Third Generation Mom/MES with Model Driven, Provides the key to Avoiding Customization

In speaking with customers from multiple industries the desire for "operational management and manufacturing intelligence" grows, but so do the horror stories of the promise vs reality. I keep asking why the horror why the disappointment, and in a series of 36 interviews it became clear that it was a number factors:

1/ vendor solution miss fit

2/ too much customization came over and over again, as key pitfall 3/ customer expectation vs reality and perpetration

Too often people initially "point fingers" looking for the vendor or implementer, but the mature, realistic interviewees admitted it was a mixture of their own preparation, culture and expectation as well as the vendor and implementer partner.

Without about a common message came across avoid customization at all cost, two interviewees talked about how they were replacing a home grown system, and took a vendor product and customized it heavily for "product x" and found that they could not take it to the second and third plant and ended up with a system similar in functionality and cost to sustain as the original home grown system.

They both admitted they did not step back and look at the new operational processes they should apply for the next 10 years but instead based their design on what they had. Both are now reviewing a new approach for the other plants. This story in different levels of complexity was not uncommon, and many had first experienced this with the ERP system.

Another huge challenge is the different cultures and operational practices on the different sites, causing significant challenges in achieving consistency in product / service quality, and operational efficiency.

There were many people where confused and bitten from applying the wrong product to a particular industry. It is important to achieve minimized customization the modern MES/ MOM system must come from that industry segment e.g. you can look at:

1.       Discrete industries

2.       Hybrid industries

3.       Continuously industries

We in the industry cause much of the problem as MES is term applied across all three, but they very different, the key to a vendor/ packaged MES is to look at it’s origin, and that will indicate the model that it fits in the above 3 broad industry areas.

So when you look at 3^rd generation MES why it really has hope is that it is model driven, avoiding customization, through allowing managed configuration that will enable companies to evolve. With the changes in the operational processes this key, as this is where you gain the significant advantages. If you choose a correct MES from your industry segment, and then apply a model driven/ workflow approach to configuring your operational practices this provides the most advantage and flexibility. Combine this with object driven platform when connecting to an automated world for real-time events, the system can become proven quickly but most of maintainable and evolutionary.  

Key is uniqueness does not mean customized code, it should be sustainable configuration based on models that can used over sites, so operational practices (your advantage) are consistent but can evolve!

Reality in the Transformation in the Industrial Training and Knowledge Transfer, With a Power Plant, Why not You?

I have discussed at length the transition of our workforce and that it is not just going to a new generation ( 42% of the workforce by 2020) but it is the dynamic culture of this workforce ( tenure ship less that 2.4 years in the role by 2020) that poses one of the biggest challenges to the industrial operational/ automation workplace.

At a dinner in Florida I was sitting next to a gentlemen in charge of "change management" at a counties power facilities major power stations. A major part of the focus is on training, and knowledge transfer. The conversation initially focused on the challenge of "baby boomer" retiring over the next 5 to 8 years and the knowledge, experience "walking out" the door. It was interesting to understand how stable his  automation and operational work force has been for the last 20 to 30 years. They are a premier employer in the area, and most of the current work force has come from trainee and full careers there, many were there when the power plants were commissioned, growing up with the plants.

Reminding me of a saying by a colleague "that when he joined the workforce on plants, the average age was his age eg 20, now that he is in his late 50s the average age of the workforce on a plant he visits is still his age ( late 50s)." As we had dinner this, saying was ringing in my head as the stories from the power plants came out.

As the dinner progressed the other challengers came out that they were building new gas powered plants, shutting down some of the older power units, and the new workforce was aged less that 35 years old, (most younger) and little to no experience. The shorter tenure ship in a role he had not seen yet, but when discussing he had noticed a trend to rotating roles, and want to progress, new challenges, and with the new gas plants with new technology and learning he saw the challenge not far off.

So he has embarked on a significant Operator Training Simulation Program. Work on three fronts:

1/ power unit operations

2/ power unit maintenance and start up, shut down

3/ environmental / energy management

These are 3 different communities of the workforce, the third being a specialized one which is becoming critical due to government regulations and the heavy fines, so understanding how to seal with different situations including weather and load is key.

The first step is to really structure the training knowledge capture from the “baby boomers" capturing their experience in operations reacting to situations, and performing maintenance. Action and knowledge they take for granted now caught in the simulation models and built in as training scenarios that can play over and over again.

His learnings are also supported in the diagram below:

He also talked about not just simulations, but his learning has been the other training tools, measurements within the OTS tools that he did not initially appreciate (or use until recently) that allow the training to evolve, and improve as well develop the team.

The big walk away I had from this dinner discussion was that he could not see how they would go through this workforce transition, and plant transition, and maintain the current operational levels in efficiency and safety without automated, simulation tools.

His whole story was micro example of what I am seeing across the world, but so many are not acting, he had been implementing a "change management" plan based around simulation learning for the last 4 years, and he was still evolving, but seeing significant returns.

On the new plants, they will have simulation as part of the plant design and operator training simulation from the outset.

Yet a week later across the other side of the world in mining, (where an even more dynamic workforce transition is happening,  with combination significant workforce rotation and retirement) the shift to simulated online training is seen as a "nice to have".

Key as we look forward there are 3 big areas that need to align, Operational performance Asset utilization / efficiency Operational workforce effectiveness. They are all equal and dependent on  each other, and the leading companies will be the ones that master the balance across the three as the dynamics in all three increases.

Next week I will expand on knowledge dissemination, learning transformation that is and will have to happen in the industrial space. 

Deployed Enterprise Historian in the Cloud Discussions with Actual Use Cases, Confirms the Opportunity for Leveraging the Cloud to Increase Uptime.

This week I had dinner with a client from the water industry who has just deployed Invensys's Enterprise Historian in the cloud. While I have had many discussions with potential users of historian in the cloud, but the opportunity to discuss 1 on 1 with one of the first implementers was too tempting.

He comes out of the water industry, and they deployed two systems, for two city water systems. They have tiered architecture with tier 1 historian on sites, feeding to the Enterprise Historian. They have used a local historian, with a 7 day storage, with the intent of using the Cloud Enterprise Historian as the analysis tool across the sites, for analysis.

I asked, " why had they gone to the cloud?"

The answer was due to increased reliability in the cloud vs hosting at the city Center and expecting someone to maintain it.

They have found over time the reliability of having a historian on site for long term data in cities is not effective, as the maintenance on the PCs/ servers, upgrading OS’s and management of archiving the data was not been done as regularly as required. With the increased regulations requiring data to be stored for at least 7 years, this risk needed to be addressed. A discussion of why this issue of historian maintenance was an issue and it came down to the Historian falling under the plant automation teams, and they did not see the PC’s and Servers as maintenance items, like they did say a pump. IT, on the other hand, would monitor the PC with performance measures to escalate issues.

So to resolve the issue the client has taken advantage of the new Enterprise Historian in the cloud offering from Invensys/ Schneider Electric where the historian is managed by Invensys/ Schneider Electric, in its cloud system working with Microsoft Azure. The uptime of the system is supplied by Microsoft with it’s Azure infrastructure, and data centers, combined with the expert managed services from Invensys / Schneider Electric. Who install and set up the system, monitor the system for data usage and archiving, and manage the operating System, and product upgrades?

Removing the whole management of the data from customer.

A series of clients hosted in the cloud are available for analysis by the user.

There was no question of security; it was assumed and believed that Cloud infrastructure is more secure than they can maintain on remote sites. This has been proven many times, and I ask people who doubt this to understand how secure your own managed historian is from an up time point of view and data security, especially as the breadth of users accessing the data increases.

It was nice to validate the original intent of building an Enterprise Historian in the cloud, and reaffirm the trend we seeing of the internet becoming a natural part of the industrial information architecture.

This case was interesting as it was new, but I suspect a year from now this will be common, as the challenge of maintaining historians and servers on remote sites, or in companies scaling back on plant engineers increases.  

The comment I heard in New Zealand a year ago “ why would I put a server on a plant site in water again” comes ringing back to my ears!  

Operations Innovation & Transformation – Transformation from Plants to an Aligned Multi Plant Value Chain

Examples in refining with multiple refineries aligning from individual plants, to "mine to Port" in mining where assets / plants that traditionally ran in isolation are now being transformed into a an agile value chain to reduce the production runs, increase the agility to chain products proceed across the sites to satisfy the market. The move to integrated operational centers (IOC)s is just one step, where planning and operations come into same environment to increase communication. 

The 4 quadrants described in the article “Operations Innovation & Transformation – the 4 Types” positions the upper left quadrant as a strategy for using a “value chain” of physical assets in a new way.

In this quadrant, a group of similar industrial operations (2 or more) adapt their performance objectives, business processes and accompanying hiring and information strategies to optimize the “value chain”.  The move is to unifying the industrial enterprise over multiple sites (in groups or as a whole), with a more holistic view in terms of operating strategy and performance management.

This innovation can be limited by the dynamic and range flexibility of some of the operations, but several corporations have achieved success with this.  One example is seasonal competitiveness, where the “chain” collaborates to achieve maximum throughput during the high demand season and maximum efficiency during the low demand season (efficiency and throughput interact differently across different groups of industries).  Another example is short-term business continuity, where the “chain” collaborates to exploit a supply or demand opportunity, or they collaborate to minimize the business impact of a supply chain problem, such as a major customer unplanned outage.  They all adapt their operations to meet a shared performance objective, such as yield or efficiency.

A key method used to sustain this strategy is operations-level feed-forward and feedback, with workflow for collaboration.  This doesn’t violate or compete with established business processes for planning, scheduling or other elements of supply chain management – in fact these strategies and business processes must work closely together.

This is a significant step beyond scorecards, dashboards or rigid workflows.  The following 2 examples show how real-time performance measures (different from traditional KPI’s) and proactive procedural automation sustain this differentiation:

·         A “value chain” of related industrial operations (one of the operations provides fuel and raw material to another) have some dynamic and range flexibility to “pace” together.  When a downstream site must slow down, the upstream site adapts its throughput of the entire site or the affected products during the duration of the slowdown.  As soon as the slowdown has ended, both sites resume their scheduled throughput and yield targets.

• Coordinators (different industries have different names for this function) use workflows to negotiate short-term upcoming changes in demand and the operating shift and the coordinators use the same visual demand, using a “tram line” display.  Information to the right of the center dashed line is forecast and planned.

The benefits include significant reductions in energy (excess energy is required to restore the high pressures and temperatures) and reductions in rework or waste.  Conventional equipment protection strategies aren’t adaptive and they are designed to handle the most extreme conditions, which is focused on safe interruption of operations.  Value-chain management focuses on safe continuity – both are valuable and necessary.

•       A “value chain” of related industrial operations (one of the operations provides fuel and raw material to another) have some dynamic and range flexibility to optimize the processing and use of fuels and raw materials in the downstream operations, such as the following example in petrochemicals and specialty chemicals.

Operations Innovation & Transformation – Transformation from Individual roles, people to a Operational Team

The 4 quadrants described in the article “Operations Innovation & Transformation – the 4 Types” positions the lower right quadrant as a strategy for using a team of human assets in a new way.

In this quadrant, teams of specialists (with the same or different areas of specialization) are grouped to provide value improvement to a group of physical assets, and the group of physical assets can be used as a “fleet” or as a “chain”.  This is much more than a passive “help desk”.  One example is where specialists use real-time benchmarking and other tools, working with new business processes with the physical assets and the dedicated workers, to unlock value of themselves and the physical assets.

The example below we have an operational team, made up of different roles, usually in different locations, and different value in the operational decision value chain. Key is all roles can work together in real time, see the same information and collaborate in real time for decisions. So a roaming user on site can take photos, look at situation, share this the operational center and virtual expert members to rapidly understand, leverage experience and make a decision. 

In the following diagrams, a team is located in different sites at the moment.  This example has 4 physical assets, A through D, and specialist 1 is mobile (working from a hotel, home or in an office within the enterprise), and specialist 3 is in an operations center.  In the left-hand diagram, specialist 1 is supporting or improving the performance of physical assets A and B, and specialist 3 handles the physical assets C and D.  In the right-hand diagram, a change in performance in asset B triggers a workflow and specialist 2, who is on call or is assigned by the team supervisor, handles asset B.  Specialist 1 does not receive workflows for asset B unless the team supervisor changes the assignments.  Overlapping assignments are also used, especially when multiple disciplines or specializations are involved.  Both use the same integrated and federated information, and these specialists become champions to help all like operations and equipment improve performance.

In the following diagram, a workflow “brings the work to the worker”, using the same integrated and federated information, on-line performance applications, and human workflow.  The supervisor(s) can easily change assignments, and the workflow can include escalation, which can be guided by the performance applications’ output compared to thresholds (simple calculations of time to reach a threshold).

The strength of these workflow is to help specialists intervene early enough, using standardized and trustworthy data, focused on trends.  This processing of information is automated as much as possible.

The specialists spend most of their time working on improvements instead of processing data and analyzing previous performance problems, and their decisions.  As a result, major overhauls are safely and reliably delayed, equipment performance is improved, and operators trust the equipment more to help increase performance at each physical asset.

Operations Innovation & Transformation – Operations Control Loops, Aligning the Operational Roles in Industrial Landscape

Constantly I am asked on how to make the operational workforce effective, key is the alignment of the different roles. Traditionally the user interfaces, the targets, kpis each role has worked to have been disconnected, a good company aligns them on paper at least, most even miss this step, with each role setting based on their view on what success is. A classical good example of role alignment is in F1 motor racing where the engineering team and driver are aligned on winning the race, if they were not the driver would drive as fast he can with little care to car to finish, and engineering would advise driver to drive in a way that avoids stress on the car or they build it to last, with alignment they aligning the driving and engineering to finish a race first and no more.

In today's dynamic workplace and market these need change in a dynamic mode at all levels, driving the rapid miss alignment of KPIs and targets.   

The 4 quadrants described in the article “Operations Innovation & Transformation – the 4 Types” positions the upper right quadrant as a strategy for governing business processes and teams of human assets in a new way. 

In the upper right hand quadrant, managers and supervisors use consistent measures and business processes to adjust targets for specialists and other workers, using the industrial automation concept of a “control loop”.  One example is where managers negotiate the next day’s production targets each day using the same business process for all specialists and industrial locations.

In the upper left diagram, supervisors use visual management to observe the ability of teams to navigate targets and constraint in order to improve team performance and adjust targets based on team capability.  This method is used in conjunction with Value Chain Optimization and Fleet Management, and these are in the left quadrants of operations innovation.  These are described in separate articles.  This visual management is used to reduce risk and evolve performance of multiple sites for the same operating shift, and multiple shifts for the same site.

In the upper right diagram, the supervisors use this business process (simplified) as a means to consistently govern targets so that a variety of activity, including different sites, shifts and responsibilities, can be adjusted at the same time and in a coordinated fashion.

Another example is where operators use visual management to address performance challenges that affect the business.

In the upper left diagram, a combination of organizational change management and visual management which helps teams to trust themselves and understand hourly operations performance with a business context.   Dark blue diamonds show a baseline for one month, and the unit cost variation is high, even at high output.  This operation has agility – a 2:1 range in throughput, and normally unit cost should be lower at higher throughput (due to higher efficiency of utilities equipment).  Magenta squares show the results of the first month; unit cost variation is still high, but most of the performance is near minimum cost.  Yellow triangles show the results of the second month, with excellent results.

In the upper right diagram, key performance indicators (KPI’s) and Operating Indicators (OI’s) are arranged together so that performance challenges at all levels are addressed in a timely and consistent manner.  For each level of the organization, lagging and leading indicators with business and operations context are linked together. “WIG” is an acronym for Wildly Important Goals, which helps to maintain focus on the most import performance measures among numerous other indicators.  KPI’s are appropriately developed from the top down, and originate with business goals and measures; OI’s are appropriately developed from the bottom up, and originate with operations targets and measures.

The results have been spectacular, including double-digit improvements in efficiency, first-quartile industry performance, and more.

No longer can we have different roles not aligned and decisions in real-time at all levels requires adjustments but alignment not just top down, but bottom up due to a situation, but approvals in real time and adjustments in real time. So workflow and linking is required so governance of decisions and actions is maintained. As seen in the diagram below. Are linking these, are you looking at the Dynamic Performance Measure approach with measure, indication and notification all aligned to the level, time, and approach.

Operations Innovation & Transformation – Fleet Management

The 4 quadrants described in the article “Operations Innovation & Transformation – the 4 Types” positions the lower left quadrant as a strategy for using a “fleet” of physical assets in a new way.

In this quadrant, a group of similar industrial operations (2 or more) adapt their performance objectives, business processes and accompanying hiring and information strategies to optimize the “fleet”.  The move is to unifying the industrial enterprise over multiple sites (in groups or as a whole), with a more holistic view in terms of operating strategy and performance management.

This innovation can be limited by the distribution flexibility among the locations, but several corporations have achieved success with this.  One example is keeping most of the locations operating at a constant or “base” portion of the combined market demand, and using the more agile locations to deliver the “swing” or variable portion of the demand.  Another example is allowing all locations to serve their local markets without contribution from any other of the “fleet”, but they all adapt their operations to meet a shared performance objective, such as yield or efficiency.

A key method used to sustain this strategy is the increased automation of work.  This is a significant step beyond scorecards, dashboards or rigid workflows.  The following 2 examples show how real-time performance measures (different from traditional KPI’s) and proactive procedural automation sustain this differentiation:

·         A “fleet” of similar industrial operations have some distribution flexibility so that they can deliver a portion of each other’s market demand.  Each operation delivery point and each operating shift for that segment are benchmarked with the others, and all delivery points and their shift performance carry a real-time performance score.  Some delivery points are more agile, and some operator shifts have fewer errors than others (quality, over/under delivering, reworks).

Coordinators (different industries have different names for this function) use workflows to negotiate upcoming changes in demand and the operating shift and the coordinators use the same visual demand, using a “tram line” display.  Information to the right of the center dashed line is forecast and planned.

·         A “fleet” of similar industrial operations within a single location or nearby locations has distribution flexibility, but they only share in real-time benchmarking (such as efficiency) and online performance guidance.  The following is an example when all physical assets are used, with equal output at this point in time:

All of the physical assets share real-time benchmarking performance on efficiency and availability.  Now consider what can happen differently when supply or demand changes suddenly, such as an unplanned outage within the industrial operation or within a client’s operation:

In the left-hand diagram, losing capacity can cause all of the “fleet” to shut down, if fast and accurate guidance isn’t available and used to either import capacity (if feasible) or negotiate reductions in demand with one or more of the customers.

In the right-hand diagram, a client’s unplanned outage causes some or all of the “fleet” to operate in zones which might be unstable and trigger unplanned outages, if fast and accurate guidance isn’t available and used to either shut down one of the physical assets or export some of the product (at a discounted price).

Proactive procedural automation, using real-time performance measures and best practices, helps the fleet to sustain high reliability and better overall efficiency, yield etc.  It is used to present the decision makers (including operators) the business and operational alternatives and their consequences, in a simple form, such as the efficiency/yield/cost without changes and the corresponding performance if one of the alternatives is implemented in time to avert an unplanned outage or a significant shortfall in performance.  This doesn’t require exotic technology, but it requires the implementation of trustworthy, on-line performance calculations and human workflows.