Stuart Morstead, COO of Arundo, shares key insights from an extensive career focused on operational performance improvement
Stuart Morstead, COO, gives insight on the opportunities and threats in the next wave of operational performance improvement
HOUSTON USA – At Arundo, we constantly think about how to create “smarter operations through industrial analytics.” But we don’t stop at just the data and the data science. We are focused on:
- How customers can get money and value from their data
- How various members of business ecosystems will be impacted by the sweeping changes that will come from pervasive big data insights
After all, it’s not data science that raises uptime, improves scheduling, or makes possible those thousand other operational enhancements that collectively differentiate the leaders from the laggards. Rather, this is a story of human capital and careful orchestration coupled with data-driven insights.
Over the last 30+ years, our team has seen firsthand the overlapping decade-long cycles of various coordinated sets of approaches applied to operational performance improvement. (Very) loosely speaking, and with timelines for effect, we might say that:
- The ‘80’s to early ‘90’s were dominated by re-engineering
- The later ‘90s and early 2000s brought a strong focus on Six Sigma programs popularized by GE
- And in the last decade-plus or so, “Lean” has clearly broken out of its historical automotive home to find favor and massive impact in areas as diverse as call center operations, software development and maintenance & repair organizations
Each of these recent waves of operation performance “disciplines” had certain common elements, including:
- Strong frameworks
- Strong discipline practitioners (e.g., Six Sigma black belts)
- Deep domain expertise
For companies that find the right formula among these elements, the impact on efficiency, effectiveness and overall competitiveness is quite staggering. I have been involved personally in lean programs that delivered efficiency gains of 40%+ with literally no automation and only limited data (e.g. nothing more than could be collected manually in a spreadsheet to provide a basic awareness of trends and effects of behavioral changes). In some cases, companies elected to trade some of the efficiency gains for vastly improved response or uptime metrics.
So what’s next?
One of the great things about working at Arundo is that we have a front-row view to a rapidly evolving market area with increasing excitement and growth. This means we get to talk directly with very smart, pragmatic asset owners and operators who are continually pushing for the next performance bump. In that context, I think most of these companies would agree that the next wave of improvement is going to be digitally-driven (think of buzzwords such as “big data,” “data science,” and “enhanced automation”). This next wave is often called “Digital Operations.”
Some companies may seek a single “perfect” use case on which to deploy a data science-driven approach, if mainly to prove a point for broader investment. However, the prevailing belief seems to be that there is not one lever (or even ten levers) to demonstrate the value of Digital Operations. Rather, such Operations will be a story of applying hundreds, if not thousands, of models and bringing the power of collective, historical, and enterprise experience to enable humans to make better decisions around individual assets, processes and problems.
This means that the historical operational performance “disciplines” mix of strong frameworks, strong discipline practitioners, and deep domain expertise will need to be augmented by new capabilities, including:
- Deep data science expertise
- A platform to scale data science (e.g., turning desktop-based models into enterprise-scale software products)
Digital Operations will affect the whole ecosystem of industrial operations.
All companies that sell products or services into asset-intensive industries will see the effects of Digital Operations. A few weeks ago, we published our perspectives on the challenges facing OEMs in this area. Other industrial ecosystem members are also likely to face significant changes in coming years.
Among those most affected by Digital Operations could be expert service providers and consultancies serving industrial companies. Such firms earn the right to serve customers every day by bringing deep expertise, frequently in the form of pattern recognition from tenured staff. Even for these experts, Digital Operations create both opportunities and challenges:
- Opportunities include the chance to bring even deeper, empirically-driven pattern recognition to insights and work, the potential to bring service-differentiating algorithms or models to market and, in the right context, to get closer to customers through digital products that are available continuously rather than through one-off engagements. However, this may require either investing in new skills and technology capabilities that lay outside historically core capabilities, or finding appropriate partners to provide such capabilities.
- There is also a potentially existential threat, however. This comes not just from technology, as Silicon Valley and other global tech hubs build software that encroaches on historically human services work – even sophisticated machine learning algorithms are unlikely to fully replace humans. However, humans assisted by such sophisticated data science and AI capabilities could be a sufficiently powerful combination to change the nature of consulting and service value propositions in many markets.
What to do? Smarter Operations through Industrial Analytics
Arundo’s focus is helping customers to deliver smarter operations through industrial analytics. Service organizations and consultancies are an important part of that market and we are starting to see early adopters ramp up their efforts to rethink their service delivery model and value proposition. As the noted author William Gibson wrote in Neuromancer, “The future is here, it’s just not evenly distributed”.
