Digital world is all around us! Disruptive Business Models is the new phenomenon! It means different things (not just internet of things) to different people and businesses.
Some industries are traditionally slow in adapting new technologies. For example, AOVC (Asset Oriented Value Chains – Natural Resources, Metals and Chemicals) have not explored digital to it’s full potential. Analytics is one of the key components of this revolution. It it not just about capturing data of business operations but to leverage actionable insights for making intelligent products, enhancing customer experience and perhaps creating Supply Chain Surplus. I believe, with advent of exponential technologies like Big data, the role of Analytics has become all the more important.
“Data is the new oil, Analytics is the engine!”
The Resources industry in grappling with profitability challenges and the focus is on operational efficiency. Industrial Assets (machines and equipment) are a large portion of balance sheet for such industries; hence there is widespread acceptance around importance of managing industrial assets to improve overall health. Gartner research suggests a growing number of organizations in asset-intensive industries are investing in advanced analytics to help them predict failure of mission-critical assets.
Today, asset performance analytics has emerged as a critical way to provide end-to-end visibility, ability to predict breakdowns and improve asset performance. It is aimed at enhancing Overall Equipment Effectiveness (OEE) and thereby improving Return on Assets (ROA). But the fact is, many organizations do not measure or monitor OEE in one single system. There is need for a comprehensive Asset Analytics solution that is tailored considering specific business requirements. Although, it sounds really simple, implementation is no easy task. It is essential to understand critical aspects of potential solution before the organization embarks on this journey. Here are key learnings from my experience implementing Asset Analytics solutions for leading Mining / Manufacturing organizations in North America.
• Define what you want to measure
This is the critical first step towards a comprehensive and standardized Asset Analytics solution. This includes defining functional areas, Key Performance Indicators (KPIs) and calculation logic i.e. mathematical formula. One can certainly leverage Industry standards from SMRP / PAS 55 / ISO 55000 for defining KPIs. It is also important to cover all functional areas like Maintenance workflow, Schedule compliance, Backlog management, Work order execution, EHS compliance, History analysis, Maintenance cost, Labor utilization and so on.
• Design the system that provides performance visibility to all
Poor visibility is a key challenge for maintenance professionals considering the size of operation and number of assets. Reporting shall leverage Asset Hierarchy and enable reporting at all levels including Asset, location, department, site, organization, business unit and so on. The analytics shall have high level dashboards for top management and detailed reports for site level maintenance professionals. Traditionally, maintenance & reliability performance reporting is done on lagging KPIs. The time is here to leverage predictive solutions and prescriptive analytics.
• Uncover and resolve Data Quality issues
Data Quality is the biggest concern for any Analytics project. There is always apprehension around accuracy of KPI’s calculated using the new solution. However the concern is more due to lack of clarity around how data quality issues are taken care of. Typically, all organizations have issues with data and sometimes that becomes a roadblock for implementation. However, in reality Analytics provides an opportunity to fix issues and start leveraging data for process and performance improvement. The analytics solution shall also report data quality ratios and confidence levels.
In today’s digital era, physical infrastructure and equipment are still the backbone of many businesses. For asset-intensive industries the cost of unplanned downtime is significant. Analytics helps in measuring asset health and efficiency of maintenance work thereby contributes in devising effective Asset Management strategy. As manufacturers continue to struggle with profitability, Asset Analytics will garner strong momentum.
“The future belongs to those who see possibilities before they become obvious.” – John Scully
This article was originally published here
