But there is a real controversy.  Quality black belts have shut the door on Model-Predictive Control Solutions even though they are constantly searching and driving for six-sigma (minimum variability) performance.  There are concerns about unfamiliar vocabulary and some feel threatened by the assumption that their processes are not “in control”.  But pull manufacturing systems and lean manufacturing concepts demand a flexible manufacturing environment and with the Production Center Solutions being delivered as part of our Rockwell Software Solutions, we as an organization doing Model-Predictive Control are becoming more and more familiar with QC vocabulary.  Production Center and many MES solutions facilitate a migration to a lean manufacturing environment directly and actively.  MPC has always delivered reduced variability, provided a stable solution for flexible processing and generally deliver where previously not possible ‘direct quality control’ that lets a processor drive directly to a quality target all the time.

There is only one area of tension, but not about delivery solutions, about traditional solution objectives. And ultimately our solution objectives are customized for each solution – so that the apparent difference is non-existent.  Traditionally MPC will reduce process variability, but we consider value delivery most easily and rapidly measured by then shifting that mean within process and quality limits to reduce costs, increase yields, increase energy efficiency or increase production capacity.  Thus where my specification range is currently 3-sigma from the current average (Cpk is 1) and I reduce variability in half (new standard deviation is half of the  previous standard deviation), then my Cpk becomes 2.

If you have justified a project on shifting the mean (a traditional MPC project objective) and running more efficiently, then you would shift the mean 1-2 sigma toward the limit, capture economic benefit, but your Cpk is back to 1.  This is where there is apparent (but only apparent) difference in QC and MPC philosophy.  When a customer can justify the value of reducing variability, in measuring a benefit and realizing value in reducing variability without shifting the mean – then you can deploy MPC, reduce variability (roughly in half) and reduce the current level of out-of-spec production.  The challenge customers have had in these discussions is described in the quality world as a traditional underestimation of the cost of non-performance.  What does it really cost your business if one out of a thousand customers have an unsatisfactory product delivered, how does it grow  your business if only one out of ten thousand or one out of a million have an unsatisfactory product delivered.  Where the value of reducing off-specification production assuming roughly Gaussian statistics by cutting standard deviation – roughly in half supports deploying MPC technology, MPC and QC become clear partners.

Part of the solution is good control design – spending the time to figure out what can happen.  Look at reams of past data (electronically) and test a control design against significant process history, interview experienced and not-so-experienced operators about what they worry about, watch a lot (and why) and compare a problem with similar solution designs in your or peers experience.  Make the design sustainable within the imperfect data and processing world of it’s environment.

The other step is to test the solution through each designed function and conceivable events.

1. What will the controller do if important controllers are unavailable, what will if do if a possible plant section is not operating
2. Is the controller designed to recover smoothly from loss and re-connectivity to your control system
3. Is the controller designed to recover smoothly and automatically from a computer reboot or loss/recovery of power.
4. How does the controller respond to projected bad data or flaws – can you test it with clearly bad data sent to it’s systems (like a zero to a controlled variable)

Your final catch-all is to confirm that the controller limits are appropriately set and at levels your controller can survive.  Manipulated Variable high and low, rate-of-change limits are to keep you out of the ditches and limits on the control system itself, required for plant, equipment and operator safety should be tested.  Confirm that if computer based control sends zeros out (which has happened on uninitialized controllers) that a lower-level control would, where appropriate reject or maintain it’s limits even with wrong values from a computer-based control system.

Operator training needs to include basic troubleshooting and required monitoring training.  An operator should know the basics of what your controller is designed to do and be able to reasonably question things that don’t seem right.  The operator should be able to turn off a suspicious control function (with on/off control swithes), but be required to write down the whats and whys.  This log is critical in your troubleshooting (along with uptime monitoring) and determining if a controller patch or operator training is warranted.  Then each event can rapidly be resolved and repeats avoided.

Mike T.

As a concept – this makes simple sense.  It matches how your mother makes cake or meatloaf.  But there have been many questions and doubts about how much good it delivers.  First almost every batch runs on a recipe – there is no problem with trying to do the same thing every time and most batch control systems include a recipe manager.

But the variation from batch to batch that you are trying to ‘control’ by repeating the same recipe is infrequently caused by failing to follow your design recipe.  Most variability is caused by: feedstock quality variation, utility system upsets, and by natural bio-organism or bio-catalyst efficacy differences.  These variations cannot be impacted by an identification of a golden batch except for the case where your feedstock tends to vary in higher or lower quality than the design and your innate process variability finds a better way to run with this shifted quality.

There is another limitation with most ‘golden batch’ recipes – time is the primary driver of when to do what. Life/bio-processes grow, digest, and convert at varying rates.  This is partially because of natural bio-processing rates, but a sense-and-respond capable control system responds to these causes of variation.  A better recipe is based on batch progress not from the simpler time.

We have delivered batch-quality control on bio- and other processes, most commonly in yeast fermentation. The focus has been on taking advantage of modified Michaelis-Menten kinetic models, tuned to a batch performance history and deploying a sence and respond system looking at all available batch state information.  We found another limit of time in that for our model-based dynamic control perspective it is not realistically differentiable (we cannot make time move faster as we can with batch progression).  A key for us is that interim QC sampling is provided so that batch progression can be confirmed or corrected while still actionable. End-of-Batch sampling is informative and can support the ‘next’ batch, but we do much better with interim results measuring quality progression. A quality model, backed-up by measurement lets you run much closer to an optimal batch trajectory every batch even with biologic, feedstock and utility variability.  This provides today’s golden batch – adjusted for what is possible with the ingredients that are available.

Mike T.

Managing the quality of product is paramount to a supplier’s profitability and even survival. If you turn on the news you can see the effects of poor quality across many companies and industries. Often supplier quality is dependent not only on parts that are designed and produced by the supplier but also the parts purchased from external suppliers. Even though the root cause of quality issues may not be their responsibility the supplier could still be responsible for identifying defective units delivered to the final customer. This can be a daunting task without an automated system that can track the work done internally to produce a part and also linking the supplier’s data of purchased parts. The ability of being able to identify quickly which parts are defective can have huge financial and brand ramifications. It’s amazing that small parts can cost a company millions of dollars in recall and containment actions not to mention the damage done to the brand image. So how do you mitigate the risk? From my perspective I would take a multi-prong approach. Obviously you need to design in the quality but you also need the information to detect and contain quality upsets faster. The first step is the implementation of a traceability system that can track when and where you produced your parts linking this information with other critical characteristics that affect quality like process information and supplier lot information. This would allow you to respond more quickly to quality upsets and help reduce the size of product recalls.

Ed Scott

If we think about this, it’s the equivalent of driving a car by looking through the rear-view mirror to confirm nothing got run over. It’ll work up to the point we encounter a pothole – or even a curve.

Many dairy plants work around this limitation by operating conservatively with respect to process targets. Knowing the variation inherent in the process (borne out by years of lab tests), we set the operating targets far enough away from the upper (or lower) control limits to make a quality limit violation highly unlikely. To use the car analogy again, we’ll drive as far away from the ditch as we have to, to keep it on the road.

When we do this, we maintain product quality, but what happens to profitability? Are we giving away product by operating away from limits? Are we consuming energy unnecessarily?

What if we could use the information base contained in the plant to predict quality in real time?

These features, while limited are simpler and easier to use than MPC for size appropriate control problems.  So sometimes a control problem needs a bigger, more capable tool (MPC) and other times a simpler, straightforward tool is appropriate.  We’ll call this advanced regulatory control (ARC) to cover anything from ratio controls to any of the more limited advanced control capabilities and looking at them together there are a lot of appropriate control challenges that are best solved with an ARC toolset.  I’ll take as a guiding principle the quote attributed to Einstein, “keep it as simple as possible, but no simpler”.

If a control problem is solved exclusively with capabilities available in MPC then MPC is the solution. If a control problem is solved with one or a straight-forward implementation of ARC tools, then ARC is the solution.  Where a debate and engineering judgment comes into play is when you are tempted to stretch ARC to the point where it becomes a maintenance challenge (Smith-Predictors, tied to decouplers or multivariate controllers; or fuzzy controllers tied to multivariate controllers with two or three variables).  I would propose that you need to look at the various block-diagrams of your controller and decide how simple it is going to be to train someone knew how to update model parameters and tune them.  If it really is easy to tune then you can build a more complex ARC, but avoid the spaghetti code of over-complexity in any case.  Before this becomes a threat, migrate an application to MPC.

Finally, we have observed opportunities to cascade MPC at a higher level to lower level ARC.  This can have significant advantages and one disadvantage.

Advantages:

1. More of the advanced control benefits are DCS/SCADA/PLC resident so that their availability is almost 100%.  Benefits increase and certainly any essential or critical safety function should be resident on the control system.
2. The MPC solution is simplified so that some complexity is removed from the MPC or optimization models by segmenting regulatory functions from closed loop, but supervisory MPC functions.

Disadvantage

1. You require two independent advanced control functions resident on two different computing platforms, which can increase training, maintenance and TCO (total ownership cost) of an application.

My opinion is that this is handled on a case-by-case basis.  Sometimes both, sometimes everything in MPC.  What do you think?

Mike Tay

Basically Washington is looking to increase the pressure on Food & Beverage manufacturers to provide full accountability back to food suppliers on the genealogy of the food they supply, and it’s looking more and more like this will be in the form of electronic genealogy.

Current practices: a paper based system that creates tons of paper daily. That challenges anyone’s ability to recall any product: the task is simply too daunting. As a result, the scope of the recall is huge just to be sure all problem products are captured, which in turn increases the costs and the breadth of the recall. It can cripple a company.

Track & Trace

Ideally, product genealogy starts back at the farm, and then tracks the path all the way up to the shelf where the product is ultimately sold. However, focusing on the manufacturing process is a big step in keeping track of what’s inside. 

Elements of a track & trace solution include identifying the “M’s” of manufacturing, including:

• Materials – Understand the supplied ingredients and their origin and usage in the product
• Machinery – Collect and track the machinery that touched the product while processing
• Manpower – track the operations staff that came in contact with the goods in question
• Methods – Capture the recipe of the finished good.

Track & Trace solutions can provide manufacturing records to upper level systems that track where those suspect products ended up on the store shelf.

All this tracking and tracing provides operational benefits (If providing safe food isn’t enough justification) but, if you need to know…Track and Trace solutions provide the added benefit of:

• Providing an accurate accounting of products consumed in processing so yields, wastes, and rework streams can be clearly identified and improved
  • Reductions in safety stocks due to increased understanding of day-to-day inventory needs
  • Improved delivery of orders since machine efficiencies are better known
  • Efficiency improvements due to increased understanding of interactions of the four “M’s”

An electronic Track & Trace solution will also satisfy the inspectors at the federal, state or local regulatory agency by providing the documentation necessary as part of a mock or real recall event.  

Supplying accurate information quickly to the public not only ensures a safe food supply, it also provides piece of mind to Food & Beverage manufactures that the products supplied are something you can put your name on.

Mike G.

1. The ‘Gray Wave’ of Baby Boomers, where a significant amount of practical experience and knowledge is about to walk past the plants’ gates and into retirement;
2. A replacement workforce that has grown up in the video game generation, accustomed to acquiring and processing  information and knowledge visually and at high rates; and
3. A dwindling graduation rates in the technical fields required to drive innovation and resolution within the day-to-day activities of manufacturing plants.

This convergence is the engine behind the recent focus on Operations Management/Enterprise Manufacturing Intelligence (EMI) solutions in the market.  Clearly, the way forward demands that a different set of application be given access to the next generations of operator.  The operator of tomorrow will be expected to make a broader set of decisions that go beyond the traditional boundaries of process control.  The drive to the top is fueling the need to ensure that minute-by-minute decisions in the control room are aligned with the corporate business strategy.  This was crisply highlighted in an Industry Week article focused on smarter manufacturing by Dave Miller, Global Industrial Sector Leader for IBM Global Services, where he highlights the fact that Manufacturing Intelligence must become prevalent for those companies that want to thrive in the future.

Unfortunately, software suppliers to the process industries have clouded the situation by confusing Manufacturing Awareness with Manufacturing Intelligence.  Elaborate dashboards displaying performance metrics tied to historical information can only provide retrospective insight, which is helpful if trying to understand what has happened.  However, Model-based Predictive Intelligence solutions have the capability to present events that are about to happen, and provide the functionality to test scenarios so operators can arrive at the optimal decision before events occur.

Predictive-EMI solutions provide predictive insight into the business impact of operating decisions by leveraging process models used in predictive control in combination with financial information from ERP systems.   The information is then presented to the control room on a role-based dashboard depicting the current state of operations (where you are), the opportunity (where you could be), and the recommended steps to capture the opportunity (how to get there).  This is the true essence of actionable intelligence.

Cement is the second most consumed substance in the world after water and changing trends in population growth and environmental pressures are introducing new dynamics to the industry.

U.S. consumption is down 25% in the current recession and the prospect of stricter environmental regulations means there is a distinct possibility cement production may move permanently offshore.

If this does happen, a shortfall in supply will occur as the world’s largest economy recovers and this demand will have to be imported from other countries.

World population growth trends are supporting this trend. Cement production and demand growth is highest in China, India, N. Africa, Middle East and Latin America.

World cement consumption will reach 3 Billion tons in 2010 for the first time ever. This will be driven by modern, energy efficient, waste burning cement plants, preferably with efficient access to seaport facilities to facilitate global shipping.

In the end it seems these mega trends will transfer production and economic wealth from “first world” economies to emerging markets.

Does this make complete sense? While it certainly assigns new capacity to uplifting new markets, does it result in a net benefit to the environment, since production is simply transferred to countries where environment regulations are less onerous?  Will importers levy a carbon tax to level the playing field and compensate for their environmental compliance costs?

The expanding producer countries are less likely to be involved in the burning of waste materials in cement kilns and will need to investigate new ways to reduce CO2 emissions from their process.

Will they become involved in growing and consuming biomass to replace other fossil fuels?

Will they introduce new raw materials and new cements to reduce the inherent CO2 component in their product?

Let’s track their moves and see. 

Corn to ethanol remains predominantly an industry organized by growers cooperatives.  This has been essential in both farmers experience in dealing with boom and bust economic conditions, in supplying feedstock and in having alternative outlays to tide entities through challenging times.  The industry has been early adopters of technology solutions and invested in new research (like cellulosic ethanol) beyond what is common in other more traditional industries.

What is amazing to see is the benefit this industry has supplied to rural areas, the effort and motivation evident in most plant operations and the industries ability to solve problems as they come along.  The WSJ and several commercial voices are betting against fuel ethanol and it’s hard to judge why, but while the industry will continue to evolve – I’ve been to 70% of the plants in the US and 25% of the plants in Brazil.  I wouldn’t bet against this industry.

Today the industry appears living in good margins with inexpensive corn pricing and reasonably high ethanol (related to gasoline) pricing.  Last year was a tough economic time for most industries including fuel ethanol, but like many other industries fuel alcohol is cautiously optimistic about the near future.