Categories: Spend Analysis

Drilling the Rig of Enterprise Data: How Oil & Gas Supply Mgmt can offer more?

If you have been thinking if there’s anything beyond rate reduction negotiations with suppliers that the oil and gas supply management organizations can offer to their enterprises, then you should know that your ask is very real. Positioned on a rig of enterprise data including demand, prices, contracts, emails and production output, along with huge online data, these supply chain management organizations can contribute to-

  • understanding of risk and cost structures
  • identifying more revenue-generating avenues
  • suggesting newer areas of automation &
  • establishing sustainable vendor-supplier relationships

“We are generating more data than ever before– 90% of the data that we have today is generated in last 2 years alone. This data is coming from a variety of different sources such as voice, text, transaction, sensor, chat, images, videos etc. To handle this fast moving, heterogenous and multimodal data we need to get more entrenched with machine learning and deep learning to make real time analytics driven decisions that will bring maximum value for the customers and companies alike.
RATNAKAR PANDEY, ANALYTICS & DATA SCIENCE HEAD, KABBAGE

Here, machine learning is a great mobilizer. You will find it very amusing to note how machine learning algorithms source data from this enterprise data rig and make predictions without very elaborate and exhaustive programming. Yes, but while technology and data are so easily accessible, we do not see many organizations able to tap enough of this rig of data.

Where is the Challenge?

The challenge is in grooming mindsets that can leverage advanced analytics. Too many supply management organizations directly starting off with the development part without a clear knowledge of the drivers. Then when the outcome is not very commendable and adds not enough value, the supply management organization loses its credibility.

Now there are many advanced analytics vendors that offer self-serving portals that do not require huge understanding or experience of machine learning, data science, or even basic statistics to help its users with predictive and prescriptive models. But  organizations that have been able to successfully implement advanced analytics approach strategically instead of diving headlong into the vantage it offers.

Key Drivers for Effectiveness of Advanced Analytics

The effectiveness of advanced analytics in supply chain management is driven by-

  • People: Once we know what are we aiming for we can then ensure if it is aligned with the company’s corporate strategy. Here leadership support is a must and that includes support from all C-suite executives. Followed by this, an assessment of the organization’s analytics talent will make it fairly easy to draw a balance with IT, business intelligence and data science.
  • Process: To help you achieve alignment & ownership, delegate responsibilities, streamline decision-making and escalation processes, quality data and governance is your guide. So it is important  data quality is measured and checked for accessibility. This will also help you determine if there is a need to grow your resource for analytics.
  • Technology: When it comes to technology, you should have an outcome in mind. This will help you understand if your technology has the capabilities of fulfilling the outcome in your mind. See the big picture in which the chosen technology is able to process and prepare your data, store and retrieve, leverage cloud computing and visual capabilities along with integrating itself to your compliance architecture.

We need to hold the hands of time and take the path it directs us to. People, processes and technology along with encouraging organizational policies can help us in derive optimum results of advanced analytics. Oil and gas supply management organizations can indeed offer support beyond price negotiations. All they have to do is to exploit the abundance of data to the advantage of its clients and companies. What do you say?

aishika bhattacharya

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aishika bhattacharya

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