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.

My experience is in predictive maintenance for Tetrapak. They have misregisters issue and using data mining/machine learning: 1. Understood different sensors and their behavior 2. Understood relation between sensors (temperature, torque, pressure, tension) and misregisters 3. Extracted features using data mining techniques. In this case, it was more on filtering the noise and extracting the required signal from sensors 4. Built an ML model to predict misregisters 10 minutes before it happens 5. Provide suggestions on the best settings to make across the system to resolve the issue Outcome: Save millions of dollars by avoiding misregisters.

Predictive Maintenance: By analyzing sensor data, historical maintenance records, data mining models can identify anomalies and predict when equipment is likely to fail. Process Optimization: By analyzing production data, machine performance metrics, and environmental factors, data mining models can suggest process improvements and optimize resource allocation. Quality Control and Defect Detection: By analyzing inspection results, and customer feedback, data mining models can identify patterns that indicate product defects and trigger corrective actions. Demand Forecasting: By analyzing historical sales data, market trends, data mining models can predict future demand patterns and guide production planning.

Data mining in your end to end process from order received -> supply -> production -> distribution will allow you to keep control on your supply chain and optimise it, it will allow you also to have visibility on every bottleneck that could cause delays in your delivery, and problem solve it real time Data mining will also allow you to continuously improve your processes, your products by delivering insight on quality, defect By infusing data into AI models you will also be able to detect more and do advanced quality check, more accurate and more often

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

Yes and— the ever elusive demand forecasting— critical for JIT and “pull manufacturing” — get it right and you benefit from being lean and responsive to your customers— get it wrong and well…. You likely need to balance multiple demand forecast and then stress test them. Historic data is great for business as usual forecasting— but what about black swan events? Anyone who experienced COVID likely understands how unpredictable predicting the future can be. Sensitivity analysis can help. So can simulation— you’re looking for asymmetrical vulnerabilities to shore up. Also forecasting and testing impact on safety stock and reorder points— the “reserves” you have in place to absorb errors and statistical fluctuation in your forecast.

The trouble with demand forecasting is that it touches so many different relationships at once. And when it's done poorly, it strains them all significantly. Suppliers need good forecasts so that both costs and risk can be better managed. Being able to find patterns and correlations in historical usage data, as well as evaluating how accurate forecasts have been--can be extremely helpful in better demand forecasting. It requires time and vigilance, but when done consistently, it can yield a much more reliable demand forecast. And better forecasts cultivate better relationships across the supply chain.

The integration of data mining in manufacturing for demand forecasting is particularly powerful. By incorporating diverse data sources and utilizing predictive models, businesses can proactively respond to market changes. This not only minimizes the risks associated with overstocking or understocking but also establishes a foundation for agile decision-making in the rapidly evolving landscape of customer preferences and external factors. This application underscores the strategic advantage of data-driven insights in optimizing operational aspects and fostering customer-centric approaches.

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.

Marketing efforts and research are in a continuos evolution of efficient products listening to consumer needs and facts about what they want of a product. Data is the key for obtain better understanding and develop efficient brands.

Data mining uncovers insights into customer preferences and market trends. Think of it as having a creative brainstorming partner, providing valuable information for designing products that resonate with consumer needs and preferences.

Data mining in product development, and even in the early stages of business planning has arrived as a powerful data driven alternative to the traditional way of doing things, we can do much less biased market research thanks to the automation of data extraction and analysis processes, the power that technology gives us is something that we should not underestimate and in fact good marketers or business owners should be aware of more than ever.

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

You can use text analytics on customer reviews, company social media, and customer service emails/calls to find issues with products and create automated alerts when there is an issue that needs addressing. These workflows are more efficient than having someone manually reading to find issues, which usually isn’t thorough and executed in a timely manner.

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.

Data mining in manufacturing presents a wealth of opportunities, including predictive maintenance, where advanced analytics and machine learning help preempt equipment breakdowns. Quality control benefits from data analysis to swiftly detect defects and boost product quality. Supply chain optimization uses data to streamline operations, reduce costs, and ensure timely deliveries. Manufacturers leverage data for product development and innovation by tailoring products to customer preferences. Lastly, data-driven analysis enhances overall efficiency and productivity by identifying bottlenecks, tracking KPIs, and optimizing resource allocation. These applications collectively revolutionize the manufacturing landscape.

Data mining enhances quality control by detecting patterns in production data. It's like having a super-powered magnifying glass, catching defects and ensuring consistent product quality, reducing waste and enhancing customer satisfaction.

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.

One of the use cases I can think of is PPDS - Production Planning & Detailed Scheduling. How to come up with a production schedule taking sales orders as input and keeping production constraints in mind so that sales orders can be delivered on a given delivery promise date? Some of the production constraints are raw material availability, assembly line capacity, manpower availability and business priorities. Imagine developing a production sequencing algorithm that accounts for the above mentioned constraints to recommend the most optimal production sequence. In addition, consider manual overrides by production team to accommodate urgent orders and also keeping production wastage minimal due to switching Quite an interesting use case!

Predictive maintenance becomes possible with data mining. It's like having a crystal ball for machines, foreseeing potential issues and scheduling maintenance proactively, preventing costly breakdowns and minimizing downtime.

While data mining offers significant advancements in maintenance planning, it's important to recognize its Limitations in settings where Machinery Lacks comprehensive Sensor Systems. To truly enhance predictive maintenance, a holistic approach is needed. This includes not only data mining but also logical and analytical calculations of component lifetimes and, crucially, feedback from on-site service technicians. Especially in cases of maintenance services following failures or claims, this human feedback is invaluable. By integrating data mining with logical analysis and real-world feedback, we can significantly enhance the efficacy of predictive maintenance strategies, even in environments with older machinery.

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

Of course, the most popular use case of ML in HR is related to attrition, which is a significant issue, especially in the production sector. By employing ML models, we can predict, for instance, which employees are at a greater risk of attrition. This allows us to make informed hiring decisions, selecting individuals whose loyalty we can be more certain about. Thus, ML can be a valuable asset in optimizing human resource allocation.

Data mining helps optimize resource allocation by analyzing usage patterns. Think of it as having a resource-efficient chef in the kitchen, ensuring materials and energy are allocated effectively, reducing costs and environmental impact.

Data mining in manufacturing offers a wide range of promising use cases that can help optimize processes, improve quality, and reduce costs. one of the use case is to help manage risks in a data-driven manner, such that they can: Determine common errors and avoid repeat losses, Predict insurance requirements ,Real-time machinery and operator work are monitored, Real-time failures and system irregularities are identified and plan risk management strategies.

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.

I have used data mining to give real time insights into production levels. This has helped our coworkers work towards a common goal in a transparent manner.

Fundamental Transformation of Manufacturing Among the applications mentioned, the most end goal of data mining in manufacturing lies in its potential to enable the development of truly autonomous and adaptive manufacturing systems. As data mining algorithms become increasingly sophisticated, they will be able to analyze vast amounts of real-time data from various sources, including sensors, machines, and even the surrounding environment, to make real-time decisions about production processes. This will allow for the creation of self-regulating systems that can optimize production parameters, adapt to changing conditions, and even initiate corrective actions in the event of anomalies or malfunctions.

Data mining in manufacturing helps ensure business processes are followed, lessening risk. It keeps operations inline with regulatory compliance, preventing fines and penalties. It can also highlight safety risks early, improve the supply chain, and prevent fraud. Overall, data mining helps manufacturers make better choices and lowers risks.

Of of the best use cases for analytics in manufacturing is material availability and production planning. Effectively visualizing material availability data, proactively communicating with suppliers, and making production plans based on this information allow for the seamless production.