What happens if equipment shuts down due to scheduled maintenance or a malfunction? What systems are affected by these stoppages? There are five key platforms in the production value chain that will help a company achieve the highest efficiency possible from their production processes.
For many years, supply chain management focused on the movement of these materials and goods. While that knowledge is helpful for optimizing production processes, profitability of manufactured goods, company infrastructure, and supplier and customer interactions, there is a vital section of this automation supply chain that is missing – machine maintenance and operations.
Nowadays, manufacturers seek the greatest efficiency possible from their production processes to maintain their competitive edge. Five key platforms in the production value chain are vital to this.
They look to implement quality improvements and changes in the supply chain that require greater collaboration with suppliers and customers. New systems often interface with customers’ and/or suppliers’ ordering or order-processing systems. These systems automatically, without human interaction, process orders – from placing the order electronically and tracking inventory levels to processing payment and arranging delivery.
The five key platforms in the production value chain go beyond functionality – whether or not a piece of equipment is operating and its related upstream effect on sales – to include parts, purchasing and materials. It also includes materials to maintenance to operations. This machine to maintenance value chain is a robust and strategic operations story when you consider the widespread impact of a production stoppage.
How are most industrial automation companies managing this new value chain?
Depending on the architecture of the equipment, instrumentation, and existing systems that companies have in place, companies approach interoperability and data communications in numerous ways.
• Third-party middle-ware applications connect systems
• Industry standard protocols, such as OPC and others, facilitate data communication
• Data collection tools, mobile devices, and applications that give organizations utilities that allow the sharing of measured and observed conditions with periodic frequency
Most experts recommend adopting five key platforms. Many of these platform capabilities are available in the cloud, and are the ingredients to enabling decision-makers and decision-making systems to “feel” and “sense” what is taking place around them. These five key platforms in the production value chain are:
- Agile data platform: This is perhaps one of the most important of the five key platforms in the production value chain. This is the technology backbone for analytics capabilities. Outdated data warehouse structures and methodologies are changed to a balanced and decentralized framework often using the cloud. Virtual data marts, sandboxes, data labs and related tools are used at this stage to create the foundation technology for agility.
- Behavioral data platform: This platform captures data from transactions and interactions and helps to connect the dots to develop insights.
- Collaborative idea platform: This helps companies keep pace with the data explosion by socializing insights across a community of analytics professionals. Such tools as democratized data, crowd sourced collaboration, incentive-based gamification, and social connections can be leveraged to connect humans and data in a fast, self-service manner that outperforms traditional centralized metadata approaches.
- Analytical application platform: This can be deployed using mobile apps – with their simple-to-use-and-digest interfaces – to make analytics accessible to anyone who needs them, wherever they may be at the moment. You move away from static applications and extracting, transforming and loading in favor of self-service apps.
- Autonomous decision platform: Decisions are made by applications and systems, allowing people to focus on higher-level strategic initiatives. A company goes beyond predictive technologies and increasingly uses algorithms, machine learning, and artificial intelligence. This permits examination of all data to detect trends, patterns, and outliers as real-time contexts for human analysts and decision makers about changes in behaviors.