Operationalizing Business Key Performance Indicators

Due to many factors including turbulent market prices, geopolitical and economic uncertainties, regulatory restrictions, changing global markets and supply risks, oil and gas companies are facing ever-increasing pressure to control costs and gain operating efficiencies. The Supply Chain Management (SCM) lifecycle, in particular, contains some of the largest opportunities for improvement, due to the sector’s heavy reliance on suppliers to actually perform the work involved in exploration, production, and delivery of the product. As a result, SCM organizations are increasingly pressured to reevaluate all aspects of their supply chain lifecycle to gain efficiencies and reduce costs, all while maintaining product quality and human safety.  

The business of oil and gas entail a very strong dependency on a network of suppliers to perform work. SCM organization must, therefore, be able to make informed decisions on the best suppliers to retain and even invest in as partners along with new ones in the industry that may strengthen their companies market position. Performance (or selection) criteria would include competitive costs certainly, but also the ability to meet demand on time and consistently deliver quality while maintaining strong safety ratings.  

In order to assess supplier performance, reporting on Key Performance Indicators (KPIs) is a must.  KPIs are typically constructed of more than one measurement to determine trends and performance in comparison to a target. The data will be likely sitting in many different places in the organization, and of varying degrees of quality and lacking standard naming conventions and hierarchy. Additionally, the data feeding KPIs would optimally be current and consistent in quality.  Creating and maintaining KPIs for as-needed management consumption has many challenges and can have a significant impact on resources, as its typically manually piecemealed together on a repetitive basis (weekly, monthly) :

  • Massive amounts of data are created within the business across many systems. Democratizing that data manually for decision making on an as-needed basis is extremely time-consuming, and not welcome workload.
  • Silos of understanding of the data and KPI “design” is pervasive when it is manually generated, prone to human error, and creates a risk when that understanding leaves the organization/company.  
  • Manually collecting and reporting on KPIs whose source data most certainly creates an outdated story by the time it gets to a meeting for discussion, potentially leading to erroneous business decisions or at best, loss of valuable time, while more current data is confirmed.

Salesforce Solution

Using the Salesforce Platform, SpringML can integrate data coming from almost any source and make it available within Einstein Analytics.  All of the KPI’s can be placed within a dashboard in one or many locations. Then data from various systems containing necessary data, such as financial data, production data, quality data or safety can be integrated to the SalesForce Platform to feed company KPI’s.  If data is not available from a source system or is indeed not reliable or ready to consume systematically, then a solution to support custom (or ad-hoc) KPI’s can be built, where the end user enters the required data for the measurements. Einstein Analytics is then designed to systematically process the data, applying equations and modifications as necessary, updating the measurements within the KPI, providing real-time and predictive insights using any one of a number of visualization forms available out of the box with Einstein.

Such automation and centralization of business data provide the ability to democratize that data which has historically sat in different systems with no way to create relationships between it and make it near real-time consumer-friendly. This approach helps companies to identify and then take necessary TIMELY action within its organization and its suppliers regarding non-productive time, safety issues, quality issues, and delivery issues.  In addition, a company can easily spot trends in its supplier community across the organization’s assets to use during sourcing and contracting decisions.

SpringML Case Study

SpringML recently completed a large project with a client within the oil and gas industry to operationalize the Asset support teams Lean-based KPI dashboards used for management reviews.  The project involved the integration of the client’s various data sources to the Salesforce Platform and the creation of over 50 KPIs (80+ measurements) to support Asset Support teams in managing their asset. The reporting areas covered cost, safety, delivery and quality (SQDC), using procurement, delivery, and completions lenses.   The data was directed to dashboards that were created within Einstein Analytics. In addition, Communities was implemented for strategic suppliers to provide a means for suppliers and category relationship managers to track and manage supplier specific KPIs using systematically integrated data.

Goal

  • Automate the generation of KPI visualizations used for Asset and Supplier Relationship management to provide a mechanism for these teams to identify hotspots and resolve issues.

Strategy

  • Create KPI’s in Einstein Analytics with data integration from several sources.
  • Use the Salesforce Platform to pull together display dashboards and other useful tools.
  • Create a custom KPI tool to support cases where source data could not be systemically and/or reliably pulled.
  • Set dataflow to automatically refresh every hour instead of weekly or monthly.  This provides the company with the most updated models possible.

Results

  • Using the Salesforce Platform has provided the company the ability to better understand if targets are not being maintained in areas of cost, delivery, quality and safety, and then to subsequently quickly assess where the problem is and what action to take.  Time previously spent collecting data and trying to interpret that data is now spent actually resolving the issue.
  • Asset Management meetings have gone under a significant metamorphosis with their EA dashboards in that their dashboards are now current, available 24×7, not stuck to a wall on a conference room and based upon actual system data maintained by the field.

Salesforce + SpringML

SpringML is the leader in Einstein Analytics implementations. We have successfully implemented over 100 projects and take pride in building dashboards that are useful to business users. We start with the end goal which is to help customers make an informed decision. We build beautiful dashboards that are aesthetically pleasing and also provide actionable insights.  If you’d like SpringML to help you realize the benefits from Salesforce Einstein, get in touch today!