How Machine Learning and Artificial Intelligence Are Reshaping Supply Chains
Terms like data science, machine learning (ML), and artificial intelligence (AI) are heard quite often, because they are changing our world. A Fourth Industrial Revolution (Industry 4.0) is currently taking place, during which we may see robots doing most of our work, and ML and AI reshaping the global supply chain.
What is Industry 4.0?
Industry 4.0 focuses heavily on interconnectivity, automation, machine learning, and real-time data. Industry 4.0 marries physical production and operations with the latest digital technology to create a better-connected ecosystem for companies that focus on manufacturing and supply chain.
The First Industrial Revolution came with the advent of mechanization, steam power, and water power over 250 years ago. This was followed by the second industrial revolution around a century later, which resulted in mass production via assembly lines using electricity. The third industrial revolution in the latter part of the last century yielded consumer electronics, information technology systems, and automation—which led to the Fourth Industrial Revolution, going on now, that is associated with cyber-physical systems.
Industry 4.0 has resulted in automated predictive analytics, which are helping numerous companies become more competitive and tech-savvy within the supply chain. Automation is mainly done via artificial intelligence and machine learning.
What Is Artificial Intelligence and Machine Learning?
Artificial intelligence is the capability of machines to “learn” different action-based capabilities, mimicking autonomy. Everything, from self-driving cars to social media, is being defined by how machines can be trained to behave like humans—and perhaps even exceed them in capabilities.
Machine learning is a type of artificial intelligence that allows an algorithm, system, or piece of software to learn and adjust without being explicitly programmed to do so. ML allows technology to teach itself over time, so that it can improve operations. ML models, based on algorithms, are great at analyzing trends, spotting anomalies, and deriving predictive insights within massive data sets. These powerful functions make it an ideal solution to address supply chain challenges.
How Can AI Be Used in Supply Chain Management?
Chatbots for Operational Procurement: Valuable time and money is wasted in trivial supply chain tasks that are conducted by humans. Businesses spend inordinate amounts of time doing manual (paper-based) processes—such as chasing invoice exceptions, discrepancies, and errors—and responding to supplier inquiries. These tasks can be automated via chatbots, which can “speak” with suppliers during routine conversations, place purchasing requests, send notifications about compliance materials, and file invoices, payments, and order forms.
Autonomous Vehicles for Logistics and Shipping: Autonomous vehicles are going to be the future norm. Companies like Tesla, Google, and Amazon are already investing huge amounts of money in this sector. Self-driving vehicles will lead to faster and more accurate shipping, reduce lead time, lower transportation and labor costs, and, most importantly, widen the gap between competitors. If this technology evolves in the near future, then it would have a great impact on supply chains, since driverless trucks can operate 24/7 without needing breaks.
Natural Language Processing (NLP) for Data Cleansing and Data Robustness: NLP is an element of AI and ML, and has the potential to decipher large amounts of data in a streamlined manner. NLP technology could streamline auditing and compliance actions that are currently rather difficult due to language barriers between buyers and suppliers.
Fraud Prevention: Risks of fraud can be reduced by using AI algorithms to automate auditing processes and detect anomalies. AI is also capable of preventing privileged credential abuse, which is a primary cause of breaches across the global supply chain.
How Can ML Be Used in Supply Chain Management?
Demand Analytics and Forecasting: By forecasting demand and supply, inventory can be better managed. If applied correctly, ML could revolutionize supply chain decision-making by making it more agile. Using ML would provide users with the best possible scenarios based on intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could improve the delivery of goods, while balancing supply and demand, and wouldn’t require human analysis.
Warehouse Management: According to Forbes, “A forecasting engine with machine learning just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies.” ML provides an endless loop of forecasting, which renders a constantly self-improving output. This kind of capability could reshape warehouse management as we know it today.
Predictive Analytics for Supplier Selection and Supplier Relationship Management: Risk assessments, audit reports, and credit scoring are important parameters in deciding the potential and value of a given supplier. ML can help ease these tasks via algorithms catered to these types of passive data gathering. The data would still be seen by humans, but the process itself would be carried out by machines, thus generating the best supplier scenarios for various cases.
Quality Inspection: This can also be simplified using ML, since it excels in pattern recognition. Accordingly, ML has many potential applications in terms of the inspection and maintenance of physical assets across an entire supply chain network.
Production Planning and Accuracy: ML is improving factory schedule accuracy and production planning by taking into account multiple constraints on the shop floor, and then devising real-time solutions.
Problems During and After Implementation:
Undoubtedly, ML and AI have the potential to continue to revolutionize end-to-end supply chains. However, with improved technology, there are always drawbacks, including:
A lack of access to reliable data, which can cause significant issues for ML in the supply chain, as some processes may “give an error” if machines are not fetched with the proper data.
Algorithms need to be checked from time to time, and updated as required. Otherwise, they will give incorrect outputs.
Incorrect decisions taken in risk-pooling activities (like detection of traffic by sensors in self-driving cars) may lead to accidents.
Automated and virtual/augmented-reality tools generate security concerns, as they may be hacked, leading to the loss of sensitive information.
A significant number of jobs will likely be lost due to automation. In January 2017, McKinsey’s research arm found that AI-driven job losses were at 5%. An Oxford University study predicts that 47% of jobs will be automated by 2033.
There are many pitfalls related to machine learning and artificial intelligence, but its benefits far outweigh the risks—especially in the context of supply chains. Most importantly, there will be new and better jobs available in these fields that will replace the ones being lost. ML and AI are shaping the future, and many routine tasks will soon be automated, which will allow us more time to grow our businesses in creative ways.
Swapnil Manglorkar is an intern at M74 and a supply chain enthusiast from Mumbai, India. He is passionate about technological changes going in the world and eager to learn more every day.
The views expressed above are those of the author and do not reflect the official position of the M74 Group, which remains neutral on all matters. Publishers assume no liability for content.