AI and IoT in Supply Chain
Artificial intelligence has the potential to play a game-changing role in nearly every facet of business, and the supply chain is no exception. By the same token, the Internet of Things can help supply chain leaders solve some of their biggest pain points and position them for a competitive advantage.
While AI in the supply chain is far from a magic salve, both it and IoT can deliver an elevated level of visibility into the supply chain so that companies can better understand their flow of goods, the status of the materials required to make them, the utilization of the people and processes developing them, and how they can avoid potential disruptions through every step of the way.
What is IoT?
The Internet of Things (IoT) is broadly defined as a network of physical devices that communicate with each other and cloud technology. These devices can range the gamut, from inventory sensors to GPS tags, and they can be wired or wireless.
What is the use of AI and machine learning in supply chain?
Both artificial intelligence and IoT can be used to provide a deep level of visibility into the supply chain. Artificial intelligence, which can work within the Internet of Things, can build in predictive recommendations to help supply chain leaders make faster and more accurate decisions.
“While planning and forecasting were instrumental prior to the pandemic, recent developments in AI have dramatically enhanced these capabilities,” says Manish Ghosh, corporate VP, industry strategy, consumer industries at Blue Yonder. “Companies are leveraging AI to predict item returns, supplier reliability, consumer price elasticity, labor needs, and much more. Generative AI is serving as a trusted, intelligent co-pilot to brands, helping augment their decision-making and problem-solving.”
Indeed, there are scores of use cases for AI in supply chain.
Demand planning and demand forecasting: Supply chain leaders can leverage AI to improve their demand planning and forecasting by quickly integrating data from both internal and external sources in order to make decisions and predictions about the ideal number and type of products they should manufacture.
And while still growing in mainstream adoption, AI’s use in this area is one of the top areas of focus for companies that have invested in AI: When asked about their top use cases of artificial intelligence and machine learning, 26% of consumer goods manufacturers cited demand planning as one of their top three, according to the 2023 Retail and Consumer Goods Analytics Study.
Inventory management: Artificial intelligence can be used in inventory management to optimize the ideal location to ship and sell products. Crunching and analyzing massive amounts of consumer behavior data, for example, could help retailers sell products that align with consumer trends through hyper-localized assortment strategies. In the Analytics Study, 23% of CG execs who said they were using AI/ML said inventory management was one of their top three use cases.
Customer service: Perhaps one of the most commonly known AI use cases, manufacturers and retailers can use AI-infused chatbots to efficiently manage inquiries and resolve problems. Quickly resolving customer service issues enables them to preserve customer relationships, build loyalty, and maintain brand value.
It’s important to note, however, that while widely recognized as an AI use case, chatbots may still have a long road to becoming truly valuable. A Gartner survey found that a mere 8% of B2C and B2B customers use a chatbot during customer service interactions and just 25% report that they would use a chatbot for assistance in the future.
Sustainability: Manufacturers can leverage artificial intelligence in logistics and supply chain to help advance their sustainability goals and stay compliant with regulatory mandates, such as by optimizing their energy use in factories and/or their transportation capacity in order to reduce emissions.
For example, Nestle is using AI to decrease its energy and water consumption, as well as improve transportation efficiency. The company has invested in digitally enabled control towers that manage more than 16,000 trucks daily and has reportedly improved cost efficiency, resilience, and responsiveness.
Cross-functional decision-making: Artificial intelligence can be leveraged to support a company’s decision-making capabilities and improve collaboration across functions. For Danone, rising consumer demand paired with supply chain disruption created a need to digitalize the company’s end-to-end supply chain planning platform so that all stakeholders could collaborate in real-time. As a result of the investment, Danone was able to run its planning processes across its commercial, finance, and operations teams; time horizon on the integrated platform; and perform real-time scenario planning to shorten decision-making timelines.
Labor optimization: AI in supply chain use cases extends to even a company’s most important asset: its employees. By using AI within their employee operations, companies can optimize scheduling and employee efficiency, potentially improving employee satisfaction and safety. For example, they can analyze the ideal number of employees to have working during a particular busy season to reduce excess costs.
How AI can solve supply chain problems
There are many different ways that AI can improve supply chain and logistics management. For example, artificial intelligence and machine learning can be used to:
- Minimize excess inventory by predicting the type and number of products CPGs should manufacture during seasonal periods
- Reduce energy waste and emissions by identifying factory equipment that isn’t being properly utilized
- Cut factory equipment maintenance costs by analyzing when factory equipment is likely to require maintenance so companies can be more proactive about repairing and replacing them
- Improve customer service through the use of chatbots
- Identify ongoing issues in product manufacturing by detecting partners in these customer complaints
- Bolster employee efficiency, satisfaction, and safety by optimizing labor scheduling
- Improve delivery times and decrease emissions through last-mile route optimization
What are the benefits of Internet of Things supply chain?
The benefits of IoT in supply chain are wide and varied thanks to the network of devices capturing and transmitting data in real-time. Heat sensors, for example, can recognize when an oven is overheating and then trigger preventive alerts or actions, while cameras can monitor product defects for root causes or count quantities to detect out of stocks, says Adheer Bahulkar, global supply chain lead of Accenture’s consumer goods and industry practice.
“IoT essentially provides real-time situational awareness that allows for rapid preventive measures or continuous learning in the case of pattern recognitions,” he notes. “Usage of IoT is currently most significant in four-wall operations for manufacturing and warehousing, as well as in transportation for real-time inventory visibility and all the way to points of sale in retail to predict out-of-stocks and gauge consumer sentiment.”
IoT when used with artificial intelligence and machine learning has the potential to transform the supply chain planning process to one that’s more concurrent, according to Amber Salley, senior director analyst at Gartner.
“If you really think about the planning process, it's very ritualized [and] very sequential. You create your forecast, and then you create some supply plans, and then you do some kind of balancing,” she explains. “But with machine learning and AI, and being able to do things faster, it goes from a sequential process to a more concurrent process, and aligned with IoT, you're constantly getting signals from the physical environment. Then you can take those signals and make decisions with them more frequently, especially in the operational time horizon.”
What are the disadvantages of IoT?
As with all emerging technologies, use of IoT isn’t all sunshine and roses. For one thing, it has the potential to overburden companies that aren’t prepared to manage their complexity and the data they provide. “For retailers, data continues to proliferate at exponential speeds,” notes IDC in its FutureScape: Worldwide Retail 2023 Predictions report. “Every new Internet of Things (IoT) sensor, camera in the store, or back-end technology solution creates another integration point that must interface with legacy data systems.”
What’s more, it can be difficult to truly unlock the value of IoT without having the right talent. “It requires technical competencies that may not exist within a retailer's pre-existing supply chain/ business model,” notes Steve Statler, chief marketing officer and ESG lead at Wiliot, a provider of ambient IoT technology.
Other challenges of IoT in supply chain include the fact that IoT can require robust network connectivity for optimal use, which isn’t available in all regions. It’s important to note, however, that the progression of 5G is expected to propel commercial industrial IoT applications, including fully automated and smart factories, according to Steve Keonig, VP of research at the Consumer Technology Association.
“5G means faster mobile broadband for consumers,” he notes. “But for commercial industrial IoT applications, it's really the greater capacity and ultra-low latency that is going to unlock so much innovation, and we're going to see that across this decade.”
What is an example of IoT in the supply chain?
There are a number of different ways that IoT can be used in the supply chain. Steve Statler, chief marketing officer and ESG Lead at Wiliot, a provider of ambient IoT technology, shared some of the most commons ways the technology can be used for CPGs:
- Inventory management: This can be particularly valuable for omnichannel retail to provide better on-shelf availability and improve customer satisfaction.
- Waste reduction and food safety compliance: IoT can be leveraged to reduce shrink and food waste
- Sustainability: IoT can enable tracking and tracing of raw materials to ensure manufacturers remain compliant with today’s ESG mandates.
For an IoT in supply chain example in action, we can look to Istanbul-based bottler Coca-Cola İçecek (CCI), which built a digital replica of its manufacturing plants, known as a digital twin, that used machine learning and IoT to help identify machine failures. The company leveraged the technology within its bottling plants to receive a holistic view of its manufacturing process and ultimately improve communication between the facility operators and IoT devices.
As a result of the investment, CCI reduced the environmental impact of CIP process and saved 1,236 kW of energy, 560 m3 of water, and 2,400 L of cleaning agent, according to its tech partner, AWS. It also reportedly optimized CIP process time and cost performance, and improved CIP process visibility for plant operators.
How is IoT used in inventory management?
Inventory management is one specific and common pain point for many consumer goods manufacturers and retailers. As mentioned above, by tracking inventory from raw materials to retail stores, using IoT in inventory management can provide manufacturers with visibility throughout the entire value chain.
“Since IoT can be deployed to real-time visibility on inventory at a case, pallet, and container level, it can be a very powerful mechanism to inform the business about the best decisions to make,” notes Bahulkar.
This real-time visibility can help with inventory management by quickly scanning available goods to ensure they are ready to deliver, as well as help ensure the right amount of inventory is on shelves. When it comes to products susceptible to spoilage (e.g. food waste), this capability can be particularly valuable.
Which companies use IoT in supply chain?
Use of IoT in the supply chain is still emerging. When asked about their current and future supply chain innovation plans, 35% of retailers and consumer goods manufacturers in a November 2022 CGT/EnsembleIQ study said they had implemented IoT devices while another 24% planned to add them.
In addition to the above example of CCI, Heineken is another promising IoT in supply chain case study: The company developed a Connected Brewery Ecosystem that marries an IoT platform with apps and analytics to optimize its manufacturing and distribution. The marriage of technologies enables the alcohol beverage company to view operational performance indicators in real-time on a scale that was previously impossible.
What is the future of IoT in the supply chain?
Coresight Research believes that IoT will become an important tool for sustainability and efficiency as artificial intelligence continues to advance, and the two can evolve together.
“As more retailers employ AI-enabled industrial robotics in warehouses, the sensors on these robots will be able to collect massive amounts of environment data and feed it in real time to warehouse operators and managers,” according to John Harmon, CFA, managing director of technology research at Coresight Research. “Comprehensive, descriptive datasets could include data about temperature, motion, lighting, GPS position, sound, and more — far beyond the capabilities of traditional sensors such as stationary cameras.”
What’s more, machine learning algorithms could analyze complex datasets to supply managers with actionable insights, predicting such factors as the labor capacity required at a warehouse, types of required transportation, and the optimal routes, he adds.
Will AI take over supply chain?
Very short answer: No.
Longer answer: Artificial intelligence is merely another tool that can be used to help companies develop a more resilient, collaborative, and sustainable supply chain.
Even longer answers, with more nuance and perspective, about whether the future of AI in supply chain includes AI taking over, courtesy of a few supply chain experts:
Accenture’s Bahulkar noted that today’s work environment needs to evolve, and the manual and transactional work that employees are often saddled with could be AI-enabled in order to provide more time for strategic and collaborative work.
“Embedded into the enterprise digital core, AI has the potential to transform the ability to optimize tasks, manage data, create faster insights, innovate with new experiences, augment workers, and connect and communicate with customers,” he says. “Every role in the supply chain has the potential to be reinvented, with people working with ‘AI co-pilots’ becoming the norm. We can also expect a large number of new tasks for people to perform, such as ensuring the accurate and responsible use of AI systems. It’s why organizations that invest in training people to work alongside generative AI will have a significant advantage.”
Wiliot’s Statler agreed that AI will be a major factor, but it still requires source data, which is where he says ambient IoT can play a role as a gateway between generative AI like ChatGPT and physical goods in the supply chain.
“AI will make supply chains leaner, will improve on-shelf availability, and will enable risk to be managed with stress tests to understand where vulnerabilities are to the external events that are constantly impacting supply chains.”
Keith Moore, CEO of AutoScheduler, a provider of warehouse resource planning and optimization technology, was even more blunt: “No way. AI is certainly going to be a bigger part of the picture as we move into the future, but we are still a long way from Skynet and artificial general intelligence.”