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Defeating Deferred Maintenance


The Geaslin Group
David Tod Geaslin, Principal
Phone (Mobile): 832-524-8214
Virtual Office:

Why Six Sigma and Other Scientific
Methods Cannot Predict Nonlinear
Operations & Maintenance Costs

A White Paper on the Management of Maintenance (Updated: 05/01/2013)

Nonlinear Video With Credits (Short 61 meg)

Nonlinear Video Without Credits (Long 120 meg)


Organizations that have successfully invested in management control philosophies such as Six Sigma, The Deming Method, Lean Manufacturing, TQM, and other solutions to improve efficiency and profitability have benefited greatly from the effort. Yet there always seems to be a point in every process where these philosophies cannot maintain control of the process. I call this the Linear/Nonlinear Boundary. Even though these programs work wonders in their Linear Arenas, the Nonlinear Arenas outside their control creates a constantly nagging parasitic drag on the profitability of the company that seems to defy all management attempts offered. I have created a new philosophy to manage the nonlinear aspects outside the linear processes.

My research has proven to me that the application of successful linear solutions such as Six Sigma, etc. to nonlinear problems will actually create operations, maintenance, and safety issues, not control them. These and other scientific management solutions are designed for application to linear processes and perform wonderfully at those challenges; however, when linear solutions are applied to nonlinear maintenance challenges the results can be dramatically inverse to the expected result. 

This white paper is offered to explore why great programs such as Six Sigma, etc. are not the complete solution to managerial challenges in organizational environments that have a heavy investment in mechanical assets and facilities that require efficient maintenance to assure continuity of service. 

My research has led me to two new discoveries in the management of maintenance that explains why process management philosophies such as Six Sigma can perform in an absolutely brilliant manner supporting linear processes and yet fail so dramatically in the nonlinear arena. In this paper I will try to explain the differences I have discovered between managing maintenance in the linear and nonlinear arenas. 

A Note of Caution: Engineers, process engineers, industrial engineers, accountants, MBA's, and other degreed professionals tend to have great difficulty with these discoveries if they think I am disparaging the scientific methods and asking them to replace the successful business and process controls they have so carefully constructed. This is not so. My discoveries have lead me to realize that there is a boundary between the linear and nonlinear aspects of management and I will try to offer this insight to the reader.

My new philosophy does not replace philosophies such as Six Sigma, etc. My new philosophy only appends onto the process at the point that Six Sigma, etc. become ineffectual because of nonlinear and/or chaotic variables. My new philosophy offers another management tool to finish what Six Sigma, Deming, and TQM started but cannot control past the linear/nonlinear boundary. 

My new nonlinear operations and maintenance philosophy of "Collecting Butterflies" is based on Dr. Ed Lorenz's pioneering research into The Chaos Theory and his famous "Butterfly Effect" and explains why it is impossible to predict and budget nonlinear maintenance. This paper is limited to the context of linear and nonlinear maintenance; however, my discoveries and more supporting information can be viewed on my web page at


Thank you for your kind interest,

David Tod Geaslin


Why Six Sigma and Other Scientific Methods Cannot 
Predict Nonlinear Operations & Maintenance Costs

By David Tod Geaslin

I earned my degree in Industrial Management & Marketing from the University of Texas in 1967 (Back during the Jurassic Period) and I can never express adequately how much I learned and what that education has meant to the quality of my life. After my sophomore year, I never had an instructor that did not "wow" me with knowledge. The constant revelations of the case studies by efficiency experts made me want to be one. However, all through my junior and senior years, there was one nagging inconsistency that haunted me. That being that after being exposed to the wonderful management tools offered in the case studies at Ford, Westinghouse, General Electric, Boeing, etc. I found that they were all but useless when I tried to apply them to my father's small, three-pump gas station and garage back at home.

My first attempts to explain and institute these types of scientific management controls were met with good natured smiles from my father (Like he knew something I didn't know.) but he supported me in attempting to put something back into the small family business that was paying for my college education. Some things worked so well, like our new inventory control policy, that those innovations actually repaid him for all my college expenses; however, even the ones that worked seemed to be some sort of photo-negative image of the real industry models.

Eventually I decided that the problem with my father was what SAP now calls a "corporate culture" issue. In my youthful examination of the challenges trying to get my dad to do all the accounting and measuring that I needed, I came to the conclusion that you can't teach old dogs new tricks and abandoned my attempts to apply the scientific management methods to the family business. After graduation from the business school at UT, I went into the Marine Corps as an aviator and was never again directly involved in the family business and you know what? It did just fine without my scientific management method and expert advice. However, I couldn't wait to get into a job where I could really apply my education.

In 1969, after finishing flight school and into squadron maintenance, I believed that my degree would allow me to make significant improvements in aircraft availability but was I wrong again! Compared to other squadrons, we had a very good maintenance function but very much less than I felt was attainable. Every attempt to apply industrial management and engineering methods to maintenance at the attack squadron level fell apart in very short order. Shortages in parts, high turnover in maintenance workers, significantly reduced pilot training, and sometimes less than yearly changes in squadron commanders cancelled each attempt to create an optimum aircraft maintenance effort. I suddenly knew why my father smiled when I told him how my high-powered management system could change his business. I realized that there were too many variables to control and the data collection necessary to get enough data points required extra personnel that we didn't have. My magnificent management sandcastles were knocked flat with each rising of the tide. 

It was about then that I made a conscious decision to stop trying to control the events that constantly thwarted my attempts at improvement. It was not an easy decision to abandon the scientific management path that had served others in industry so well. I made a plan to stop trying to control that which I could not control and institute a policy of early detection and early intervention to give me some advantage over each maintenance event. I decided to catch and repair every problem as early as possible. In effect, I decided to aggressively treat each symptom of trouble as soon as detected. Since I was not allowed to have the resources to eradicate maintenance diseases, to succeed I would catch each "illness" in the earliest stages and short-stop each disaster whenever possible. Although it was not the solution that I wanted, it was better than a sharp stick in the eye. My life as a manager of maintenance improved immediately. 

I stopped letting the lack of higher strategic leadership be the driver of my maintenance resources. I began ignoring that which I could not control and concentrated on what I could. I let each detected individual failure event of people, parts, and leadership become the driver of my resource allocations to snuff out each cigarette butt before it grew into a forest fire. Early detection and early intervention became my mantra. It worked! However, again I noticed that my solution seemed to be like a photo-negative of the textbook scientific management solutions. I was having superior success, but each success was reactive rather than a scientifically planned solution.

I decided to leave the Marine Corps and in 1975 and started my own business applying my aviation maintenance techniques to fleet preventive maintenance. My company was a mobile PM service for commercial truck fleets. I applied time and motion studies to the servicing of trucks and became the local preventive maintenance guru for fifteen years. I was happy as a clam. I now owned my own business and was not limited in my management options. I attempted to implement what I thought would be a good scientific business control function but quickly realized that I could "control" my workers production interactions with the vehicles using time and motion studies, technology, and training but I had little control other than that. I could be efficient servicing a truck, but I had no control over when and where I would get a customer's truck to service. Again I went to the "photo-negative" and began responding to each scheduling "symptom" with gusto by creating an alternate PM Plan for my clients. It worked again but I was not happy with the lack of control when we were servicing up to a thousand trucks a month.

I enrolled in an MBA program in my search for a method of control but withdrew because the university offered no new options that would apply to my type of business. I began to read the many authors such as Peters, etc. always looking for a new option for control. After the energy crisis of 1973 I began to hear about The Deming Method and although I didn't attend any of his classes, I studied everything I could of his method and became a big fan. I tried to implement his method as best I could but again there was always a point where the "photo-negative" of The Deming Method provided better results. I studied the Six Sigma and TQM methods and found them to lose effectiveness at the point where I needed them the most. 

I would like to make it clear that I have never doubted the effectiveness of these methods. The proof is there that they can work and work well. I had the highest confidence that I was capable of implementing these types of programs and yet I knew they would not work in my maintenance application. These methods worked well in other maintenance applications such as refineries, production lines, and process intensive operations so I asked myself, "If my failure to control was not the Six Sigma process and it wasn't me, what could the problem be?" In 1992, I discovered the answer.

In 1988, I had began to offer consulting seminars in the management of maintenance. First for truck and forklift drivers and then to management. In 1990, I sold my fleet PM service and began consulting full time. In 1992, while doing some budget work for BFI, I read the book "Chaos" by James Gleick. The name for this field of study is a misnomer. The Chaos Theory is the study of finding hidden order in dynamic and apparently chaotic systems. It is the organized study of nonlinear systems that seem to be unpredictable in their result. The book constantly compared linear systems that can be measured, quantified and analyzed to nonlinear systems that cannot. Nonlinear systems have so many immeasurable variables that it becomes impossible to quantify or predict their outcome no matter how many accurate data points you have. 

Reading about The Chaos Theory was like that cartoon "light bulb" coming on inside my head! The companies that have successfully used The Deming Method, TQM, and Six Sigma to manage their operations and maintenance issues are linear business processes. Each station in the linear production process is embedded or physically attached to the process. Each linear station is dependent on the station before it and responsible to the station after it. If any station becomes unreliable the entire process is threatened and because the process is measurable, the cost of the unreliable station can be computed, risk/reward decisions made, training quantified, and actions taken. Philosophies such as Six Sigma excel in this environment because the process can be defined, measured, analyzed, improved, and controlled

These philosophies work wonderfully in the linear arenas such as scheduled airline operations, food processing, steel production, automobile assembly plants, refineries, pharmaceutical manufacturing, human resource management, and accounting functions such as accounts payable and accounts receivable. When any part of these processes are interrupted, the final delivery to the customer is threatened. That creates an immediate alert and executive management becomes personally involved in the solution and guarantees the necessary resources at the right time to avoid the challenges in future operations.

My revelation revealed to me that the processes that I had been supporting were nonlinear. Nonlinear processes such as truck distribution systems and tactical aircraft (Unscheduled) operations were not initiated by scheduled events and were in fact tasked by unpredictable events and demands. These types of operations are not physically attached to the process and in many cases the assets are interchangeable when delivering remote services. If one truck or fighter plane fails to perform, the entire flow of the process to the end user (Customer) is not threatened. Another interchangeable asset is diverted and the critical part of the process continues at the expense of a non-critical part of the process. 

Top management attention and resources are not brought into the problem/solution until enough individual pieces of the non-critical parts of the process can no longer support the critical parts of the process. Then the bells and whistles go off and executive management becomes involved only after the process has deteriorated and heroic interventions are needed to assure the final flow to the customer is restored.

Nonlinear maintenance functions in arenas such as agriculture, health care, construction projects, trucking, facility management, warehouse operations, spare parts inventories, and pizza deliveries seldom get top executive management attention until the customer complains or a collapse is imminent.

What I have learned from The Chaos Theory and Dr. Lorenz's "Butterfly Effect" is that no matter how hard you try,  nonlinear events cannot be predicted with any accuracy in other than the very short term. I now have a true understanding that the maintenance of assets in nonlinear applications cannot be predicted. An example:

  • The maintenance needs and operating cost of a slurry pump in a linear-process steel mill sump that is used to transfer suspended material that is consistently uniform can be predicted with a high degree of accuracy.

  • The same slurry pump used to empty or dewater sinking barges in a ship repair yard may encounter any unknown liquid or material imaginable and in any concentration or level of abrasiveness. The maintenance needs and operating cost of this pump is not predictable

All of my professional life I had believed that if I had more and more and better and better data points about the assets to be maintained that I would be able to predict maintenance needs and maintenance costs...

I no longer believe this to be true! 

I came to question the validity of trying to apply successful linear maintenance solutions (Such as Deming, Six Sigma, etc.) to nonlinear maintenance applications. While examining the point at which linear maintenance functions become nonlinear I realized that I needed to define a crux, a pivot point, at which the change occurs and it was not an easy task. To do so I went back to some of my resources and looked for a key.

I consider Six Sigma to be a very effective tool for managing linear processes and the maintenance to support them so I dusted off my reference text titled "Six Sigma" by Mikel Harry and Richard Schroeder and found the reference I was looking for. In discussing Six Sigma's breakthrough strategy on page 23, I quote, "The Six Sigma Breakthrough Strategy is a disciplined method of using extremely rigorous data-gathering and statistical analysis to pinpoint sources of errors and ways of eliminating them.

I do not believe that there is a better way to describe the philosophy for scientifically managing linear processes and the supporting maintenance. In a linear process, this is the best type of approach one could apply and it works. However; I have learned that no matter how hard you try to control a linear process, production line, refining process, or manufacturing environment, at some point the process will always become nonlinear, chaotic, and impossible to measure

I can think of nothing that is more disastrous than to try to manage a nonlinear process with a wonderful linear method like Six Sigma because the nonlinear processes cannot be measured.

When a statistical process such as Six Sigma, The Deming Method, or TQM attempts to control a nonlinear process:

  • At best it fails

  • At the worst it succeeds and defines, measures, analyzes, and renders a solution that is no longer valid.

A linear process control like Six Sigma does the most damage to a nonlinear process when it succeeds in gathering data points and attempts to fix a problem that no longer exists! Nonlinear processes are extremely dynamic. By the time enough measurements have been taken to formulate a solution, the problem vector has either subsided or morphed into something very different. 

When management has a high confidence they have found a solution (Through Six Sigma, etc,) based on measurement, they become very energetic in the application of that solution. Unfortunately, in nonlinear processes, by the time enough data points are collected to act, the problem has evolved, changed, or no longer exists and the energetic application of the now false solution only creates confusion. In the case of cyclic or periodic nonlinear events, the scientific method will actually reinforce the changed vector producing something like pilot-induced oscillations in aircraft and will amplify the problem.

There is no one-stop solution to managing maintenance and attempting to apply linear solutions to nonlinear problems actually creates problem maintenance and safety issues.

In discussing this concept at a steel mill I told an executive, "You can control your linear process costs to a penny a ton inside the walls of the melt shop and rolling mill; however, your process becomes nonlinear at the door going out and into the scrap yard, and becomes chaotic at the gate when your trucks pull out onto the Interstate.

I was trying to explain to him that he had achieved excellent linear process control inside the mill but outside in the yard where scrap was being brought in on rail cars and by truck, he had very little of the process control enjoyed in the mill. Forklifts, front loaders, cranes, grapples, switch engines, rail track, switches, conveyor belts, scrap shredders, magnets, metal sorters, and a thousand other moving, grinding, bumping, sliding, and reciprocating actions defied all attempts at control through monitoring or measuring. The sum of these functions amplified by unpredictable rail delivery schedules resulted in an almost totally nonlinear process.

Even worse than that was when his hundreds of trucks left their yards with the products, all control was lost! When the drivers entered the Interstate Highway System and municipal streets, the result of that interaction was totally nonlinear and almost chaotic. Constantly changing street and traffic conditions, weather, vehicle and tire condition, driver mental state, and the random acts of other drivers offered too many variables to predict any useful management information. All managerial process control had been lost at that point and any attempt to measure and predict those nonlinear activities was futile.

An example could be the attempt to quantify the causes of suspension maintenance in a delivery fleet. A quantitative solution would be to measure the problem over a period of time to determine the cause of the damage, but would that actually work? Assume that the road surface in Houston at the interchange between IH-10 and IH-45 has degraded into potholes that are damaging the fleet traveling from San Antonio to New Orleans.  After two months of study, the decision is made to divert all east/west delivery operations onto Loop 610 North. 

Unfortunately, during the time it took to measure the problem, the state, always behind on their street maintenance, has repaired the IH-10 / IH-45 interchange and the surface of the Loop 610 has begun to deteriorate. Applying the measured solution to a problem that no longer exists has created two more problems. 

  • The new path is now causing or will cause the same suspension damage.

  • Sixty miles have been added to the round trip cost for delivering the product and about 1.5 driver hours and 10 gallons of fuel.

Logic and data collection has provided a very good solution to a problem that no longer exists. This is where an alternate solution to the scientific method is needed. This is where the "photo-negative" solutions to nonlinear problems can provide much better solutions. 


Is Your Process Linear or Nonlinear?

To consider an alternative management solution, you must first decide if your maintenance function supports a linear or nonlinear process. Remember the slurry pump mentioned above? You may have like assets used in both types of operations. Or you may have a linear process that can be controlled only to a point by the scientific method and then it becomes nonlinear and chaotic. 

If you are having doubts as to whether nonlinear processes and the supporting maintenance actually exists, all I can do is ask you to look at that point in your total process (From raw materials to the final delivery of your product to your customer.) where your linear process control fails to provide the control you expect. That is about the point where your process becomes nonlinear. It is somewhere around this point that you begin to get managerial feedback like:

  • "The solution is undefined because the data points lack accuracy.

  • "The data is not being collected correctly."

  • "There is a 'corporate culture' problem in that department and they refuse to learn or buy into our solution.

  • Or "We have told them what needs to be done but they just won't do it." 

My experience is that this is a pretty good place to look for the boundary between linear and nonlinear processes. If your solutions almost work, or they worked before but the problem just keeps coming back, or if the challenge or problem has been put on the "back burner" because the resources needed to measure do not warrant the result; then you have begun to define your linear/nonlinear boundary. Another way to define your linear/nonlinear boundary is to examine the credentials of your managers in each department.

  • Linear processes have engineers, process engineers, industrial engineers, MBA's, and accountants as managers. 

  • Nonlinear processes do not! 

You can perform a quick litmus test. How many manufacturing companies do you know that operate large truck distribution systems and have a mechanical or automotive engineer as the Fleet Manager or Fleet Maintenance Manager? 

Assume that a fleet consists of 1,000 tractor/trailer units valued at $150,000 each. That is an investment of $150 million in high production and high risk assets. Would it not be a wise and prudent policy to put an automotive engineer over the maintenance of that $150 million in assets? Yet how many transportation departments have an engineer managing those truck assets? I personally have never been in a company that had a mechanical or automotive engineer directly responsible for a truck fleet. I am sure there must be one somewhere, but I have never met them.

Another quick test of whether a process stops being linear is to examine the credentials of the managers of the maintenance functions in an industrial or manufacturing company. The Process Maintenance Manager will be an engineer and the Fleet/Mobile Equipment Maintenance Manager (With a thousand trucks and trailers, loaders and forklifts.) will be a former mechanic. The Facility Maintenance Manager (In a $140 million building.) will probably be a former janitor or building maintenance worker.

Why is this so? It is because one process is linear and the other is nonlinear. I believe that this is because engineers will not take a nonlinear management position even though the asset investment should seem to demand it. Engineers are solution-oriented people. Their whole adult lives has been devoted to creating scientific solutions and it is my opinion that engineers will not take these truck maintenance (Nonlinear) type positions because they know (At some level.) that their scientific management techniques cannot succeed. A qualified engineer can have their choice of jobs and they are not going to accept a nonlinear task that has no scientific solution or at worst could result in failure. People do what they like doing and engineers like to solve problems that can be solved. These points that I have mentioned are not written down anywhere, they are just quick-test answers that keep proving themselves day after day. 

If you apply these quick tests and feel they are accurate, then you must consider that the scientific and quantitative solutions taught in our business and engineering schools have limitations that begin at the linear/nonlinear boundary.


Managing Nonlinear Maintenance Challenges


If you canít Define, Measure, Analyze, Improve,
Control what can you do? 

Attempting to force a linear solution onto a nonlinear problem only creates more problems and safety issues and management should look for an alternate solution. I have created a management and maintenance philosophy (The Geaslin Method) based on "Early Detection" and "Early Intervention" that is designed to identify the linear/nonlinear boundary in an organization and create a vertically integrated, self-financing program to tame the "Inverse-Square Rule for Deferred Maintenance". It appends to the successful linear management systems at their linear/nonlinear boundaries. 

I consider the prime detractor of linear solutions in nonlinear applications to be that the scientific collection of data points can only create a "revenge" solution. An example: 

  • In the early days of man-portable infrared antiaircraft missiles the Redeye missile was a "revenge" missile. The IR sensor technology only allowed the defensive missile to lock onto the enemy aircraft's hot exhaust pipe metal. Therefore, you could only take a revenge shot after the enemy plane had already bombed you and was flying away!

  • As technology improved, the IR seeker became "all-aspect" and could lock onto an enemy aircraft's hot exhaust plume while it was flying toward you and you could shoot him down before he could bomb you. This was a significant proactive improvement to say the least!

Six Sigma and other philosophies are "all-aspect" solutions in linear applications because they can predict the future by measuring the past. 

Six Sigma and other philosophies are "revenge" solutions in nonlinear applications because they only record the past disasters. Because nonlinear problems change faster than useful data points can be collected, trying to use these data points to predict the future is futile.

I have discovered that when coping with a nonlinear situations, a manager must create a method that maximizes the "Early Detection" and "Early Intervention" of every single maintenance event and they must aggressively treat each symptom as it presents itself!

In dealing with nonlinear problems, management must not wait for revenge information to be gathered and analyzed to create an action plan. To delay maintenance action in nonlinear situations will trigger my "Inverse-Square Rule for Deferred Maintenance" that states, "Any part that is known to be failing and left in service until the next level of failure, will create an expense equal to the SQUARE of the cost of the primary failure part." 

The penalty for not using early intervention will be the square of the primary failure part or a minimum of 15-times the difference between the early intervention invoice value and the breakdown invoice value! Example:  

If a $100 bearing in an industrial electric motor is failing and not repaired when the machine demands it (Timely repair invoice value of $667 parts and labor.) and is allowed to remain in service until the bearing fails and the rotor damages the stator, the repair and rewind invoice will easily exceed $10,000.

The "Inverse-Square" rule proves itself every day and it is easy for an executive to confirm without exhaustive "revenge" data points. Do you have a maintenance event in your "action file" that is so stinky that it will have to be examined by top management? If so, I have another quick test for that repair order. 

  • 1st ~ Put the direct maintenance cost into your calculator.

  • 2nd ~ Add the indirect cost associated with the event to the RO value. (Idled workers, ruined materials, lost production or sales, customer service, etc.)

  • 3rd ~ Total the direct and indirect expenses.

  • 4th ~ Then tap the SQRT (Square Root) button on your calculator.

  • 5th ~ Is that value the price of the primary failure part? 

Does that value represent the cost of the part that if repaired in a timely manner would have avoided the excessive costs and disruptions to operations and customer service? If so, you have just seen the evidence to prove the validity of my "Inverse-Square Rule for Deferred Maintenance" and the disastrous consequences of deferring maintenance.

When it comes to deciding the resource allocations to achieve "Early Detection" and "Early Intervention", I suggest that managers pretend that they are in a submarine and feel a drop of water hit them on the top of their head. They should not stop looking until they know where that drop of water came from! It may be condensation, but it may not be. It might be a leak in the hull! The risk/reward penalty difference between condensation and a hull leak is dramatic and they should aggressively investigate and treat each symptom as if it was a hull leak. That same urgency should be applied to any nonlinear maintenance event.

Once we learn how to recognize the linear/nonlinear boundary, it becomes possible to identify:

  • How maintenance budgets fail. (The Inverse-Square Rule)

  • The trigger that initiates the failure. (Attempting to apply linear solutions to nonlinear problems.)

  • How to create a SELF-FINANCING SOLUTION to improved maintenance. 

  • And how to create a "corporate memory" for operations, maintenance, and safety to remember "lessons learned" across multiple financial periods, turnover in technical personnel, and changes in leadership to assure the continuity of adequate maintenance funding to avoid the Inverse-Square Rule. 

My new philosophy of "Early Detection" and "Early Intervention" will produce the lowest maintenance cost per unit of production possible in nonlinear systems. All other methods cost more. 

If you should care to discuss how the application of wonderfully successful linear philosophies such as Six Sigma, The Deming Method, Lean Manufacturing, and TQM are actually creating problem maintenance and safety issues in nonlinear processes, I am at your service. 



David Tod Geaslin

Comparing Linear & Nonlinear
Operations in the Same Process

Linear Processes
(Process Predictability Good)

Nonlinear (Photo-negative) Processes
(Process Predictability Poor)

Steel Mill Operations

Melt Shop Scrap Yard & Marshalling Area
Rolling Mill Trucking Deliveries of Rebar to Construction Sites
Raw Product Inventory Methods Retail Rebar Inventory Methods
Mill Maintenance Truck & Crane Maintenance

Aircraft Operations & Maintenance

Scheduled Airline Operations Nonscheduled Firefighting Aircraft
Scheduled Airlift Operations Jet Fighter Operations

Electrical Power Generation & Distribution

The High-Line Distribution Grid High-Line Bucket Truck Operations
Power Generation Plant Maintenance Office Facility Maintenance

Food Production

Beef Processing Cattle Ranching
Campbell Soup Manufacturing Grain & Rice Farming
Cal-Maine Egg Production "Open Range Chicken" Egg Production
Sugar Refining Sugar Cane Farming

Mining Industry

Conveyor Operations Dump Truck Operations
Crushing / Sorting Operations Dragline Operations
Dewatering & Pumping Systems Dust Control Tanker Operations

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The Geaslin Group
David Geaslin, Principal
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