Using AI to fast-track manufacturing operations

In situations where visual inspections are required, AI, with a trained dataset, is effective in eliminating human bias and providing accurate outcomes.

Let us consider some common use cases of visual inspection. For instance, in a glass bottle manufacturing plant, there is usually a conveyor through which, post-production, bottles pass an inspection station where quality control operators accept/reject bottles based on visual defects such as scratches, blemishes, shade gradation variation, bubbles and minor spots. Some of these defects are only visible to a highly trained eye, and given the high degree of focus required, statistically there are possibilities of type 1 (reject a good bottle) or a type 2 (accept a defective bottle) error. Sometimes such mistakes can cause significant customer dissatisfaction and to avoid this, there is one more inspection on a sampling basis before the final dispatch. This is not only effort intensive, but also a wasteful process.

AI’s role: An in-line camera sensor which can capture a 360-degree image of the bottles in the line and has the ability to map the data back to the various categories of defects and provide an accept/reject outcome could solve this problem and improve the quality and efficiency of the inspection process. As the number of inspections increase, the data gets populated further and the outcomes become more accurate. However, the prerequisite for AI to be implemented in the process is to have large volumes of data/images for each of the defect categories so that the AI algorithm can be trained to eliminate the bias in its assessment.

As the next step, the ability to monitor and plot control charts, use the seven principles of control chart interpretation (outliers, 2 consecutive points above 2 sigma limits) and map them back to the process parameters that resulted in these defects can further help in enhancing the process control through a feedback loop.

Over a period of time, the system would then be able to analyse process parameter deviations from the norm intelligently and, instead of noticing control chart parameters on finished products, the system can start monitoring the control chart for process parameter deviations and correct them at source or provide alerts to supervisors for correction. This would help businesses move from a reactive to a pro-active stage.

This proactive stance in a manufacturing environment can enable businesses to address defects at the source even before they appear in the product. The maker-checker process can also be minimised resulting in a reduction in the number of inspections.

Another use case would be in the automotive industry which requires an accurate visual defect identification of painted surfaces. Currently, due to manual inspection, this process has certain levels of subjectivity which can be addressed using the power of AI. AI can learn to classify the defective painted surfaces from non-defective ones through vast data, eliminate subjectivity and provide more accurate outcomes.

The use cases of AI pertaining to quality control are evolving rapidly and companies need to adopt AI-based solutions on a rigorous evaluation of return on investments. Accordingly, the skill set required for a quality control personnel has to evolve in tandem with the newer ways of working. Resources will no longer have to specialise in measurements but will need to be equipped to interpret the signals which the AI provides. They also need to be made aware of the technology behind the AI, at least at a basic level, with a focus on the use cases where AI can be beneficial for their day-to-day tasks. AI can effectively improve the measurement system.

With the ability to track the mean time between failures (MTBF) trends and detect early warning signals, AI may effectively prevent unplanned outages. This is particularly useful in critical equipment where uptime requirements are close to 100%. Maintenance functions have been trying to move from preventive to predictive maintenance, and with AI this goal is well within reach.

At present, most companies follow time-based maintenance for their equipment and have schedules such as preventive maintenance once in six months. However, this does not guarantee that an equipment failure will not occur in the interim.

AI’s role: With a wide variety of failure modes and causes pertaining to an equipment being fed in as training data, AI-based tools can start looking for similar likely events when the equipment is under operation. For example, if a critical equipment has had repeated motor failure due to various causes such as heating of bearing or overload of equipment, the company can invest in thermal scanning of bearing while the equipment is under operation and load parameter monitoring. This way any deviation from expected temperature and load can trigger an alarm/alert for the maintenance technician to take quick action.

An added benefit of adopting AI is the ability to manage requirements for spares since AI will be able to predict the failure frequency. Experience suggests that spares are generally managed through a vital, essential and desirable classification with certain levels of inventory holding. In most manufacturing environments, ageing of spares is an important concern. With AI, one may be able to accurately predict when a spare would be required and then plan to procure it when needed. This not only reduces non-moving inventory, but also helps recalibrate the vital, essential and desirable spares.

There is a wide variety of use cases of AI in predictive maintenance, and a good application of the technology could be in planning the shutdown maintenance of a kiln in the ceramic industry. Most shutdowns today are planned based on time/cycles of operation, however, with AI, this can be shifted to maintenance based on proactive triggers.

Figure 2: A well-trained AI tool can help in reducing variations emanating from the ‘reproducibility’ parameter

For businesses which operate on responding to tenders, it is important to quickly bring out a solution at the bidding stage so that the bids can be submitted on time. The computational ability of AI can design solutions quickly and objectively with the help of training data. In an engineer-to-order environment, products may have to undergo incremental changes to meet certain client-specific requirements. The incremental changes which are required in the product could be a new feature or a new performance parameter requirement. For example, one of the requirements could be a lighter product for the same strength specifications. In such scenarios, if people need to look for solutions or prototypes with various permutations and combinations, the time to find a solution will be very high.

AI’s role: If all product features, and corresponding performance requirements are made available as data points to an AI algorithm, the AI, over a period of time, will be able to suggest the best material and process combination that can meet the performance parameter for any incremental changes that are required for the product.

This aspect of AI can be useful in an engineer-to-order environment, such as the defence sector, where companies can leverage AI to get the best strength for a protective equipment for a given cost and weight consideration.

AI, coupled with a vast amount of point of sales (PoS) data and an ability to recognise patterns, can accelerate sales for a variant/depot combination. Recommendations made on supply chain networks and footprints based on emerging trends in consumption can contribute to boosting the sales of a product.

Take the example of rooftop solar systems. The panels and accessories are generally supplied through a channel partner which is replenished by a depot. Since this is a sunrise industry, the demand can be quite unpredictable and the customers for these categories also have options to switch if a local dealer doesn’t have stocks of the product that they are looking for. For end customers, this is a cash and carry option, therefore, it is important to have the right inventory close to the source of consumption.

AI’s role: Based on historic consumption and demand patterns, analysis of lost sales due to stock-outs, and cross shipments across depots, AI can help identify the right inventories to be kept at each consumption point. Over a period of time, based on heat map of consumption and replenishment points, AI can also provide intelligence on how the network should be organised to feed the demand using certain decision parameters such as consumption frequency, replenishment time and customer’s willingness to wait. AI can help rationalise inventory holding across the supply chain, in addition to enhancing sales.

Material handling, which involves movement, control, storage and protection of goods through the different stages of manufacturing, distribution and consumption plays a crucial role in supply chain management and logistics with efficiency and productivity being the focal areas of attention.

AI’s role: The adoption of AI tools, enhanced by deep learning, can optimise transportation index within a plant for mobile material handling equipment and can also suggest layout modifications to reduce transportation index. Leveraging AI in planning milk runs within a plant for movement of materials according to the production run, along with the optimised transportation index may boost the production capability of the plant.

Tooling is essential for manufacturing, production, assembly and other operations to ensure that tasks are performed with precision.

AI’s role: Through monitoring and analysis of tool usage and tool wear, AI technology can be leveraged to predict the remaining useful life of the tool and spares management for tools. An example of this is in fabrication shops where the punch and die process can be closely monitored through image scanning and a plan for refurbishment of the tools can be made well in advance.

While the example demonstrates AI’s role in manufacturing operations, the technology can also be used in other business processes such as workforce planning, procurement, logistics and store management. The application of AI will depend on the type and nature of the industry, maturity of operations, product life cycle and other factors.