Artificial intelligence (AI) is one of the big “buzzwords” of 2024, which is a shame because the technology’s analytical capabilities have a lot to offer supply chain planners – if you can cut through the hype.
I’m never one to jump on the bandwagon with emerging technologies. Sure, they’re cool, and many are worth keeping an eye on, but investing in something that’s flashy and promises to solve all of your problems in a new and exciting way is probably just what it sounds like: too good to be true.
AI and machine learning (ML), however, are a different story because, even though they’re cropping up in some weird and wild products (think robotic bees and rap lyric generators!), it may surprise you to learn that these technologies date back to the 1950s and have been used in computer-aided design (CAD) and computer numerical control (CNC) machines since the 1970s. All hype aside, AI is really as simple as applying advanced analytics and logic-based algorithms to raw data to generate actionable insights, and it’s already widely used in manufacturing, finance, healthcare, retail and many other industries.
It’s safe to say that AI has long moved past buzzword status. As I discussed in a recent article in Food Logistics, it’s a powerful tool for predictive analytics, demand planning and business forecasting that shouldn’t be overlooked.
How is AI Improving Supply Chain Management?
Predictive analytics, demand planning and business forecasting have always been core functions for supply chain planners. How else would we know which products consumers will want next, where to source inputs from or how much of an item to produce?
The trouble is, these functions have historically relied on the best-available data and statistical modeling. In other words, looking at what we knew to be true in the past to predict the future. The future, however, isn’t static. There’s no way to predict with certainty what consumer buying habits will look like one month, one year or five years from now. The same goes for harbingers of supply chain disruption like bad harvests, droughts, wildfires, the spread of disease and geopolitical headwinds. We don’t know what we don’t know.
AI and ML don’t necessarily solve this; they aren’t magic, but these technologies, applied strategically, can bring a new level of adaptability, accuracy and efficiency into supply chain processes. And supply chain leaders are taking note; 37% are already using AI or plan to do so within the next two years. Forty-seven percent of established organizations are integrating AI and automation into their supply chain processes.
AI Takes the Guesswork Out of Predictive Analytics
AI’s role in predictive analytics is an interesting one. If you’ve checked out QAD Redzone, you already know we’re big fans of technologies that empower humans rather than work against them. I look at the connected workforce movement as an opportunity to eliminate time-consuming manual tasks, increasing productivity and giving your people more time to focus on doing what they do best.
The best use of their time probably isn’t filtering through mounds of raw data to forecast human behavior and other trends. It’s extremely labor intensive and human analysts can only do so much considering the sheer volume of data flowing through your organization. This is a perfect use case for AI.
AI can sort through massive amounts of data in seconds or minutes, not days or weeks, and identify complex patterns, correlations and anomalies with unparalleled speed and efficiency. Take forecasting consumer buying patterns for example. We all know that people are starting their buying journey online, looking for innovative new products and are increasingly conscious about sustainability factors.
AI-driven predictive analytics can take a holistic approach to modeling that includes everything from past purchases to online interactions to accurately forecast future buying patterns. For business leaders, that means data-driven marketing strategies, optimized inventory and, most importantly, happy customers.
Demand Planning With AI: Predict the Future, Don’t Just React to It
Now that you’ve taken the guesswork out of predictive analytics, the next step is to apply that knowledge to your demand planning activities. This is another area where AI shines.
Accounting for shifting market conditions, seasonality and unexpected disruptors can be tough at any time, but this is especially true when using conventional demand planning techniques that rely on fragmented data only going back about 24 months. AI-based models can analyze dozens of parameters at once, providing businesses with real-time insights into variations in demand.
AI is so effective in this application that McKinsey & Co. estimates that it can reduce supply chain errors by 20% to 50%, mitigating the risk of lost sales and product unavailability by as much as 65%.
Accurate demand forecasting requires a blend of quantitative and qualitative techniques, and AI and ML offer the best of both worlds because you can account for all kinds of factors, including weather conditions, economic trends and sales data. The technologies offer an unrivaled opportunity to take inventory optimization, waste reduction and product quality to new heights.
Future-Proof Your Operations with AI Business Forecasting
Uncertainty is the only thing that’s certain about the future, and that’s why it no longer makes sense for businesses to utilize static, linear forecasting models. Building out business forecasts takes time and they’re relatively inflexible considering all of the factors that come into play. AI-enabled forecasting can adapt to changes on the fly, quickly adjusting predictions in real time to reflect the latest information.
As a result, business forecasting has become a much more dynamic landscape. Not only can AI make quick sense of quantitative, structured data, it can also absorb unstructured, qualitative information that’s much more difficult to digest using conventional techniques. AI can process everything from news articles and social media trends to customer reviews, bringing a rich layer of contextual information to otherwise rigid business forecasts.
With AI-driven business forecasting, you’re in control. You can use such tools to identify emerging trends, predict variations in market behavior and take a proactive, rather than reactive, approach to uncertainties.
Considerations for Implementing AI-Based Supply Chain Solutions
While AI-based solutions for flipping burgers or judging beauty contests (yes, they exist!) are buzz-worthy in terms of media trends (and literally so if we’re talking robotic bees as pollinators), AI and ML are proven technologies with staying power, especially when it comes to predictive analytics, demand planning and business forecasting.
That said, it’s still never a good idea to adopt any new technology without first considering your unique requirements and potential challenges. Data quality, human expertise, ethical concerns and internal infrastructure are a few that come to mind with AI. All of these are manageable, but we must ensure the implementation of AI, like any technology, adheres to ethical, societal, and environmental standards.
Is AI right for your business? Read the full article and explore Pragmatic AI to learn more.
