What are the most promising use cases for data mining in manufacturing?

Data mining is a game-changer. Here's how data mining can be a big help: 1. Predictive Fixing: It can tell us when machines might break down. 2. Quality Check: Data mining can spot problems in our products early. 3. Supercharged Processes: It can help us find places where we can work better and faster. 4. Supply Chain Wizard: Data mining can make our supply chain work like clockwork. 5. Energy Efficiency Guru: Data mining helps us see where we can use less power. 6. Problem Detective: When something goes wrong, data mining helps us find out why. 7. Supplier Scorecard: It helps us see which suppliers are doing a great job. Data mining is like having a secret weapon that makes our manufacturing process better and our products top-notch.

There are various domains that apply data mining techniques , the example of use case includes Higher education, social media analysis, financial.sector retail sector, gaming, customer satisfaction, science and engineering and many more.

Demand prediction is an important use case, not only for the manufacturing industry, but also for any business. For demand prediction projects, I relied on time series analysis algorithms, statistics and machine learning based. Using historical data - i.e. past demand values - together with context data - i.e. holiday calendar, seasonality, etc... -, it is possible to obtain a quite precise estimate of the upcoming demand.

My experience, it was a combination of demand forecasting & recommendations. Using data mining/machine learning: 1. You can forecast the demand for a product at a store level 2. You can predict the next best day when a store will order product 3. You can cluster stores and then predict product at store segment level 4. Combine 1,2 and 3 and get the overall demand and based on this demand, plan production & supply chain Benifits: 1. Optimized production 2. Right sales at the right time 3. Predict what a store needs and optimize delivery & van loading

Data mining helps us identify customer needs and how it's our product or service compared with their expectations, for example measuring some variables abstract as feelings turning it to a numbers or categories

The voice of the customer has been a tenant of sound product development methodologies for decades. Unmet needs manifest themselves in lost sales reports and complaint logs, and failure mode effects analysis methods challenge stakeholders to prioritize for diverse customer needs. The tendency, though, is to drive most into being fast, cheap followers. In the past decade, since "Moneyball" and "Everybody Lies," new analytical techniques are being leveraged to uncover latent, unexpressed needs. Simulation (the digital twin) and Bayesian techniques offer paths to be agile, to accelerate development, to differentiate, and to innovate.

Churn Prediction: Predicting customer churn by analyzing user data to proactively retain valuable customers and reduce attrition.

Data mining can help you to better understand your potential customers, actual customers and their segmentation to target the most fitting products or services for them. And also in case you have hesitated customer that might churn your product data mining helps you to better understand their actual need and why they tend to leave you, these information will help you to re-target them with better offers and retain them. As well as it can help the company with customer life cycle and how their purchasing behavior changes during their journey with you.

Within predictive maintenance, I have worked on anomaly detection. The problem with anomaly detection is that there are not many examples of the "anomalies" to train any machine learning algorithm. Here a change in perspective is needed, since we can only work on data describing a working system. On those data, then, we can implement alarm criteria warning us of "anomalies", i.e. possible failures. Anomaly detection is often based on outlier detection techniques for static data. For time series data, flow charts used to be the state of the art. A more modern implementation applies the flow chart techniques to machine learning prediction errors rather than to the raw data.

Finding a more accurate mean time to failure can be incredibly helpful to reduce unplanned downtime. If you find that a part of a machine consistently after 500-550 hours of runtime, by scheduling maintenance or part replacement at 475 hours can greatly reduced unplanned downtime and issues. This can also lead to more accurate uptime planning. More accurate uptime calculations can easily be fed into demand scheduling and production planning to create more realistic production schedules and forecasts.

Another one I would like to add here, "Decision making", because the manufacturing department involves multiple processes, and it generates different types of data. All these data we should be able to analyse properly and then take right data driven decisions. Decisions in process is important, and when we want to enhance or optimize any process, the data collected will help us to take the decisions. For this we can utilize decision trees, and based on different process we can have decision trees created and it will help us to identify the slow process, the resource allocation,duration and other related information. All these data will help us to build decision trees and then we can take proper decisions in business.

Resource allocation is an important process for all organizations, regardless of size or industry. By effectively allocating resources, organizations can: Improve their efficiency and productivity Increase their customer satisfaction Reduce their costs Achieve their strategic goals more quickly

There are hundreds of KPIs out of manufacturing, but the top two in my book are very easy to digest. On time to contract OTTC and turnaround time. If you are on increasing your percentage of on time to contacts and reducing your turnaround time, it’s going to be a successful story. Measure OTTC by calculating all of the contracts and find the percentage of on time versus late. Your on time should be much higher than late and increasing everyday. Also, TAT is simply when you started to when you finished the contract. This should get shorter and shorter with the insights gained from optimization and visualization.

Another major advantage of data mining over other experimental techniques is directness. Data mining helps to identify the problems and proffer solution accurately, thereby assuring directness overall. The use of data mining reduces the troubleshooting time that most companies lose in trying to get to the roots of the issues that give rise to consumer dissatisfaction because the data points to the root causes. Data mining also allows the problems to be tackled head on, by implementing the best, affordable solutions to resolve consumer dissatisfaction.