Digital transformation is fast enabling enterprises to make the strategic shift from being customer focused to customer-centric. Enterprises are turning to digital transformation to combat fee pressure, cost constraints, shifting regulatory environments and sluggish returns. The need to digitize assets and acquire digital investments is being fueled by this demand. As asset managers look to prioritize core service digitization such as trading execution and risk management, the transformation of customer engagement is falling behind in contrast to consumer brands. Smart asset management through cloud-based IIoT platforms is one of the key objectives for digital transformation.
The main objective of asset management is 100% utilization of an asset through downtime reduction and ensuring efficient runtime. To ensure these two objectives are met, regular maintenance of asset is necessary. The maintenance can be scheduled, condition based or predictive. The other aspect that asset management has to address is reduction of maintenance cost
More Connected Ecosystems in Manufacturing
IIoT-generated data from equipment’s/devices provides benefits to up and down supply chains. These include remote asset monitoring for increased performance, critical supplier data, or improved product distribution. For example, companies can use the cloud to analyze data accrued via sensors in order to improve time-to-repair metrics. Another example is manufacturers leveraging connected, product-centric ecosystems in supporting overall operational excellence alongside designing products which align with customer specifications.
Specific Requirements for IIoT Implementation
To capture asset efficiencies, machine vendors need to install certain technologies for data capture, visualization creation, and to get product operating condition. Data capture to be sent to the cloud for analysis and to create visualization screens. Based on the asset, visualization might differ. Such an investment cannot be achieved overnight, and it is expensive. Enterprises can use available built-in libraries and readymade solutions to create visualization forms and to analyze the data. The pre-built AI machine learning algorithms help to analyze the asset’s current and historical data, find out wear and tear, and understand operational efficiencies and to make predictions. This results in faster time-to-market solutions.
Benefits of IIoT for Asset Management
Across multiple industries, smart systems assist in making significant progress in engineering, transportation, and design. For example, in the automotive industry Telematics relays important performance information. The data influences future designs speeding up production cycles to improve time-to-market speeds. Such smart systems comprise multiple consistent elements including security protocols, M2M communications, IoT-networked sensors, and intelligent automation.
Compared to traditional asset management methods that are static and reactive, IIoT-based operational data bolsters more responsive, predictive and proactive methods for asset renewal and maintenance. Most connected ecosystems employing cloud-based IIoT include the following:
A critical element of industrial hardware is the capability to add sensors to assets. These allow the transmission of data via heavy equipment and machinery from inside and data becomes very relevant to make more accurate decisions. To increase one’s return on assets (ROA), manufacturers are leveraging digital capabilities to develop remote service monitoring frameworks. These work towards enhancing both reliability and servicing of all IIoT-connected assets. Additionally, latest sensor technologies are helping to place it inside machines to collect relevant and important data.
Machine-to-Machine (M2M) is a mainstay of the manufacturing sector. In specific parts, the IIoT represents a sophisticated version of that technology. With manufacturing plants increasingly adopting more wireless networks, these interconnected ecosystems offer extensive means for relaying asset information for operations to instantly respond as required via mobile-based tools. The inter-dependent machines in a process or manufacturing plants can coordinate better and make more relevant decisions at device levels.
3. Data Management
Siloed data has long posed challenges to companies. Thanks to increased digital capabilities, such companies are now moving away from traditional databases. Currently, operational teams are relying on big data, Data Lake, in-memory databases to store, share and act on data with ease. This is improving overall asset management.
Data security is another important aspect of the solution where its whole purpose is to make sure data at and transactions are secure. It is important to maintain data integrity, authenticity, confidentiality and availability.
5. Analysis and Intelligence
Analytics tools are evolving to meet intelligence requirements of all sorts. Diverse platforms, improved processing and the ability to analyze growing amounts of unstructured data allow manufacturers to generate insights from each production aspect and optimize asset performance. The solutions enable the end user to never call for service again. Failures are predicted in advance resulting in automatic service requests raised to the machine manufacturer according to the equipment’s condition.
Once shop floors gain increased mobility they can tap into asset-related information and act on that data with regards to maintenance and repair. Technicians now possess the capabilities to receive alerts and notifications, tap into knowledge bases and capture data improving asset control and oversight. The mobility helps both equipment manufacturer and end user.
Sasken is helping manufacturing companies tap into a product-centric ecosystem of smart manufacturing, connected supply chains, and connected customers. We leverage and implement the most advanced technologies in sensors, software, and networks in delivering data-rich insights ready to be acted upon. As we help more companies adopt IIoT through cloud-based platforms alongside edge analytics, their assets benefit from zero downtime, faster time-to-market strategies, and predictive maintenance.