Proving the Business Value of Advanced Analytics


In talking to my peers about the value of advanced analytics, IoTmachine learning and predictive analytics, they are often intrigued by the potential to:

  • Reduce working capital by improving demand forecasting
  • Reduce downtime and customer satisfaction by predicting maintenance needs in advance
  • Reduce quality problems by detecting anomalies in manufacturing processes

But making the rubber meet the road seems a bit illusive.  The business leaders I talk to often don’t how to begin this new type of digital business initiative.  And frankly, it’s a lot to ask a new or even existing customer to “just trust us.”

So, in concert with Microsoft, we are making that first foray into digital business an easy, low risk one.

Proving Value Quickly

The Proof of Value engagement illustrates how a business problem can be solved and transform your business with a measurable ROI.  Whether you want to reduce working capital by improving demand forecasting, reduce downtime and improve customer satisfaction by predicting maintenance needs in advance, reduce quality problems by detecting anomalies in manufacturing processes or improve your organization in some other way, a proof of value engagement quickly reveals what’s possible and gives you the ammunition and business justification to move forward.

Ideal candidates for these types of engagements have:

  • A well-defined business case
  • Data that relates to the problem being solved
  • Measurable, quantifiable measures of success
  • The will and desire to operationalize the solution

A Few Examples

To make these proof of value engagements seem more real, here are a few examples:

Manufactuing: Anomaly Detection

Let’s say you are a manufacturer.   You have more than 20 locations and you’d like to reduce the number of defects.  So, you test the viability of building an advanced analytic model with historical data to determine if you can reduce the manufacturing defect rate by 20%.  If you do, you could end up saving more than $100K per month per plant or more than $20M annually.

Retail Distribution: Demand Forecasting

What about retail? Let’s say you sell products through retailers across the US. You have already reduced costs across your supply chain as much as humanly possible, but still believe there is too much cash tied up in excess inventory. So, you develop an advanced analytic model and feed it historical data to determine if you can get better at your forecasts. In this case, you could significantly reduce working capital.

Utilities: Revenue Protection

Utilities depend on their meters to measure consumption. Electric meters can slowly degrade over time, showing usage which is lower than the true usage. Instead of dispatching a crew to every failing meter, what if you could predict usage using historical meter data and external data sources like weather? Then you could monitor when actual usage differs from predicted usage, allowing the utility company to alleviate erroneous zero balances and protect revenue over the long term.

Revenue Loss Protection
Using Advanced Analytics to Protect Revenue

Let’s Get Started

If you have a nagging problem that you’d love to solve, let’s talk.  Together, we can determine if the problem is worth solving.

To learn more, download our Proof of Value FAQ or contact us.

Phil Morris
There's nothing Phil enjoys more than helping customers create game-changing solutions to their business problems. He's especially enthusiastic about developing digital business programs that converge business strategy with digital technology to improve customer experience, optimize business operations and develop new business models. Phil is happiest when he's meeting with customers, listening to their stories and digging in and helping. Phil oversees the strategy, business development and finance departments at Mariner, staying focused on new trends in business and technology. Away from the office, he enjoys spending time with his wife, kids and grand kids, crafting award-winning beer and playing guitar in a blues band.
Phil Morris

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