“Operations leaders know they have a problem but aren’t quite sure what the solution is.”

This statement continues to echo around the meetings I attend, the challenge is there are many parts to the dynamic situation we find ourselves and they are all converging at the same time.

The Top 3 operational challenges faced by executives tells us that functional silos of people and systems continue to frustrate them and they need help justifying potential solutions to address these challenges.

It also explains why software categories like Manufacturing Execution Systems has limited awareness outside the plant. Also the growing discussion around platforms to accommodate the variety and provide basis for absorption of differences, while applying consistent changes.

A big discussion last week with 3 different groups, but it all came back to trusted , validate data that decisions can be made on. It was clear that much of the recorded data when actually taken and moved to a basis for business decisions, that people had to stop with grand plans of information and knowledge, they had to go back to getting basics sorted with validate, trusted data.

While the diagram above indicates 48% had issues with collaboration across departments, (very true, just transparency and communication is an issue) but in two sessions it was clear terminology and alignment for these conversations was a basis for significant part of the problem. Between systems/ applications, and people.

What shocked me in the conversations was how people were taking a very pointed (local) approach to solving the issues of terminology and structure, and not looking at how to make it “sustainable innovation”. The models and approaches must not be “band aids” they must structured and sustainable, avoiding anything that is not “managed”.

The process of delivering goods and services better, faster and cheaper sounds simple but can sometimes be unpredictable and lead to shortages or surpluses. Over the past two decades, the supply chain journey has evolved through a number of distinct phases along with a shift in power from suppliers to customers. Over the course of this evolution, operations professionals have expanded their perspective and philosophy from an inventory-centric view in the 1980s to an order-centric view in the ’90s to a product-centric view today. As product lifecycles shrink, innovation has risen to the top of the CEO agenda. But product innovation cannot meet the business objectives of lifecycle profitability without supply chain process considerations.

Future operations professionals need to get involved in the product development process to enable both product and process innovation. The product lifecycle perspective becomes more important as it provides a holistic view across disparate enterprise silos to provide a coordinated response to the end-customer — who is the ultimate driver of demand. Integration of product lifecycle and supply chain management can provide fresh perspectives and critical insights that are often missed due to the extreme fragmentation of functions within the enterprise and across supply chains. This is the new frontier for value creation, an untapped area of opportunity to create competitive differentiation and growth for businesses

Making money is no longer from a transaction. It is from a customer experience for a lifetime.

As companies grapple with their own journey to “Operational Excellence” they must gain control on their information and data, otherwise the alignment and collberation, across teams, for actionable decisions will fail.

More and more of the problems we face today don’t have easy answers. Solving these hard problems require “integrative thinking”, a concept put forward by Roger Martin in his book, The Opposable Mind. Martin defines the term as follows: “The ability to face constructively the tension of opposing ideas and, instead of choosing one at the expense of the other, generate a creative resolution of the tension in the form of a new idea that contains elements of the opposing ideas but is superior to each”. Rather than accepting conventional tradeoffs where you choose either X OR Y, integrative thinking is about pushing the boundaries and searching for creative resolutions which give you X AND Y.

Convergence on Wisdom (applied Knowledge), and Industrial Analytics / Operation Intelligence grow in importance!!!

It seems like a while I have been talking about Operational Intelligence/ Industrial Analystics, and then the movment to Wisdom (Applied Knowledge) all as separate threads but I was asked the question last week:

 “how do they relate?” .

They are different, but all related to empowerment of operational workforce to make faster decisions, and take actions. As I pointed out last week one of the big drivers to platforms is to manage varience. We talk Supervisory, MES, Information, Simulation platforms, but as we pointed out must a “People Platform” that covers:

·         Collaboration between people

·         Supports the hosting of “Activities” with their embedded information/ knowledge and their associated actions.

·         Transformation of Information to Situation ally aware for the particular user interested/ interacting.

·         Management of Operational Work between team members

·         Notifications

·         Plus more

This will abstract the turnover of the workforce, abstracting the different skill levels, and experience levels, with embedded “Applied Knowledge (Wisdom),  so the experience is now in the system. A key concept for the this upcoming Operational Transformation.

Industrial Analytics provides the shift from the past through the present and into the future based on high fidelity models(from experience). Providing a new dimension to the workers tools, and thru the decision they are about to make. Combining the “Future”    providing answers to “what will happen!!!” with the recommended actions to take.

Providing the answer to “What should I do Next?” with experience, fore thought, and understanding. Operation Intelligence also aligns with this by providing a screens, presentation of the situation or “ know Questions” with context and awareness.

Operational Intelligence providing the worker an understanding of “Now” , where he is, and what the future holds, simple and clear. Increasingly I am being asked for this type of “Operational window” and view; it is not analysis it practical information around my current situation and immediate future. No configuration just a simple view of task or question provides the view and clear awareness, providing an answer.

Are these different experiences, No, they are all functional value expansions on each other, and should seen as building blocks in the road to providing and Operational Execution knowledge platform, with built in experience. Providing a foundation for absorbing turnover, transition in the workforce while maintaining operational consistency and efficiency.   

Mastering Variety in Industrial Production, Issues a Challenge for Industrial Architectures and Drives the Requirement for Platform Strategies

For many businesses, variety (or choice) is core to the strategy where its effect cascades down to the execution level (as well as upstream in the B2B value chain.) The operational challenge of variety (or variability) is that it can create waste and inhibit velocity. The challenge and opportunity is with companies, especially as they move to unified value chains (multi plant manufacturing). “How do you manage this Variability, so that production consistency, agility and increased production output are achieved?”

“Standardization is not a business goal – it is a means to an end.
The goal of business is to make a profit.”

                                                             - Continuous Improvement Leader

Thus, any standardization effort must distinguish between the different types of variety in a way that maximizes profit without constraining the business strategy. Thus, the business challenge can be summed up (using the Food & Beverage example illustrated on the above) as follows:

• Mastering necessary variety: More brand choices drive the number of order line items (SKUs) and master recipes, which in turn drive the resulting plant-level recipes that must accommodate the variations in process equipment as well as ingredients. This type of variety is necessary and must be mastered in order to survive and succeed against the competition. Other “necessary variability” are material composition variance from different suppliers or regions, raw materials will vary. Location delivery in skus due to language, for example, the same product will have to be delivered to different countries in different language or different quality requirements. All must be mastered to optimized production.

• Accommodating unavoidable variety: Situations like M&A make it difficult to standardize on any single automation vendor, where “rip-and-replace” isn’t economically viable despite engineering’s desire for a more homogeneous environment. The growing one in this area is the “changing workforce” how do have a system that can accommodate a changing, (rotating) workforce while maintaining timely decisions and consistency in actions.

• Eliminating unnecessary variety: Anything other than the above two scenarios would be eligible for standardization.

This challenge is driving companies to adopting “platform strategies” that abstract the variability and can absorb variability while provide a platform of services that enable standards to be built on. Providing the architecture for “sustainable innovation” through managed standards that can evolve over time. The word of standards can be operational models in supervisory for alignment of context and structure, as well as operational actions to guide users through tasks in a consistent way. Also, configuration of control strategies should be over multiple vendors, where common control standards for process can be deployed over multiple controllers but managed in structured way.

Does this mean one platform? NO, not for the industrial landscape different layers of the industrial operations landscape have different roles. Providing different services and different ability to absorb variety, but the common services between these platforms must enable them to “tightly aligned but loosely coupled”.

 As we have pointed out the key to success in this dynamic but changing world is the ability to “Master Necessary Variety” in your business, while “Accommodating Unavoidable Variation”, eliminating all other variation for efficiency.

Food for thought!

Applied Knowledge/ Wisdom Foundational to Internet of Things, and "Time to Performance" of Operational Teams

For the last couple of weeks, Stan DeVeries and I have been brainstorming around articulating this core area of the operational transformation, "the ability to have a system that can absorb workforce change/ turn over". Good example of this is with one company on the 2025 vision of "all knowledge/ experience in the system", this is capturing as much of the tribal "applied knowledge" that the experience operational staff are making decisions, and taking actions on and moving it to the system. If then applied in a "activity/task" based operational experience, a younger skilled user has the ability to select a "activity" and the associated knowledge/ information, and action are presented to him. Dramatically reducing the "Time to Performance" and increasing the consistency of operations, while increasing flexibility in operational workforce management.

The results of the discussions has brought the discussion around "Federated Wisdom, applied knowledge":

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/Applied Knowledge” – prescriptive advice and procedures to help achieve targets such as safety, health, environment, quality, schedule, throughput, efficiency, yields, profits etc.

The cost to store and share data has dropped significantly, and a simplistic expectation is that although storage is growing by a factor of millions in only a few years, that somehow the following pattern evolves:

Although the pattern might seem to be convenient, it is actually a nightmare, because it becomes much harder to discover and translate knowledge and wisdom from another operation, especially in another location, to the local needs.  But there is a solution.

To understand the problem better, let’s consider the definition of “knowledge” – it includes context.  This context begins with local context – time, location, process or machinery configuration, raw materials, energy and products being processed or produced.  It is already valuable to have “wisdom” to achieve and sustain best performance for the community, customers and the corporation.  This local context only needs to know its immediate information, if it has enough “wisdom”.

Now let’s consider what happens when a single site, a fleet of similar sites, or an enterprise have numerous similar operations.  How can local “wisdom” be enhanced by using “wisdom” from the other operations, especially when all of these operations are sufficiently different?

The reason that solving this problem is important is for operations transformation, such as operating physical assets as one (in a chain or as peers), and by supporting the multiple operations with a flexible team of remote experts.

One approach to solving this problem is to take advantage of a technique used in distributed databases, where a technique called “federated information” is used, especially in industrial operations management architectures.  This technique does not change the local information’s naming or structure, but provides multiple translations, both across the database for multiple similar structures, and for multiple contexts such as what financial, technical support, scheduling, quality and other functions require.  This technique is an alternative to the fragility and complexity of attempting to force a uniform and encompassing naming and structure that attempts to satisfy all applications and users.

The same approach can be applied for “wisdom”.  Currently, hobbyists and enthusiasts around the world share “wisdom”, for restoring cars, making furniture, playing a musical instrument, gardening etc.  Anyone with no experience at all can ask for “where do I get started?”, and most respondents will provide kind advice; in the same forum, experts can share wisdom that is valuable and understandable by them at their level of experience.  This “wisdom” is extremely decentralized, and the experts are providing the translation.

In the industrial operations environment, federating “wisdom” is partially automated by expanding the local context.  This expansion includes information about adjacent operations, information about the chain or peers if these operations are being managed as one, and then “knowledge” is expanded by applying the context of group targets and performance.

Some enterprises have hundreds or as much as tens of thousands of similar operations, supported by dozens or fewer experts.  Discovery of wisdom is greatly enhanced by maintaining an architecture which enhances local context without modifying or attempting to force burdensome structures on local operations.

Expect this discussion to continue as expand on the systems, and approaches to make this real, while enable sustainable operational innovation. This will be core to Industrial Internet of Things as we align smart devices, operational practices and humans into a dynamic but coordinated operational force.

Real time information Platform vs. traditional historian, Why it is Key to Pushing “Actionable Decisions”, foundational to the “Industrial Internet of Things” and Empowering the Teams.

Again last week I was presenting to a set industrial companies in water, food, and mining, and the topic of a "real time information platform" many questions.

My immediate answer is “what are you trying to do? " " who are the users targeted to interact with the system, and what decisions and actions are they expected to take?". These last two questions usually leave a complex blank expression on people's faces.

Many are engineers who have been asked to investigate, and they centered on the traditional approach of a "data centric" historian centered  world, leading with a technology strategy. The question of what people will use the data for, what roles and actions to be taken are secondary in their minds! 

Why is this when if someone had wanted a "historian" they would have asked for it. So why a platform, what does real time mean, and key is information.

It all comes back to one of the quadrants we talk about in the "operational transformation" around networking a series of assets, plants into a a "trusted" information system. That "actionable decisions" can be taken by a ever increasing community of operational people across the operational landscape.

To me it is understanding this community of consumers and what their requirements, uses are is key:

1. What activities, decisions, and actions they are expected to take?
2.  Their roles, skills, and approach is their time frame, location relative to the data
3. Their context and understanding of the plant, asset or process in question, as their is a growing trend of highly educated skilled people on assets, process. With little or no practical experience on the asset, and more than likely will not have visited site.

On investigations you find you have the traditional process engineers, who need the trend analysis and discovery of potential improvements. 

However, there is a growing tribe of people who need to make actionable operational decisions. They will not monitor the system must best "self-aware, and living" (exception based) capture the data, transformation  it into information.  Apply experience and knowledge, clear understanding of the situation, and what are typical actions with "best operational process" provided to take action.

This is very different to everything getting data stored and then extracted, yes in this new world there is history as it provides the history for reliable knowledge and basis for wisdom or " application knowledge".

The real key is the change in approach from “predictive to prescriptive” which embeds the “actionable decisions into the model. Empowering the operational team, no matter the location or experience with decisions and associated actions.

Understanding this maturity curve and evolution is what we see as foundational to the success of “industrial Internet of Things”. Through the embedded practices provides a basis for the changing workforce to act and make decisions in a timely manner.

However, these two communities in the industrial landscape are interlocked for success. The two communities are:

• Community 1: Process, performance, optimization team that accesses the data with trending, analysis, and predictive tools. Identifying the trends, conditions by applying their experience combined with “big data” techniques allows these conditions, to be seen in the “to be state”. If captured in a managed configuration framework, that will allow roll-out over sites and sustainable evolution. These become embedded into the system, for adoption by the operational team.

• Community 2: Operational Team: This is the dynamic team, from roaming people on the plant to central operational teams, to virtual expert teams, collaborating together in real time to enable “actionable decisions” no matter role, location, and experience.

The diagram below shows the this maturity of capturing this “applied knowledge” as Managed “Actionable Discussions” that interact with people, assets and process as key, very different a traditional historian approach.

The “Real Time Information Platform” provides a real-time "living" model that is self-aware that captures validates the data with rules aware of it is current state. Storing this data in context and rules and calculations in that provide motivation, embedded operational process, and awareness to correct people. Fundamental is the "trust" worthiness of the information, without impacting current automation systems. The ability to have sustainable evolution and scalability, through managed components that represent the assets and processes (actionable decisions) to the model is available on storage side in history and real-time.

You cannot do this with Historian (data centric) architecture and solution. Make sure you looked at who the communities of users you are satisfying now and in the immediate future?