How AI Is Transforming Industries With Smart Inventory Management
Smart inventory management could lead to greater efficacy for industries. Find out other ways AI is enabling transformation.
Edge centers, gateways, cloud data centers, supercomputers, and IIoT hardware and software are the tools the technology industry is bringing to supercharge the movement. And AI is at the heart and center, says Ryohei Fujimaki, founder and CEO of dotData.
Supply chain disruptions, inflation, seasonal shifts in demands, carbon emissions, and energy usage. These are the most significant global challenges the manufacturing industry faces, and all these issues converge into inventory management. From raw materials to finished products, optimizing supply and demand, all roads in manufacturing lead to inventory processes.
Traditionally, inventory managers work the product lifecycle using manually constructed spreadsheets. Decisions in this model are made using the available information, and the experience of leaders becomes a major differentiator. While several industries still hold on to the old ways, the post-pandemic digital transformation and acceleration of manufacturing have been a tectonic shift for the industry. Most companies have already digitally transformed or have plans to do so.
A Forrester report commissioned by Bounteous found that failure to transform digitally translates into revenue growth impact, return on investment, and bottom line. More than half, 66% of companies surveyed, say they are transforming to have the agility needed to meet the demands of customers, workforces, and the environment. 42% of digitally advanced companies are experiencing double-digit growth.
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Industrial IoT (IIoT), 5G and low power connectivity, edge-cloud computing, and smart platforms driven by machine learning (ML), business analytics apps, artificial intelligence (AI), and digital twins are today the tip of the sword of inventory management. Big tech companies—staying ahead of the demand curve—are stepping up their offerings to support the industrial transformation. For example, Lenovo, in mid-2022, presented the broadest infrastructure portfolio in its history, with more than 50 new and enhanced products and services for the new era of digital transformation.
Industry 4.0: Real-Time Monitoring, Optimization, and Forecasts
Optimization, automation, and innovative technologies are driving the fourth industrial revolution and the shift to the new data-driven era. For industries that deploy hundreds of thousands of IIoT devices, employ large workforces, and have hundreds of processes running live simultaneously, the only solution to real-time visualizations is edge-cloud platforms driven by AI.
Visualization is a critical component of inventory management. If a company does not know what materials will arrive, which are needed, the status of their production line, people and devices, logistics, and demand, they are navigating blind. Minor disruptions, for example, a machine in downtime, can lead to an entire shutdown of operations. Therefore visualizations require monitoring and tracking as well as preventive forecasting.
Top vendors turn to AI for its ability to rapidly process Big Data, propose solutions, find patterns, and prevent disruptions while minimizing human errors, increasing performance and reducing costs. “Material goes in one end of the factory, and finished goods come out the other—what happens in between is often mysterious and costly,” IIoT World reported in January 2023.
As data flows from sensors to devices to edge gateways to cloud platforms, visibility, ML, and AI become the best allies for decision-makers using Just in time (JIT) inventory management—ordering and producing only as they are needed for the production process— and other cost-efficient models. But real-time does not provide the necessary speed companies require to pivot rapidly.
Leading manufacturers have switched from real-time to predictive modes to cut costs while delivering production, better manage supply chains and partners, and manage inventories, production, sales, and shipping. AI algorithms help companies forecast inventory by factoring in data features like season demand, economic environment, supply chain status, in-house workers, and customer trends.
Predictive maintenance is also essential for inventory management. Decision makers need to have a bird’s eye view of the processes throughout the lifecycle, and keeping their resources in the best health is primordial to avoid deviations or disruptions. AI software can run auditing and financial procedures, automate inventory system transactions and find new alternative routes when needed while preventing issues with machines, devices, and people by identifying the root causes of problems and solving them before they occur. In this cutting-edge predictive inventory management world, digital twins are born.
Digital Twins: Advanced AI-Powered Simulations
Microsoft describes digital twins as “virtual replicas of a physical object, machine part, system, process or entire lifecycle.” Not only can digital twins enable real-time monitoring and control using ML and AI, companies autonomously update, do preventive maintenance, and improve systems and designs.
But digital twins shine the most when they are used to creating advanced simulations of possible future environments. Inventory managers can simulate how businesses would be impacted if they change their supply chain, release new products, market new regions, and more. The possibilities for simulation scenarios are infinite and customizable for every company.
Traditionally, any environmental or physical change in the industrial process was done in real life, and even when changing the slightest component or factor, new unseen variables will arise. With digital twins, the unexpected becomes tangible as AI applications executive very realistic simulations revealing a 360 vision of something that has not yet happened.
AI can provide a snapshot of the possible future, and it can also go beyond, creating living realities that change as the environment shifts. From product design to factory optimization, supply chain management, energy, carbon emission optimization, and new physical spaces, digital twins allow companies to simulate inventory management processes and issues—often with millions of dollars in the line—without risking losses.
NVIDIA reported on December 16, 2022, on the opportunities presented by the U.S. CHIPS and Science Act, which includes a $13 billion research and development investment. The leading technology company explains that to get manufacturing right, not only a highly skilled workforce is needed.
While companies like BMW are already building a digital twin for their new plants, and “no one has built anything as complex as a digital twin of a chip fab yet, the goal is now within reach,” the company known for its AI processors and innovation assures.
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But digital twins and AI technologies alone are not the complete manufacturing solution. Forbes reported in early 2023 that while there is growing excitement about advanced AI solutions and their role in solving the supply chain crisis, AI analytics only helps with one-third of the problem. Execution is two-thirds of the solution, according to the media.
Leading Transformation
Innovation must be met with frameworks and new approaches. JIT, on-demand manufacturing, and direct-to-consumer models, where companies open new digital and physical sales points that they own, manage and operate, are some of the new trends rising for their potential to navigate the complex contemporary world and its socioeconomic and political factors.
In the near future, most leading manufacturing plants are expected to have deployed AI and digital twin technology. Reaching AI maturity level, transitioning to operational digital transformation status, and combining technology with processes, people and frameworks is how leading industries transform inventory management.
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