Last updated on December 14th, 2021 at 11:52 am
Machine learning could change the way businesses think about producing goods and services.
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If there’s any technology on the market right now that could transform production management on the whole, it’s Machine Learning.
ML is a tech trend that is reshaping the manufacturing industry. Amidst the changes it brings, there are a host of benefits for production managers, from greater transparency to real-time data streams that inform supply chains. With these benefits, the application of ML is boosting production across industries.
From its role in food, product, and material production, Machine Learning supports advancements in management technology that are changing the way we go about all kinds of manufacturing and agricultural processes.
Before we relay all the applications of Machine Learning in the production sector, it helps to define ML. Machine Learning is a subset of Artificial Intelligence (AI) — one that is capable of making inferences from patterns it discerns on its own. An ML system can dynamically conclude data sets, but it differs from Deep Learning in the number of layers explored by its mathematical models.
The implications of these tools are incredible when it comes to managing a production floor. That’s because modern data tools allow us to draw vast amounts of information from virtually any business process, which can then be explored through ML systems.
But all that is abstract. Instead, here are some examples of how ML is already being applied in food, product, and material production.
One of the most compelling instances of ML in food production is the application of this technology by Plenty, Inc. in California. Plenty builds vertical indoor farms that grow everything from strawberries to lettuce with the help of thousands of sensors and monitors, all connected to the Internet of Things (IoT). The IoT is instrumental in making Machine Learning possible, as it provides the network for raw data needed for ML systems to adapt to a process.
With Plenty’s tech, ML systems are capable of adjusting the water use and environmental conditions of the cleanrooms in which the food is grown. This allows them to produce 2 million pounds of clean lettuce every year while only using 1% of the water needed by conventionally grown food.
There are also companies using ML systems to innovate their manufacturing processes. In one example, Suntory PepsiCo teamed up with Pacific Hi-Tech to develop “Machine Vision” for their packaging and labeling procedures. Cameras, sensors, and AI algorithms all running on the IoT make this system possible by evaluating labels to determine if they are missing or illegible. From here, the company can automate and streamline its quality control efforts. This demonstrates how ML is enabling greater automation in manufacturing and shaping a more efficient tomorrow.
Finally, there are applications for ML within the material production and innovation space. These uses of Artificial Intelligence may be vital for developing the kinds of materials we need to support sustainable production in the long term, as proven by the work done by companies like Citrine Informatics.
Citrine uses ML systems to optimize material and chemical properties, streamline production guidance, and promote systemic reuse of materials. This allows production managers to make the most of their supplies and cut down on needless waste, while the ML process learns better ways to integrate materials efficiently.
Production management, regardless of industry, can be simpler with the help of these highly automated and powerful technologies. After all, ML is designed to dive deep and automate processes that were too complex for machines previously. Now, decision-makers in food, product, and material manufacturing all stand to benefit from the applications of these tools.
Machine Learning is evolving the production industry. From food to materials, we can use this tool to apply the insights of big data to the ease and convenience of automation, allowing for all kinds of business improvements. From oversight to risk mitigation, Machine Learning is the future of management because it empowers fact-based business decisions with the most recent data.
These are just a few of the ways that ML systems are stepping up to advance data-driven decision-making in production management:
- Allows for predictive calculation of supply and demand
- Enables automation of trade and market analysis
- Streamlines oversight of supply chains through data communication
- Enables equipment surveillance and predictive maintenance
- Allows for IT threat reduction through pattern recognition of cyber threats
Because of these benefits, all kinds of production facilities are adopting ML processes. The popularity of these tools has continued to increase as the technology improves. Now the Machine Learning industry is worth over $6.9 billion, with a CAGR of 43.8%. This represents a rapidly growing market share because it translates to endless value for the companies that apply it successfully.
As you explore the potential of AI and Machine Learning in your production management, keep in mind the range of benefits ML can offer you. These perks are only the beginning.
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