9 AI Use Cases Pivotal to Developing a Highly Productive Manufacturing Business
Digital Transformation | 9 min READ
The Rise of AI-based Models: Importance of Applying Machine Learning to Processes
Machine learning is a growingly robust tool to accelerate automation which can be gauged from the fact that businesses are looking forward to optimizing and streamlining processes. In contrast to rudimentary automation based on standard rules commonly employed for standardized, foreseeable situations, machine learning can learn from the data without depending on rules-based programming.
Deepak Gupta
Deepak Gupta

AVP, Practice Head

Data & Analytics


As a result, it can steer more complicated processes, ushering in more efficient and accurate results. However, many organizations continue to be in a pilot mode wherein they have likely forged some distinct use cases, despite that they are yet to discover broader applications of machine learning and leverage its most modern forms. Results of the latest global survey conducted by McKinsey show that 15% of respondents have profitably scaled automation spanning many business segments. While only 36% stated that machine learning algorithms had been rolled out past the pilot stage.
The primary stumbling block comes in the form of institutional knowledge since it is never completely codified. This stems from the fact that many decisions cannot be boxed within a set of rules. Most of the avenues of information that are essential to scaling machine learning are high-level and very scientific to make them actionable. This proves to be a real challenge for ML leaders to train their teams on adopting Machine Learning algorithms.
The magnitude of value derived by applying ML to processes is paramount. This can be gauged from the fact that top organizations have successfully improved the efficiency of their processes by 30% or higher and grown their sales by 5% to 10%. For example, using a predictive model, claims classified across different risk classes saw the number of claims paid automatically surge by 30%. This also resulted in minimizing human effort by 25% at a healthcare company. Businesses will achieve higher scalability and resilience if they build ML into processes.
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Signals of the Progress of the Most Versatile AI-based Tools: Machine Learning Models
As per a Deloitte report, at Microsoft, specialized chips have sped up the training of machine learning models at a dramatic rate, reducing the time to develop a speech recognition system by 80%. The same report stated that technology vendors could use transfer learning to cut the need for training data by several orders of magnitude. MIT researchers have exhibited a method to train a neural network that brought both precise predictions and the rationales behind those predictions. Leading tech vendors are searching for ways to cram robust machine learning models onto mobile devices. In addition, new tools are being developed to automate tasks occupying up to 80% of data scientists' time.
Barriers to the Adoption of Machine Learning
Although ML is one of the most advanced, versatile, and robust AI-based tools, its adoption faces a lot of obstacles. A 2017 survey of 3100 executives in SMBs and large enterprises across 17 countries found that less than 10% invested in machine learning. Many factors are slowing down the pace of adoption of ML, including talent shortage. Immature and still evolving tools and frameworks employed for ML work are other factors responsible for the slow adoption of machine learning. In addition, it can be hard, time-taking, and expensive to get the large data sets that some machine learning model-development techniques require, per Deloitte.
Challenges faced in Silo AI Projects
Some major challenges faced while working in silo AI projects include non-reusability. Workflows cannot be copied and pasted. Enterprises are doing it in pockets, solving individual problems. Because of Ops complexity, the movement from exploration to production is slow. Everyone does it well but does it differently. Last but not least, there is a challenge in industrializing this capability and making it available at scale.
Benefits of Our AI Factory
Our AI Factory develops and deploys Artificial Intelligence on a common platform. The production deployment is faster with standardized Ops. There is a common data architecture under compliance and security needs. Our AI factory allows higher collaboration, making teams more productive by reusing and expanding faster to other units and use cases. This is how our AI factory overcomes challenges posed by Silo AI projects.
How Our AI Factory Addresses These Challenges
Technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence, Machine Learning, and Automation are instrumental for the smooth functioning of smart factories. We will discuss each of the AI projects in the following section in more depth and comprehensiveness, intending to put the benefits of our AI Factory in perspective.
Remote Monitoring
When public health advisories restrict on-site work, AI in factory automation enables organizations to continue operations by remote equipment monitoring and controlling. As a result, it becomes imperative for businesses to connect critical assets to cloud-based control software to implement such services. Industrial control software vendors and machinery OEMs provide most equipment software extensions and connectivity kits. Meeting the highest security standards for the security of their organization and customers, employees establish access to these tools from home.
Asset Tracking
AI-based models have empowered organizations across various sectors, including manufacturing, to respond to market changes at a more rapid and efficient pace by making quick adjustments to production capacity while augmenting remote operations when there are hindrances to access to any facility. According to McKinsey, remote monitoring, controlling, assistance, and maintenance tools, including asset tracking tools, cut the field-service costs, particularly travel, by 10% to 40% by decreasing the need for in-person visits. The benefits are especially higher at machinery OEMs with a large installed base.
Fleet Management
According to McKinsey, the global market size for fleet telematics hardware, software, and services will be $75 billion by 2025. Fleet telematics technologies are gaining traction at a rapid pace. This is clear from the fact that nearly 15% of vehicles have telematics installed as standard, and there are about 100 million telematics units in operation globally. Telematics enables businesses to store and analyze fleet operations data. This helps companies predict failure and determine proper intervals for preventive maintenance, making fleets more efficient and reliable by optimizing vehicle usage to enhance fuel efficiency and service levels.
Drug Discovery
Drugs work only when they stick to their target proteins in the body. Analyzing that stickiness is a major hurdle to the drug discovery and screening process. New research using an amalgamation of chemistry with machine learning could help lower that hurdle. MIT researchers have developed a novel approach, DeepBAR, that provides accurate calculations of the binding affinity of a drug molecule with a target protein at a much faster pace than the previous sophisticated methods. It is predicted that DeepBAR could help to speed up the momentum of drug discovery and protein engineering.
Inventory Management
IIoT-powered inventory management helps manufacturing organizations reduce inventory and free up liquidity directly. For example, sensors use ultrasound to monitor container-fill levels at a single site. Other applications use geotags combined with integrated mobile communication to track the flow of materials over long distances. This real-time transparency allows the logistics team to better manage the material flow and reduce inventory by ordering raw materials and other inputs closer to the required date. As a result, the Industrial Internet of Things (IIoT) helps reduce inventories by up to 36%, per McKinsey.
Manufacturing Maintenance
Amidst the Industry 4.0 environment, manufacturing maintenance is not just limited to downtime prevention of individual assets. Because machines are now interconnected more and more along the production line, and a faulty machine could pause the entire production process, the adoption of predictive maintenance becomes imperative. On average, per Deloitte, predictive maintenance enhances productivity by 25%, minimizes breakdowns by 70%, and reduces maintenance costs by 25%. In addition, predictive maintenance in manufacturing could improve uptime by 9%, lower costs by 12%, decrease safety, health, environmental & quality risks by 14%, and increase the lifetime of aging assets by 20%, according to a PwC report.
Workforce Management
Workforce management tools based on IIoT enable enforcing important steps for physical distancing when sites are open. If employees agree and regional regulations allow, workers, put on positioning devices that show their movements within site. This data is converted into intelligent algorithms, helping managers in workflow optimization and contact minimization at critical points, such as shift changeovers. For instance, based on IIoT insights, an organization could stagger breaks and arrange shifts at a drastically faster pace, enabling the company to maintain operations while dramatically decreasing employee contact. In addition, some workforce tracking solutions limit access to already crowded areas automatically.
When workers are COVID positive, organizations can notify employees with whom they have been in contact by using the positioning data available from their wearable devices. For employee privacy reasons, all worker-centric data must be anonymous. In scenarios where COVID-19 causes worker absenteeism because of ill health, the devices inform the concerned department about the staff shortage areas to recognize operational areas where risks such as slowdowns can occur.
Besides employee protection, workforce management solutions can increase employee productivity and decrease cycle times. According to McKinsey, workforce management solutions improve employee productivity by 10% to 30%, based on the factory setup, such as the count of machines and processes.
Paperless Manufacturing
The industry is witnessing a remarkable digital transformation. This transition will transform plant floor operations and begin a new era in manufacturing, Industry 4.0, which is powered by the Industrial Internet of Things (IIoT). Technological advancements, legislation changes, and a growing marketplace are some factors that will drive the need for paperless manufacturing. In addition, manufacturers are under pressure to go paperless owing to the demands of the regulatory and compliance authorities for the generation of automated digital records. Embracing paperless manufacturing has several benefits, including improved accuracy, high-quality electronic record keeping, and the availability of easily shareable data.
Warehouse Management
Warehouses offer several automation opportunities, such as automated material storage and retrieval systems, shuttle systems, smart picking robots, smart shelves, collaboration robots (cobots), intelligent and automated systems that sort, pick, and pack, besides drones that execute inventory inspection. In addition, digital Twins creates duplicates of warehouses digitally to better understand the results from myriad digital technologies, helping design optimum warehouse operations. AR tools that make picking numerous orders at once less difficult and more productive and exoskeletons to minimize accidents from repeatedly handling heavy materials are other Industry 4.0 solutions that provide immense support to the warehouse workers.
There are myriad use-cases of AI in manufacturing. Our AI Factory helps drive AI projects, including remote monitoring, asset tracking, fleet management, drug discovery, inventory management, manufacturing maintenance, workforce management, pollution monitoring, warehouse management, paperless manufacturing, and shipment monitoring seamlessly. Early adopters of smart factories reporting higher efficiencies in operations and revenues are some factors that have provided an impetus to the rise of factory automation systems. In addition, several manufacturing companies have piloted machine learning models, signaling the magnitude of opportunities in store for the future.
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