Any success-driven organization with significant physical assets can tap some or all of the preceding sources of profit improvement using Asset Performance Management. Yet as in all things, success depends on setting goals and establishing metrics that can be used to manage and measure results. This is especially true when an organization directs Asset Performance Management toward specific asset-related operating problems. The more calibrated the metrics, the more powerful and measurable the results.
Take the example of a large regional grocery chain with 140 stores in the United States. In the grocery business, perhaps the most critical capital asset is the refrigeration equipment that keeps high-dollar items cold like meat, produce and other refrigerated and frozen foods. If that equipment fails and the problem goes undetected, spoilage costs can be enormous. In the past, this organization, like many grocery companies, monitored refrigeration equipment store by store. It lacked visibility into the performance of the equipment company-wide. As a result, it was forced to be reactive-if equipment went down at a given store, the only hope was to discover the problem immediately and fix it as quickly as possible.
Now, with Asset Performance Management, the company is implementing a proactive solution for optimizing performance of the coolers enterprise-wide. The solution features a centralized, unified view of all refrigeration systems across all stores. This enterprise-wide solution adds the ability for the company to make decisions that directly improve operational performance, through monitoring and analysis of specific operational metrics such as:
* Temperature vs. energy consumption and cost
* Maintenance cost vs. temperature trend
* Cooler efficiency by store
* Preventive maintenance compliance vs. refrigeration alarms, spoilage incidents, and refrigeration leak rates
These metrics go beyond the standard transactional maintenance metrics of EAM because they depend on data from multiple systems-asset, finance, operating and others-not just the maintenance system alone. In addition, the modeling and trending tools offered by Datastream 7i Analytics enable a forward view on that data, unlike traditional reporting tools which simply visualize current transactional data. By analyzing these cross-silo metrics, the company can now decide which coolers run most efficiently, how temperature control affects cooler performance, what the appropriate maintenance cost per cooler should be, what level and schedule of maintenance is optimal. It can, in short, be completely proactive about optimizing equipment uptime at a lower operating cost.
Take the example of a large regional grocery chain with 140 stores in the United States. In the grocery business, perhaps the most critical capital asset is the refrigeration equipment that keeps high-dollar items cold like meat, produce and other refrigerated and frozen foods. If that equipment fails and the problem goes undetected, spoilage costs can be enormous. In the past, this organization, like many grocery companies, monitored refrigeration equipment store by store. It lacked visibility into the performance of the equipment company-wide. As a result, it was forced to be reactive-if equipment went down at a given store, the only hope was to discover the problem immediately and fix it as quickly as possible.
Now, with Asset Performance Management, the company is implementing a proactive solution for optimizing performance of the coolers enterprise-wide. The solution features a centralized, unified view of all refrigeration systems across all stores. This enterprise-wide solution adds the ability for the company to make decisions that directly improve operational performance, through monitoring and analysis of specific operational metrics such as:
* Temperature vs. energy consumption and cost
* Maintenance cost vs. temperature trend
* Cooler efficiency by store
* Preventive maintenance compliance vs. refrigeration alarms, spoilage incidents, and refrigeration leak rates
These metrics go beyond the standard transactional maintenance metrics of EAM because they depend on data from multiple systems-asset, finance, operating and others-not just the maintenance system alone. In addition, the modeling and trending tools offered by Datastream 7i Analytics enable a forward view on that data, unlike traditional reporting tools which simply visualize current transactional data. By analyzing these cross-silo metrics, the company can now decide which coolers run most efficiently, how temperature control affects cooler performance, what the appropriate maintenance cost per cooler should be, what level and schedule of maintenance is optimal. It can, in short, be completely proactive about optimizing equipment uptime at a lower operating cost.
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