Comment les algorithmes d’IA peuvent-ils réduire les déchets dans l’industrie manufacturière ?

- Product design optimization can be done using AI where multiple scenarios are built using simulation (AI). First scenario will give specific shape and size based on certain level of performance, cost, weight etc. Second scenario will have a different share and size, based on another level of performance, cost , weight etc. Few thousand scenarios can be built, and then designers can choose from these scenarios.

When deploying an AI model in production, defining clear objectives becomes the foundation for its success. These objectives serve as the guiding principles, outlining precisely what the model should achieve and how to measure its performance. Whether it's accuracy, response time, or any other relevant metric, having well-defined goals is crucial. Setting acceptable thresholds for these metrics act as guardrails, preventing the model from drifting away from its intended performance levels or encountering unexpected issues. By establishing these parameters upfront, you ensure that your AI model not only meets expectations initially but also maintains its effectiveness over time, contributing to the long-term success of your AI deployment.

AI can analyze data on resource consumption and recommend ways to optimize the use of raw materials, water, and other resources in the manufacturing process. Also, AI based automated sorting helps in efficient recycling, ensuring that materials are properly segregated for reuse, reducing the amount of waste sent to landfills. That's how we can reduce the waste.

AI algorithms can be like the creative brain in manufacturing, helping to come up with designs that use less and do more. It can also suggest designs that need less material but still stay strong and look good. It's like having a clever buddy who knows a lot about design and can provide fresh ideas that you might not think of. Also, AI can test quickly to find the ones that meet the goals, whether it's reducing cost, saving material, or making the product last longer. This way, before the real making starts, a good design is ready, ensuring less material is wasted. By giving new design ideas and quickly testing them, AI helps make sure that the products are designed in the best way from the start, saving materials and costs in the long run.

Another avenue where AI algorithms can significantly reduce waste is in predictive maintenance. By analyzing data from sensors and historical maintenance records, AI can predict when a machine is likely to fail or require maintenance. This allows for timely interventions, reducing unplanned downtimes and wastage of resources.

Yes, AI algorithms can be used to optimize schedule and planning. For example, quantity that will be produced on hourly basis and what will be product mix produced by plant can be decided on the basis of availability of employees, skills of the available resources in each shift etc. To get higher optimization levels, machine needs to work will millions of data points to build the schedule for a day, which is not possible for humans. Hence when supervisors/plant managers when doing planning they focus on the more important items like fast moving products, resources who are highly skilled, effectively manage inventory which is running low etc.

In planning and scheduling, AI's ability to predict and adjust to dynamic variables is invaluable. For instance, by predicting demand spikes, AI can guide resource allocation efficiently, avoiding overproduction or underutilization of resources. Moreover, timely maintenance scheduling, driven by AI's predictive analytics, could significantly reduce unexpected downtimes. Through better-orchestrated scheduling, manufacturers not only meet delivery timelines but do so with minimal waste, showcasing a tangible shift towards a more sustainable and efficient manufacturing landscape.

Through planning and scheduling optimization, artificial intelligence (AI) algorithms can minimize waste in the manufacturing sector. These algorithms analyze production data, demand projections, resource availability, and other variables to develop effective production plans. Manufacturers may optimize production sequences to increase productivity while minimizing waste, cut down on setup times, and shorten idle time between operations by utilizing AI. In order to further maximize resource usage and reduce waste, AI algorithms can also dynamically modify schedules in real-time based on changing situations, such as equipment failures or rush orders.

This works well IF you have the needed quality data, which is the biggest dilemma in many manufacturers. Like with Design Optimization, this is the tip of the iceberg. One of the problems with normal production planning is your timeframe. For example, the ultimate plan for today, may not look the same if we looked at it for a week, or a month. AI opens this possibility to have a longer window of Planning and gains all the advantages being discussed.

AI algorithms are like a behind-the-scenes helper in manufacturing, making sure things run smoothly with less waste. For example, AI can guess when a machine might stop working by looking at past data, and plan a fix before it breaks. This means fewer surprise breaks and more steady work. Also, AI can help with smart shopping for materials. It can tell when materials will run out and order more, so there's no overbuying and wasting. Plus, AI can help save energy by scheduling machine work when energy is cheaper or more available. By doing these things, AI helps not just in planning and scheduling but also makes sure every resource, like machine time, materials, or energy, is used well, reducing waste in many ways in the making of products.

- Let me share my experience. Keeping buffer is a common practice in inventory management, which is known as safety stock. However, way more importance is given to deliver an order which comes as compared to optimizing inventory. This results in holding much higher inventory than safety stock. - AI can be used to predict with higher consistency inventory requirements, and demonstrate that there is no shortage happening, to build more faith with sales professionals and plant managers that we will not run short of inventory. That's when you can gradually bring inventory down to lower levels with acceptable levels of buffer stock.

By employing AI in forecasting, a company can more accurately predict future needs, reducing the likelihood of over-ordering or under-stocking. For example, AI can identify patterns in past sales data to predict future demand with higher accuracy. By doing so, businesses can keep a lean inventory, reducing holding costs and minimizing waste from unsold goods. Moreover, AI can automate re-ordering processes to ensure a consistent supply of necessary materials, further streamlining operations and contributing to waste reduction in the manufacturing sector.

Using AI for demand prediction could be the primary use case around which the entire value chain for manufacturing can be optimized. Accurate demand prediction requires astute customer insights and requirement management.

Done right this is one of the biggest value propositions for manufacturing. That "just right" inventory level is almost impossible to get to without rock solid forecasting. Look at the entire scope, forecasting is difficult enough, but historic based forecasting is limited in value. Backlog is solid and is easily predictable, but, depending on how long the sale cycle is, and the needed turnaround after order, this gets more complicated. Add in Supply Chain, so you know when you have to order that widget.... This is a perfect use case for AI, but, again, good data is critical.

Inventory management becomes more proactive due to AI's ability to spot patterns. AI and more especially supervised learning models, are very good at interpreting complex patterns that would take a long time and effort for humans to find. Supervised learning algorithms identify patterns that are associated with supply disruptions, demand spikes, and even return rates by analyzing past data to forecast future trends. For example, AI is able to detect, often undetectable to the human eye, the subtle effects of consumer behavior patterns, economic movements, or seasonal variations on inventory demands. However, rather than using AI as a stand-alone decision-maker, it's important to use it as an enhanced reference tool.

There are the areas where I have built AI solutions, let me share my experience - Predictive maintenance - Some of the large million dollar machines used in plants (an example being large printers in printing press etc) are fitted sensors which capture temperature, pressure, rpm and other metrics for individual components of these large machines. When the metrics cross the thresholds Ai system detects it and alerts are sent out. The alerts can be through mails, in a dashboard, can be more visceral like a siren (yes, that also happens :)) Quality control - Images of output from assembly line are taken from multiple angles. AI matches these images with images which are labeled as good quality. When gap is high, then an alert is raised.

Within Biopharma and Pharma AI could make significant improvements to the cost of goods in reducing the requirement for a significant portion of the headcount to be focussed on quality assurance. When you look at a Pharma site, they normally have a history of hundreds of deviations, change controls and CAPA’s both in play and also completed. No individual can actually know the content of all of these documents leading to the ever present risk of a quality management system slowly spiralling out of control. With AI there is an opportunity to use a dedicated (as in not linked to the web, for IP reasons) system to learn your quality system. Instantly alerting you if there is a similar document. Potentially huge saving in headcount.

Success of AI systems hinges on the quality of data they're trained on and their integration with the existing manufacturing infrastructure. Proper training and continuous learning are crucial to ensure that these systems remain effective and relevant in the ever-evolving manufacturing landscape.

🔁 The goal is to shift the paradigm from reactive to preventive. By leveraging anomaly detection and predictive analytics, we can not only foresee equipment malfunctions but also significantly diminish downtime and extend machinery life. Plus, integrating machine learning with visual and acoustic monitoring can vastly enhance defect detection, ensuring products meet quality standards before they ever leave the line. 👋 However, it's important to navigate the integration challenges that come with such solutions (connectivity, data siloing, compatibility with legacy systems, processing requirements, scalability, ...). Nevertheless, the potential for leaner, greener, and more efficient manufacturing becomes increasingly tangible.

Maintenance and quality control assures Quality Assurance and an UpKeep for a project ! AI can detect equipment breakdowns by examining sensor data, allowing for early maintenance and a reduction in the waste brought on by unplanned downtime and expensive emergency repairs. For example : General Electric (GE) uses AI for predictive maintenance in their power plants and aircraft engines. Sensors and AI algorithms analyze equipment data to predict when maintenance is needed, reducing unexpected downtime and avoiding the waste of energy and resources.

It's important that right expectations are set about time needed to build these AI systems, limitation of their accuracy, time commitment of the SMEs from manufacturing plant. AI leaders need to inform that for 6 to 12 months there is investment without minimal returns. Post that one can expect significant returns. Also failing fast is critical. AI team should do analysis on whether quality data is present, patterns are there in data to predict with required accuracy, plant managers should decide whether their SMEs can devote time for AI project. I prefer this analysis to be done within 3 months. If situation is not favourable for building an AI solution, then a tough decision needs to be taken.

Scalability is a concern. While an AI solution might work well in a controlled or pilot environment, scaling it to handle larger, more complex manufacturing processes might introduce unforeseen challenges. Integration with legacy systems, which are prevalent in many manufacturing industries, can also be complex and resource-intensive.

Data quality is essential. If the algorithm is trained on erroneous data, expect failure - potentially at scale. The entire data production chain needs to be monitored and tested. Here, AI can help detect anomalies or data drift. This is upstream from algorithm training. Ensuring this data quality is the role of DataOps, or even AnalyticsOps if the data used comes from a BI report. The model deployed in production has a limited lifespan. Its action produces new data that will be used to generate the next model. It's the role of MLOps to monitor the model drift and replace the old model with its new version. Versioning ensures that the model remains explainable over time. It is essential to be able to replay a situation a posteriori.

One facet of AI is that it cannot solve problems that we can not solve manually using our own skill. It is a powerful tool. However, our success with it is dependent on our ability to provide high quality input. Can it provide a way forward with the challenges in manufacturing? Yes, but only if we can provide sufficient input to tell it how. In this regard, a Human-led AI is perhaps the best way forward to maximize the potential of AI to assist in enhancing manufacturing

One of the most important challenge is data quality, there might be many reasons for a machine to stop in the post, if you don't have that parameter in your database, it won't be easy to predict if it will happen again soon or not. Example is low-quality fabric used in textile manufacturing might cause it to break off and production stop, but your machine learning model will try to predict machine failure by using other parameters about the working conditions.

AI must be used to do a reasonably good demand forecast which basically will help produce what is actually needed instead of producing to stock. Inaccurate forecasting is a major cause of waste in manufacturing. AI can help reduce WIP a lot by refining the production schedule so that only right product in right quantity at right time is produced. AI can help in machine maintenance by improving predictive maintenance to a much higher level.

The initial investment in AI technologies can be substantial. It's essential to conduct a thorough cost-benefit analysis to ensure that the long-term benefits, such as waste reduction and efficiency gains, justify the upfront costs. Also, change can often meet resistance. The introduction of AI might be viewed with skepticism or fear by employees concerned about job security or the impersonal nature of automation. Addressing these concerns openly and proactively is crucial.

Standardization for the solution providers would be the wise way rather than aiming to provide solutions for every industry / every machine. These are very specific environments and require a lot of data to learn from, customizing the solution for each client might not be viable for the business of the vendor.

As I've stepped into the CyberSecurity world , I strongly believe that Security should be a Primary key taken into consideration when talking about AI and Automation Future. The integration of AI may introduce new cybersecurity risks that need to be managed. A real life example would be : when using AI for quality control, a pharmaceutical manufacturer must make sure that their AI systems abide by FDA guidelines. They have to show that when it comes to identifying flaws in pills or capsules, AI-driven inspections are just as trustworthy as conventional techniques. Threat: Knowing the AI model, an attacker can avoid AI security detection by creating false network traffic that introduces malware or grants unauthorized access to the network.

One thing missing from this article, which is often overlooked is improving OEE with AI. Most solutions out there focus on gathering as much data as possible and presenting insights to the production manager, so they can take action. We have built an AI copilot that directly suggests better process parameters for various industrial machines. It empowers machine operators to significantly increase the quality and efficiency of their production. For example, clients that use #Optimyzer to find better laser cutting parameters end up cutting 20% faster, on average, with an improved quality and lower gas consumption. Solutions like Optimyzer help manufacturers get the most out of their machines and ensure an efficient production.