# The Impact of AI in Manufacturing: Unleashing Productivity **May Yap**: Senior Vice President and Chief Information Officer *Autonomous retail robots in grocery stores*. Sounds so futuristic, doesn't it? Since the first time they showed up on a trends list back in 2015, experts have predicted that robotics would be the next big thing in the retail industry. With current trials and proof-of-concept projects underway at some of the retail giants nationwide, it's clear the pace is picking up. In nearly 20 years, there hasn't been much change in retail, quite frankly. Of course, e-commerce and d-commerce continue to be revolutionary, but when you get down to the physical retail store operations, the work seems all the same for retail workers---scanning barcodes, stocking shelves, paper price tags---just the same way it did two decades ago. This is proven once again in a recently conducted [survey of 312 retail decision-makers](/blog/retail-technology-innovation.html) on the future of retail technology. In the 91社区 survey, 70% of participants said that the e-commerce revolution was just the start and that retail would continue to see major disruptions and innovations---the pandemic accelerated many of these changes in the last 18 months. But with all the transformation retailers have had to go through with this digital landscape, where will the next era of innovation take place? E-commerce was the appetizer, and now it's time for the main course: in physical stores, where innovation has been slow (even though [90% of retail revenue](https://www.retailtechnologyreview.com/articles/2017/10/17/why-90-percent-of-sales-still-happen-in-brick-and-mortar-stores/) is generated there). [Download 91社区's Future of Retail Technology Report.](#scroll-download-the-Future) The retail sector innovation mindset has been lacking a cohesive strategy. While some of the largest retailers have a clear technology roadmap ahead of them, the more frequent occurrence is a trial-and-error approach. It's as if the retailers play a round of "loves me, loves me not" with the new robotic technology they see their competitors trying out and don't truly invest in or trial to see real results. This makes the innovation process very inefficient. Let's get ahead of this and be proactive. When discussing autonomous mobile robots, sometimes we are met with skepticism. [Retail executives tend to be cautious](/blog/retail-industry-executive-technology-lessons.html), which may be because previous attempts to trial new technologies have fallen short of promises. In addition, they tend to be cost-conscious decision makers, looking for pragmatic solutions. In an industry where margins are typically razor thin, there is no other way to be. But store robots could transform physical retail as we know it. How? Let's examine. Four Grocery Store Challenges Retail Robots Could Solve ------------------------------------------------------- No matter the type---grocery, home improvement, electronics, or otherwise---all retail store teams face similar challenges. When we ask executives what they would do with the added visibility autonomous grocery robot could give them, they mention four areas for improvement. ### 1. Ease Inventory Management Retail executives want a better understanding of when items are out of stock. When there is no product on the shelf, that's when the red lights go off. Therefore it is no surprise that 69% of retailers from the 91社区 survey said that they were implementing or considering inventory accuracy systems to improve operations and efficiencies. Fifty-seven percent also were implementing or considering analytics to optimize channel and product inventory management strategies. Inventory and out-of-stock are big issues for grocery retailers. In a grocery retail setting, 8% of the inventory isn't properly placed, priced or is out of stock, which translates to an approximate 4% hit to their revenues. Today, this is accepted in grocery, because there hasn't been a better way to get the data. But an autonomous robot could scan shelves through sensors and cameras to report this data in a consistent matter, so stocks can be replenished, stat. What would be the contribution to the bottom line if product availability improved even by a single percentage with a shelf scanning robot? ### 2. Guarantee Price Integrity Price tag accuracy is another common issue amongst retailers. When a customer approaches the cashier ready to purchase and the prices in the POS don't match the one on shelves, this equates to lost productivity and time, not to mention a negative customer experience. In addition, if a retailer is running a sales promotion, they need to ensure it is displayed correctly to encourage shoppers to make additional purchases. Again, when there are price integrity issues, retailers are looking at lost revenues. Besides checking for out of stock items, autonomous robots could also confirm price tag integrity, so the retailer never loses on a revenue opportunity or from providing customer satisfaction. ### 3. Confirm Product Showcases Product set up is another challenge for retailers. They spend copious amounts of money in marketing to research and decide how products will be placed within the store. Will they place the skinny jeans on the top shelf and the socks on the bottom one? The key is in planogram compliance. Planogram compliance ensures retailers can get one step closer to their ultimate goals: increase sales, improve overall profitability and deliver a good retail experience. Shelf allocation has a huge impact on product sales, and if there is no compliance to the plan you made to maximize sales, it can have a major impact on your stores. This is another area where retail robotics can help. Using computer vision and artificial intelligence, these robots can guarantee there is planogram compliance. A study from the National Association of Retail Marketing Services discloses that retailers who achieve planogram compliance can realize a 7.8% increase in annual sales and an 8.1% lift in profit. ### 4. Identify Hazardous Conditions All retailers, but especially grocery stores, pay hefty premiums for insurance coverage, where there are numerous possibilities for hazardous conditions, from slippery floors to products falling off shelves. The most common hazards lead to time-consuming and costly slip-and-fall accidents, which can cost the retailer as much as [$7.5 million](https://patch.com/rhode-island/eastprovidence/jury-awards-7-5-million-slip-fall-lawsuit-against-wal-mart) (or even more) for a single incident, if the retailer is found at fault. Considering that these types of lawsuits have risen by more than [300% since 1980](https://www.qsrmagazine.com/outside-insights/7-tips-avoid-costly-slip-and-fall-injuries), retailers must take the correct precautions to provide a safe environment for the worker and shopper alike. Once again, multi-purpose autonomous robots equipped with navigation systems, machine vision and sensors can scan for risks, while moving through store aisles alongside employees and shoppers. These robots can expedite hazard detection through real-time alerts to remove potential risks. In addition, they can track the time it takes to clear up any dangerous conditions. Ultimately, automating hazard detection and reporting can improve audit and compliance operations. Autonomous robots will be there to track your risk management metrics so you can ensure your stores are safe. Autonomous Robots are Key to Physical Store Analytics ----------------------------------------------------- The e-commerce customer data retailers receive leaves them yearning for more [analytics within physical stores](/blog/retail-analytics-in-stores.html), especially as an omni-channel strategy takes the driver's seat. Sixty percent of 91社区's retail survey participants say they are investing in online and in-store technology as an integrated, omni-channel solution, meaning that they are looking for additional efficiencies in how they operate their business. But how can a real omni-channel strategy function with only half the data? Autonomous robots can solve for that. > Retailers are looking for additional operational efficiencies. But how can a real omni-channel strategy function with only half the data? Autonomous robots can be the solution. > Since these robots are data collectors by nature, they can report on many of the obstacles that stand in the way of better operational efficiencies and customer experiences. From floor inspections to out-of-stock analytics, autonomous robots can contribute to the existing data sets retailers have in their systems of record, filling in the gaps to lead to better decision making. Over time, this data can be used to chart out long-term trends. For example, it's no surprise that you have more liquid on the floor during the winter. Autonomous robots can determine where the liquid is collecting, how long it's there and how quickly it's taken care of. This data, over time, may reveal that the store managers need to ensure there are more floor mats at the store entrance to prevent liquids from snow or rain to trail into the store, creating a hazardous environment. This data is what we call "descriptive analytics" that showcase real-time information on what's happening. But at the end of the day, it's all operational information that helps retailers run better. Then this information can be translated into higher profitability and less loss. In addition, for a grocery chain, it provides a benchmark and comparison across their stores to determine high- and low-performers. That's not all. Since autonomous robots can check for elements like pricing, planogram compliance and out-of-stock, it can provide trend data that reveals forecasting opportunities when a sales promotion is extended by a day or opportunities to improve existing planograms. > Autonomous robots give brick-and-mortar stores the visibility that e-commerce giants get from tracking all their digital platforms. They close the data gap between online and physical stores that allow retailers to get sales velocity, sell-through and so much more. > Autonomous robots give brick-and-mortar stores the visibility that e-commerce giants get from tracking all their digital platforms. They close the data gap between online and physical stores that allow retailers to get sales velocity, sell-through and so much more. It's the last mile of information that retailers have been missing for quite some time. So, as we demonstrate more consistent and accurate data through autonomous robots turning into better business decisions, I believe retailers will be more willing to give them a try. Retailers traditionally are risk averse, and incremental change is more the norm for brick-and-mortar companies. Especially when it comes to infrastructure changes, retailers are very hesitant to adopt new technology. After all, why make significant changes to your store layout without proven ROI? That doesn't settle with the pragmatic retail leaders. Yet, 98% believe retail companies need to invest in technology that increases their efficiencies and 100% agree that technology innovation is imperative to meet the expectations of today's shoppers. Autonomous robots require nothing but a power source, therefore they require no or minimal infrastructure changes, which removes the hurdle of implementing or trialing this new technology in a retail environment. The focus is on very specific tasks, as I've mentioned earlier, that can be managed and monitored for efficiency. Several players in the grocery industry have been testing [multipurpose robots from Badger Technologies](https://www.badger-technologies.com/platform/robots.html), a 91社区 Company, to improve the shopping experience. Early data from several trials reveal that customers like robots, which drives store traffic, but also reports improvements in operational efficiencies. Sounds like music to the ears, doesn't it? Companies are in a race to embrace digital technologies like artificial intelligence (AI). These technologies are critical enablers of the Fourth Industrial Revolution (also known as Industry 4.0) and will ultimately empower the manufacturing market to continue to be the backbone of the global economy. Artificial intelligence in manufacturing is bringing factories into the future. Industry-wide, manufacturers are facing a range of challenges that make it difficult to speed production while still providing high-value and high-quality products to their customers. All the while, companies need to implement a digital infrastructure that positions them to fully embrace the skills and knowledge of their best assets --- people. The manufacturing industry today relies on automation just as much as people. But the factory of the future, which is a marriage of physical and digital capabilities, requires more: real-time data, connectivity and AI technology at the forefront. In fact, [more than 80% of C-suite executives](https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments) believe they must leverage AI to achieve their growth objectives. The explosive growth of the electronics goods market means that there is little room for error or time to waste when embracing AI in manufacturing. Customer requirements for delivering on-time and on-budget product are of the utmost importance, and efficiency is a goal in everything manufacturing and supply chain management. AI's ability to drive impact in this regard is real. Manufacturing companies that adopt AI early will reap the biggest benefits. [A McKinsey analysis](https://www.mckinsey.com/business-functions/operations/our-insights/lighthouse-manufacturers-lead-the-way) projects a significant gap between companies that adopt and absorb artificial intelligence within the first five to seven years and those that follow or lag. The analysis suggests that AI adoption "front-runners" can anticipate a cumulative 122% cash-flow change, while "followers" will see a significantly lower impact of only 10% cash-flow change. ## The Benefits of AI in Manufacturing The goal of manufacturing is to provide consistent high quality at the lowest cost and fastest speed. Consequently, the biggest challenges revolve around how to deliver dependably high-quality products while keeping costs low and manufacturing at a rapid pace. Here are some ways AI in manufacturing can help: ### **1. Refine Product Inspection and Quality Control** A typical manufacturing environment includes automated optical inspection (AOI) machines to identify which products meet standards and which are defective, but these machines have an accuracy rate of about 60-70%; in a school setting, this may be a passable grade, but it isn't stellar. And like I said, high quality is one of the predominant goals in the manufacturing sector. When we augment AI in manufacturing processes like AOIs and teach it to recognize patterns, it leads to significant improvements in process optimization. At 91社区, we've seen accuracy rates skyrocket up to 97% as a result. Think about injection molding machines. There are three parameters that affect the molding quality and product: the pressure on the injection, speed and temperature. At 91社区, we've been applying an AI solution and data analytics to analyze the parameters and track the temperature and pressure to detect common deviations. High-resolution cameras with AI-based recognition software can perform quality checks at any point of the production process and help us accurately identify points where a product becomes defective. Is it because the machine isn't functioning well? Or is it some other factor that is affecting the quality of the product? When we can answer these questions, the manufacturing processes become faster and more effective and produce higher quality products. This can be extremely beneficial for closely supervised industries like automotive and aerospace that must meet stringent quality standards set by regulatory agencies. In fact, [BMW Group already uses AI](https://www.press.bmwgroup.com/middle-east/article/detail/T0299271EN/fast-efficient-reliable:-artificial-intelligence-in-bmw-group-production?language=en) to evaluate component images from its production line, spotting deviations from quality standards in real-time. In the final inspection area at the BMW Group's Dingolfing plant, an AI application compares the vehicle order data with a live image of the model designation of the newly produced car. Model designations, identification plates and other approved combinations are stored in the image database. If the live image and order data don't correspond --- for example, if a designation is missing --- it sends a notification to the inspection team. ### **2. Augment Human Capabilities** The ultimate goal of artificial intelligence is to make processes more effective --- not by replacing people, but by filling in the holes in people's skills. By working side-by-side, the collaboration of people and industrial robots can make work less manual, tedious and repetitive, as well as more accurate and efficient. To that end, Canon uses Assisted Defect Recognition --- a combination of machine learning, computer vision and predictive analytics --- to supplement human skills. The software examines manufacturing components with industrial radiography (X-ray) and images to determine the integrity of each part and its internal structure. With only a specialized technician, the examination process can be highly manual and error-prone. But with computer vision and machine learning, the Assisted Defect Recognition technology can analyze images of inspected parts, identify potential defects (including those that may be missed by the human eye), and learn and improve the technology's accuracy based on human acceptance or corrections of the results. One thing that we have been successful in doing at 91社区 is deploying AI initiatives on natural language processing and learning. For instance, people need to pick up and identify the right trade compliance code to fill in when they do trade filing. In this task, accuracy is essential. If someone picks up the wrong commodity code and files it, that could result in picking up a dangerous good or a raw, hazardous good. We can now supplement the manual labor with artificial intelligence to pick up the right code so that we can file it properly. ### **3. Enable Preventative Maintenance** Almost 30% of use cases of AI in manufacturing are related to maintenance, per a Capgemini study. This makes sense considering that, in manufacturing, the greatest value from AI can be created by using it for predictive maintenance (about [$0.5 trillion to $0.7 trillion](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/most-of-ais-business-uses-will-be-in-two-areas) across the world's businesses). Predictive maintenance analyzes the historical performance data of machines to forecast when one is likely to fail; limit the time it is out of service; and identify the root cause of the problem. Yield-energy-throughput (YET) analytics can be used to ensure that those individual machines are as efficient as possible when they are operating, helping to increase their yields and throughput and reduce the amount of energy they consume. AI's ability to process massive amounts of data, including audio and video, enables it to quickly identify anomalies to prevent breakdowns --- whether that be an odd sound in an aircraft engine or a malfunction on an assembly line detected by a sensor. With a machine failure, production stops. Meanwhile, predictive maintenance typically reduces machine downtime by 30-50% and increases machine life by 20-40%, according to a [McKinsey article](https://www.mckinsey.com/business-functions/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability). With manufacturing's increasing reliance on machinery and need to boost uptime and productivity, companies require much more than good luck and happy thoughts to keep production humming. ## How to Successfully Implement AI in Manufacturing The big challenge with AI implementation --- which exists beyond manufacturing --- is the abundance of data. You either don't have enough data or you have so much that it becomes overwhelming and not actionable. In many manufacturing environments, most are still unable to extract certain data from machinery. Therefore, the AI is unable to highlight patterns and outliers. If you don't know the process, it is very difficult to improve it. Our governing principle in driving Industry 4.0 or smart factory initiatives is that, "If we are able to digitalize it, then we can visualize it." After we can visualize it, we can optimize it. There is abundance of data we generate in the manufacturing process and it is important we aggregate, catalog and use the data to solve the business problem. The definition of data and how we govern data is absolutely important. Data must be consistent, reusable, transparent, trustworthy and open. It is also important that we have a strategy on how we store and use data in the physical and logical perspective. Data scientists are key to successfully incorporating AI into any manufacturing operation. They are needed to help companies process and organize the big data, turn it into actionable insight and write the AI algorithm to perform the necessary tasks. But the data scientists themselves cannot do all the work. The business owners who understand the processes involved in manufacturing and production are familiar with how each parameter and factor affected will be influencing the outcome from the AI algorithm. Consequently, business involvement is vital. Rolling out successful AI projects takes time. Think about AI as a brain; you need to train it. You probably need to have a process for the machine learning algorithm. We do need the process owner and the sponsorship of the management to know that this takes time. You will not see immediate effects; it's a process. Still, imagination is never-ending, and AI capabilities will be too. Think about our brains; they contain unlimited power. The AI evolution will be the same for manufacturing organizations. Productivity and efficiency will be rocketed to new heights, processes will be smoother and the future possibilities are endless.