A New Model For Enterprise Resource Planning — Part 2

In our last blog we spoke about the current model of Enterprise Resource Planning (ERP). Particularly what it’s not doing to help companies capture digital value.

The problem is an outdated model where for every dollar an organization spends on technology, they spend five in consulting. The solution is an Platform-as-a-Service (PaaS) model.

This enables solutions providers to take on the three T’s of ERP challenges.

Technology — Many ERP systems were built to handle data from a limited amount of data sources. Today, all data from many sources (e.g. CRM, asset data, weather, etc.) needs to be cleaned and verified to ensure data Integrity.

Tradition — ERP was built around a traditional model in which it was simply expected to ingest data and report on it. It then relied on a series of consultants and development shops to create solutions, without ownership of the ultimate goal. Moreover, the business itself assumed all of the risk.

Time — Today, a market leader can be disrupted in practically the blink of an eye, by competitors that arise seemingly from nowhere. A slow, overly complex model of data processing is death to innovation.

These three challenges demand three key capabilities of the PaaS model: intelligence, speed, and leverage.

Intelligence – Given the advancements in data science and machine learning, solution providers should assume risk with the customers, and not customers assuming 100 percent of risk alone. That’s because the technology is creating a new level of certainty. With that new certainty for future outcomes, the service providers can sell outcomes.

Speed – The promise of digital is great, but digital transformation appears costly with its challenges seeming enormous. That’s because it’s assumed that in order to use technology, an organization must change its workflow and manage that change with more consultants. Today, however, there are more and more opportunities to add value by integrating with your workflow rather than disrupting it.

Leverage – As CIOs or CTOs will tell you, most technology and IT budgets are flat or even declining. Yet at the same time, the volume of data is rising exponentially. And the need for pinpoint insight is ever-more acute. PaaS treats insight as a source of sustainable value. By monetizing that insight, these solutions providers can enable businesses to create new cash-flow streams that can be then reinvested in further data management and insight creation.

Predictive maintenance is a great example of what is possible today.

It’s a great concept. But not if workers on a locomotive or a remote drilling platform have to wrestle with complicated, non-intuitive interfaces that interrupt their workflow. Or if companies are paying for the setup and reconfiguration of technology, but not the actual outcomes.

The Technology exists today that allows us to clearly identify what the problem is on a machine, even weeks before it causes a problem—or worse, an accident or injury. We infuse that intelligence directly into the workflow with an intuitive interface—cutting time in the process. Finally, by leveraging a model where we deliver outcomes, like uptime, fuel efficiency, and safety, we can leverage reinvestment in new innovations and further data applications.

We see a parallel with cloud storage. It promised obvious advantages, but it didn’t take until companies like Dropbox offered a seamless, easy solution that didn’t upset the workflow. Predictive maintenance is set to explode the same way, but only if it’s easily accessible and low impact.

Technology, Tradition, and Time. With cutting-edge advances in data science; a business model that delivers outcomes; and an agile, analytics platform that offers frictionless solutions, the promised value from digitization can finally be realized.