Technology has become an all-pervasive force which has impacted all facets of our lives; and the manufacturing sector is not any different. A tectonic shift is underway in how manufacturing processes are executed.
Manufacturing processes are moving to the Industry 4.0 paradigm, signified by an exponential increase in the use of technology. Extensive use of various software solutions are present, including: enterprise resource planning (ERP), manufacturing engineering systems (MES), and computerized maintenance management systems (CMMS). On top of software, the technology building blocks required for industry 4.0 are:
- Internet of things (IoT)
- Cloud computing
- Low latency network connectivity (5G/WiFi 6E)
- Big data analytics
Big data is the technology that plays a pivotal role in improving manufacturing processes.
Big data and AI
IoT sensors in the various parts of the manufacturing process help to capture all data generated. They are capable of sending this data over a low latency network to the cloud infrastructure. The data collected is continuous and happens round the clock. Due to this, the volume of data collected is huge. A single factory could generate terabytes of data every single day. This large volume of data collected and stored is generally referred to as big data.
Analyzing such a large volume of data is a difficult task, and traditional analytical methods are not effective at analyzing big data. While conventional models work for small volumes of data, they do not scale to big data. As such, artificial intelligence is required to work with big data. The use of artificial intelligence to gain insights from large volumes of data is called big data analytics. For more advanced use cases, machine learning and deep learning technologies are used. It is critical to improve manufacturing processes with technology.
Big data and AI have an impact on manufacturing in a myriad of ways. The five most common impacts of big data and AI on manufacturing processes are covered in the following sections.
1. Supply chain improvement
Supply chains of businesses are extremely complex. Multiple vendors, suppliers, customers, warehouses, and distribution centers are part of the supply chain. Managing such a complex network of moving parts is a cumbersome task. One mistake in the supply chain can have a cascading effect.Â
The supply chain of an organization generates a large volume of data. Traditional supply chain management procedures cannot even have complete visibility of the data. AI has the ability to overcome those challenges. It can process large volumes of data and give the optimal way to manage the supply chain. AI capabilities can also be used to synchronize with data from vendors, suppliers, and other supply chain partners. This helps to have a streamlined end-to-end supply chain.
2. Costs
Another significant way AI can help in the manufacturing process is reducing costs. Manufacturing facilities are cost-centers for organizations. Minimizing the cost of manufacturing operations will help to improve the profit margin. Every organization should try to use AI for cost optimization.
AI can map the cost distribution in the manufacturing operation. Historical and live data of expenditure can be used to perform a deep analysis. The AI model also has other pertinent information regarding manufacturing operations. Therefore, the model would be able to determine the importance of each line item in the expense table. AI can also help to identify wastage in the operations. Lean maintenance can be incorporated with AI models to detect and rectify wastage.
A cost minimization function can be performed by an AI model. The real-world efficacy of the output from the model can be discussed and implemented in cohorts. This helps to see if some of the recommendations from AI are working before proceeding with the rest. Slowly but steadily, AI can reduce the running cost of manufacturing operations.
3. Predictions
AI, combined with a large quantity of historical data, can make reliable predictions about the various aspects of manufacturing operations. The prediction could be of expected future demand. Reliable prediction of future demand helps to optimize the production according to the demand. This ensures productive capacity does not go to waste.Â
Predictive maintenance can also be done where machine breakdowns are predicted. This helps to perform maintenance activities before the breakdown occurs. This avoids unscheduled downtime and saves from many other such costs. Inventory management can also be made efficient with the help of AI. Reliable predictions of inventory requirements help to have minimal storage space and also reduce wastage.
4. Quality control
Artificial intelligence can be used for quality control using hi-resolution images to identify defects. Machine learning and deep learning algorithms compare the products produced with the standard product. This helps to identify defective products and remove them from the assembly line.
The defects in the products can also be used to identify the underlying reason for the defect. AI can be used to conduct root-cause analysis of defects. This result of analysis can be used to rectify the production process and improve quality in the future.
5. Product development
As much as AI helps in manufacturing products, it can also be used to design new products. The usage patterns from consumers are available as digital data. This can be used to identify unfulfilled needs of consumers to develop new products. AI can be used in all areas of a new product launch from design, testing, production, marketing, and even after sales.Â
Adopt AI to improve manufacturing
The use of AI will eventually be as ubiquitous as smartphones are today. Manufacturers will have to adopt big data and AI to improve processes, reduce costs, and eliminate wastage. Streamlined manufacturing with the help of AI is a definite advantage to win in the competitive markets.
