History of Growth of Manufacturing Analytics

 

History of Growth of Manufacturing Analytics

Introduction

The evolution of manufacturing analytics has been driven by advancements in technology, data availability, and the need for more efficient and effective production processes.

Key developments in manufacturing analytics:

Early Years: In the early stages of manufacturing, data collection, and analysis were limited. Manufacturers relied on manual record-keeping and basic statistical analysis techniques to monitor production and quality.

Introduction of Automation: With the advent of automation technologies in the mid-20th century, data collection became more standardized and automated. Machines began generating data points, such as production counts and cycle times, enabling basic performance monitoring.

Statistical Process Control (SPC): In the 1980s, the concept of SPC gained prominence in manufacturing. SPC uses statistical methods to monitor and control processes, detect variations, and ensure product quality. This approach involved analyzing data from sensors and quality control checks to identify patterns and take corrective actions.

Enterprise Resource Planning (ERP): The 1990s saw the emergence of ERP systems, which integrated various business functions, including manufacturing, into a single database. This allowed manufacturers to capture data from different areas and gain a holistic view of their operations. Analytics capabilities within ERP systems provided insights into inventory levels, production planning, and resource allocation.

Advanced Analytics and Machine Learning: With the increasing availability of computational power and big data, advanced analytics techniques started gaining traction in manufacturing. Machine learning algorithms were employed to analyze vast amounts of data from multiple sources, including sensors, production lines, and supply chains. This enabled predictive and prescriptive analytics, helping manufacturers optimize production, improve quality, and reduce downtime.

Internet of Things (IoT): The proliferation of IoT devices and connectivity in the early 2000s brought a significant transformation to manufacturing analytics. IoT sensors embedded in machines, equipment, and products began generating real-time data, providing visibility into every stage of the production process. Manufacturers could leverage this data to monitor equipment health, identify bottlenecks, and implement predictive maintenance strategies.

Industry 4.0 and Digital Twins: The concept of Industry 4.0 emerged, combining digital technologies, automation, and data analytics to create "smart factories." Digital twins, virtual replicas of physical assets or processes, became popular for simulation and optimization purposes. Manufacturers could use digital twins to predict the behavior of their production systems, test different scenarios, and make informed decisions based on real-time and historical data.

Cloud Computing and Edge Analytics: Cloud computing enabled manufacturers to store, process, and analyze massive amounts of data without significant infrastructure investments. It facilitated scalability and allowed for easy collaboration and integration across multiple sites. Additionally, edge analytics, where data is processed at the edge of the network, gained prominence. Edge analytics reduced latency, improved real-time decision-making and enabled local data processing for critical applications.

Artificial Intelligence (AI) and Cognitive Analytics: The convergence of AI, machine learning, and cognitive analytics has further revolutionized manufacturing analytics. AI-powered systems can autonomously analyze vast amounts of data, detect anomalies, optimize processes, and make intelligent recommendations. Natural language processing (NLP) and computer vision capabilities enable systems to understand unstructured data, such as maintenance manuals or visual inspections.

Integration with Supply Chain and Business Intelligence: Modern manufacturing analytics systems are becoming more interconnected with supply chain management and business intelligence tools. Data from different parts of the supply chain, such as suppliers, logistics, and customer demand, is integrated, providing end-to-end visibility and enabling proactive decision-making to optimize inventory levels, production schedules, and customer satisfaction.

Conclusion

Overall, the evolution of manufacturing analytics has been driven by advancements in technology, data availability, and the quest for operational excellence. The integration of advanced analytics techniques, IoT, AI, and cloud computing has transformed manufacturing processes, allowing for continuous improvement, higher productivity, better quality, and enhanced decision-making capabilities.

Reference

1.     Westerlund, M., & Holmström, J. (2014). Manufacturing Analytics and Big Data: The Route to Manufacturing Performance Improvement. International Journal of Production Research, 52(21), 6346-6358. doi: 10.1080/00207543.2014.932633

2.     Thirumalai, M., & Sinha, R. (2018). A Systematic Literature Review of Analytics and Big Data in Manufacturing. Journal of Manufacturing Systems, 49, 141-159. doi: 10.1016/j.jmsy.2018.07.007

Hitansh Lakkad

Business Analytics intern at Hunnarvi Technologies Pvt Ltd in collaboration with nanobi analytics.

VIEWS ARE PERSONAL

#manufacturinganalytics#businessanalytics#business#hunnarvi#nanobi#isme

 

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