ERP: what can we expect in 2018?

At the end of each calendar year enterprises’ usually take stock of overall operations then look toward the future. However, next-step investigations relating to technology are typically more ethereal than other, more mundane business reflections.

Part of this has to do with tech’s constant drive toward innovation, and another has to do with sometimes unknowable cost requirements; particularly when compared with more static accounting processes. However, when it comes to trying to fathom the direction of ERP platforms, things can become even more tension-filled because resources-based systems tend to consume innovation and cost directly, while also altering critical enterprise operational rules at the same time.

Consequently, many ERP managers just don’t want to look deeply at past operations when considering the future, and instead hurtle toward whatever ‘new-and-improved-thing’ is on the horizon without accounting for where their operations evolved from and what they may have learned along the way, then ultimately find themselves with the wrong tech, at the wrong price.

Therefore, rather than doing just another start-of-year prediction piece, I thought I’d offer a suggestion that lends themselves toward a more universal technology that can be applied to ERP, rather than simply delivering one or more brand-specific recommendations.

ERP and ‘machine learning’ (aka automated algorithm)

During the last couple of years interest in developing automated processes that can be externally applied to ERP have emerged. One of the more evolved elements here involves ‘machine learning’, and how it might support the future growth of ERP as an intrinsic enterprise platform.

 However, in order to comprehend the value of ‘machine learning’ one must first understand its basic level utility. Accordingly, in the B2B world, machine learning relates to the science of getting computers to act without being explicitly programmed.

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Now, from a current ERP perspective, which of ERP’s core processes lend themselves to machine learning, and why do supposed those processes might be important to the enhancement of automated efficiency? Well, I guess you could do a better job of automatically managing individual inventory records within a platform, or perhaps calculative processes could be streamlined and executed in an unattended mode.  However, in the end of day, any of these processes along with everything else in an ERP platform come down to a central target; raw or refined data, and by extension, the ERP platform’s fully-integrated data-store.

According to Elastic, one of today’s applied proponents for the ERP environment, “Complex, fast-moving datasets make it nearly impossible to spot…business issues as they happen using rules or humans looking at dashboards. (Elastic’s) X-Pack machine learning automatically models the behavior of data trends in real time to identify issues faster, streamline root cause analysis, and reduce (errors).”

Given the description of this product’s attributes, it would appear that this type of product is a perfect fit for ERP’s ever evolving data requirements, as supported by a number of projected future analyses.

How much will machine learning scale during 2018?

According to comments from Gartner "by 2018, more than one-half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries, and by 2020, the use of intelligent business analytics, information will be used to reinvent, digitalize, or eliminate 80% of business processes and products from a decade earlier. Algorithm marketplaces, says Gartner, will disrupt the analytics ecosystem and likely even the whole software ecosystem.”

Therefore, and congruent with my early ‘universal’ suggestion, if there’s a single recommendation related to 2018 and beyond, this technological focus area will likely become one of the most important innovations for the foreseeable future. Granted, this is a completely subjective assessment, but based on what I’m following from a research perspective, it trumps virtually any other system development driver by at least an order of magnitude