ai in manufacturing

7 Ways to Use AI in Manufacturing

The manufacturing industry is on the verge of witnessing an unprecedented boost in operations with the incorporation of AI in the supply chain and accelerating research and development. The major companies of the world are increasingly adopting digital means of production and distribution for a host of advantages it offers, right from the board executive, down to the foot soldiers doing the actual labor to bring in the revenue.

Many companies are yet to tap into the full potential of AI, and one of the reasons they are lagging is the inadequate knowledge and practical applications of AI, machine learning, and deep learning algorithms. This article discusses how the manufacturing industry and its facets can benefit with AI in the front seat, driving the firm straight into profitability and efficiency.

How AI has benefitted the Danone group?

What AI does for your company depends upon how AI is used within an organization. Let’s take the example of Danone Group, a French food manufacturer that used machine learning models to improve the accuracy of the forecast for its demand. After replacing the traditional means for the forecasting gig, the company observed the following improvements;

  • A 20% reduction in forecasting errors
  • A 30% reduction in lost sales
  • And a 50% decrease in the workload of demand planners

While this is how Danone Group reaped the benefits of the emerging technology, AI and ML models have a lot to offer to the manufacturing companies of the world, regardless of what goods they produce.

7 uses of aI In Manufacturing

Some companies within the manufacturing industry have already yielded remarkable results with AI in the picture. Here are some of the key uses of AI in manufacturing.

  1. Quality Checks:

Quality checks are conducted by manufacturing companies to ensure that the units are tested thoroughly and given a green light before they hit the shelves. This phase ensures that the final products do not have any defects in them, and thus, is one of the most crucial stages of the whole manufacturing process.

At times, even experts can overlook certain things, and fail to detect minor flaws. AI can see what the human eye cannot. With Machine learning technologies, companies can conduct and even thoroughly quality checks. These models can flag even the minuscule defects, improving the efficiency of quality checks magnificently.

  1. Forecasts failure of equipment:

Manufacturers have to often deal with machinery and equipment failure within the production facility. This poses huge challenges to the manufacturers, including loss of money and delay in delivery, among other things. AI and ML models can forecast the failure of equipment based on some quantitative and qualitative input about the machinery, something that humans simply cannot do.

Moreover, AI/ML models can also create predictive maintenance systems that can communicate equipment failures, and take actions to prevent that eventuality. This would save the company from a lot of losses, especially in the long run.

  1. Supply-Chain Management:

Supply-Chain Management is a multi-faceted stage that deals with procurements, information management, inventory management, supply chain planning, asset management, supply chain strategy, and logistics management.

Additionally, various actors are involved in this phase, including suppliers, manufacturers, warehouses, stores, and customers. With AI’s integration into the supply chain management, not only can manufacturers benefit thanks to computer vision, image and speed recognition, and robotics process automation, but warehouse staff could also make their operations smoother.

  1. Product demand forecast:

Another major benefit of AI in manufacturing is its ability to forecast product demand in the market. Human effort in market research can only do so much. AI on the other hand can use the advanced predictive analytics tool to efficiently predict what the customers in the near future, based on the current and past trends. Such predictions are made after data on the customers is gathered from multiple sources, and the same is processed and interpreted by the AI models to give valuable insight into consumer behavior.

  1. Price forecast:

With data collection and processing under the belt, ML algorithms can also be used by companies to predict the prices of the products, and come up with competitive prices to stay ahead of the curves. Naturally, companies that employ AI would drive more profitability than firms that still rely on traditional methods of price forecast.

  1. Generative design:

Generative design can use ML algorithms to assist designers and engineers in creating countless design options by inputting certain design parameters. Such parameters could include, size, weight, raw materials, manufacturing means and methods, and other specifics. Once the ML model receives input on these, it can create the possible outcomes with the parameters and make it much easier for the manufacturer to choose from several designs.

  1. Process optimization:

Manufacturing consists of several processes, which AI can optimize to reach sustainable production levels quickly. One of the chief ways through which AI can simplify and optimize the manufacturing process for companies is by providing a process mining tool. With this tool in the bag, manufacturers will be able to compare the performance of multiple factories in varying regions on the basis of process steps, cost, duration, and the individual behind the step, to ensure timely delivery of goods to the consumer.

These are only a few uses of AI to name in the manufacturing industry. The manufacturing sector is a multi-facets industry, with several complex processes and sub-processes involved. Converting raw materials into finished goods is a lengthy and intricate process. AI can contribute to closing the gap between production and delivery, and making the whole process extremely cost-effective.

Moreover, with AI in the game, the pressure on the human force to keep up with the demands of the customer reduces, as the AI/ML models take it upon themselves to deal with both, mundane tasks, as well as complicated activities. In some cases, AI can also be trusted to make critical decisions on behalf of humans. While a lot of manufacturing companies are yet to embrace the wonders of AI, many companies have already begun to use AI to streamline their operations and business processes.

2 Real-World Examples Of Use Of AI

  1. Suntory PepsiCo and Quality control:

Suntory PepsiCo is a beverage manufacturing company with several factories in Vietnam. The company faced inaccurate scanning of manufacturing and expiration labels on the goods due to smudges on the surface of the bottle. Needless to say, this led to several challenges, including production delays and stoppages, which took a toll on the company’s finances. To deal with this challenge, the company sought to integrate machine vision inspection with its operations.

The AI-powered solution, equipped with cameras, and sensory units can read code labels and provide accurate information in it, including whether or not the label is properly stuck on the bottle, whether it is correct, or whether it is unreadable, smudged, and more. If there are any inconsistencies, the issue would be flagged to the manufacturers and corrective steps could be taken on time.

  1. BMW and Production:

BMW is one of the top automobile manufacturers in the world. The company has now sought to boost its production rate by integrating AI-powered solutions to take care of mundane activities, which were otherwise done by humans. These include quality checks and control, logistics management and coordination, and virtual layout planning.

The manufacturing industry has still a long way to go to truly embrace all the aspects of AI. But with an increasing number of companies willing to invest in gaining knowledge about artificial intelligence and subsequently dishing out money in training the employees to use AI in the daily operations of the firm, we are working our way towards an AI-first future. This would prove to be beneficial to both corporations and customers. If you’re a company considering adopting AI-ways, it’s vital to consult professionals to learn the next steps.

What is ANAI and how can we help?

ANAI is an end-to-end machine learning platform to build, deploy and manage AI models at a faster rate saving a ton of time and money spent on building AI-based systems. It enables entities to handle and process data, create exploratory and insightful visuals, and make the data ML ready.

ANAI’s AutoML pipeline, utilizes the transformed data and extracts the correct features from it so that the model learns the most important details from the data. The data is then passed to the ML pipeline where various ML models are trained and only the best out of them are selected for deployment. ANAI’s MLOps allows users to keep a tab on their models even after deployment to check for model drifts and performance issues.

But due to all this automation of AI, there’s always a chance of an untrust regarding the model results and as AI models are already termed black boxes, because they provide no insights within their functioning, it again becomes more difficult. To solve this ANAI also has a model explanation pipeline called Explainable AI (XAI) that generates explanations on the model’s results allowing us to look behind the curtains, remove biases and other inconsistencies, and ultimately create a trustable, fair and responsible AI system.

ANAI can help in strategizing to enable companies to capitalize on the AI options available to them. This would help in boosting business prospects, as well as help manufacturers from avoiding downtimes and save money on multiple resources.

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