How To Use AI ML To Optimise Manufacturing Costs

60% of those surveyed reported that AI was yielding significant cost savings, with the greatest benefits being shown in manufacturing and supply chain functions. Two years on, KPMG reported that 93% of their respondents in industrial manufacturing had already implemented moderate or fully functional AI into their processes. AI adoption (ML in particular), was visible in several main areas:

• Predictive maintenance
• Defects monitoring
• Network planning
• Detailed scheduling
• Forecasting accuracy

Predictive Maintenance

One of the biggest noticeable impacts made by AI and ML is in predictive maintenance. Unexpected machine downtime is a normal, and usually expensive, expectation for most manufacturing businesses. A sudden breakdown can lead to increased unit costs, production delays – and paying premium freight charges so that orders can be rushed out once your factory is functional again.

Predictive maintenance does away with all that. Predictive AI algorithms collect live data from sensors on your factory floor, and this big data is analysed to help increase efficiency. It starts by identifying locations or circumstances where your equipment's useful life might be diminished. ML is also able to correlate specific factors, indecipherable to any human planner, with impending breakdowns. When these factors are identified and programmed, you'll be alerted about an impending breakdown. You can then take proactive measures to determine the least disruptive moment when maintenance can be carried out. In this instance, ML provides early identification of maintenance requirements before your factory needs a complete shutdown.

In AI-enabled production environments, planners have a better picture of what to expect in areas like demand and capacity restrictions. Many industrial manufacturers have already begun implementing AI tech into their maintenance operations, and a significant proportion believes that preventive maintenance will be the strongest factor in their company’s future success. Many early adopters succeeded during COVID-19 because of their AI capabilities, while other companies are planning to implement more ML so they can catch up.

Defects Monitoring

AI is contributing to the enhancement of production quality control. Advanced connectivity includes installing sensors within or above the production line. These can identify and correct process defects in real time, instead of waiting until the production process is complete. In this way, you can significantly reduce production line downtime and wastage. ML can leverage the data to analyse the results of any defects for trends or patterns which could help to identify the root cause. As much of the overall manufacturing cost of your products is in source materials, this capability could make a significant difference to your operations. Minimising scrap materials and rework will also help to cut down costs and increase profit margins.

Network Planning

The principal aim of AI/ML adoption is optimisation. These technological innovations are all designed to streamline and speed up processes, reduce production costs and deliver a better quality end result. Reducing the immediate costs of materials and labour is the first step to optimisation.

The next is to optimise the entire production network. This involves deploying algorithms, as before, to identify trends and patterns that couldn’t possibly be discerned by human eyes. This time, though, it's on a larger scale, looking at the big picture. What you want is to find more efficient, less wasteful ways to structure not only your factory floor, but your whole production network. The goal of a more efficient network is a smoother overall operation and the effective reduction of logistics costs. In addition, you'll cut down on any replanning work that might be caused by a mechanical process or supply chain disruption. Your operation will become progressively leaner, with the knock-on effect of reducing your future capital commitments.

Today, this kind of optimisation is frequently being done with the aid of digital twins. These are valuable in both design and planning environments, and in speeding up construction and trialling of equipment. They can also be valuable in providingreal-time simulations of your supply chain. The constant input of data allows the model to reflect the actual conditions existing in your supply chain as they evolve. This provides you with a collaborative environment where you can test out and verify different hypotheses using AI-powered models.

Many manufacturing and logistics enterprises are already deploying some kind of supply chain digital twin to assist their planning. This means that the biggest obstacle facing many companies trying to optimise their network is that of scaling. Digital twin technology has many different use cases, and will require some nominal investment, but as industry becomes more accustomed to AI, it'll be worth it.

Detailed Scheduling

When you're on the real factory floor, planning and forecasting comes down to the detailed scheduling of production runs. Human beings previously planned their schedules simply by using their eyes and brain to assess the situation. You might have moved on to incorporate a digital planning solution, using simple software that doesn’t have integrated data analytics capabilities. These were all very well in their day, but today's production programs incorporate many more constraints, and you might find it much harder to create an optimal route forward.

Taking into account the global disruption caused by COVID-19, climate change and political unrest, it's much harder to plan ahead for every eventuality. Production constraints include such things as raw material requirements and availability, machine and manpower capacity, and customer requirements. In today's climate, when technologies such as CAD and 3D printing allow for a much greater degree of customisation, you also need to consider what parts and materials are necessary not just for individual products, but for individualised items.

All of this creates a complex array of potentialities that can be difficult to evaluate. This is where AI has the upper hand, as it's designed to deal with precisely this kind of complexity.

If you adopt an AI-powered planning solution for your operation, detailed scheduling will be a breeze, even in a job shop environment. You'll be able to structure any type or volume of workload on an unimaginably minute level of detail. It will all be accomplished in much less time than ever before, enabling much greater agility in your operation. You'll respond more flexibly to both actual and projected orders, reduce wastage and downtime, and improve your on-time performance (OTP) rate.

This OTP rate is amongst the most commonly measured and critical supply chain KPIs when it comes to eCommerce, and your customers will benefit greatly from the improvements that AI can deliver.

Forecasting Accuracy

Once you move into the more abstract realms of an AI-driven technology ecosystem, you're looking at financial planning and accounting. This involves forecasting future demand so as to improve your sales and operations process. You can use AI to analyse historical data and combine it with current market trends. It can then deploy algorithms for more predictive modelling, so that your forecasts will be much more accurate.

You'll also be able to manage the costs associated with shortages and surpluses, by matching demand and capacity more effectively. In the long run, you'll have a better prediction of what you'll need in your supply chain, thus improving your margins and boosting your top line.

The COVID-19 pandemic has caused significant delays in obtaining and shipping parts and finished goods, with the automotive industry being visibly impacted. In addition, the supply chain is still suffering from one-off impacts such as the 2021 blockage of the Suez Canal. This has influenced the global manufacturing sector to accelerate adoption of AI/ML capabilities, so that companies are better able to model such previously inconceivable scenarios. Recent news of another grounding by a container ship from the same stable as the Ever Given may well underscore manufacturers' concerns.

Increased forecasting accuracy will also enable manufacturing companies greater precision in purchasing, and thus help to reduce storage costs. The lag time that pushes up costs should be eradicated, and the necessity for expedited shipping reduced – along with penalties imposed by customers for late delivery. The AI/ML enhancement of forecasting will affect other accounting procedures, such as assessments of asset impairment recoverability, and obsolete or excess inventory reserves. Risk assessments will also be affected as these underlying accounting processes evolve.

Optimisation Through AI And ML

AI, ML and big data analysis can offer substantial business benefits to industry. The average modern automated factory produces enormous caches of data every year, and these technologies are information-driven. AI and ML algorithms are thriving on analysis of this data, in order to optimise efficiency and save costs. Many manufacturers are beginning to leverage analytics technology to enhance their existing capacity, improving performance and reducing downtime.

Digital transformation will spread exponentially, as the adoption of AI technologies reveals further opportunities for industry to adapt its cost structures. There are many other areas that will be influenced, including changes in jobs and staffing. Manufacturers should study these carefully to find the best way of optimising their efficiency and costs.

This article was originally published at: How To Use AI/ML To Optimise Manufacturing Costs