impossible to deny the fact that supply chains can apply big data and machine learning disruptions to their supplier relationship management strategies. Though this application is still in its early stage, it is important to note that future supplier relationship management strategies will heavily rely on big data and machine learning. Businesses in the supply chain sector need accurate supplier data for them to come up with effective relationship management strategies. Much of the data they need is in the form of qualitative data. It includes evaluations, audits, and assessments.

Machine learning and its intelligent algorithms can help these businesses collect and analyze this type of data. This disruption would also help them compile the actions and information of the suppliers over a long period of time for present and future use. Procurement professionals will also be in a position of carrying out intelligent and predictive supplier selection activities. This initiative would improve transparency in procurement activities and create more opportunities for long-lasting relationships.  

Supply chains will also get access to up-to-date information that is readily available for human inspection and use. Through machine learning and big data, they will have diverse parameters for gauging which supplier matches their needs and demands. The future of procurement activities will be brighter than ever thanks to these disruptions.

Final Thoughts

It is good to note that supply chain professionals are on the verge of embracing the latest technological disruptions at their disposal. These professionals increasingly need accurate forecasts on risk points related to their business for operational efficiency and transparency. Despite the size of a supply chain business, having these forecasts comes with lots of exciting benefits. Machine learning and big data both give procurement professionals a chance to witness how data can forecast itself. It allows them to be part of the intelligent analytics that draws accurate inferences on large data sets within a short period of time. An example of this analytics is predictive analytics, which they can use to evaluate and manage supply chain risks.

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