| As the manufacturing industry goes, so goes the future. Perhaps no other industry has seen a greater impact from technological advancements than manufacturing. And this is particularly true today, as automation driven by Artificial Intelligence (AI) and Machine Learning (ML) is once again sending shockwaves through the industry.
Sudeep George,
Vice President Engineering,
iMerit
As the world is experiencing yet another Industrial Revolution driven by advancements in AI and ML, it is critical that the Indian manufacturing industry keep pace with technological change. It was in 2015 when the Government of India launched its Digital India initiative to empower India on its journey of digital transformation on the global stage. And with this digitisation comes a mountain of data that is being used to power technologies such as AI, ML, Decision Intelligence, Robotic Process Automation (RPA), Edge Computing, Quantum Computing, and much more.
Data is essesntial
The explosion of digital technologies has driven a new wave of business development here. According to a report by NASSCOM, ‘Resilience to Resurgence: Technology Sector in India 2022,’ India recorded a spectacular 15.5 percent growth in the technology sector, generating up to $227 Bn in revenue.
Today, data is used for forecasting possible risks, demand, unexpected waste, market patterns, and the requirements of the customers to keep up with high-quality standards and quality metrics. For the digital future, companies are incorporating highly collaborative technologies like the Digital Twin to ingrain superiority in their products. A Digital Twin is a virtual representation of an actual product developed by real-time and Artificial Intelligence and machine learning models. An extremely effective technology, Digital Twin technology can help identify defects and predict the end of life of a product. However, this is just the beginning, and it is irrefutable how the upcoming technological transformation benefits the manufacturing industry.
Addressing challenges
This technological shift can lead the manufacturing sector to expand its horizons and deliver bigger and better. It can solve the numerous challenges the manufacturing ecosystem has faced conventionally.
- Inventory Management: Inventory management is one of the biggest challenges in the manufacturing industry. Over time, due to a lack of structured data, companies fail to assess their inventory, which often leads to problems like overstocking and understocking. Both being obverses of the same coin led to inefficiency in production. While overstocking leads to increased costs and a tiering of profits, understocking can lead to a poor output of products and the loss of sales and customers.
- Demand Forecasting: Demand patterns are a major challenge in the manufacturing industry. Due to irregular demands, companies fail to manage inventory in the warehouse, which can result in poor quality products. This severely affects customer satisfaction, resulting in the loss of customers.
- Floor Management: It is no secret that human error is always possible. These errors can affect improper floor management. Although manpower is the biggest asset in such a complex business environment where an insignificant change in figures can lead to massive losses, companies cannot rely merely on human resources. Human error can be a result of a lack of proper training, a lack of communication, distraction, and training.
- Machine Maintenance: With a major focus on core areas of the business and key aspects of developing products, workers fail to detect machine abnormalities that can cause the machines to malfunction or stop working. An unexpected downtime caused by a malfunction in the machine affects production to a great extent.
- Inflation: Due to the global economic downturn, inflation is soaring at an unprecedented rate, directly affecting the profit margins of manufacturers. Due to the unavailability of data on changing market patterns, manufacturers fail to identify the cost of raw materials, which leads to losses.
- Quality Analysis: Due to the untimely availability of feedback, manufacturers lack the ability to produce products as per customers’ changing requirements. With the market being cluttered with cut-throat competition, this is a huge setback for any business as it drives customers towards competitors. It is a fact that a chief product officer cannot simply rely on predicting that customers will like the product.
However, in line with recent tech transformations, what has gained more relevance over these years and also become an important wheel for the manufacturing industry is data. Realising the value of data, companies have started coupling Artificial Intelligence models with data to deliver data-driven products that align with customers’ needs. According to a report by MarketsandMarkets, the global Artificial Intelligence adoption in the Manufacturing Market, which is currently valued at USD 2.3 billion, is anticipated to reach USD 16.3 billion by the end of 2027, growing at a CAGR of 47.9%.
Leveraging data on facets of manufacturing
Today, manufacturers have large volumes of real-time data collected from different sources, such as reviews, industry patterns, social media, and personal care devices enabled with IoT technologies. This data can be used as a competitive advantage by providing meaningful insights. Manufacturers can improve overall production by leveraging this data on several facets of the manufacturing process.
- Improved Decision-Making – By analysing big data, companies in the manufacturing industry can make better decisions. The data can also assist managers in identifying trends and challenges so they can allocate resources according to their needs.
- Precise Forecasting – Big Data can help in identifying demands and production by analysing historical data. By enabling companies to predict upcoming demand patterns, big data can significantly become a proven advantage to enrich performance.
- Concurrent Monitoring – With the availability of concurrent data generated by devices running on IoT technology, manufacturers can optimise the quality of production, which helps them gain a competitive edge over their competitors.
- High-Quality Products – Assessing Big Data can help in manufacturing products as per customers requirements, which leads to a higher customer satisfaction rate.
- Timely Maintenance – Data generated through machines’ internal dashboards can be used for detecting failures and the life of machines, suggesting preventive measures, and predicting expected downtime in production in case the machine stops working suddenly. This can considerably help manufacturers figure out alternative steps to continue production.
- Effective Pricing Strategies – The data extracted through markets can be used to analyse the cost and demand to finalise the price of the manufactured product in the market to yield maximum profits.
As we tread the digital world governed by tech-savvy consumers, data comes in handy to increase customer satisfaction rates. It is an undeniable fact that the role that data plays in the overall brand-customer journey is critical. But data alone is not enough. It is also critical to ensure the date is given context from which a business can extract value and use it for a positive outcome. This is where data labelling comes in. AI needs to be trained on structured data to be effective, and labelling creates this structured data. By labelling data gathered from different sources and extracting meaningful insights from that data, companies can thrive among their competitors.
Leveraging data with robotic machinery
Here are a few examples of how companies can leverage the power of data by transfusing it with highly powered robotic machinery.
- Enhance Industrial Automation – By coupling video, image, and sensor data with AI models, companies can optimise robotic processes. Integrating data with AI/ML models enables machines to carry out operations 24/7. Since the output is carried by machinery, there is less scope for human error. This increases factory output.
- Data Cleansing – Using AI-powered annotation tools, machines are trained to extract meaningful information by cleansing irrelevant information from unstructured data. After data cleansing is done, these models can leverage the information more efficiently, since duplicate or irrelevant data has been removed. This results in several benefits, including a higher ROI, improved efficiencies, and minimised compliance risks.
- Automatic Order Management – Annotated data can be used to train machines to track market fluctuations, demand shifts, consumer expectations, and material shortages. By leveraging this data, machines can be trained to automate order management processes. But it is important to understand that the AI models that track and react to these types of fluctuations cannot perform without properly annotated data feeding the model.
- Quality Control Management – To maintain high standards, the products often go through quality control management processes. Quality control management, historically a job for humans, requires strict identification, detection, and classification of any issues prevailing in the final product. To ensure a lower scope of errors, manufacturers are replacing human vision with computer vision by training machines to extract relevant information from the data gathered.
Computer vision, which is a branch of AI, has today become the go-to tool for manufacturers in the quality control management process. Some of the key reasons for this include the detection of issues that cannot be detected by humans (i.e., minute cracks, leakages, etc.), improved quality control, decreased labour costs, high-speed processing capability, and 24/7 continuous operability.
The field of manufacturing is undergoing massive advancements by making use of synergies in data and AI/ML technologies. It is a fact that today’s market is cluttered with competitors. Thus, it is mandatory for organisations to shift from a model-centric approach to a data-centric approach.
