Top 10 Ways To Remodel Manufacturing With Machine Learning

In today’s scenario, machine learning models are being put to use to enhance every aspect of a business. Right from marketing to sales to maintenance, there are numerous opportunities where machine learning proves to be handy. Some of the industry verticals which make use of machine learning for their businesses are manufacturing, retail, healthcare, Transportation, Oil& Gas, etc. Out of these, this article will focus on the manufacturing domain.


Data is a valuable resource for any organization. But big data is becoming increasingly popular nowadays. As most of us are aware, big data refers to a collection of data which is huge in size and grows exponentially with time. Such data is very large and complex that no traditional data management tools will be able to store it or process it efficiently. This is where machine learning comes into the picture. Machine learning models help manufacturers reach the one timeless manufacturing goal, which is producing high-quality products at minimum cost. The global machine learning market is all set to grow from $1.41 billion in 2017 to $8.81 billion by the year 2022. Having said that, let us now understand how machine learning remodels manufacturing.


What Is Machine Learning?


Machine learning is a subset of artificial intelligence which focuses on developing computer programs which can access data and use it to educate themselves. It is one which provides systems with the ability to learn automatically and improve based on experience without being explicitly programmed. Machine learning applications have the ability to learn, change, grow, and develop by themselves when exposed to new data. In simpler terms, machine learning is one where computers find insightful information on their own without being told where to look. Machine learning helps analyze large sets of data.


Ways in Which Machine Learning Remodels Manufacturing


1. Machine Learning To Revamp Quality Control


Machine learning and artificial intelligence are adopted for product inspection and quality control. Machine learning algorithms have the ability to learn from a set of examples and are skilled at distinguishing the ‘good’ from the ‘flawed.’ According to Forbes, automated quality testing done through machine learning helps increase detection rates by up to 90%. Even if advanced manufacturing techniques can be implemented, there is an inherent limitation when it comes to using humans to spot errors. Machine learning uses algorithms to inspect products and identify flaws more quickly. Besides the products, machine learning can also improve the machines which make those products.

2. Predictive Maintenance


Predictive maintenance refers to proactive repair work rather than reactive repair work. Maintenance is usually conducted after a problem occurs owing to the high costs incurred. Usually, when a piece of equipment is down, managers are faced with the impossible choice of taking the equipment offline. Machine learning helps identify the ideal moment to make that choice and eliminate costly and stressful guesswork. By predicting equipment breakdowns with machine learning, companies can be proactive and ensure that the machines are serviced regularly. The results of this are fewer errors, lower human-capital costs, and less downtime.

3. Machine Learning Minimizes Equipment Failures


Every time when a machine is taken out for maintenance, it implies that it is doing its job and that it may require factory downtime until it is repaired. Frequent equipment repair leads to losses, and infrequent maintenance leads to costly breakdowns. Machine learning algorithms help determine optimal repair time and balance multiple sources of data. It uses historical data to identify patterns in equipment failure and determining the regular maintenance schedules. This helps increase the speed and efficiency of machines and reduce manpower costs.

4. Inventory Optimization


The costs of storing inventory are usually massive as they account for around 20-30% of the cost of a product. Even a slight reduction in holding costs by 10%, helps minimize per-unit costs by 2-3%. We will have to pay for storage space if we hold unsold or undelivered products. Though this may not look like a major problem, it has an immense effect on cash flow. Machine learning is used here to calculate when to sell/hold inventory and when to increase/reduce the production of inventory. This is managed by monitoring the supply chain elements, holding costs, market prices, and production capacity.

5. Supply Chain Optimization


The task of optimizing supply chains becomes more challenging as the economy becomes more complex. Change in fuel prices, damages ships, and a single shift in weather can greatly impact our business. Machine learning optimizes each element of your supply chain by taking all these complex factors into account. It helps calculate the extra time which must be given for a shipment and deciding where to ship products from based on potential hurdles or possible weather patterns. Machine learning algorithms help increase supply chain efficiency by 10%. Machine learning helps in efficient and reliable production.

6. Machine Learning For Electricity Consumption


Electricity is one of the greatest inputs for any factory. While most of the factories operate 24 hours a day for optimal efficiency, it is possible to schedule energy-intensive activities at different times. This will ensure that those activities will occur when power is the cheapest. But this is not that simple as a myriad of factors need to be considered. Again, we use machine learning as it has the ability to process large amounts of data. This can help schedule the perfect time for performing energy-intensive activities through equipment maintenance, by minimizing inventory, and considering energy prices along with labour costs. This will also help intelligently invest in electrical infrastructure. Machine learning algorithms can help quantify the value of your factory’s electricity at any given time.

7. Providing Relevant Data


Machine learning provides relevant data which aids operations, finance, and supply chain teams to better manage factory and demand-side constraints. In some companies, IT systems are not integrated. This makes it difficult for cross-functional teams in accomplishing shared goals. Machine learning provides a new level of insight and intelligence to these teams and make it possible for them to achieve their goals of inventory, Work In Process (WIP), optimizing production workflows, and value chain decisions.

8. Machine Learning Revolutionizes Product And Service Quality


Manufacturers often face challenges in making product and service quality to the workflow level a core part of the functioning of their businesses. Quality is most often isolated. Machine learning revolutionizes product and service quality by determining internal processes, workflows, and factors which contribute the highest and lowest to the quality objectives being met. Machine learning the contribution of quality and sourcing decisions to Six Sigma performance within the framework of DMAIC (Define, Measure, Analyze, Improve, Control).

9. Increases Production Capacity And Lowers Material Consumption


Smart manufacturing systems are designed to capitalize on machine learning and predictive data analysis. These have the potential to improve yield rates at the plant, production cell, and machine levels.

10. Machine Learning Optimizes Shop-Floor Operations


Machine learning and real-time monitoring are now optimizing shop-floor operations. It also provides insights into machine-level loads and production schedule performance. Knowing a machine’s load-level in real-time leads to better decisions managing each production run. Machine learning now helps optimize the best set of machines for a given production run.




Modern manufacturing houses have now begun to incorporate machine learning throughout their production process. Machine learning algorithms are now being used to plan machine maintenance adaptively, rather than following a stringent schedule. Though modern manufacturing is largely automated, it is still reliant on the human workforce. But in the future, there is a huge chance for a large part of manufacturing being taken over by robots which would be flexible enough to cooperate with humans and perform tasks as humans do. With machine learning increasingly touching every aspect of our daily lives such as fraud detection, Amazon, Netflix, speech recognition, etc., it is certainly here to stay!