Businesses need to continuously look for ways to maximize profit without sacrificing product quality or employee productivity. The balance between these three aspects of business is can be hard to achieve, as most of the time, high quality usually demands greater attention and more expensive inputs and raw materials. Because of this, modern-day companies are making considerable efforts in automating parts of the business where they can produce efficiencies.

Some of the recent inventions that modern businesses can take advantage of are those that involve artificial intelligence, extensive data analysis and storage, and cloud computing. The significant technological strides in these areas have been improving manufacturing environments, thus, also improving performance. AI has also been providing critical information that helps key decision-makers make better decisions. Automation yields quality products more efficiently and quickly while also providing crucial information that can help managers develop an informed business decision.

Here are more specific ways AI and IoT can work together to help businesses improve their productivity.

Data-based Demand Forecasting

Machine learning and AI enable systems to test a plethora of production mathematical models and outcome possibilities and make precise analyses while adapting to sudden demand changes, supply chain disturbance, and new product introductions. Because of machine learning, companies that use it reduced their overall inventories by 20 to 50%. AI also introduced conveniences and efficiencies with simple physical inventory taking. For example, an inventory task big grocery chains could finish in a month can only be completed in less than 24 hours with the use of sophisticated drones flying through the entire warehouse, scanning items and checking misplaced goods.

Predictive Maintenance

The internet of things undoubtedly has made huge headways in creating impressive results in this business area. To improve businesses’ operating efficiency, they invest in predictive maintenance initiatives. These solutions have an immediate positive effect on a business’ bottom line. With sensors tracking equipment conditions and analyzing data continuously and in real-time, the company can minimize downtime and costly repairs. The same solution also enables organizations to implement equipment servicing only when necessary instead of on scheduled maintenance routines.

With the help of AI, machines can assess their own condition, place orders for their replacement parts, and even book a repair technician should they malfunction. This takes the guesswork out of the situation and minimizes errors in handling machines for repair. Big data algorithms can also be used to forecast future machine failures. Through this, companies can prepare and reduce instances of system downtime.

Optimized Manufacturing Processes

Recently, machines that could run learning algorithms and be capable of improving their manufacturing efficiencies were introduced. These machines significantly improved the way things are done in the factories. Since then, AI systems were used to monitor cycle times, quantities used, lead times, temperatures, downtime, and errors to highly optimize the manufacturing environment’s production runs.

Here, AIs were deployed on ‘operator assist’ mode, allowing them to run silently in the background while suggesting smart answers to its operator. These AI systems will then process the operator’s decisions and use the same to learn and master how the human brain functions and runs. These decisions could then be deployed to allow the same system to run on ‘operator replace’ mode. Further into the future, these AI systems are expected to analyze and transform numbers and data from a vendor-agnostic environment into intelligence, allowing it to speak to other machines in the same machine lingo. This is expected to increase manufacturing efficiency even more.

Automate Raw Material Procurement

Machine learning combined with analytics can record, analyze, and critique everything. It can go through the quoting stage and effectively establish a supply chain. Experts predict that machine learning could lessen forecasting errors by over 50%, effectively lessening transporting, supply chain administration, and warehousing costs by 25 to 40%.

In Summary

Productivity, efficiency, and accuracy are essential for businesses to keep their customers satisfied while also allowing them to save resources. While machines could never replace the human mind, the internet of things opened a vast window of opportunities for inventions and innovations, along with AI, big data analysis and storage, and cloud computing business has never been better positioned to improve productivity. These simplified processes have made production more streamlined and efficient than ever. Those who stand to profit the most from these innovations are the companies that take advantage of them and their respective target markets.

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