How close are we to supply chains that can run themselves? Are production, warehousing and transport ready to become lights-out operations, where no human being is needed? Artificial intelligence can already automate much of the supply chain. It can also make a supply chain that learns and adapts by itself. There is enough AI technology today for supply chain operations that can run unattended for long periods. Maybe indefinitely.
We know that the AI supply chain industry is worth $9 billion, predicted to rise to $30 billion by 2030. This is not just prevalent in Western countries either, as China, Japan and India are increasingly adopting AI in their supply chain businesses for tasks like demand forecasting, inventory management and route planning optimisation.
But if this is so, why are we not all putting our feet up as supply chain turns into the new spectator sport? Money is one reason. It costs money to put solid AI and lights-out solutions in place. On the other hand, surprisingly perhaps, concerns about unemployment are less of a reason. Artificial intelligence still works best when combined with human brain power. Workforces may need to be retrained and reassigned. However, businesses run entirely by robots is still a myth — for now.
The First Question to Be Asked: How is AI Used in Supply Chain?
New technology can sometimes be a solution looking for a problem. If AI is to be useful and profitable in supply chain, it should be driven by issues that affect operations today. Of course, it can also open new opportunities for tomorrow.
However, to get management support (aka budget) the fastest, the first question should be this one: “What needs to be improved in supply chains right now?” The following are examples.
- Hard to plan for demand due to supply chain disruptions
- Excessive safety stocks and bullwhip effect
- Supplier unreliability
- Transport network unpredictability
- Demand by customers and partners for access to a “real person” (or a very good imitation)
- Seeing the real bottom-line impact of supply chain decisions
In different shapes and forms, AI can help with all these problems. However, AI is no panacea. To see where and when it might help, we can start by looking at the types of AI available.
Global supply chains are complex systems that involve multiple stakeholders — such as suppliers, manufacturers, logistics providers, and customers. Many supply chain managers feel under pressure to optimise their operations and reduce costs, which is understandable seeing how fast the AI market is moving.
But where do you start? Well, AI can help supply chain professionals navigate many different tasks, and there are a lot of potential benefits to adopting it into your system.
Benefits of AI in Supply Chain Management
Some of the key advantages of using AI in supply chain management include:
- Improved demand forecasting and inventory management: Because AI can analyse historical data and identify patterns very easily, it can be used to predict future demand more accurately based on extensive data. Doing this properly can go a long way in helping businesses maintain optimal inventory levels.
- Better supply chain visibility and predictability: With AI, you get real-time insights into all of your supply chain operations. This can allow your supply chain and logistics managers to monitor your business performance and then identify potential issues, potentially before they even have a chance to form or escalate.
- Optimised transportation operations: Optimising transportation pathways with AI by looking at your routes and schedules for transportation can help to reduce delivery times and lower your operational costs.
- Improved customer satisfaction and loyalty: As a result of all the above — better demand forecasting and inventory management — your business can ensure that all of your products are available when customers need them, which then leads to higher satisfaction and loyalty.
Tools in the AI Toolbox
AI technology is not so new. As far back as the 1950s, technologists had visions of artificial intelligence. They saw it at the service of society and business. While the ideas were good, it took some time for reality to catch up. We can put AI tools into three categories.
- Tools to make smart decisions. A key example is the expert system. This AI tool is based on data and rules. A classic case was the project to create an expert system for the Campbell Soup Company. The firm’s leading expert on large-scale soup production was soon to retire. So, the idea was born of trying to capture that human expert’s knowledge in an expert system. The human expert estimated it would take a few hours to explain what he knew. The project finally took many months. Supply chain planners can leverage these tools to run what-if scenarios and prepare for potential disruptions.
- Tools to act like humans. Machine learning is now gaining ground. With more data and greater processing power now available, computers that learn by themselves have become more feasible. Using ML, a computer automatically finds patterns in data. It can use them to draw conclusions from new data. E-commerce recommendation engines do this. They find the patterns in how visitors navigate on the e-commerce site. They also note the end results of those navigations. Above all, they note if the visitor bought something and, if so, what. If a new visitor arrives and shows a similar pattern of navigation, the engine swings into action with “You might also like (insert name of frequently purchased product here)”.
- Tools to think like humans. Current research focuses on neural networks. The neurons in the human brain and the way they interact are reproduced in software. Human-like thought processes and reactions are the result. Image processing is an example. In logistics, an artificial neural network (ANN) that understands images can help self-driving vehicles to manoeuvre.
Putting AI to Work in Global Supply Chains
The first applications of AI may not be spectacular. However, they can be vital just the same. Price pressure and the need to make profits have left many supply chain organisations in a tight corner. Their answer has been to apply cost reduction and lean practices. This in turn has led to a smaller staff. AI can help by:
AI-powered supply chain solutions from platforms like AWS and SAP are enhancing operational efficiency and visibility.
- Doing lower-level tasks faster and more reliably. This frees up their human counterparts for work that AI cannot do, such as creating a new supply chain strategy.
- Suggesting actions after reviewing analytics. Data analytics are part of a growing number of systems. Yet the insights they offer may overwhelm a smaller team. AI can help pick out the insights with the most impact and suggest precise actions.
- Creating a knowledge base that new workers can access, based on the know-how of older workers, as in the Campbell Soup Company example.
AI Inspirations and Considerations for SCM
Despite being called “artificial”, AI for supply chain management and other domains is still rooted in the world of the living. IBM describes its AI-based technology for supply chain as a swarm of bees, working together.
Elsewhere, “ant colony optimisation” imitates the social habits of ants and their innate ability to find the shortest routes to food and shelter. For supply chain, useful problems solved with this approach include vehicle routing and production process planning and selection.
Fuzzy logic is an AI solution for handling ambiguity and uncertainty. It can help make rules for using subjective criteria. One example is supplier performance evaluation. Other uses are finding out how expensive a product price is perceived to be, and controlling the cost of stocks. Agents are entities that act to achieve goals.
They can also compete and cooperate with other agents. An AI agent can use knowledge and deal with errors. It can also learn from what is around it, and talk to others in natural language. In supply chain, agents can improve shop floor control, logistics planning, business negotiation, and customer relationship management (CRM).
AI techniques can also be combined for further effect. “Chatbot” AI software can use natural language processing (NLP) for a dialogue with humans. The chatbot can then translate a request from natural English into the instructions needed to drive a back-end IT system.
The chatbot converts the IT system’s output back into easy-to-understand language or graphics. For example, a supply chain manager’s question might be the following: “What is the impact on profitability of using only air transport for deliveries?” With voice recognition, also an AI technology, the question does not even need to be typed in at a PC. It can simply be spoken.
AI-driven warehouse management systems can optimise stock levels and improve workplace safety.
However, avoid relying on AI to plug holes or paper over cracks that should not exist anyway. For instance, machine learning can be used to forecast the demand distortion that comes from lack of collaboration of supply chain partners (the bullwhip effect). Yet a smarter solution might be to get the partners collaborating properly in the first place.
Braking Factors in Supply Chain Disruptions
So far, applications of AI have tended to be for problems that are well understood. Very complex problems or issues that are harder to define, such as supply chain planning, have been less prevalent. Uptake of AI in supply chain can also stall because of the following.
- The name. AI has often been presented as science fiction (thank you, Terminator!), or as academic and abstract. Some rebranding might be useful. For example, “chatbot” is a shorter and more intuitively obvious way of saying “autonomous, interactive agent with natural language processing”.
- Lack of skill sets. It takes certain skills to put data (including big data) and AI tools together for useful results. Some solutions manage to be effective while requiring little or no technical skills for use. Others still need a team of specialists.
- Poor usability. This is linked with the point above about skill sets. Even the most technically minded can get tired of a clunky interface. New trends like chatbots and NLP are, however, making things easier.
- Machine stupidity. Everyday AI is driven by computer software, not by brains. It has no notion of “free will”, “initiative” or “creativity”. Workforce skills and knowledge of AI must continue to be developed to help AI to be used in a truly intelligent way.
- Too dumbed down. In trying to simplify, the risk is to strip AI of its real-world relevance. There is a happy medium to be found between rocket science and comic books.
- Glut of information. Market researcher IDC suggests supply chains have 50 times more data available to them today than five years ago. Yet only a small part of it is being analysed. The problem is often knowing where to start.
- Stale or bad data. Data often has a short shelf life. “Garbage in, garbage out” is true for AI too, especially when out-of-date data is being used.
Return on Investment
AI systems also require investment. Hiring data scientists to prepare and model data for AI systems can be expensive, as can full lights-out supply chain operations. Often, the monthly ongoing expenses of a “standard” human workforce are easier to handle than a big investment in smart automation.
Investing in AI technologies can significantly enhance the resilience and efficiency of the global supply chain.
However, there are degrees of investment, just as there are degrees of AI. A macro in Microsoft Excel or Word is a primitive form of AI. You could call it the amoeba of AI, able to react in basic ways to the data around it. In addition, once you have a licence to use Excel, the macros are free.
Macros are also a good example of machine intelligence helping people with tasks that must be done, but that take a lot of time for not much value. AI in general has a big role to play in ensuring that repetitive tasks are done faster and more reliably. AI doesn’t get bored, skip a step, or forget to file the results. It can’t think outside the box, but it can flag errors and anomalies for a person to then sort out.
Management consultancy McKinsey & Co puts it this way. AI — and more specifically machine learning — is great at “relentlessly chewing through any amount of data and every combination of variables”. Based on what it has been told to look for and what it finds, it can then handle necessary but time-consuming tasks, freeing up people for other work with more added value.
Successful AI Implementation with Real-Time Data
The following points can help make a success of AI use in supply chain.
- Focus on business objectives. Lowest internal costs and highest customer satisfaction are typical goals. If an AI solution cannot clearly link to either one, it may be ahead of its time or simply irrelevant. Instead, favour AI solutions that contribute to meeting relevant business goals and solving business problems.
- End-to-end thinking and data. Silo working in supply chains is bad news. Interdependencies often run from one end of a supply chain to the other. Correctly balancing the different components is crucial. If AI is restricted to isolated parts of the chain, the results may be no better than using standard SCM systems.
- Change management. Using AI is a change. AI is also changing fast, meaning still faster change for your enterprise. People often resist change or at least need time and help to feel at ease with it.
- Continuous improvement. Kaizen got it right, decades before AI was in vogue. In many cases, data is always growing and changing. Campbell’s Soup may have found success with a rule-based system because raw food ingredients do not change. In other sectors, change is the only constant. AI then needs to constantly examine new data, learn from it, tune itself and stay up to date. Effective supply chain planning is crucial for leveraging AI to its full potential.
- Scalability. Large supply chains can have millions of stocking locations. AI solutions must be able to make the right decisions, fast, and at scale.
- Good user experience. AI and human intelligence must work together. Whether in customer interactions, on the production line, in the loading bays, or on the road, people need to feel at ease with whatever AI tool is helping them to work better.
Conclusion
Look for the practical benefit of AI. Faster, cheaper, better — or whatever — if AI has a place in your supply chain, it is because it offers clear business improvement. Remember, AI still has limitations and people also need to accept it as a friendly helper, not a rival for their jobs. Supply chain is also still a hugely complex activity, for which AI is a useful tool, but not a solution by itself. Or at least, not yet!
