Artificial Intelligence in Supply Chain Management

How Does Artificial Intelligence in Supply Chain Management Work?

You don’t have to look far to see where artificial intelligence is having an impact, including artificial intelligence in supply chain management. Virtually every industry is utilizing the various technologies to one extent or the other. Adobe reports that 12 percent of organizations are already using artificial intelligence and another 60 percent have plans to do so in the very near future. As artificial intelligence gains steam in supply chain management, the market is expected to reach $1.3 billion by 2024. 

PwC reports that 72 percent of executives believe artificial intelligence will be critical for their success, saying they anticipate it will give them a business advantage. According to McKinsey, of those who have already implemented artificial intelligence into their supply chains,  61 percent report a decrease in costs and 53 percent report increased revenues as a direct result of their AI investment. 

Transportation and logistics, a crucial part of the supply chain, is cited in the PwC report as being the fourth most impacted industry. Why transportation and logistics? Artificial intelligence collects and analyzes massive amounts of data from different data sources, giving transportation and logistics leaders insights they never had in the past. It is opening up capabilities across the industry, from optimizing routes and automating trucks to predicting transit times and avoiding weather events.

What Is Artificial Intelligence in Logistics?

Gartner defines artificial intelligence as a technology that applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. It also says artificial intelligence can be broken down into two categories. It is either used to augment human capabilities and reduce errors, or it is used to automate key processes. When it comes to transportation and logistics, both use cases apply. 

Logistics leaders often lack the tools they need to accurately and rapidly identify, assess, and mitigate risks, much of which is due to the fact that they do not have access to all of the data they need to do so. The tools they do have are disconnected and fail to provide a comprehensive view of their risks, the probability, and severity of those risks, and exactly where, when, and how those risks could impact individual shipments. They use legacy systems and manual processes to collect and crunch numbers from disjointed systems, all of which take time, introduce the risk for error, and result in an incomplete view of what matters most.

Risk is inherent in the supply chain but with artificial intelligence, companies have real-time data to anticipate risk and take action before those risks turn into issues. Leaders can make data-backed decisions early in the shipping process when mitigation efforts have the greatest chance for success. Readwrite says artificial intelligence in supply chain management, “helps to improve different areas of [the] supply chain like supply chain transparency and route optimization…Based on the weather, real-time sales and other factors, it provides continuous forecasts in a loop.”

Related: Modernizing Transportation Risk Management to Meet Weather and Climate Trends

The key to fully optimizing artificial intelligence in the supply chain is to use it to forecast and make predictions. Predictive analytics is an important aspect of this advanced and intelligent technology, giving leaders a way to go a step beyond the identification and assessment of risks to predict what could happen based on reliable data. In fact, predictive analytics is cited by 82 percent of respondents in an MIH survey as a technology they plan to adopt within the next five years. 

In order to respond quickly to identified risks, which is the most important aspect of mitigation efforts, leaders need their decisions to be more automated. As Gartner said, automating processes, including the decision-making process, is where artificial intelligence shines. Predictive analytics and machine learning are powerful artificial intelligence tools that when combined, deliver unprecedented capabilities at scale.

Predictive Intelligence Adds a New Element

Actionable, predictive intelligence uses machine learning techniques to make risks more predictable, even risks as seemingly unpredictable as the weather and environmental conditions and natural disasters, such as floods, extreme temperatures, and severe storms. Wildfires, social unrest, infrastructure outages, and other risks are all included in the risk assessment. This intelligence also helps with decisioning. 

Tools now exist that automate the task of finding lower-risk alternatives to current shipment plans. The technology automatically scans shipments up to three days before and three days after a planned pickup date to compare risk levels. Companies can define their own risk tolerance and set up alerts that notify them when a risk threshold could be met. It also recommends the lowest-cost equipment options that maintain freight integrity, as well as which mode of transportation and lane are most likely to result in shipments being on time. 

Related: Predictive Analytics and Supply Chain Network Optimization

In a Research and Markets report, “Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, Planning and Logistics, Inventory, Fleet, Freight, Risk), and Region 2019-2024,” AI-supported supply chains were found to be 45 percent more effective at on-time delivery with fewer errors. In addition, the report says that leading solutions enable decision-makers to “have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion.”

It isn’t just real-time data that is important for decision-makers. Artificial intelligence in supply chain management provides historical context so that leaders can identify trends and use those to inform decisions and hone their processes so that they are always learning and improving. The better and faster they become at identifying, assessing, and mitigating risks, the more bite they can make into marketshare.

The predictive analytics shines a light into potential future events and recommended actions. With past, present, and future perspective, leaders are more aware than ever of their entire ecosystem and able to make decisions with greater confidence, understanding context, dependencies, and scenarios. They can use this data to justify their decisions within the company and externally with customers, setting appropriate delivery expectations, and keeping lines of communication open. 

How to Bring Artificial Intelligence Into Your Supply Chain

If you are considering an investment into artificial intelligence for your supply chain, your decision will come down to buying an artificial intelligence application from a vendor or building it in-house. Supply Chain Dive says when citing the MIH survey, “Bringing the required talent on board can be a struggle. Fifty-six percent of respondents considered hiring a top challenge in the current environment and 78 percent said there was high competition for the talent available.”

Instead, you can focus your budget on software purposely built for logistics leaders who want to make risk more predictable, actionable, and manageable. With comprehensive risk identification and analysis, dynamic planning, and automated decisioning, all of which utilize artificial intelligence, leaders can have greater confidence they can deliver more loads on time and in full.