How Can AI Improve Manufacturing Operations?

AI has been proven to be a viable solution to the growing problem of energy waste in manufacturing. Manufacturers are experiencing increasing environmental pressures while energy demands also increase. A recent report from the EIA found that 60 percent of generated energy is wasted, and this presents a massive opportunity for manufacturers to improve their energy efficiency and lower their environmental impact while still increasing production year over year.

AI can be put to work in optimizing energy intensive processes such as HVAC resulting in significant energy savings. AI can also be very effective at analyzing complex demand environments and coming up with accurate forecasts. This demand forecast data can, in turn, make it easier for factories to utilize renewable energy sources. If implemented correctly, AI can be an invaluable tool for reducing both energy consumption and costs (Rambach, 2024).

How to Correctly Implement Manufacturing AI

The effectiveness of any AI tool lies in the quality of its data. In the manufacturing space, the relevant data includes:

• details pertaining to the operations of each employee and machine in the production line
• details regarding the movement and status of all components and finished goods within the factory
• data concerning the movement of goods through warehouses and across shipping lanes including the movement of raw materials from supplier to plant
• external demand-related data

Obtaining all this data can be a daunting task but manufacturers are increasingly turning to RFID technology to provide them with a valuable view into operations. RFID, or radio frequency identification, uses electromagnetic frequencies to uniquely tag objects or people (Amsler & Shea, 2021).

31 percent of manufacturers are already using RFID to increase efficiency and production quality. This adoption rate is expected to rise to 66 percent by 2029. This widespread adoption can be credited to the fact that many of the world’s largest manufacturers still find themselves largely in the dark about the granular details of their own operations.

The optimal process for implementing AI into manufacturing operations begins with analyzing business objectives and finding the correct metrics to evaluate them, then using this as a frame of reference to begin setting up a data capture plan. Data capture could come from sources such as RFID, RTLS (real time locating systems), machine vision systems, data feeds from robotics, or external sources. It is important that the data feed be established prior to implementation of AI tools. The data needs to be engineered in a way that enables AI or the huge effort involved in implementing will fail to drive any business value.

It is worth mentioning that most manufacturing executives pushing for increased levels of robotics, AI, and automation are not viewing these technologies as a way to replace the frontline worker, but instead as tools to be placed in the hands of a now more empowered frontline worker (Hickey & Hickey, 2024).

Additional Benefits of AI in Manufacturing

If implemented correctly, an AI optimized manufacturing operation could benefit from the energy savings mentioned above but also,

• fully automated supply chain planning,
• increased efficiency through the development of production schedules that maximize throughput at minimum changeover cost,
• predictive maintenance schedules,
• automated quality inspection,
• new product development based on market trends,
• more empowered, happier, and therefore more productive employees

How ACS is Utilizing AI

ACS Industries has not yet taken the leap to a fully AI optimized manufacturing workflow, but we have begun the process of gathering the right data and implementing localized machine learning solutions. Data feeds from our industrial PC’s have been converted into TTIP language and unique IP addresses have been established and catalogued for all of these machines. This allows us to collect telemetry data and utilize machine learning systems to optimize for both scrap and downtime. Our Romanian plant is leading the AI charge within the ACS manufacturing network: machine learning has already been fully implemented at a plant level in this location. ACS’s other manufacturing locations also utilize AI enhanced robotics, AI enabled computer vision systems and much more. 50-60% of our accounts payable functions are now controlled by AI. Robotic Process Automation has been implemented to replace tedious manual data entry tasks, combatting attrition within these roles. ACS’s Mexico Plant 3 has begun robotizing inventory management in accordance with Lean 6 Sigma production. All of these new technological tools are serving to significantly increase production efficiency, limit scrap, improve employee retention, improve demand forecast accuracy, and optimize all of our diverse workflows. We are excited to see what future benefits stem from our continued technological advancement.