May 17, 2022
The global spread of COVID-19 has created unprecedented situations for manufacturing industries. While the post-COVID-19 impacts are challenging, if not brutal, manufacturers have a tremendous opportunity to holistically analyze the data generated through the procurement, production, and delivery processes.
Data analytics is a game-changer for all manufacturers who encounter relative challenges. According to research by Mordor Intelligence, the manufacturing sector’s big data analytics was valued at USD 904.65 million in 2020 and is expected to reach USD 4.55 billion by 2026 (CAGR of 30.9%).
This blog post aims to highlight data-related challenges and how manufacturing analytics is a catalyst for digital transformation in Industry 4.0.
Industry 4.0 revolution
The concept of Industry 4.0 isn’t a straight path; it is tied to the continuous development of technologies like artificial intelligence, big-data analytics, self-service business intelligence, robotics, and other emerging IoT-powered sensors and devices.
Industry 4.0 delivers the capacity to gather, store, and process generated data. Whether the organizations seek to improve their supply chain, marketing, and operations, or desire to meet production challenges, the adoption of Industry 4.0 is the way ahead.
Manufacturing industry: Challenges for becoming data-oriented
- Unify data integration: Disparate sources challenges
One of the most critical data challenges for manufacturers is unifying data from multiple sources. The legacy transaction-oriented ERP and CRM systems are the most challenging factors because they were designed as independent applications; they were not explicitly developed to unify data from disparate data sources.
- Security compromises
Various connected tools and industrial applications can hinder the capacities of connection gateways. Moreover, for many, limited computing will become a serious threat that might lead to unauthorized access, security, and governance issues.
- Lack of support for growing data
Another issue is that manufacturing units have several devices, generating more data simultaneously. For many, dealing with big data is complex. Newer tools are necessary to integrate existing setups like ERPs, control systems, execution systems, and planning systems.
Manufacturing analytics: Today’s approach to success
It is an exercise of capturing, sifting, and analyzing machine-generated data to achieve operational excellence through real-time insights. Manufacturing analytics collects and manipulates large amounts of unstructured and structured data to show insights for business automation and real-time response.
Manufacturing analytics can predict future requirements, prevent failures, and successfully forecast areas that need to be modified.
What organizations can achieve?
Manufacturing analytics provides real-time awareness and gives decision-makers a natural competitive edge by digitizing the entire business. This leads to cost optimizations, quality improvement, and innovation, which redefine the entire customer experience. Here are some other value-driven opportunities manufacturers can achieve by adopting a manufacturing analytics approach:
- Efficient supply chain management
Manufacturing data analytics enables organizations to achieve excellence in the entire manufacturing life cycle. The supply chain is one of them. Forecasting the proper demand enables manufacturers to manage their inventory, optimize storage, and purchase materials in an efficient manner. Through, analytics businesses are enabled to:
- Demand forecasting
- Inventory management
- Order management
- Maintenance optimization
- Suppliers’ performance
- Early warning system
- Overall Equipment Effectiveness (OEE) approach
The purpose-built manufacturing analytics software can collect data from operators, sensors, or directly from machines. This data can outline downtime, shortstops, slowdowns, and product trends and improve overall OEE quality. The manufacturer can track overall equipment and effectiveness at any granularity for the machine, work unit, or company.
- Visual management: Dashboards and reports
A large amount of data can be consolidated and summarized into intuitive dashboards in real-time. The dashboards and reports provide access to data by role, so users can prioritize the roles without getting any organization-wide distractions. Also, purpose-built manufacturing analytics software can be developed to find anomalies automatically.
- Logistics, robotics, and automation
Analytics can improve organizational efficiency by viewing ongoing processes, operational costs, and resource estimations. Large organizations can leverage this analysis to unveil robotization or automation opportunities, reducing time.
Wrap-up: Data is the new fuel for manufacturers too!
As the path for Industry 4.0 is already being carved, data-driven manufacturing is becoming a strategic necessity. The faster the adoption, the greater the chances are to leap ahead. Although the manufacturing sector has one of the highest BI adoption rates, the journey ahead is long. With technologies like cloud and Analytics-as-a-Service, organizations do not have to invest heavily initially. With self-service BI & analytics, they can redefine time-sensitive decisions right on time.