The Workforce Crisis of 2030!! And how to start solving it now

“People, workforce planning will become more important than financial planning.” Rainer Strack

This is a statement from good TED talk by Rainer on his interpretation of the workforce challenge of 2030. Supporting much of what this blog has been looking at this past year, but bringing another angle. The key we both trying to get over it is not about the changing workforce it is about the operational workforce transformation to a new workforce landscape of skill shortage, labor shortage and cultural and people change.

You can go to Ted talk with the following link.

http://www.ted.com/talks/rainer_strack_the_surprising_workforce_crisis_of_2030_and_how_to_start_solving_it_now

A couple of extractions:

The investigation and determination of a significant workforce shortage by 2030, but starting now:

The required strategies that will dominate much the thinking

Rainer is talking general workforce, if you take this and bring it into manufacturing, industrial world, the shortages will be all more acute. As pointed out in other surveys from Accenture and others.

This shows in US survey results on filling skilled and highly skilled roles, below is worked example of mid sized company in mid west.  

This is why the move from knowledge to wisdom is key, and emedding of knowledge and wisdom , actionable decisions into the systems is key to accommodate the transformation to dramatically reduced dependency on skilled people. From Rainer’s and other investigations that Operational Systems of the future in the industrial market have 50% + reduction in dependency in skilled and highly skilled workforce.This Ted talk supports this will be the biggest issue in the next 10 years to sustain competitive agility. Like we have seen in 2014, I believe 2015 this acknowledgment and strategies building around workforce change will intensify.    

Knowledge must transfer to Industrial Systems to Combat the Skill Shortage/ Lack of Experts

A couple of weeks ago I talked about tacit knowledge and how it is key due changing way experts will be deployed, and short fall in skilled workforce. This was driven home again this week, with the release of skill assessment by “Accenture 2014 Manufacturing Skills and Training Study” that showed some real truths to the situation in the USA.

The diagram below show the current split of jobs that Highly skilled to skilled to unskilled, then the second shows that percentage of the Highly skilled and skilled jobs will grow over the next 2 years.

This is in total contradiction to the next set of questions around how severe is the skill shortage in customers.

The take away is that we putting in more complex processes, and systems in order to compete in the “flat world” which requires increased skilled people, yet there is reducing capacity in skilled, and highly skilled talent, actually the pool is shrinking.

The study goes on to talk about training and the use of operator training systems, and the need for intuitive (self learning) systems. But my thoughts rang back to discussion on Tacit Knowledge and how critical it is to capture experience not in words, or videos, but in systems that enable actionable decisions (built on embedded captured operational experience). Examples are Embedded operational Procedures, simulation for the future, pattern recognition on conditions to see events before they happen, all new techniques just entering the operational systems.  

Tacit knowledge is not easily shared. Although it is that which is used by all people, it is not necessarily able to be easily articulated. It consists of beliefs, ideals, values, schemata and mental models which are deeply ingrained in us and which we often take for granted. The key to acquiring tacit knowledge is experience. Without some form of shared experience, it is extremely difficult for people to share each other's thinking processes.

Tacit knowledge has been described as “know-how” – as opposed to “know-what” (facts), “know-why” (science), or “know-who” (networking). It involves learning and skill but not in a way that can be written down. On this account knowing-how or embodied knowledge is characteristic of the expert, who acts, makes judgments, and so forth without explicitly reflecting on the principles or rules involved. The expert works without having a theory of his or her work; he or she just performs skillfully without deliberation or focused attention. Apprentices, for example, work with their mentors and learn craftsmanship not through language but by observation, imitation, and practice.

The study goes on to provide examples of the impact of this shortage for a midsized $500m complaint to be $4.6m loss annually:

Food for thought!!! Tacit Knowledge must be captured in a form that new users can take actionable decisions in a reliable and effective way, through knowledge and experience moving from the experts to the systems in a sustainable approach that can evolve.

Operation Intelligence(Enterprise Manufacturing Intelligence) vs Business Intelligence (BI) the Difference and it are the time it be recognized!

So many times when I visit a customer site or discuss with product develops, or engineering houses people get confused over what are the roles of each system, and they must work in conjunction but they not the same. Especially when companies already have a business Intelligence strategy and tools, but they also have process analysis tools (trending) but let's move the focus away from engineers to the consumers of the information and their transforming role in achieving operational excellence.

The question of “why a company should implement an EMI solution if they already spent money on a BI solution. They already have the “slice and dice” and analytical ability within BI, so why waste money on an EMI solution?”

The realization is that users in the real time operations require empowerment capability to make decisions, to be able to access “trust worthy information” quickly and easily. Quickly seeing status of plant, operations, and easily been able to apply limited operational analysis to answer well known “Operational situational questions”. EMI and BI have different purposes, and they are aimed at a different audience. Manufacturing-specific reporting and intelligence are different in content, context and data frequency than the data in BI.

I had a long discussion with Gerhard Greeff (Divisional Manager: Bytes PMC, MESA trainer), on this subject, and he totally agreed in the miss understanding, that people have and how often they tried to use BI tools to build operational dashboards for operations and they do not get accepted or used. Also, this exercise results in significant IT projects to build the tools, and gather the data, so often to be far less effective that MS Excel, which many operational people will configure what they want. The requirement now is for consistency of information, and measures, causing a transformation in the market caused by “Apple” that time to access and value is far more critical than “perfection on information layout” introducing the concept of “good enough” will do. Like we do on many applications on smart phones where down loaded applications based upon a functional need, and have limited ability to change it, except the basic configuration, but it works and is delivering value fast.

You may be asking can you clarify the difference, so I have used some text from Gerhard’s paper in “The Mom Chronicles”.

“Data in a BI solution is typically at the same low frequency as that of the ERP system such as daily values. For a Plant manager that wants to know what is happening on a shift or hourly basis, BI will thus be inadequate. BI tools are typically not designed and implemented to take into account the real-time nature of manufacturing operations and its very large data rate. As such, BI are not able to handle the high frequency of data receipt and the required fast response-times of reporting/visualisation required by manufacturing operations.

Executives use BI as strategic analysis and decision-making tools for the company. From their BI systems, they can see the profitability of individual plants and sites and, as such, can make the decision to close down a plant or to change the manufacturing strategy. They typically work on confirmed and validated numbers and results as they want to ensure they have accurate data when they make the decision. These validation/confirmation or auditing steps often add considerable time between the actual event and the time the data end up in the BI solution.

Site-level production personnel however cannot wait for the niceties of auditing and validation before they take action. If a report or an EMI dashboard indicates that something is wrong, it is their responsibility to investigate and take corrective action. If a feed-rate is lower than planned, the production manager is not going to wait for the confirmed result in the BI system tomorrow before he takes corrective steps. No, he is going to investigate or have someone investigate for him. If it turns out to be a false alarm, then he is glad as it is a crises averted. If something is wrong, he takes corrective action, or at least knows and expects the bad results from the BI system tomorrow. Production executives hate surprises, even good ones.

EMI systems thus have a two-fold purpose:

1. To provide early warning in real-time for potential problems in order to make decisions or take action, and

2. To provide “slice and dice” data on historical data and Operational data for process improvement, and operational status, delivering the information in time, equipment, and operational context.

EMI has data available at the granularity and frequency delivered by the individual applications. This can be from seconds to days, depending on the specific operations requirement. The data is also available per individual piece of equipment, line or processing unit and can also be rolled up into hours, shifts, days or weeks for any of these. The granularity of EMI systems is closer to real-time, and they are often used as real-time dashboards for Operations Executives.

BI may be able to provide the historical “slice and dice” data, but typically, not at the level of granularity required by operations managers. BI will not be able to provide the real-time early warning required by the plant. Both of these are thus needed to support manufacturing companies adequately.”

The challenges vendors have is how to deliver this operational information in a rapidly consumable form, with minor time and effort outside of operations. The system will need to evolve, with more operational questions answered out of the box, or an experience which enables operational people to answer these questions.

Information vs Data Leads Discussion on the Future of Operations!

“Gather all the plant data and analyze afterwards” are common words you hear about the market, but when the discussion happens this approach a “putting head in the sand” approach, with limited bigger picture consideration. Today the key to agility is empowerment of decisions and actions in the NOW. This does not require data it requires trustworthy, in context information.  The last couple of weeks has enabled some fascinating and productive engagements. In a discussion,  last week at a Mining Thought leadership on the future a sizable group of interested people attended and took part in discussions.  

A key concept of “mine of the future” and actually for most industries oil and gas, power, food etc. is the agility to take more holistic operational view of the system and day to day operations. This requires alignment at 3 loops (the diagram illustrates these loops) of operations with the alignment in decision and actions. Foundational to this is the information that decisions are based on, requires not HISTORIANS but Plant/ Operational Information Systems, that align information and actions for effective use my different operational roles. Companies that make this foundational move will have a system where data structure, validation is “managed” not coded, that the system is trust worthy so people will depend on and use the system.

To many times the discussion with mining and process end users who are implementing an information system for increased decisions support, that they require a re look at the data sources and how to put it in context, and validated. An example of this was a coal company in South Africa they had spent significant time working on an information system and the historians and data warehouses, but lost effectiveness through:

• Data alignment across sources, E.g.| Finding different data streams that effected the same asset calculation for say energy.
• Data validation
• Data structure

The conversation remarkably quickly ended up going back to redo of the structure of data coming into the historians, and getting this data structured, validated before it went into historian. They had two choices either going to source in this case PLCs and adjusting the running code (not a smart idea), or put a structuring layer in which would structure the data, validate the data, and provide high availability and single names space to manage over the distributed historians.

This is a departure from the story he was told that just put a historian in and capture the data worry about analysis afterwards, that is old and in effective saying. As discussed in the “mine of the future” discussions  the challenge is to “federate” the existing data sources on a plant, E.g.| Historians, alarm event data logs, operator logs, and delay, downtime data bases, E.g.| The alignment of the data sources into effective information that actions can be taken. The concept of “self service” becomes necessary, as there was a lot of comment around of trying to minimize the process analysis phase and role in the data, and try to get effective information to operational people quickly. Another example is an oil and gas plant’s decision system that is effectively been run 24 hours out of phase with the plant, by only have decision able reports/ dashboards from the past 24 hours at 2 to 3 in the afternoon so. Again this delay was due to data gathering, data alignment, validation, in MS Excel manually done by 3 people, the company was exploring ways to eliminate this manual creation, so the whole process is “near real time”.

A clear message from the last couple of weeks is we need to step back, align the existing systems, to provide that key foundation for operational empowerment, absorb significant milestones such as advancement in the communication infrastructures; Example putting 4G communications in the Pilbra mining area (remote north western Australia), providing significant data capability. (A topic for next week). This need to absorbed into the industrial / operational Architecture, internet will be a natural part of the backbone, leveraging computing power remotely for functions, such as storage, analysis, model running, help accelerate the decision support.   

Dynamic Simulation needs to become a Natural Feature of the Industrial Operational Landscape

To empower operational teams, workforce, each role must understand the context of the future, as well as current state and history. In the traditional industrial solutions, the worker is provided with tools for understanding history and NOW. This is the same as driving a car by only looking at the dashboard of the car, and rear vision mirror. Can you drive the car without looking out 100 meters or longer into the future the answer is NO, you would miss judging the next corner?

So why do we think we can run plants in this same manner?

The leading companies are defining the operational experience of empowering the different roles in the plant operations with the ability to “understand the future”. This will then empower the user with:

·         Understanding of the NOW situation

·         Understand the History

·         An expectation of the future based on NOW situation

·         Access to Experience

The concept or words used to describe this capability is “What If” capability. The ability to have at the finger tips of the worker the ability to ask “what if” question and the system will provide the best estimate based upon the current state of the process/ plant, and history. This provides maybe a “times 10” in time look forward so that it provides another reference point for the worker to base their decision on.

An example of how the general day to day market has come to accept using simulation models as day to day is in the weather. I do a lot of ocean sailing; today a natural tool I use on the boat is weather simulation. On my Ipad,  I have two applications, which connect to a cloud application that will allow me to select a location, and run a number of weather simulation models, based upon the current condition. There are a few models with different characteristics that I use to 2 to 3 models and compare feedback, combine this with reading the weather maps, and do a “gut validation”. It is this future, plus my experience from the weather map reading plus applying local conditions such as mountains that allows me to gather enough information to make decisions on what to do. This effects safety, comfort etc, there were examples of friends on other boats that just listened to general weather broadcast especially in remote places, and they made decisions based on this, but these forecasts were over 1000s of miles and extremely general, and open for interpretation as they were line item. In the same situation,  I ran the models; play off scenarios “what if” based upon local navigation, mountains, and my boat. On at least 3 occasions,  these friends left and where caught with young families in life threatening situations. This is decidedly different to when I was a teenager sailing the Australian coast, where we only had the weather maps, and a number of peoples opinions, we got caught a couple of times in particularly severe weather especially 5 hours out. Today I am able to use the models to look 7 days into the future for a trend, and greater accuracy at 12 and 24 hours. Today these models are a natural part of my navigation tools, having on board capability to download the results of the model, and then tools to ask questions based upon routes I want to go, easy to interpret graphic experiences to see results. (As seen below the weather is overlayed on the charts and uses colors to show intensity, I can work through time forward 5 days by hour).

It is easy to receive model results even at sea by sending an email of current locations, areas I am interested by a text email and the results come back in a data file that I load into my local system with my boat parameters, navigation charts, and I can now easily see graphically the results layered on the navigation charts and ask questions, because of this simplicity to use I now use this many times a day to make and validate decisions.

So why, not on the plant? No matter the role, it could be maintenance, performance engineer, safety, operations, where decisions are having to be made, and experience varies, the ability to get these reference points is key.

This must use high fidelity simulation models to predict the future, but the capability of adding this to a solution must be small. Traditionally simulation systems have been offline, and large after thoughts, especially outside of the oil and gas industry. “Why cannot we just add this future capability similar to adding alarm or historic capability to a device supervisory control?”. The answer should be yes. This will require smaller foot print simulation, dynamic and libraries of simulation models for different devices/ processes etc. Again in the oil and gas world this has been done for years, and companies such as Exxon and others have built libraries of optimization, and simulation capability. All industries require this no matter the complexity, but to achieve reality it must be easy (a natural act) and rich this library of simulation models must be rich, and easy to apply, plus domain experts must be able to develop these models easily, these could be device / process vendors, domain experts, and companies. With the models naturally able to be applied to the automation/ operational platform as just another function.

Yes, dynamic simulation is one of fastest growing aspects of Industrial Operational Systems, as we design systems we must assume that this capability should be included.