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Introduce Asset Monitoring and Predictive Maintenance in Your Business

Client: A US-based multinational company in industrial manufacturing. 

Problem: The client is one of the largest suppliers of industrial and environmental machinery to multiple other industries. One of their major products are pumps that are utilized in power, oil, gas, chemical and other industries. They require a way to help their customers manage the performance of their pumps. We can think of this issue having three parts:

  1. Transforming the nature of maintenance at the client’s customer’s factories, moving from a reactive approach of maintenance to a proactive approach. 
  2. Increasing the visibility of real-time processes and therefore allowing active monitoring and predictive monitoring. 
  3. Creating analytical systems that utilize past trends and historical events to predict maintenance requirements before they become an issue. 

VT’s Solution: We combined our IoT (Internet of Things) solution and implemented it with analytical systems. In doing so, we set up and deployed the cloud services to form the backbone of the project. Installed sensors to monitor the pumps, and built analytical models and systems personalized for the maintenance prediction of the client’s pumps. Integrated the solution with the OSI PI system.

We utilized Enhanced Condition Data Point Monitoring (eCDPM) which combines 24/7 equipment monitoring (via sensors) with traditional route based condition data point monitoring. Full spectrum vibration analysis reports provided maintenance and reliability teams with improvement recommendations that increase mean time between failure (MTBF) & process efficiency while reducing total cost of ownership.

This helps give a clear understanding of your equipment’s remaining life, most likely failure modes and recommended actions so you can respond to adverse equipment conditions before they impact your organization.

Key Points:

  • Achieved client’s business requirements by introducing technologies that monitor their assets and predict maintenance before major issues arise. 
  • Built an end-to-end implementation to monitor the processes as per client’s needs, deploying cloud connected IoT sensors for assets, building the cloud framework to support it gaining real-time visibility for the assets, and created analytical models to provide predictive maintenance capabilities.
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Strategize to move from the past legacy system to the cloud

Client: A US-based company providing internet services

Problem: The client is one of the most well-known internet service providers. The client collects a high-volume of information on customer data, such as customer browsing trends, financial information, or customer use of service after purchase. The client was using legacy systems in various aspects of their business, however with the increased demand, they were looking for ways to modernize their systems. In the eyes of the client they could expand their on-premises legacy data storage systems or move to a Cloud computing model. The client wanted an in-depth analysis of the costs and savings, highlighting the various opportunities and threats that were involved in both solutions. 

VT’s solution: We started the solution by mapping out the various business processes that exist in our client’s organization, preparing information flow and organizational charts. For the analysis we looked at various ways in which the client’s large data system could be expanded, and how it would compare to different types of Cloud migrations. Given the large scale of the legacy data system and its utility in some departments, it was also important that we provide a strategy that allows for growth, but also integrates some aspects of the legacy systems with it. The solution we presented to our client was a phased migration into the cloud, with integration of the legacy data system as an auxiliary or support system focusing in on a couple of departments. The phased roll out allowed departments to understand the new system as it was rolled out, with gradual modernization that met the clients data needs, and reduced their costs. 

Key points

  • Achieved client’s business objective by providing a cloud strategy from a migration roadmap to analyses focusing on the future growth of the client’s enterprise. 
  • Integrated the legacy data system to maximize savings for our clients, as the legacy systems were built on-premises and could therefore provide support to the modern cloud-based systems without incurring many additional costs. 
  • Showcased multiple strategies for the roll out of the project, highlighting the various aspects of each approach and different ways to integrate and expand the legacy systems in a cost-effective manner. 
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Modern Data Warehousing and Business Intelligence

Multicompany with roots in food supply needed a way to create a data-driven culture within the enterprise, requiring a way to store and utilize their customer, finance, distribution and manufacturing data to make better business decisions.  

Client: A global multicompany known for being one of the largest integrated growers, shippers and packers of multiple fruits, nuts, wines and more. 

Problem: The client was looking to harness the data that they had collected to make better decisions. They were specifically looking to create reports and dashboards to support the Financial, Distribution and Manufacturing divisions. In addition they were looking to find Key Performance Indicators (KPIs) in these business processes to manage their operations efficiently. The client laid out the business objectives as follows:

  1. Nurture a data culture at the client’s enterprise by promoting data-driven decision making, employing data analytics and BI to find actionable insights and future opportunities.
  2. Provide enterprise level, interactive BI reports & dashboards for process areas like Accounting to Reporting, Forecast to Plan, Plan to Procure, Procure to Pay, Order to Cash and more

VT’s solution: To utilize the data gathered by the client, we first needed to build a data warehouse for easy access of data, for which we used Snowflake’s Cloud Data Warehouse. To add structure to the data, we built an automated data ingestion and loading system using Informatica Cloud. After establishing the data source, we created a BI and analytics system, to run data models to find the KPIs for the client and create modern BI reports and dashboards to provide insights and help make data-driven business decisions. 

Key Points: 

  • Achieved client’s business objective by providing the base to build a data-driven culture. By establishing a data warehouse and BI system, we provided the tools necessary for gaining and using data insights to make business decisions.
  • Built a Data Warehouse to allow easy access to data, establishing a single source for all data needs. 
  • Created BI and Analytics system to provide enterprise level, interactive BI reports and dashboards for the various departments in the client’s organization.  
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