EBOOK AI in Manufacturing Modernize Your Demand Forecasting, Supply Chain, and Predictive Maintenance EBOOK | AI in Manufacturing AI in Manufacturing Modernize Your Demand Forecasting, Supply Chain, and Predictive Maintenance Imagine you’re paying your bill at your favorite restaurant. You walk outside and see a UPS delivery man dropping off a package. What comes to mind when you see the famous brown truck? Is it simply the brown and yellow logo and clean cut uniform or does something else come to mind? Perhaps you contemplate UPS’s innovative delivery options or the facilitation of international trade. Taking a deeper dive reveals what is actually under the hood of the famous brown truck that made UPS a global leader since 1907. In 2018, the World Economic Forum, with knowledge support from McKinsey, recognized nine of the best factories in the world — or Lighthouses — including Fast Radius with UPS (Chicago, IL). Fast Radius with UPS made the list for “meeting increasing demand for fast-turnaround and...proprietary operating systems that drive real-time analytics and orchestrates, design, production and global fulfilment.” The Lighthouses were selected based on a successful track record of incorporating Fourth Industrial Revolution (4IR) technologies — ”from artificial intelligence (AI) to the Internet of Things (IoT) – into daily manufacturing and supply chain operations, effectively creating the most advanced factories across the globe.” McKinsey notes that while Fast Radius with UPS is at the forefront of adopting new technologies, over 70 percent of businesses investing in technologies that are unable to compete with Lighthouses are “still languishing in ‘pilot purgatory,’ unable to bring manufacturing innovation to scale.” 1 EBOOK | AI in Manufacturing The McKinsey report highlights six enablers that are setting these manufacturers apart across the value chain: 1 2 3 An agile approach makes A technology ecosystem Industrial Internet of iteration continuous. enables new levels of Things (IIoT) academies An alternative to yearlong collaboration, facilitated boost workforce skills. pilots designed for by a digital infrastructure. 4IR leaders use internal perfection that are quickly Manufacturers can and external expertise outdated once completed. leverage partnerships as to reskill and resource large amounts of data are transformation teams. exchanged with suppliers, partners in unrelated industries, and customers. 4 5 6 IIoT/Data architectures Agile digital studios A transformation office (“stacks”) help ring in facilitate ideation. supports enterprise-wide the next generation of Co-locating data engineers, change. technology capabilities. IIoT architects, and data Manufacturers that Lighthouses such as Fast scientists with product achieve scale have Radius with UPS provide managers ensure results established governance their workforce with a are delivered in sprints and models to encourage technology infrastructure iterated fast. best-practice exchange that allows innovation in and prioritization. a matter of weeks. By embracing AI and IoT, cutting-edge manufacturers have made incredible progress towards ingraining 4IR technologies into state-of-the-art production. The McKinsey report states that these 4IR developments are forecasted to generate up to $3.7 trillion in value by 2025. Let’s take a more extensive look into how AI and IoT can support the transition to 4IR by augmenting demand forecasting, supply chain, and predictive maintenance for manufacturers in any industry. 2 EBOOK | AI in Manufacturing Planning Around Erratic Consumer Demand Peaks and Valleys Shouldn’t Cause Pain When the calendar flips to September in the U.S., school starts and so does football season. Football season equals big parties and nights out awash with chicken wings. As Thanksgiving and Christmas approach, the demand for turkey rises, and demand for chicken plummets. Welcome to a simplified view of the highly intricate world of demand forecasting, where a company’s ability to compete depends on providing customers with what they’re looking for at the time when they want it in a cost-efficient way. Predicting such seasonal ups and downs is a real pain point for many companies, especially those pushing perishable fresh products. Smart Chicken is a poultry distributor committed to focusing on the humane treatment and care of their chickens. In order to get their organic birds to customers when they want them, they use a blend of live analytics in Qlik dashboards and machine learning assisted forecasting with DataRobot. First, Smart Chicken segments their customers using machine learning. Smart Chicken not only talks to representatives in the field, they also rely on DataRobot to methodically cluster and segment similar customers and products based on volume and ordering behaviors — rather than simply applying a single forecasting algorithm. Second, DataRobot auto-generates unique forecasting models for each individual cluster before diving into its library of predictive models to rank its recommend models based on the following: PERFORMANCE ERROR RATES SPEED TO PREDICTION Third and last, DataRobot delivers a recommendation for a forecasting model to be put into production. Working with DataRobot allows Smart Chicken to leverage the prowess of automated machine learning to tighten customer demand forecasting — all without having to maintain an in-house data science team. 3 EBOOK | AI in Manufacturing REDUCE FORECASTING ERRORS AND BOOST SUSTAINABILITY EFFORTS, SUCH AS REDUCING FOOD WASTE The ability for companies such as Smart Chicken to more easily utilize AI is really good news for manufacturers. The McKinsey report Smartening up with Artificial Intelligence (AI) recaps the following benefits of AI-based approaches to forecasting: 30 50% 25 40% Reduction of costs related to warehousing Reduction of forecasting errors in some settings 510% Up to 65% Reduction of costs related to transport Reduction of lost sales due to stock-outs 20 50% Potential inventory reduction A reduction in forecasting errors is not only positive for a healthy bottom line, it can also improve global sustainability efforts. Japan is the world’s third leading economy, yet it’s number one in one unfavorable category — it has the highest food waste per capita, disposing of approximately 6 million tons of edible food at the cost of $19 billion annually, or approximately 51 kg (112.44 lbs.) of waste per person annually. Convenience store chain Lawson Inc., one of three major convenience stores in Japan, reports that its average daily non-food waste per store amounted to 38.7 kg (85.32 lbs.) in 2019. Hence, Lawson sought to reduce overstock by 30% and hopes to cut food waste at all of its stores in 2030 compared to 2018 numbers. To implement this goal, Lawson has begun to use DataRobot to help estimate how much product on shelves, from onigiri rice balls to egg and tuna sandwiches, may go unsold or fall short of demand. By working with DataRobot, Lawson is now saving money, reducing waste, improving profitability and doing its part to comply with Japan’s stringent new laws aimed at halving food waste and reducing food loss by 2030. 4 EBOOK | AI in Manufacturing TAKING DEMAND FORECASTING FURTHER With DataRobot’s efficiently operationalized demand forecasting solution, a company can not only deliver accurate CASE STUDY 2 and timely product placement but also optimize many of Harris Farm Markets its most critical business subunits and components with CHALLENGE Traditional demand forecasting helps with capacity Grocery chains everywhere struggle to predict their perishable needs, but Harris Farm Markets, a grocery retailer in New South Wales, Australia, has facedespecially challenging circumstances. First, wildfires made obtaining supplies unpredictable, and then COVID-19 drove sudden spikes in demand. All this made the difficult task of demand forecasting that much harder. With an expanding geographic footprint, the chain needed a way to consistently meet consumers’ demand for variety and freshness. Harris Farms turned to DataRobot to implement an AI and machine learning platform that could generate accurate predictions with minimal labor on the part of the IT team. SOLUTION Working together with DataRobot, the team put in place data-driven decision-making for the chain’s stocking operations, starting with a core subset of products and then later included additional demand forecasts. The system needed to take into account a wide range of data points, from seasonal impact to customer numbers, to generate accurate forecasts. minimum operational latencies, overhead, and spending. planning, inventory planning and allocation, and product replenishment, which allows companies to predict and plan operations for a specific time period in the future. With DataRobot, companies like Smart Chicken can take demand forecasting even further by making extremely specific predictions such as the growth trajectory and final weight of chickens — numbers that can greatly impact the planning process of anticipated supply. Being able to keep sharp tabs on supply limit surprises empowers the customer support team to be informed of exactly how much product they have available to distribute. Understanding anticipated supply shortages enables the customer support team to manage expectations more effectively and exhibits the power of AI-driven demand forecasting. We’ve looked at one essential component of the supply chain process — now let’s delve deeper into other areas of supply chain management. Turn Forecasts into Actionable RESULT In total, this has resulted in 400 automated models in production for demand and 30 models for customer-number forecasts, using 25 individual models for hourly numbers and five clustered models for daily numbers. The net result has been more accurate predictions, a vastly increased capacity to manage perishable inventory, and reduced waste. 5 EBOOK | AI in Manufacturing Intelligence to Ramp Up Supply Chain Efficiency COVID-19 hit people and businesses in so many different ways. For Stanley Black & Decker (SBD), the world’s largest toolmaker, the pandemic started poorly. Headlines read “Coronavirus Hits Stanley Black & Decker Supply Chain’’ as its production operations in China were hit hard. Their operations in China make up approximately 40 percent of their overall production, an $8.66B global operation with 42,670 suppliers. To make matters worse, spring is to tools what football season is to chicken wings: tool enthusiasts are ready to start their newest home renovations and hungry for products. Attempts to ramp up production in Mexico fell short, however, and SBD stock plummeted by March of 2020. Companies around the world soon fell upon an unsettling analysis regarding preCOVID-19 transparency/visibility investment: they were trapped in an overwhelming inward focus. MIT Sloan Management Review notes: Organizations might scrupulously monitor regulatory compliance and track disrupted “ stock-keeping units (SKUs), stores, categories, distribution centers, or logistics on their networks. But observing how other actors — suppliers and partners — improvised or failed to cope with crises frequently proved impossible. Actionable data and analytics for evaluating downstream impact simply weren’t there.” Anil Kaul, EVP at Infogain, continues this sobering analysis: If you think of data analytics, all the models, tools that have been put together for “ forecasting sales or forecasting any of the things just stopped working once COVID hit. They didn’t make sense. There was nothing historically in the data that would be able to inform you what is going to be happening.” This position resonated with SBD and led to a deeper relationship between the company’s data scientists and supply chain managers. Aleksandar Lazarevic, VP of Advanced Analytics and Data Engineering at Stanley Black & Decker, recently wrote to the MIT Sloan Management Review: With over 100,000 SKUs coordinated by complex procurement and production processes, better demand management forecasts could measurably improve prioritization. 6 EBOOK | AI in Manufacturing For SBD, supply chain teams could reprioritize customer and channel signals. During a DataRobot AI webinar, Lazarevic noted that meeting the transparency/visibility We chose DataRobot to allow our data scientists to efficiently determine and use the best machine learning model for specific problems. — Aleksandar Lazarevic, VP of Advanced Analytics & Data Engineering, Stanley, Black & Decker. challenges required translating the demand forecasts into formats that described what was needed to take meaningful actions immediately. The forecasts were worthless unless their users had visibility into the actionable next steps that they should take. By understanding that forecasts needed to be converted into clear actionable intelligence, solutions that did not give users insights into the essential data were scrapped. Essentially, operational visibility led to organizational buy-in. Lazarevic and SBD also believe they ensured “the most effective business adoption” when they selected DataRobot as its Enterprise AI Platform in 2019 for its development and deployment speed, accessible UI, and ability to convert business analysts into citizen data scientists. Before choosing DataRobot, SBD had a data science team implementing a wide range of open source code to crack a variety of analytics problems for over 30 brands. With a focus on clear actionable intelligence, SBD quickly turned things around with the following milestones: 2019 MILESTONE 2020 $199.1M The final three months of 2020 saw profits of $458 million, 130 percent higher than 2019’s fourth quarter. $458M $956M Year-over-year, SBD’s profits rose 26 percent with $1.2 billion in 2020, compared with just under $956 million in 2019. $1.2B $3.7B Sales in 2020’s fourth quarter were $4.4 billion, 18 percent higher than 2019’s fourth quarter sales of $3.7 billion. $4.4B By early spring of 2021, SBD was ready for the spring rush of homebound people buying SBD tools to work on home improvement projects, and by May of 2021, SBD stock was at an all-time high. The global giant also expects to free up between $100 million and $150 million a year by further increasing efficiency and cost savings. 7 EBOOK | AI in Manufacturing MEET SUPPLY CHAIN CHALLENGES AND ENSURE THAT PLANS CAN ADAPT TO VARIABILITY EFFECTS While SBD was able to turn COVID-19 pandemic disruptions into unparalleled success, not every company has such good fortune. Many companies have suffered supply and demand disruptions. These companies need to enhance their central-planning capabilities sooner rather than later. A recent article from McKinsey, Succeeding in the AI supply-chain revolution, states that “enhancing the relevance and size of supply-chain or business-planning teams is not enough to achieve better performance.” McKinsey suggests that companies need to be ready to handle several more additional challenges: Predicting demand across multiple product segments and geographies Identifying trade-offs with thousands of interlinked variables and innumerable technical constraints Integrating AI solutions (such as processing optimization, predictive maintenance, or master data quality) to manage the wider value chain Supply Chain Challenges Ensuring that plans can adapt to variability effects (such as demand shocks, production stoppages, and transportation disruption) in a timely manner. To meet these supply chain challenges, DataRobot helps build retail- and manufacturing-specific forecasting tools for an imperfect and unpredictable business landscape. AI drives accurate demand forecasting in the real world, and these accurate predictions, in turn, improve demand response times and decrease unnecessary overhead with these five steps: Lay a strong data foundation Prepare data for modeling Create accurate models Update forecasting models Connect forecasts to planning 8 EBOOK | AI in Manufacturing • Lay a strong data foundation. Separate signal from noise in your data so that you know your data is giving you an accurate picture. This includes internal data as well as third-party data from vendors and logistics providers. • Prepare data for modeling. Not all data is useful. Filter out bad, irrelevant, and biased data to correct the problems that inaccurate data can cause. Unreliable data often comes in the form of inaccurate logistics and delivery details. • Create accurate models. Use a library of hundreds of advanced AI models, along with any other proprietary models you have, and put them to work in parallel to determine which is the best one to drive accurate forecasts. These models could be predictions for on-time deliveries, inventory stock-outs, or customer demand. • Update forecasting models. Keep a close eye on forecasting models to ensure they consider changes in data, such as a competitor opening a nearby store, changes in consumer habits, or swings in pricing. • Connect forecasts to planning. Accurately create an efficient system for on-demand ordering, production planning, and on-time delivery with detailed AI-driven demand and sales forecasts. The immense changes to supply chains, consumer demand, and shipping logistics have also inspired DataRobot and Palantir to join forces to create a custom, newly developed framework that will empower companies to take on a more nimble strategy to demand forecasting, eliminating the time and resources spent on manual data cleansing and one-off manual modeling. The custom framework combines the best of Palantir Foundry and DataRobot model development capabilities to give customers the ability to create and test data-driven, easily updated forecasting models in minutes — not months — from a single platform. With a holistic view of the retail ecosystem, brands will be able to avoid previous blind spots, and make better and more impactful business decisions via the following steps: DataRobot’s Time Series models combine multiple data types into model development and dramatically enhance demand forecasting. The best models are then brought back into Palantir Foundry and implemented into operational workflows, delivering massive scale data and AI to business users. The models are constantly updated and trained by DataRobot to keep them relevant and fed back into the organization’s integrated data asset. Future modeling becomes even faster — allowing each project to take advantage of Palantir Foundry’s data assets and previous modeling outcomes. 9 EBOOK | AI in Manufacturing Stay One Step Ahead with Predictive Maintenance As increasing efficiencies and productivity and growing revenue are always top of mind, manufacturers should look to explainable AI, a field We’ve already seen how unevenly the COVID-19 pandemic can hit manufacturers. Let’s look at the numbers. A recent PwC report, AI Predictions 2021: Industrial Products, states the following COVID-19-related maturity curve for manufacturers: 54% 31% Manufacturers, that have accelerated AI efforts due to the COVID crisis. Manufacturers, that have delayed AI efforts due to the COVID crisis. where producing “outcomes that humans can readily understand and track backwards to the origins” is the fundamental litmus test. The most practical application of explainable AI in manufacturing is predictive maintenance, or the ability to use volumes of data to anticipate and address potential issues before they lead to breakdowns in operations, processes, services, or systems. As the Wall Street Journal reports that unplanned downtime costs industrial manufacturers approximately $50 billion annually with equipment failure causing 42 percent of this unplanned downtime, having strong predictive maintenance tools in place is essential. A predictive maintenance system is no longer a luxury — it’s a prerequisite for any manufacturer that wants to know when the next machine failure will take place in order to prevent potential interruptions in services. Before implementing a cutting-edge predictive maintenance system, it’s important to secure a deliberate strategy. In the recent article Prediction at scale: How industry can get more value out of maintenance, McKinsey has identified five golden rules for the successful implementation of predictive The study then drops a blunt question: What are manufacturers looking to get out of AI? The top goals of manufacturers’ AI strategies are clear and straightforward: 51% 37% Manufacturers, that identified increasing operating efficiencies and productivity as a primary goal. Manufacturers, that marked growing revenue as a top goal. maintenance at scale: 1 2 3 4 5 Be judicious about which assets to include Consider the right partners Provide sufficient time to improve models Put people first Build predictive maintenance into the organization’s wider digital ecosystem When it comes to considering the right predictive maintenance partner, DataRobot can enhance the efficiency and productivity of several verticals that rely on assets requiring frequent repair or those that are critical in the production process. 10 EBOOK | AI in Manufacturing Manufacturers can use predictive maintenance techniques to implement safeguards that notify the right people when a piece of equipment needs to be inspected. Using their existing historical data, such as electrical current, temperature, vibration, and sound generated by equipment, manufacturers can build models to anticipate the likelihood of a potential breakdown before it occurs. These models can identify which equipment is at greatest risk of failing, allowing maintenance teams to respond accordingly. The insights from the models that fit to historic data can also help point to the root cause of the problem and inform operators of underlying issues. For example, Smart Chicken has also turned to DataRobot for predictive maintenance. As the smallest change in tension can indicate a worn-out ball-bearing, or a minute increase in motor temperature could mean an increased level of strain, IoT sensors constantly measure conveyor belt motor amperage, temperature, and tension. By analyzing the steps that led up to previous parts failures, Smart Chicken can start predicting when the next failure is likely. Proactively pinpointing issues before failure lets manufacturers get what they want out of AI: increased operating efficiencies and productivity, as well as revenue growth. PRACTICAL APPLICATIONS OF PREDICTIVE MAINTENANCE Supply chain operators can also use predictive maintenance analytics to plan around equipment downtime and potential disruptions. Model insights can inform the supply chain team how long an asset, system, or component could be offline, allowing them to plan accordingly. Original equipment manufacturers (OEMs) can provide predictive maintenance as a service. By collecting data from multiple customers’ equipment, OEMs can build models with data collected from a wider customer base to provide individual customers with insights and equipment-specific maintenance schedules. Off-the-shelf services, however, also carry the risk of recommending overly-conservative plans that lead to overmaintenance of assets. An in-house predictive maintenance model can provide balance and a sanity check to OEM recommendations. Government agencies can also benefit from implementing proper predictive maintenance techniques. Automated machine learning for predictive maintenance can help officials understand when new parts, components, and overhauls will be required for military equipment like helicopters, aircraft, and weapons systems. Using predictive maintenance, models that rely on AI and machine learning can help public sector agencies operate more efficiently, keep expensive assets in usage longer, and enhance supply chain operations. DataRobot can help government and other public sector officials address timeconsuming Failure Mode, Effects, and Criticality Analysis (FMECAs) by running models that can predict patterns based on different assets’ environments. These predictive maintenance models can lead to more accurate asset and component lifespans and can be deployed for other use cases, including accident analysis and labor optimization. 11 EBOOK | AI in Manufacturing The More Things Stay the Same, the More They Change We’ve delved into tools, chicken, and the iconic UPS truck. Now, let’s Imagine you’re a journalist who’s just been sent to Dayton, Ohio to cover Kroger opening its first Ocado distribution facility in Greater Dayton. After the event you drive past Wright-Patterson Air Force Base before stopping for lunch. During your meal you Google who Patterson was and read all about the Patterson family and their work with Charles Kettering — inventing an easy credit approval system in 1905 and National Cash Register’s introduction of the electric cash registers in 1906. You learn that in nearby Troy, Ohio, the first-ever Universal Product Code (UPC) scanned a pack of chewing gum in 1974. This not only got rid of the need for price stickers, but also ditched the highly inefficient “eyeballing it” reordering method which used gut feelings over reliable data and demand forecasts. The UPC also spawned the SKU inventory revolution. A few years later, Kroger was testing its first automatic self-service checkout machine. Today, Kroger Edge is a leading smart supermarket, and Kroger is also using Ocado’s automation technology around the U.S. to select the products ordered by customers from shelves for pickup and delivery as well as for in-store sales. Whether it’s 1906 or 2022, two things never change. Supermarkets want to deliver fresh and innovative products via cutting-edge technology and walk away with happy customers and a handsome profit. Customers want their favorite foods and household items at the lowest price with an optimal shopping experience. With AI, supermarkets and manufacturers can improve demand forecasting, streamline supply chain, and reduce operational risks with predictive maintenance. The problems and needs don’t change, but the solutions do. By choosing DataRobot, businesses can work with an enterprise AI platform committed to solving their most urgent manufacturing and business needs. 12 DataRobot AI Cloud is the next generation of AI. The unified platform is built for all data types, all users, and all environments to deliver critical business insights for every organization. DataRobot is trusted by global customers across industries and verticals, including a third of the Fortune 50. For more information visit datarobot.com v09222021.0316 © 2021 DataRobot, Inc. All rights reserved. DataRobot and the DataRobot logo are trademarks of DataRobot, Inc. All other marks are trademarks or registered trademarks of their respective holders.
