Final Post: The Journey of Data Transformation to Wisdom to Enable Smart Work

This post will be my final one in this blog, as forum does not seem to be working as it once did, and we exploring new approaches to getting thought leadership out.
Thank you for your interest if you feel like we should continue in some form, please post a comment.

This final post I will post a paper Stan DeVries and I have just completed that summaries the journey, and pitfalls on that path we see leading companies following to achieve Operational Excellence.

In today’s “flat world” of demand-driven supply, the need for agility is only going to move faster in the next 10 years.  This is driving leading companies to transform their operational landscape in systems, assets and culture in a shift to “smart work.”  Agility transforms their thinking from a process-centric to a product and production focus, which requires a dynamic, agile work environment between assets/machines, applications and people.  Aligned decisions and actions across a multi-site product value/supply chain is crucial, and  requires the ongoing push of a combination of automated (embedded) wisdom and augmented Intelligence (human wisdom).

The paradigm shift from the traditional “lights out manufacturing concept” of fully automated systems  in an agile world  shifting to scheduled work that can be dynamically planned requires both:

• Automated embedded Intelligence and knowledge
• Augmented Intelligence using humans to address dynamic change

Foundational to this shift is an environment where a worker can have the mind space to understand the larger changing situation and make augmented intelligent decisions and actions.  To provide this, data is transformed naturally into operationalized information upon which decisions can made, then extended to be combined with “tacit, applied knowledge;” it can provide incredible value when taking operational actions.

The explosion of information across industrial operations and enterprises creates a new challenge – how to find the “needles” of wisdom in the enormous “haystack” of information.

One of the analogies for the value and type of information is a chain from “data” through “information” and “knowledge” to “wisdom.”  In the industrial manufacturing and processing context, it may be helpful to use the following definitions:

• Data  – raw data, which varies in quality, structure, naming, type and format
• Information  – enhanced data, which has better quality and asset structure, and may have more useable naming, types and formats
• Knowledge  – information with useful operational context, such as proximity to targets and limits, batch records, historical and forecasted trends, alarm states, estimated useful life, efficiency etc.
• Wisdom  – prescriptive advice and procedures to help achieve targets such as safety, health, environment, quality, schedule, throughput, efficiency, yields, profits etc.

An example of this transformation: imagine driving along a freeway in California which you do not know.  You rent a car with a GPS.  

• Data: the GPS knows that I am on a freeway, at 80 mph.
• Information: it is “situationally aware” that I am heading south, on the I-405 freeway
• Knowledge: it works with other services to determine that 10 miles ahead the traffic is stopped, and provides me with a warning that I will be delayed due to a traffic hazard. It has combined traffic knowledge with my location, speed, and destination to provide timely, advanced decision support knowledge that I can use to potentially take an action.
• Wisdom: the GPS provides two alternative routes on the display giving me time and characteristics of the two alternatives. Without me having to take my eyes of road and use an A-Z directory, I have been:
• warned ahead of time of a potential issue in achieving my destination on time
• given two actions at my fingertips based upon route characteristics I desire, to guide me through a neighborhood and route I have never been on.

There is no reason why this same journey of data transformation to wisdom cannot apply to manufacturing operations.

Avoiding the Pitfalls Going to Wisdom

Many companies stand on the edge of a data swamp that is growing quickly, with the Industrial Internet of Things and Smart Manufacturing providing access to an exponential level of additional data from their industrial value chain.  This data influx can either “bog them down” on growth, or if leveraged to achieve proportional growth in “knowledge and wisdom” could propel a new level of operational agility.  The Fourth Industrial Revolution (Industrie 4.0) provides a potential framework for leading this ubiquitous transformation.  Major industrial organizations are now realizing the incredible value that can be extracted from data and are applying the time, resources and new technologies such as big data and machine learning, combined with a new evolution in operational culture to leverage this potential.

Those operating in manufacturing have been living for decades with vast amounts of data located in historians, equipment logs and across their extended supply chain network. Data, in and of itself, is not of much value.  The same can be said for reams of paperwork that document best practices-- it isn’t of much value sitting on a desk or in a document management system.

The same discussion could be used for companies running on paper-based operations and paper “black book” operations (as one operations department mentioned), where the knowledge and wisdom to take correct actions based upon experience or “instinct” also becomes an unsustainable model.

The diagram below shows the quadrants of potential situations where companies can find themselves with “experience vs data.”  Today, many companies find themselves on one of two ends of the spectrum of knowledge and data, and both ends leave companies in a state of limited agility to change to required speeds.

Summary

Manufacturing is in a constant drive to improve performance, and transformation of work has become the main method to achieve and sustain this.  Higher capacity or more efficient machinery and processes aren’t sufficient any more.  Manufacturers with agile and cyclical operations need a method to remain cost competitive during the lower throughput periods, yet remain responsive enough to take full advantage of high throughput or high margin conditions.

Implementing systems which transform work using higher value information and reliability change when, where  and/or how users make decisions, and is the foundation for this next level of improvement.

Operational Transformation through Smart Work is a journey, and technology is only one of the key elements.  The user culture must adapt, similar to the previous waves of quality, safety, health and environmental improvements.  The journey advances with work process improvements, as applied to sections of a site or an entire site.  Existing software must be assessed in terms of delivering knowledge and wisdom, supporting mobile and traveling workers, with the goal of significantly reducing the skill and effort to maintain them.  The journey is worthwhile, practical and essential for manufacturers not only to stay competitive, but thrive.