12 MINS READ
Manufacturing firms globally are grappling with constantly evolving consumer behavior, their preferences, and a hyper competitive market scenario.
International geo-political factors and competition from cheaper sources have further complicated the market and intensified the need for manufacturers to integrate, automate, and deploy smart technology on their shop floors, embracing Industry 4.0 best practices. Improved productivity and operational efficiency, minimal equipment downtime through machine learning (ML)-led preventive and predictive maintenance, resilient supply chains through improved forecasting and digital auditing, and more, are some obvious benefits of digitalization. Automation of routine plant production and maintenance with minimal human intervention activities also free up manpower hours and executive time to focus on innovation, research and development (R&D), and strategy.
Digital tools for manufacturing transformation
Capabilities such as remote plant operations management via internet of things (IoT) and 5G integration with smart technologies, including machine learning (ML), digital twin, and mixed reality in manufacturing, have become a necessity not just for business continuity but even for innovation, personnel training, and informed executive decision-making.
Data and analytics will play a critical role in achieving this transformation, but manufacturers face numerous challenges.
Data and analytics constraints impacting manufacturers
While most manufacturers realize the imminent need for a robust data and analytics strategy, they face challenges adopting and implementing them.
We have summarized the challenges into five buckets, as described below:
a. Structured data from enterprise resource planning (ERP), manufacturing execution systems (MES), product lifecycle management (PLM), supervisory control and data acquisition (SCADA), and others.
b. Time series data from data historians.
c. Unstructured data from various social media and market research reports.
The challenge of managing diverse data from disparate sources is that integrating this data simultaneously can make the process too complex. Also, rigid governance processes and fixing quality issues, which are time consuming, can diminish the value of data if not delivered at the right time