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Five Ways AI Can Help Create New Smart Manufacturing Startups

smart manufacturing, AI, machine learning

AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.

PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.

 Abundant AI startup opportunities in smart manufacturing and industry 4.0 

According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.

Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.

AI, Industry 4, smart manufacturing

McKinsey and the Massachusetts Institute of Technology (MIT) collaborated on a survey to identify machine intelligence leaders’ KPI gains relative to their peers. They found that leaders achieve efficiency, cost, revenue, service, and time-to-market advantages. Source: Toward smart production: Machine intelligence in business operations, McKinsey & Company. February 1, 2022.

Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:

  1. AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
  2. Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
  3. Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
  4. Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
  5. Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.

Talent remains an area of need 

Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings’ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.

5 Ways Integration Is Enabling The Factory Of The Future

  • factory-of-the-future-report93% of global product leaders say that predictive maintenance combined with real-time equipment monitoring enabled by integration is a must-have for factory planning today.
  • 75% of global product leaders plan to implement factory of the future initiatives and programs in the next five years or less, starting with Industry 4.0
  • 67% of automotive executives expect that new technologies enabled by real-time integration will enable their teams to reach and exceed lean management and continuous improvement goals starting this year and accelerating through 2030.

Boston Consulting Group’s recent article, The Factory of the Future provides insights into a recent global survey the consulting firm conducted of more than 750 manufacturing product leaders from leading companies in three industrial sectors: automotive (which includes suppliers and original equipment manufacturers, or OEMs), engineered products, and process industries. The survey’s objective is to define the vision for the factory of the future in 2030.  Determining long-term benefits and the roadmap to implementation are also goals of the study Boston Consulting Group (BCG) and its research partner, the Laboratory for Machine Tools and Production Engineering at RWTH Aachen University, achieved. The Factory of the Future is a vision for how manufacturers should enhance production by making improvements in three dimensions: plant structure, plant digitization, and plant processes.

5 Ways Integration Fuels The Factory Of The Future’s Growth

Real-time integration based on intelligent objects that connect diverse enterprise systems including SAP, Salesforce and others is the foundation that manufacturing companies must adopt to excel in their Factory of the Future efforts. These real-time objects illustrate the future of Application Programmer Interfaces (API).  APIs that will fuel and drive the Factory of the Future will enrich each real-time integration points across manufacturing networks. Intelligent Objects pervasively used today are the precursors to the most valuable APIs that will enable Factories of the Future tomorrow. With APIs continually improving and gaining the capability to provide insight and intelligence, the essential role of real-time integration in all factories of the future becomes clear.

The following are the five ways integration is enabling the Factory of the Future today:

  1. Real-time integration enables the value chains supporting the Factories of the Future to continually accelerate, excel and improve with additional insight that drives future growth strategies. Bringing greater intelligence into each integration point across the value chains supporting the Factories of the Future leads to new technologies delivering greater lean management benefits. Real-time integration will deliver strong benefits in the areas of lean management, predictive maintenance, modular line setups, and the orchestration and collaboration of smart robots.

factory-of-the-future-1

  1. The Implementation Roadmap for the Factory of the Future shows how critical real-time integration is to the Factory of the Future’s vision being attained. Multidirectional layouts, modular line setups, sustainable production, the orchestration of smart and collaborative robotics and attainment of big data and analytics plans all are dependent on real-time integration. The following graphic from the study illustrates just how central integration is to the optimizing of plant structure and plant digitization.

factory-of-the-future-2

  1. By integrating large-scale enterprise systems including those from SAP, Salesforce and others with legacy, 3rd party and homegrown systems, every area of production quality will improve. The most urgent need global manufacturers have is finding new ways to improve product, process and service quality without raising costs. Improving the quality of these three dimensions makes any manufacturer more trusted and successful in selling next-generation products.  By aggregating data using real-time integration so that Big Data and advanced analytics can be used to find new patterns, some of the world’s most well-known manufacturers are excelling on product quality. To produce cylinder heads at its plant in Untertürkheim, Germany, Mercedes-Benz uses predictive analytics to examine more than 600 parameters that influence quality. Mercedes-Benz is an early adopter of using Big Data and advanced analytics to improve quality management and bring high precision to engineering. Bosch has implemented software that analyzes data about its production of fuel injectors in real time. The software monitors process adherence and recognizes trends. It automatically transmits information about deviations to operators, allowing them to improve the process accordingly.
  1. Real-time integration across and within manufacturing systems enables multi-directional layouts of production workflows. The Audi R8 manufacturing facility in Heilbronn, Germany, does not have a fixed conveyor so the teams there has greater multidirectional flexibility in building customized vehicles.  Real-time integration across the Audi factory floor is essential to provide R8 production teams with the specifics of how they can best collaborate and deliver the highest quality vehicles in the shortest amount of time. Real-time integration is enabling driverless transport systems, guided by a laser scanner and radio frequency identification technology in the floor, which moves the car bodies through the assembly process. These systems enable assembly layout changes quickly with no impact on existing production. Enabling real-time integration often involves extensive field mapping between different systems, which is a lengthy and error-prone process. Integration technology provider enosiX has developed a unique, real-time integration technology that obsoletes the need for field mapping and supports bi-directional data updates.
  1. Enabling the Factory of the Future’s production operations to flex in response to rapidly changing customer requirements is entirely dependent on real-time, reliable integration of production and customer-facing systems. The implications of the study on the future of manufacturing underscore just how critical it is for manufacturers to be agile enough to create entirely new business models while gaining insight and intelligence into how they can continually improve lean manufacturing. When real-time integration unifies a value chain for any manufacturer, their speed, scale and ability to simplify the complex processes required to serve customers turns into a formidable competitive advantage.

 

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