Manufacturers across the world are fighting to carry out sustainable products within a profitable time-span. However, the degree of complexities associated with product manufacturing, AI power predictive analysis, such as R&D, product trial, trial period feedback analysis, and so on, make the final product launch a stressful matter.
Since the last 7 to 10 years, lots of manufacturing defects stories have come in front of us related to different products.
In 2017, a man residing in Mumbai, India bought a car which included a defective turbo charger. After lots of legal proceedings, the manufacturing company had to pay a large amount of monetary compensation to that man.
But why such issues? Do manufacturing companies now care less about product quality and testing?
Definitely not. Now every business decision is made on the basis of several real-time data driven outputs. For such car manufacturing companies, the types of data include type of customer expectation, demographic choice, market trends for features, competitor analysis, and many more.
With ongoing time, the expansion of the data-set is getting bigger and bigger. So manual effort, and basic technology driven strategy for such a large amount of data analysis is not anymore a feasible way.
Data science has come up with a number of solutions to such critical issues in the manufacturing domain.
Let’s see, in which way, data science and AI are securing the future of the manufacturing domain and AI power predictive analysis.
- Fault Prediction and Preventive Maintenance
As I have mentioned a real-life scenario of getting a faulty car, let’s start discovering the use of data science in manufacturing in terms of fault identification and respective precautionary measurements.
Starting from real-time analysis of an engine behaviour or any other product feature to the possibility of identification of variable cause of engine failure, everything needs continuous monitoring.
Although conventional technical models have already surrendered to provide effective output in such causes, AI and ML have shown us a new way. With the help of complex ML algorithms and using expert system application of AI collection, analysis of such data is no longer a headache.
With the help of AI power predictive analysis, tons of small to small causes of engine failure, along with their predictive solutions can now be listed out along with respective issue solving recommendations.
1. Development of products and designing
Amongst all other branches of data science, AI power predictive analysis has the greatest degree of contribution in product development and designing.
Increasingly unpredictable consumer demands and satisfaction results make it difficult for the design and manufacturing personnel to maintain productivity. Not only does consumer loyalty affect product design and production, but so do other factors such as financial benefit, environmental outcomes, and legal constraints.
However, in recent years, most global product firms have begun to incorporate expert structures within their design and manufacturing units.
As a result, computer-integrated product design and data-driven, highly sustainable product manufacturing have emerged. A few examples include the manufacture of camera lenses, the construction of automobiles and smartphones, and so on.
2. Efficacy management with computer-integrated application
Irrespective of the size of the manufacturing business, what is the key concern as of now? Sustainability. Yes, sustainability is the concern that drives every manufacturing company, from small to large.
While customer expectations are obviously a critical concern, at the same time the manufacturers have to keep an eye on several environmental issues. Assume that carbon emission standards are the most important concern for the automobile manufacturing industry, and that radiation levels are the most important concern for the smartphone and electronic goods industries.
So, with traditional technical analysis, if you try to solve such a problem, to meet one criteria, you have to compromise with other criteria.
Here the intelligent logic directs a manufacturer to concentrate more on the environmental issues rather than the feature demand of the customer. But what is the ultimate result? The product now meets the legal standard, but will the customer invest in this as it will not fulfil his expectations?
AI comes with optimum solutions for such scenarios. The latest AI-Powered quality controlling strategy based on identification of manufacturer’s goal, detection and classification of predictable issues via computer vision provides the best-fit quality and most demanded product options.
Moreover, the options obtained from data analytics can be visualized by applying appropriate algorithmic models. Further modification of such algorithms can effectively draw insight into customer preferences, market demand, even further scopes of improvement.
In a simpler world, through an ML algorithm powered by computer vision, you can visualize the future of your target product.
Don’t you think this makes the efficiency management of manufactured products much easier?
3. predatory data analytics for performance & quality management
After design and development, the second most crucial measure of the manufacturing industry is quality management. Quality does not mean only the used material/technology/process/durability of the product.
In a competitive marketing environment, quality also includes measures such as meeting customer expectations, the scope of future modifications, as well as price increases for the same product, and so on. So, for the analysis of KPIs for product quality again needs predictive real-time data analysis. AI-optimized KPI identification applications can now come up with best-fit lists of key performance indicators.
The manufacturing strategy of products based on such KPI’s offers best quality products along with effective resource management (such as scrap reduction).
4. Competitive but sustainable pricing
If you have made an extremely advanced and attractively designed product, without proper pricing, you can survive on the market.
Sometimes, even after successful marketing, price-effectiveness, and high demand, manufacturers can continue some product. Why so?
It may be because of raw-material shortage, raw-material price increments, product modification, etc.
AI powered, mainly expert system-enabled ML algorithms can aggregate and analyse the cost-effectiveness of products both from the market competition and internal manufacturing source measures.
Lots of AI-powered price optimisation tools are now available to do a sustainable and profitable cost optimisation within a minutes. The ML algorithms used in such price optimization tools can optimise promotional strategy, customer segmentation, product segmentation, etc.
5. Automation, and robotics-The smart optimization of supply chain management
Automation translates into large investment. The journey of systems engineers and system architects is charted by the advances in data science and technology to lead them to cost-and time-effective solutions. Predictive and analytical methods are employed to assess optimal cost-benefit strategies
Finally, these ideas are applied to the design and production process, which allows companies to use their R & D ROI in robotics and advanced automation technology.
Today’s best manufacturing facilities are now using data science approaches to design and optimization, according to the article. Real-world data has enabled the application of new technologies, designs, and equipment to the manufacturing industry.
6. Inventory Control and Demand Forecasting
It is no longer the case that each customer interacts with just one seller. In fact, we’re seeing a supply chain going more and more towards B2B: multiple organisations are collaborating to provide one shared or linked inventory of inventory pictures in a straightforward, intuitive manner. Multi-party inventory sharing and visibility of the supply needs strong demand management in order to maintain good lead times.
So lead generations and inventory management is also becoming data-driven. Hence, there is no choice left other than to adopt AI and ML for such tasks. It’s again the expert system application, in which the system itself analyses the real-time updated data as well as the already collected historical data to reach the right inventory management strategy.
For the last phase before manufacturing of a product, there is the demand forecasting. This has just about everything. It involves both marketing and finance. High-volume forecasting is critical to identifying and managing the lifecycle of products. Again, AI is capable of performing these kinds of functions. Advanced artificial demand prediction may include a competitive outlook that includes both current and future scenarios, alongside likely competition and materials.
It is now apparent that manufacturing is just like other industries and will gradually transition to being data science based, too. If you work in manufacturing, it’s now or never will be. At present, the immense scale of the data science applications in industry does not pose much of a challenge to data scientists and the number of new positions open positions are still plentiful.
There will be several job openings during your career transition, so don’t waste time on waiting until it’s over. As long as this level of rivalry persists, the difficulty will increase. A good opportunity awaits you.
Don’t waste time. Today, statistics, data science, and artificial intelligence (AI) are used. Confused about how to start? Visit www.skillslash.com and submit your profile for review.