December 20, 2022
By: Isabell Bücher
Nine Key Use Cases for Manufacturing Analytics
Manufacturing analytics adoption is growing rapidly among process manufacturing companies, with the market predicted to expand from $8.6 billion in 2021 to $27.6 billion by 2027 at a CAGR of 21.4%. The number of top-tier manufacturing businesses that have adopted AI-based solutions such as manufacturing analytics skyrocketed from nearly a quarter in early 2020 to 76% by 2021.
Manufacturing analytics is one aspect of the larger trend of smart manufacturing and industry 4.0, applying artificial intelligence (AI) and machine learning (ML) to process enormous datasets from sensors and Industrial Internet of Things (IIoT) devices and produce insights to optimize production quality and plant operations. Manufacturing analytics also involves other advanced technology, like edge computing and 5G networks that can ensure data is transmitted to analytics platforms in real or near-real time for updated insights.
But while plants are quickly adopting manufacturing analytics – often as part of the trend for digital transformation – they aren’t always sure about the best ways to implement them, which use cases to begin with, or how to make the most out of their investment.
Here are 9 key use cases for manufacturing analytics for plants to consider.
Predictive maintenance applies manufacturing analytics to estimate when equipment is most likely to wear out, so as to calculate the most effective times to schedule maintenance and reduce unexpected downtime. Optimizing maintenance schedules helps ensure that maintenance professionals don’t arrive when the equipment is needed for production runs, and that their services are timed to provide the most value. Predictive maintenance also helps plants coordinate purchase orders for replacement parts so that they are on hand at the right time.
For example, cement companies – like many other process manufacturers – operate around the clock. It’s not easy to pause production and they lose money for every hour of downtime, so they want to make the most of that maintenance time whenever it occurs. Predictive maintenance allows them to plan a shutdown when it’s least disruptive and the greatest number of items of equipment need to be dealt with, so that maintenance can happen less often overall.
Predictive monitoring takes predictive maintenance forward an extra step to monitor conditions across the entire plant, rather than only specific items of equipment, using real-time information. By comparing current conditions to “normal” ones, predictive monitoring systems can deliver alerts about anomalies before employees can notice that anything has changed.
Early detection for anomalies allows issues to be resolved while they are still relatively minor and require less time and money to fix, preventing small glitches from snowballing into lost batches, part failure, or unexpected downtime, while also extending the machine’s lifetime. At the same time, predictive monitoring can speed up root cause analysis by delivering vital data about plant conditions when the issues arose.
To give just one example, oil and gas companies typically run many processes that are hazardous for humans to inspect while also working their systems very hard. Operating conditions can shift from good to poor within a short time frame, resulting in dangerous situations if the deterioration goes unnoticed. Predictive monitoring ensures that processes are checked constantly without requiring employees to put themselves at risk, helping reduce the chances that something could go wrong without being noticed.
Optimizing Production Quality
Manufacturing analytics help plant managers gain better insight into the plant’s true capacity, including spotting and removing bottlenecks to production, and identifying when speed or other issues cause quality to suffer (a threshold which can vary for different product items).
With these insights, managers can organize production to be more efficient, improve production quality, and identify areas where processes can be improved with relatively small investments. For example, a manager in a petrochemical plant might use manufacturing analytics to understand how much of each type of petrochemical can be produced in each cycle; calculate the extent to which production can be speeded up without harming quantity or quality; and set optimal production targets for a given period.
Manufacturing companies have often struggled to accurately predict customer demand, and the challenge is more acute today, when the pandemic has generated a new set of consumer expectations and the economy is fluctuating wildly. Historic data is not enough to guide predictions about customer demand in the short, medium, and long term. With better insights from manufacturing analytics, plants can prioritize the right product, stock up on the necessary raw ingredients at the optimum time, and avoid producing items that won’t sell.
This is especially relevant for pharmaceutical companies, which saw demand rise significantly overall during the pandemic, but bring major fluctuations, requests for more small batch and customized products, and far shorter order times. If the plant isn’t ready to turn around a complex request within the expected time period, the customer will go elsewhere, but if they produce more than is needed of a batch of customized medications, they won’t be able to easily sell them.
Refining the Supply Chain
The supply chain still hasn’t healed post-pandemic, and plants are still struggling with fractured supply chains that can delay or damage shipments and/or raw materials. Advanced analytics give visibility into even an extended supply chain, so that plants can understand the impact of delays; calculate the risks posed by adverse weather and traffic issues; measure the cost and reliability of suppliers to make better decisions and negotiate contracts more effectively; and track shipments from supplier to plant and plant to end customer, to spot and correct faults in the chain.
Food and beverage plants are particularly in need of this use case. They can have long supply chains with components that need to be kept in specific conditions, such as cold chain. If shipments are delayed or spend too long in the sun, damp, or cold, it can destroy items and/or create a health hazard. Visibility from manufacturing analytics keeps plant owners informed if a batch of raw materials will be late, so they can choose other suppliers or find another solution.
Running a smooth manufacturing company involves considering how and where to store the finished product so that it won’t deteriorate by the time it’s delivered, and can be retrieved efficiently when it’s time to ship it onward.
Chemicals companies, for example, may have hazardous products that need to be stored securely, as well as temperature- and/or humidity-sensitive items that require specific storage conditions, and they all need to be stocked in a way that lets them be found easily for rapid shipment. Analytics help ensure that they produce items at the right pace so they don’t run out of storage room, and guides them to the most efficient way to arrange items in the warehouse.
Robotic process automation (RPA) relies on data from manufacturing analytics, which is converted into appropriate instructions through AI algorithms. Analytics also help reveal the best opportunities for automation or robotization within the plant, guiding executives to the right place to begin and ensuring that the first processes to be automated are those that will deliver the most value.
Industries such as food & beverage and oil & gas can involve a great number of small processes, so it’s not always clear which ones can best be automated or would have the biggest impact if automation was carried out. Manufacturing analytics can help leaders to make the best decisions to implement RPA effectively.
Traditionally, product R&D is carried out manually, but plants are applying manufacturing analytics to big datasets to speed up modeling and innovation. By using manufacturing analytics, R&D teams can create more accurate replicas of real world conditions to improve research into new products, and predict the impact and increased revenue potential for potential new products to select the best new option.
For example, pharmaceutical companies are highly driven by the need to develop new products that can replace previous revenue-drivers that are about to go off patent. The pandemic also emphasized the need for fast responses to new diseases and viruses, pushing R&D to move up a gear. Digital twins, which are a subset of manufacturing analytics, allow researchers to quickly model the effects of potential new cures and formulas without the risk of working with live materials.
Finally but most importantly, manufacturing analytics can boost overall equipment efficiency (OEE) in process manufacturing plants. With analytics, plant managers can understand the impact that a minor issue or inefficiency can have on the entirety of the plant, and act to prevent them from arising or to resolve them more quickly.
OEE results from the combined impact of optimizing maintenance schedules, reducing downtime, improving product quality, and more. For example, cement companies can use manufacturing analytics to lower their costs, because more efficient systems waste less water, energy, and raw materials; extend machine lifecycles; and also increase product quality and meet customer demands more fully.
Manufacturing analytics can be the key to a more profitable plant
As you can see, manufacturing use cases are manifold and highly impactful, spanning the gamut from predictive maintenance and monitoring to warehouse management and product R&D, with the potential to serve as the gateway to further tech adoption such as RPA. Adopting manufacturing analytics could thus be a consequential decision by plant owners, and there are many valuable places to introduce it.
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