The manufacturing industry is in full transformation. In today’s digital age, customers, suppliers, and business partners are requiring greater agility, speed, and scale from manufacturers. As such, in 2018 and beyond we will see increasing numbers of firms moving to the use of data science in manufacturing. Fully-integrated, collaborative manufacturing systems will respond in real time to meet changing demands and conditions in the factory, in the supply network, and with customer needs. Smart manufacturing is supported largely by big data analytics. IDC forecasts that by 2021, 20% of the largest manufacturers will depend on a backbone of embedded intelligence built on the Internet of Things (IoT) and cognitive data applications (e.g., artificial intelligence and machine learning) to automate large-scale processes and speed execution times by up to 25%. As we start the New Year, we’re predicting eight implementations of data science in manufacturing in 2018 as they move from static, backward-looking data metrics to the next generation of analytics based on real-time monitoring and predictive algorithms.
Data Science in Manufacturing – 8 Trends to Watch
1. Reduction of Supply Chain Risk
By the end of 2020, one-third of all manufacturing supply chains will be using analytics-driven cognitive capabilities. Supply chain dependencies can be major sources of risk to manufacturers. Given the recent increasing frequency of natural disasters and political tensions globally, supply chain risk will become a strategic priority for manufacturers in 2018, and data will be at the core of manufacturing supply chain strategy. Supply chain risks can be greatly reduced using emerging data-based processes and tools. Big data analytics can be used to map out, among other issues, how and when potential delays or weather patterns might have an effect on product shipments or which producers are poor on quality or demand forecasting and why.
We will thus see manufacturers start to utilize cloud-based data networks for logistics management and correspondence along their supply chain, with an aim to enhance supply chain planning and partner collaborations in a real-time environment. They will accomplish this using sensors and wireless technologies that capture data at all stages of a product’s life, such as a delivery truck’s speed, fuel consumption, and oil temperature or those that detect defects or damage along the way. Computer modelling will use this sensor data, e-commerce order transactions, and other systems to produce reports on when orders were shipped or received and to identify risks and delays in the fulfillment process. Manufacturers will also begin using and sharing this cloud-based data with their supply chain partners with a view toward strengthening and building commercial relationships.
2. Optimization of Operations to a Higher Degree than Ever
A Gartner survey on the projected use of manufacturing analytics over next two years showed that 88% of companies plan on utilizing data metrics to improve manufacturing responsiveness, 81% to improve capacity utilization, 74% to understand their true costs, and 75% to make faster and better decisions. Optimizing efficiency of manufacturing assets today involves being able to make real-time adjustments. This calls for managing production capacity by having a real-time view of equipment performance and production processes, along with identifying assets locations, which includes those of products and people. Data generated by IoT is enabling new opportunities for manufacturers to improve operational performance of production assets to a degree never possible before and in real-time. Manufacturers will once again leverage cloud-based systems with analytics that provide real-time visibility and facilitate operational flexibility. They will be investing in controls and sensor technologies that can make autonomous decisions during production and report them back to front-end management, allowing for more flexibility of production processes. For complex, longer-term tasks, sensor data can be combined with other structured and unstructured forms of data and analyzed for deeper insights, such as how production input affects overall yields. This analysis can, in turn, provide prescriptive actions or business rules for improved operational efficiency to include insights on ways to change existing processes.
3. Perfecting Quality as a Competitive Advantage
A full 92% of manufacturers say product quality defines their success in the eyes of their customers. They constantly seek ways to reduce waste and variability in their production processes to improve efficiency and product quality. The mainstreaming of big data technology that captures sensor data from shop floor tools and equipment will allow manufacturers to take an increasingly granular and enterprise-wide approach to quality control, instead of “quality” being its own isolated department. Going forward, manufacturing data analytics will integrate quality and compliance management as core strengths across all locations, allowing manufacturers to shift from a reactive to a predictive approach to quality.
We will see manufacturers continue to invest in data capture at the machine level and the storage and analysis of such data in their company’s integrated systems. In addition, manufacturers will make greater use of combining quality management programs with manufacturing data to obtain a full view of their operations across all their production centers. They will also be able to determine which factors, processes, and workflows impact quality. Using data analytics, manufacturers will ultimately obtain more accurate predictions about how their sourcing decisions and overall operations contribute to company-wide quality.
4. Predictive Maintenance to Reduce Costs
Predictive maintenance, as a market, is set to provide a constant rate of return of nearly 30% until 2021. Generally, it is predicted that such a focus will enable manufacturers to realize as much as a 50% reduction in costs by 2022. We will see manufacturers moving from a focus on preventive maintenance of production assets to a focus on predictive maintenance by leveraging data produced from IoT and connected assets, to include insights gained by advanced predictive analytics. We will see more investment in smart machines that can alert floor managers to problem occurrences and analyze past experiences with relevant information to predict the need for preventative maintenance. This will also help to detect deteriorating machine performance and prevent larger problems. Manufacturers recognize that they can now optimize asset usage in a truly end-to-end way by using data analytics capabilities and, in turn, use this ability as a competitive differentiator.
5. After-Sales Service Improvements
Economic pressure, demographic pressure from Millennials, and the proliferation of social media on a global level have resulted in a power shift from the manufacturer to the consumer. Manufacturers are coming to understand that their actions after making sales are as important as the efforts they put into preparing for the sale, which all has an increasingly significant impact on their company’s financial performance. A recent Service Council study found that 27% of manufacturing companies’ total revenues came from service. Another report suggested that an average gross margin of 39% could be attributed to after-sales service. This figure is much higher than normal profit margins realized from the production and sale of most manufactured products. Undeniably, high-quality service is important to obtaining financial success. Therefore, manufacturers will need to utilize predictive analytics to optimize their after-sales service and product parts business performance. For example, if a customer has an upcoming appointment to bring his car in to the dealer for a service, and the automobile manufacturer’s data analytics determine that a part on the car only has another 30 days of life before it fails, the car dealer will be notified by the manufacturer’s data system and will have enough time to ship a replacement part to the right location at the right time for the customer’s upcoming appointment. This avoids a situation where the customer either has to bring the car back in for a repair in the near future or has to wait for a new part to arrive. Using predictive analytics in this way improves customer loyalty, saves time, and reduces costs.
Manufacturers will move away from using outdated technologies and business practices for inventory and after-sales management that provide little visibility and control. We will see them investment in cloud-based solutions that incorporate machine learning and provide manufacturers with greater visibility and control over inventory management.
6. Mass and Individual Customization of Products
The power shift from manufacturers to consumers described earlier is also driving investment in product customization capabilities that are largely made possible by advancements in big data usage, IoT, cloud computing, and advanced analytics. When those elements are combined in big data systems, connected enterprise, and smart manufacturing devices, manufacturers can create a direct line to customers and provide tailored products. As consumers customize products, they provide extensive data about their preferences and behaviors that manufacturers can use to inform future product development. Big data analytics then allows companies to analyze customer behavior and develop methods of delivering products in the most timely and efficient way possible.
We will thus see manufacturers moving data out of silos and creating a data ocean of customer information with the goal of becoming more agile and responsive in making products to individual requirements both in B2C and B2B environments. This contrasts with the traditional focus on providing high throughput, low variability, and mass production. Intense personalization of products such as those offered by Nike, which has its NIKEiD program that allows consumers to customize running shoes and have them made and shipped direct, represents typical customization that all manufacturers will strive toward. In addition to individualized customization, near-customization will become possible using big data to predict product features and combinations that will be in highest demand. For example, the Opel Adam offers more than 61,000 variants for its exterior and nearly 82,000 for the interior; in total, Opel offers 4 billion combinations.
7. New Data-Driven Revenue Sources and Business Models
Manufacturing analytics platforms gather data from multiple sources to gain benefits not only for the manufacturer but also for business partners, other industry players, and even customers. Whether a manufacturer decides to analyze all of its collected data or sell it to other companies, the data is valuable as a potentially actionable monetary asset. In recent years, with the creation of and participation in industry-based data clouds, manufacturers have been experimenting with data capitalization and data monetization opportunities that open new markets to other companies and customers. Faced with excess data and unique information that was never available before, the monetization of industrial and manufacturing data will become a reality. More companies will be looking into possibilities for data exchange and industry collaborations through projects such as the Industrial Data Space to create new revenue streams, new services, and even new business models. Moreover, new types of data and analytical tools, such as video, geospatial, simulation, and time series analyses enable never before possible insights. Companies will be looking for ways to leverage such data.
One long-standing example of this movement to leverage data as an asset is that of Deere & Company, manufacturer of John Deere farming equipment. In 2013, the company launched myjohndeere.com to develop direct connections with farming industry consumers. The platform had first been established for John Deere customers to have access to equipment parts and to bring their attention to other company offerings. In running this website, Deere found a rich source of assets in what amounted to extensive data collection. Deere has since installed Wi-Fi enabled sensors to its mobile equipment owned by consumers. These sensors record data on metrics that are useful to farmers, such as, for example, fuel consumption. John Deere’s Field Connect™ monitoring equipment collects additional data that is important to farmers, such as temperature, rainfall, soil moisture, and wind speed. Because of the data John Deere provides to individual farmers, they are better able to improve productivity in their farming operations. There is also, however, speculation that agricultural companies could find such data to be an important commodity. Caterpillar, Komatsu, and other heavy equipment makers are also testing what might be called a hybrid approach to data capitalization. They are providing sensor data to customers to strengthen business ties, but the data is also fed back into the companies’ proprietary analytics systems.
8. From Local to Enterprise-Level Data Analytics
On a global level, manufacturers do not have appropriately scaled systems in place to make use of smart technologies and associated data generation. However, large factories that produce and manufacture goods are being broken up and distributed around the world to become much more flexible. In addition, next-generation analytical systems and applications have been developed to integrate existing shop floor data and technologies, such as artificial intelligence, IoT, blockchain, and operational technologies, which can facilitate the movement of both structured and unstructured data from anywhere to anywhere. These developments will push manufacturers to move from big data usage at the individual department or factory level to implementation of global, enterprise-wide intelligent analytics. Companies are realizing that as manufacturing becomes more networked as an integrated intelligent system, the key to strategic growth and continued effectiveness is to make use of the data generated in the system. Companies like GE, with its more than 500 factories and thousands of factories in its supply chain, are taking advantage of data analytics at the enterprise level. Predictive applications geared toward in-line production quality measurements require algorithms that can specifically handle and make use of all incoming and recorded data. Benefits of such systems include overall improved quality with fewer downtime operations, faster cycle times, and greater savings realized in labor costs and improved operational efficiencies across the enterprise and the globe.
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Wherever your manufacturing business is within the continuum of leveraging data science, StrategyWise is here to help. Call one of our manufacturing industry specialists today to take the next step in leveraging all your data has to offer.