Why Now for AI in Manufacturing?

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Click to learn more about author Venkat Eswara.

Technology always evolves faster than our ability to adapt. AI is becoming mainstream in the consumer and enterprise sectors. So, it’s not a question of if but when AI will become the norm in the manufacturing sector. In fact, it already is.

On the one hand, the COVID-19 pandemic has heightened uncertainty for the global economy. On the other, it has created an opportunity for new service providers to leverage technology and business innovation in the areas of design, manufacturing, operations, and supply chain. For example, the concept of remote product service enabled by digitization and automation using AI is becoming a reality. These new disruptions, coupled with ongoing capital constraints, are causing manufacturers to look for new business models and growth avenues outside their traditional CapEx-led business models. Customer-focus is becoming a new way of differentiation in manufacturing, which has traditionally been an intense hardware-focused sector. However, this shift is in its early stages, and some early adopters in manufacturing are seeing early success in implementing digital and AI-enabled solutions.

Recent data from manufacturing surveys reported Manufacturing PMI at 59.3 percent in October. These results show resiliency as manufacturers and suppliers are becoming more proficient at expanding output by implementing some of these emerging technologies. The value of AI applications in manufacturing was projected to increase to a whopping $18.8 billion by 2027, with a CAGR of 41.2 percent. But more than the actual projected numbers, it is the directional view that is key to unlocking value in areas such as production, supply chain, and customer service. Technologies like AI and ML create numerous opportunities for OEMs to drive cost savings across the value chain, improve operational efficiency, and enhance customer service with additional revenue opportunities from aftermarket sales.

Emerging technologies such as AI, ML, and IoT are providing OEMs with the necessary building blocks to evolve their business models towards better customer service while focusing on becoming more agile in the face of the unprecedented pressures facing them as a result of the COVID-19 pandemic. With that in mind, here are some ways OEMs can adopt AI to address current needs. 

Inventory Optimization

Inventory management in the supply chain is a key area where OEMs can leverage AI to achieve efficiencies in the supply chain network. Currently, inventory management in the OEM and dealer network is siloed and plagued by inefficiencies that result in a high chance of error that impacts business operations and customer service. For example, one manual data entry error can set off a string of delivery delays that directly impacts the customer experience. Luckily, AI for inventory helps to improve productivity, efficiency, and accuracy by eliminating unnecessary manual entries.

Additionally, many OEMs and part shops today still employ outdated inventory management systems that aren’t sophisticated enough to handle instances of drastic unpredictability where circumstances change from second-to-second — such as during a global pandemic. AI- and ML-powered systems, on the other hand, give OEMs and part shops the ability to be more agile and responsive to large-scale unpredictability by allowing them to rapidly analyze and synthesize data to make adjustments and decisions on the fly. Moreover, they can constantly run risk scenarios and develop pre-programmed responses to each scenario via automation.  

Simply put, through AI-enabled inventory management, OEMs can gain better visibility into their inventory operations. This allows them to better forecast needs, understand market conditions, keep customers happy, and eliminate the heavy overhead costs. 

Adaptive Pricing

Pricing can make or break a business, which is why it’s so important to have advanced pricing tools and strategies in place. The keys to pricing success today are relatively straight forward: Dynamically address underpriced items and capture margin potential, maximize revenue potential for parts with low market share, find opportunities for price differentiation, and adapt to local market conditions. Unfortunately, these tactics are virtually impossible to implement properly without advanced computing techniques. Fortunately, though, these computing tools exist and are helping OEMs and part shops thrive. 

AI-powered adaptive and dynamic pricing tools have been a game-changer for OEMs and part shops. For example, in the automotive industry, adaptive pricing plays a key role in maximizing revenue and customer satisfaction by allowing OEMs and parts shops to constantly adjust online and in-store pricing. This allows them to provide the best price possible to consumers based on real-time market conditions and competitor movements. These advanced pricing insights can also help inform other areas of these businesses, such as procurement, ordering, advertising, and more. 

Service Uptime

The downtime of equipment has a direct impact on customer experience. Keeping equipment up-and-running — without any disruptions — is key for manufacturing end-users. The longer a piece of machinery is down, the more money is being lost. But what if it was possible to predict when a piece of machinery was on the verge of failure and fix it before it shut down? Thanks to AI, ML, and IoT, it is. 

Predictive maintenance utilizes a wealth of historical and real-time data of equipment usage, coupled with advanced machine learning models to predict failures well before immediate action has to be taken. It avoids any unplanned situations and maximizes uptime for the end-customer.  

In addition to minimizing unplanned downtime, predictive maintenance can have a significant multiplier effect on other areas of the OEM’s business, as well. For example, predictive maintenance can optimize inventory management and ease spare parts logistics by eliminating instances where parts need to be “rush” delivered due to a surprise breakdown. Further, the real-time performance data gathered can help R&D departments with the future design and reliability of products, reducing the need for long-term testing and speeding up the time of each new part to market.

The COVID-19 economic crisis is placing unprecedented pressure on the entire manufacturing industry. However, as more OEMs adopt AI and other digital solutions, they’re beginning to find ways to navigate this extraordinary time and drive efficiencies across the network. It provides a key enabler for OEMs to evolve their business model and to be a data-driven customer-centric organization.

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