Here's how you can optimize processes in Lean Manufacturing using data analytics tools.

It's all about leadtime. With today's analytics capabilities, I would start to build up a process twin therefore I would use a process mining engine to analyze processes not only on the shop floor, but more and to end. by doing this, you would be able to analyze, for example rework, missing inventory and material availability, rescheduling of orders, deviation in plant and actual times or processing times, unnecessary transportation, etc. with negative impact on leadtime. On top of this process twin, I would put a real time engine not to have only historical analysis, but also to act on the processes running in real time. This would enable shop floor controllers to act on the most critical orders by using prediction on real time data.

Every Manufacturing process involves two key parameters, the Cost and the Time. Time is time, however Cost has several components. You can control, optimize and improve, only when you are able to measure it. Data analytics tools begin the process with data capture i.e. measurements and recordings. If there is no data, there is nothing to analyze, optimize and improve. Analyzing the real time data, comparing with historical data (when the process was new with ideal manufacturing conditions) and the ZERO budget (ideally No Loss) calculations gives us an idea of where is the drain and key areas for optimization. Data recording is most important, once this is available there are several tools in the market to analyse and get help to optimize,

The power of direct observation and recording actual data by involving real people will be an effective substitute for installing real time data capture systems. Can we stop automating things as an immediate recommendation and involve our experts, teaching them the importance of studying the process for themselves. And, it’s far less costly initially.

In Lean Manufacturing, data analytics tools streamline processes by capturing, analyzing, and leveraging data for continuous improvement. Utilize IoT sensors to collect real-time production data, identifying bottlenecks and inefficiencies. Implement predictive analytics to anticipate maintenance needs, reducing downtime. Harness historical data to optimize inventory levels, minimizing waste and costs. By integrating data analytics into Lean principles, businesses can drive efficiency and competitiveness.

Process optimization is simply as where are we now, where we want to be, what is the gap, "how" are we going to bridge the gap and measure the progress with a continuous feedback loop. This article talks most of the said aspects other than "how" we should improve a process. In Lean Manufacturing, we are given 8 tools: 5S, Kanban, Workload Balancing, Error Proofing, QCO, TPM, STW and PDCA, that can be applied to identify and eliminate wastes in it. Accurate usage of above tools to the relevant scenario will definitely lead to effective and efficient process optimization in a Lean way!!!

One thing I found useful is process mapping. " Love the Details". In a typical one cycle of the process we have floor to floor activities pick up component load it, press start button, observe process, note down parameters, inspect component from earlier cycle. Unload the component and load new component. We need to write a list of activities man and machine doing. Make a ratio of machine time and man time. Find out opportunity through man and machine idling. Involve Team. Find out options of solutions. Take a team decision and expert opinion in organization. Implement solution. Repeat the process of process mapping. Actually this study needs to be done on bottleneck process so that line output improves. Waste elimination is a result.

Track production flow with real-time data to identify delays, waiting times, and areas for improvement. This helps streamline processes and reduce overall lead times.

It provides some help to put organization's actions in the best sequence possible in order to perform production without interruptions. Also, helps whenever someone request visualized feedback of current operations and perform them more effectively.

Definir bien el proceso, estandarizar los cambios con herramientas smed, potenciar la detectabilidad y la ocurrencia y sobretodo, hacer cumplir los estándares

Whenever you've caught the vital information, use process planning to imagine your assembling tasks. Information examination apparatuses can assist with making point by point flowcharts that recognize each move toward the creation cycle. By examining these guides with the gathered information, you can detect shortcomings and redundancies that add to squander. This visual portrayal makes it simpler to impart complex cycles and expected upgrades to all partners included.

Process mining is a great option to analyse waste as well: since it can be quantified - you can consider the amount of waste as a process metric with some optimisation potential you're looking for. And by using process mining tools and approach you can locate the events and subprocesses that have the most impact, dive into their root causes, find the ways of possible reduction and compare the expected value for different change strategies with the help of process simulation and What-If analysis.

In Lean there are 7 + 1 waste. TIMWOOD = transportation, Inventory, motion, waiting, overproduction, overprocessing, and Defects. The eighth waste is opportunity lost by no people involvement. Typical Lean TOOLs for identifying waste is value stream mapping, string or spaghetti diagram, cost of poor quality and idea program. The process is draw current state map identify opportunities of improvement. Brainstorm solutions and draw future steam map, calculate benefits for the organization. Plan to implement future state map with minimum line stoppage. Implement future state map. Verify benefits. Prepare a report. Present it tk tol management. Congratulate team and AWARD them as appropriate. Do the same process in other line or same line.

Lean assembling centers around decreasing seven kinds of waste: overproduction, pausing, transportation, over handling, stock, movement, and deformities. Utilize data analytics to quantify these processes' wastes. For instance, investigate creation information to recognize examples of overproduction or use movement following to limit superfluous developments. By focusing on unambiguous kinds of waste with information driven techniques, you can roll out additional effective improvements

Waste analysis is also playing major role in LEAN Manufacturing so Analyze data on scrap rates, rework needs, and downtime to identify areas of waste and implement targeted solutions.

A waste analysis provides with a focus on steps that will not create value but are no possible to eliminate because of the current equipment and sensors. Therefore, to make the necessary changes it is recommended to have a continuous conference with all of the shareholders in order to have a consensus.

Definitely, we need a measurement and improvement system that allows possible the sustainable development of monitoring operations and engage the entire workforce in order to support business strategies. After that, there is no excuse for management to predict financial results to meet shareholders goals

Lean is basically a continuous process of bringing improvements. YoY setting new goals, providing resources to meet them and appreciating the results will bring a sea change in the work culture and customer satisfaction enhancement. A regular check on the progress and being with the team to motivate will go a long way to keep the cycle of improvement always on.

Real-time data analytics represent the optimal scenario, particularly when employing machine learning models capable of directly intervening in processes that exhibit issues, thereby minimizing waste within the organization. These models act precisely when needed, supported by a continuous feedback system that utilizes sensors and active supervision of equipment. This approach not only optimizes production processes but also enhances our operational efficiency, ensuring swift and accurate responses to the dynamic conditions of the manufacturing environment.

Tener capitalizado todos los problemas y como se solucionaron en producciones anteriores, ayuda a mejorar los estándares y a hacer un proceso más robusto.

Zur kontinuierlichen Verbesserung gehört die Definition von Standards um Abweichungen zu erkennen. Diese Standards müssen jedoch ebenso weiterhin in Frage gestellt werden dürfen falls sich ein Prozess ändert.

PM is a system of checks and balances in place to identify the flaws in the equipment and determines what cause this. It is important to note that PM needs to not replicate firefighter actions. It prevents the organization from executing action to counterproductive and combat efforts to use it in fraudulent manners.

Predictive Maintenance: Prevent Downtime: Leverage sensor data and machine learning to predict equipment failures before they occur. This allows for proactive maintenance, minimizing unplanned downtime and production disruptions. Optimize Maintenance Schedules: Shift from reactive maintenance to preventive maintenance based on data-driven insights on equipment health. This extends equipment life and reduces maintenance costs.

With information investigation, persistent improvement turns into an information driven process. Lay out key execution markers (KPIs, for example, process duration, throughput, and imperfection rates, and use investigation to follow them over the long haul. This permits you to quantify the adequacy of changes and guarantee that enhancements are maintained. Consistently survey your information to recognize new open doors for improvement and make changes depending on the situation.

Este es el verdadero secreto del rendimiento de una línea y que se plasma en los indicadores KPI´s. Formar a los empleados, empoderarlos y tener muy claro que todo el equipo de m.o.i. está al servicio de ellos. Son nuestro principal cliente interno, que tengan los conocimientos y herramientas para ser excelentes y lo más autónomos posibles.

People are still an important success factor, it's not just about processes and IT. Empower people to use the data collected from the processes and improve the processes by using the right methods.

Analyze machine performance data and cycle times to pinpoint bottlenecks that slow down production. This allows you to focus improvement efforts on areas with the biggest impact. For these analysis employee engagement is playing vital role.

Ultimately, include your workers in the information examination process. They are the ones collaborating with the apparatus and cycles everyday and can give significant bits of knowledge. Create a culture where employees are encouraged to participate in data collection and analysis and to use their feedback to make changes. At the point when representatives see the substantial advantages of information driven changes, their commitment and obligation to incline standards extend, driving further interaction advancement.

En última instancia son las personas las que mantienen las organizaciones, y son este mismo eslabon al que tenemos que fomentar.

Don't forget the supply chain, especially regarding the important inputs that feed those processes you want to optimize -- data regarding inventory, lead time variation, supplier performance, logistics, and more! Collecting the right kind of data on your supply chain can help you measure and make decisions about risk and changes needed to make a direct impact to manufacturing. For example tools that do Collaborative Planning, Forecasting, and Replenishment (CPFR) like Logility and Kinaxis can help facilitate collaboration between supply chain partners on planning and forecasting... I've seen these personally improve forecast accuracy and coordination... leading to better alignment of supply and demand and reduced inventory costs too.

Take a holistic approach. Don't just focus on production, but also analyze and improve the supporting functions such as planning, maintenance, quality, logistics, etc. There is usually a lot of potential here.

Quality Improvement: Real-time Quality Control: Integrate data from sensors and automated inspection systems to monitor product quality in real-time. This enables early detection of defects and prevents production of faulty items. Root Cause Analysis: Analyze quality control data to identify the root causes of defects. This allows for targeted solutions to prevent recurring issues.

I'd consider the order that you do these tasks, employee engagement and defining the problem should come as early as possible. Its likely a few conversations with the right people from the shop floor could give you several hypotheses to go and test. You can then focus a data collection project on data specific to that problem. Measure its current state, plan ways to improve, and see the results (DMAIC process). Then iterate after showing some early wins.