IBM Case Studies Lowering maintenance costs using predictive analytics for enterprise asset management

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	Lowering maintenance costs using predictive analytics for enterprise asset management IBM

Lowering maintenance costs using predictive analytics for enterprise asset management

IBM

Technology Category	Analytics & Modeling - Predictive Analytics Functional Applications - Enterprise Asset Management Systems (EAM)
Applicable Industries	Retail
Applicable Functions	Maintenance
Use Cases	Asset Health Management (AHM) Predictive Maintenance
Services	Data Science Services System Integration
Challenge	Migros Zurich (GMZ) operates a vast network of stores, specialty markets, and food outlets, serving around 1.5 million customers. The company's reputation rests on its ability to provide high-quality goods and services at reasonable prices with clockwork efficiency. However, the company's maintenance staff needed a way to aggregate, consolidate, and analyze information from asset inventories, service requests, work orders, maintenance records, equipment service contracts, warranties, service level agreements, and service and technical documentation, much of it archived in physical form in the company's central offices. These records contained thousands of data points needed to gain a comprehensive understanding of the current condition of its resources and transform its maintenance practices from reactive to proactive. In addition, to promote efficiency in the field, the company needed a mobile solution that would provide field technicians with anywhere, real-time access to the company’s asset management system. Read More
About Customer	Migros Zurich (GMZ) is a major retailer in Switzerland, operating 97 stores, 26 specialty markets, and 47 food outlets. The company serves around 1.5 million customers and generates annual revenues of around CHF 2.47 billion (USD 2.5 billion). GMZ employs about 8,800 people. The company's reputation rests on its ability to provide high-quality goods and services at reasonable prices with clockwork efficiency. However, the company's maintenance practices were proving costly, leading to the recognition of the need for a proactive strategy to optimize the performance, reliability, and longevity of enterprise assets. Read More
Solution	To transform its asset management operations, Migros Zurich engaged IBM and IBM Business Partner, Ascom Solutions, to implement an analytics-based solution built on IBM® Maximo® Asset Management and IBM Maximo Everyplace® software, which offer a comprehensive view of company assets. Ascom Solutions provided excellent consulting and implementation services during the deployment. GMZ now uses predictive analytics to assess years of maintenance data and accurately determine asset lifecycles. Maintenance analysts can assess facility, system, and equipment health across the company to better predict when systems will require maintenance, repair, or replacement. With these insights, Migros Zürich can prioritize and schedule maintenance tasks to extend the service-life of individual assets and optimize the allocation of staff needed to maintain them. The solution integrates service contracts, warranties, SLAs, and historical service records that were previously inaccessible to maintenance teams in the field. Now, technicians can access these and technical manuals and diagrams remotely using mobile devices, such as smartphones and tablets from the site of service calls, eliminating the need for time-consuming office visits to consult hardcopy documentation. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Functional Applications - Enterprise Asset Management Systems (EAM)

Applicable Industries

Retail

Applicable Functions

Maintenance

Use Cases

Asset Health Management (AHM)

Predictive Maintenance

Services

Data Science Services

System Integration

Challenge

Migros Zurich (GMZ) operates a vast network of stores, specialty markets, and food outlets, serving around 1.5 million customers. The company's reputation rests on its ability to provide high-quality goods and services at reasonable prices with clockwork efficiency. However, the company's maintenance staff needed a way to aggregate, consolidate, and analyze information from asset inventories, service requests, work orders, maintenance records, equipment service contracts, warranties, service level agreements, and service and technical documentation, much of it archived in physical form in the company's central offices. These records contained thousands of data points needed to gain a comprehensive understanding of the current condition of its resources and transform its maintenance practices from reactive to proactive. In addition, to promote efficiency in the field, the company needed a mobile solution that would provide field technicians with anywhere, real-time access to the company’s asset management system.

About Customer

Migros Zurich (GMZ) is a major retailer in Switzerland, operating 97 stores, 26 specialty markets, and 47 food outlets. The company serves around 1.5 million customers and generates annual revenues of around CHF 2.47 billion (USD 2.5 billion). GMZ employs about 8,800 people. The company's reputation rests on its ability to provide high-quality goods and services at reasonable prices with clockwork efficiency. However, the company's maintenance practices were proving costly, leading to the recognition of the need for a proactive strategy to optimize the performance, reliability, and longevity of enterprise assets.

Solution

To transform its asset management operations, Migros Zurich engaged IBM and IBM Business Partner, Ascom Solutions, to implement an analytics-based solution built on IBM® Maximo® Asset Management and IBM Maximo Everyplace® software, which offer a comprehensive view of company assets. Ascom Solutions provided excellent consulting and implementation services during the deployment. GMZ now uses predictive analytics to assess years of maintenance data and accurately determine asset lifecycles. Maintenance analysts can assess facility, system, and equipment health across the company to better predict when systems will require maintenance, repair, or replacement. With these insights, Migros Zürich can prioritize and schedule maintenance tasks to extend the service-life of individual assets and optimize the allocation of staff needed to maintain them. The solution integrates service contracts, warranties, SLAs, and historical service records that were previously inaccessible to maintenance teams in the field. Now, technicians can access these and technical manuals and diagrams remotely using mobile devices, such as smartphones and tablets from the site of service calls, eliminating the need for time-consuming office visits to consult hardcopy documentation.

Impact #1	Achieved faster repairs by allocating tasks more sensibly between technicians
Impact #2	Delivers deep insights into the type and frequency of equipment breakdowns
Impact #3	Cuts time and cost of maintenance by automating many tasks

Impact #1

Achieved faster repairs by allocating tasks more sensibly between technicians

Impact #2

Delivers deep insights into the type and frequency of equipment breakdowns

Impact #3

Cuts time and cost of maintenance by automating many tasks

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Overview

Lowering maintenance costs using predictive analytics for enterprise asset management

Operational Impact