Those Who Do, Do Better – and Take Action Upon Opportunity

In a previous post on enterprise operational analytics, we explained that process industry organizations have started to recognize that the data created within their plants is underutilized.  Quite often, that data doesn't extend past manufacturing operations. As a result, many companies are now taking steps to provide greater visibility and integrate plant data into wide-ranging business and operational analytics.

How Do Some Companies Outperform, Outdistance the Competition?

This awareness raises a several complex questions.  How do superior performers close the loop with visibility and analytics to drive initiative-taking and proactive decision-making for measurable impact? What is the difference between companies that stop at metrics, or stop after the analyze step in analytics (even when providing prescriptive options to consider), and those that make decisions and take action to generate results?

Studies by LNS Research show that top performers exhibit five key characteristics that separate them from the pack:

1. KNOWLEDGE IS POWER ― they realize that superior decisions are based on having good data plus a deep understanding of the business and plant operations

2. DATA MODEL A MUST-HAVE ― they understand that there can be no gaps in data and they must have full access to data, so they have an enterprise data model and architecture or an initiative to address the issues

3. EXPANSIVE VISIBILITY IS ESSENTIAL ― top performers value visibility —not just plant operations, but across the portfolio and value chain— so they know what’s happening now, what happened in the past, and what could happen in the future (hence the role of both metrics and advanced analytics)

4. TECH IS A TOOL, NOT A GOAL — these companies view technology as a means to an end and not an end in and of itself; they organize and rally around generating positive business outcomes, not software implementations

5. CHALLENGE THE STATUS QUO — top performers first (and constantly) ask what they could do better if they had new or different tools to enable change

Digital Twin as Transformational Technology

Let’s examine these characteristics in context with some common technology dilemmas process companies face right now.  One example at the plant level is digital twins for predictive and prescriptive analytics.  What would or should a plant do differently if it knew with high confidence that a piece of critical equipment was going to fail in 60 days?  If the planning, scheduling maintenance, and operations processes don't change when it knows the impact, then what's the point of using digital twin technology?  The opportunity for improvement is lost.

Let's consider a different scenario, this time at the corporate level. In this example, the operating company has two plants, each manufacturing the same specialty chemical using the same process. One plant meets and often exceeds volume targets but has trouble with quality, while the other produces consistently high-quality product but struggles with downtime. Both plants supply product to the same set of customers in multiple locations. How can the operating company get a handle on the disparities, and what should it do differently?

Each plant will undoubtedly understand its unique situation, but does corporate understand the details? How can the company compare and analyze differences without the right visibility and tools? Further, how can corporate evaluate the impact on customer satisfaction? What’s required so they two plants can share experiences and help solve each other's problems?

Change Not Easy; the Right Change Entirely Worth It

These are just two examples of what companies can do with the right tools, a willingness to change the way they work, and taking action when the opportunity arises. True, there is always risk in change, but without change, there can be no improvement and no operational excellence. Frankly, not changing in the face of today's dynamic environment is riskier. The question is always what, where, and how to change.

Top performers aren't afraid to act on opportunity. That doesn't always mean huge changes in one giant leap. It does mean planned, focused, disciplined action supported by the right tools and technologies that provide insight and anticipation. Top performers are far more likely to know what they want, have access to the data they need, have prepared business use cases, have an enterprise and plant architecture and data model in place (or are well on the road to establishing them), and know what role they want enterprise operational analytics to play. The numbers from our research show that companies that are "doers" undeniably produce better business outcomes.

Where does this leave us? If a company seeks enterprise-wide improvement with analytics, it needs to assess and understand where it is on the journey to provide easily accessible data, and the right analytics tools to help everyone throughout the organization make better decisions. Software vendors that demonstrate an understanding of the industrial customer's situation, illustrate how change will impact business outcomes will be appropriately rewarded.

Learn how industrial companies create visibility in the recorded webcast featuring LNS Research, “Driving Profitability with Plant and Process Data”.