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Although companies understand the enormous value of an efficient supply chain to address a variety of mission-critical factors and maintain a competitive edge, there are plenty of things that can stand in the way. That’s why industry leaders are looking for the most effective tools to help with supply chain management (SCM)—including the cutting-edge solutions offered by artificial intelligence (AI) applications.

In a 2019 survey of supply chain executives about “key innovation investments across retail, manufacturing and logistics,” 77% of respondents cited “supply chain traceability and visibility” as their highest area of investment, with 82% planning to deploy or test cognitive analytics, and 62% planning to do the same with AI and machine learning (ML) technologies by 2021.

Survey respondents also provided specific ways they found AI/ML technologies valuable in their organizations—with 51% saying it helped them to optimize inventory, 45% saying it helped with predictive distribution, and 42% saying it helped to optimize distribution networks.

Here, we’ll delve into defining AI; its implications for SCM; and factors that influence success for companies hoping to make the most of what AI has to offer.

Defining Artificial Intelligence (AI)

Noting that definitions of artificial intelligence vary, since “AI isn’t just one thing,” Accenture defines artificial intelligence as “a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. …Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.”

Referencing supply chain planning, Deloitte defines AI in the context of the “digital supply network” (DSN): “Often associated with machine learning and cognitive computing technologies, AI enables supply chain monitoring systems to learn complex patterns, collapsing tactical planning and execution while automating much of the decision-making in DSNs. This enables a shift from purely history-based forecasting to one that can effectively predict reorders and forecast demand.”

Since supply chain planning is a data-rich, analytical process, Deloitte says the “intelligent technologies” of AI are uniquely suited to help organizations make better decisions when it comes to planning; reducing costs; ending a reliance on “tribal knowledge;” gaining more accurate insights into their supply chains; improving decision-making; and increasing DSN agility.

Applying AI to Supply Chain Management (SCM)

In “Application of Artificial Intelligence in Automation of Supply Chain Management,” the authors note the complexity of supply chain management, since it intersects with so many business operations—including marketing, logistics, and production. In this light, they describe how technological progress over the past two decades has led to the current AI era in which computers are now able to receive and process vast amounts of data to provide the analysis and recommendations organizations need.

A key value the authors say AI provides for SCM is through its ability to forecast demand and optimization: “As AI can process, analyze (automatically) and more importantly, predict data, it provides accurate and reliable forecasting demand, which allows businesses to optimize their sourcing in terms of purchases and orders processing, therefore reducing costs related to transportation, warehousing and supply chain administration, etc. In addition, it discerns trends and patterns which help to design better retailing and manufacturing strategies.”

Other applications for AI within SCM include:

  • Shipping: Algorithms can identify optimal shipping routes and uncover inefficiencies in the transportation process.

  • Logistics: AI algorithms can inform specific processes—such as those along a transportation route—that can make a big difference in fuel savings and CO2 emissions.

  • Manufacturing process: AI can predict customer orders, thereby guiding the use of manufacturing resources, as well as monitoring inventory and automatically reordering when needed.

  • Delivery date predictions: Available-to-promise (ATP) metrics can be generated through a comprehensive visibility of global demand made possible by real-time data collection across multiple systems.

Factors That Influence the Success of AI in SCM

In the survey of supply chain executives discussed previously, “resistance to change” was noted by nearly half of respondents as the number one challenge to adopting AI more fully—which was attributed to the inability to visualize and quantify the value AI can provide. Relying on legacy technologies that aren’t up to date can also get in the way, slowing down those working within the supply chain as they try to process data and crunch numbers from a variety of global systems.

Quoted in a Forbes article, AI expert Dr. Michael Feindt touts the critical role of AI in supply chain management and says that making the most of what AI has to offer in this area will require that a few foundational factors be in place:

  • To get a complete picture, the system must be able to “read signals” and process billions of pieces of information” from disparate data sources—including all factors that may influence the supply chain.

  • To provide solid forecasts, the system must be able to “look into the future” without “rules-based” approaches that may do more harm than good.

  • The technology must be able to “overcome human nature”—ie, the tendency of humans to want to “fix things ourselves.” If trust is built in the system to automatically make the right decisions without human intervention, humans can be freed up to attend to the things only they can do.

Finding Success With AI for Your Organization’s Supply Chain

In summary, there are many advantages to applying AI in SCM, including:

  • End-to-end visibility—which uncovers cause-and-effect issues, logjams, and opportunities for improvement based on near real-time data.

  • Actionable analytic insights—which supports timely, agile decision-making based on business intelligence data.

  • Reduction of manual workload—which is a time-waster for supply chain professionals and prevents them from attending to tasks only humans can perform.

  • Informed decision-making—which is supported by cognitive automation and AI-enabled predictions.

However, finding and deploying artificial intelligence solutions to optimize supply chain efficiency requires partnering with the right expert who can help you do it.

At CLN Worldwide, we have the answers you’re looking for to help address your supply chain needs.

Most companies transporting internationally face reoccurring logistics problems. At CLN Worldwide, our management service coordinates the entire supply chain and solves transportation and regulatory challenges. We allow you to focus on your business.

Do you have more questions? Reach out to us today to schedule a free demo. We look forward to working with you.

CLN Worldwide

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