As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Lenley Hensarling, the Chief Product Officer at Aerospike, explains how AI and ML technologies can enhance the supply chain throughout manufacturing...
As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Lenley Hensarling, the Chief Product Officer at Aerospike, explains how AI and ML technologies can enhance the supply chain throughout manufacturing processes.
For the past three years, we’ve seen that many industries are tugging on a strained supply chain, especially in the manufacturing sector. Quantities are limited, high prices are unsustainable, and delays are anywhere from frustrating to catastrophic. While the problem is evident and tangible and is hurting businesses and consumers, there has yet to be a viable, holistic solution. Could artificial intelligence (AI) and machine learning (ML) be the silver bullet for fixing the manufacturing supply chain?
AI and ML bring countless benefits to solving supply chain issues in manufacturing and, once honed, could be the solution we’ve been desperately anticipating—not just because automation helps a business grow and remain competitive but because it can also lead to lower total costs.
With legacy technologies, large amounts of data can hinder speed and performance. However, AI and ML technologies function at their best with more data; they have insatiable appetites for it. More data means better model training so that a system can learn underlying patterns. More training, tuning, and validation will lead to greater accuracy.
Streamline Logistics
Logistics requires pulling disparate signals’ data from various sources—including data centers in other countries. Using AI tech to process this data in real-time from the global supply chain can enable more streamlined logistics. The real-time benefits that AI brings can yield more efficiency, too. For example, it can help distributors optimize delivery routes and schedules by providing up-to-date information on traffic, weather, and other factors affecting delivery times. Companies can pivot from looking through a foggy rear-view mirror to viewing situations through a crystal-clear windshield.
Machine learning also enables more descriptive and predictive analyses, helping teams shift from asking, “What happened?” or “Why did it happen?” to “What is happening?” or “What will happen?” These insights can lead to wiser, real-time decision-making and fuel effective planning.
Enhance Visibility and Traceability
Leveraging data through AI also results in enhanced visibility and traceability. AI-driven models can support the tracking of materials and products from the source to the end-user in an instant. This can help companies identify quality issues (by pinpointing a single flawed component) to track recalls for the highest level of safety and ensure compliance with myriad government regulations.
Additionally, manufacturers should consider implementing AI-based tech to gain real-time visibility into logistics, transportation, wholesalers, retailers, suppliers, and other audiences. Gaining real-time visibility into supply chain operations by collecting and analyzing data from various sources is crucial in helping companies identify bottlenecks, delays, and other issues, allowing them to take corrective action quickly.
Boost Collaboration
Maintaining manual communication and collaboration with all the teams and individuals it takes to move from material to market can be time-consuming and costly. Minutes spent identifying and correcting a problem minimize the minutes available to exploit innovation opportunities, leading to dollars lost across the board.
The data supported by AI technology enables stakeholders in the supply chain to collaborate more effectively by allowing them to share real-time information. This can reduce errors, improve communication, and ensure all parties work toward the same goals. Consider automating control systems in a warehouse, using facial recognition to ensure security, or using a chatbot to interact with online customers. Even small businesses can use AI-driven ChatGPT to perform routine tasks so employees can fill more strategic roles.
In fact, a recent study from the National Bureau of Economic Research of 5,179 customer support agents finds that workers with access to an AI-based conversational assistant were 13.8 percent more productive than employees who did not. The newest workers received the most significant benefit, reporting that the tech helped them work 35 percent faster than without the AI’s help.
Improve Inventory Management
Leveraging real-time data through AI also contributes to better inventory management. Implementing an online system that streams data and makes decisions in milliseconds optimizes this process. Goods can disappear from the shelf faster than teams can record the shipment or replenish supplies, but automation can overcome this gap and ensure that goods are produced faster. Companies can manage inventory more effectively by tracking stock levels in real-time to notify purveyors and partners of depleting resources or to rectify overages. This can help reduce waste, optimize storage, and ensure that products are available when and where needed.
Through real-time systems designed to make instantaneous decisions, AI and ML can enable companies to streamline logistics, improve visibility and traceability, enhance collaboration, and optimize inventory management. This technology saves time and resources so companies can focus on their core business priorities rather than troubleshooting and routine tasks. Today’s complex supply chain ecosystem requires companies to automate to keep up with demand, stay competitive, and lower operating expenses.
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