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Disruptive Power of Repair Management Method DPRMM

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Introduction[edit]

The DPRMM and OTRME models are relatively new. One of the most important decisions made in any company is how much to spend on replacing expensive equipment. CAPEX, which is capital expenditure are machines, cars and equipment that are used on a daily and constant basis and usually cost a lot of money, in many cases bought either with the aid of loans. Examples of Capex include Large Manufacturing Machines such as CNC, Robotic Welders, Printing Presses such as Intaglio and Offset Printers, Metal Die-Casting, PCBs, Cars, Buses and Freight Vehicles ranging from Boats to Airplanes.

The cost of the equipment is such that decision making has to include more than just the cost of the machine, it must include the service factors, the cost of installation, the costs of repair and maintenance including accessibility to spare parts. It must also factor in whether the machine is a bottle-neck unit, which means it is the only one of its kind and if it is down for repair or maintenance the production process is stopped.

Capex investment is all about pricing the overall expectancy of the item within a system, and evaluating the cost per hour even when the unit is not being used.

The final costs are depreciation and scrap or resale value.

The decision making process for which machine to use is a complex one the more complex a system becomes. In the case of investing in a building or a vehicle, the costs are much more easier to contain and control. With machinery in a production process the costs become much more complex and the effect of downtime due to failures is a concern for all companies.

OTRME[edit]

The optimum time to replace malfunctioning equipment (OTRME) model takes the current acceptable models (RCM, TPM), differentiates between repair time and maintenance, and adds two new repair-based factors that are not present in the decision-making process. These are the disruptive power of repair (DPR) and the malfunction ripple effect (MRE) that a repair downtime has on a whole system. The proposed decision-making model is a focused model, applicable and cost-efficient for the replacement malfunctioning equipment reasons and not technological advancement or substitute technology reasons for change. The OTRME model is implementable within any industry and service sector that uses equipment in a production or service process. This model is directly related to Kärri, T. (2007) T,[1] and is an extended product of his research.

The OTRME and DPRMM Research Team[edit]

Equipment Life Cycle[edit]

Every piece of equipment, irrespective of its complexity, has a life cycle. The life cycle is based on the effectivity of providing a quality service to reach its purpose. For example, a hammer can be made of one or two pieces of material, and its purpose is to use force on another object for either smashing it, beating it or hammering it into a shape or into another object. The life cycle is based on the hammers effectivity in reaching its purpose. Hammers have a long-life cycle and have different price ranges based on design and material as well as specific purpose. On the other end of the complexity scale, we can find many items including Magnetic Resonance Imaging machines (Grover et al., 2015), CNC Machines (Akturk and Avci, 1996) and Mine Drilling Machines (Al-Chalabi et al., 2015). The cost of these items is usually very high, and they require skilled operation teams as well as a complex and regulated manufacturing process and calibration before and after usage. Irrespective of the equipment’s complexity, all have a lifespan that is defined by their ability to perform within cost and quality constraints.

Equipment life cycle phases

Equipment life cycle has three phases (New, Operational, and Replaceable):

  1. The new phase is the phase when an item is introduced into the market, be it a new technology or an old one introduced in a new form;
  2. The operational phase is when an item is being used, and during its use, will malfunction, requiring repair;
  3. The replacement phase is where the frequency of malfunction is too high, costing its user money and time, and must be replaced with either a new model or a substitute equipment. The other reasons to replace equipment is technological advances and emerging substitutes that make the older versions obsolete.

Equipment Procurement Process

When choosing a new piece of equipment, the procurement process uses many variables to determine viability. The variables include the product functions that are set by the primary stakeholder. The primary stakeholder is the individual or department that sets the attributes of the equipment, including the equipment’s dimensions, functionality, and productivity based on the various physical attributes and production process that the equipment must integrate into. These attributes are the stakeholder defined ranges or “constants” that cannot be changed without the stakeholder’s consent.

The procurement variables that the primary stakeholder allows the buyer to consider when creating a list of makes and models to choose from are price, supply-chain, installation, training, maintenance, warrantees, and in some cases, substitutes that might be considered where they prove to perform better than the original concept. Procurement professionals have a wide range of tools to use for determining the equipment they intend to purchase. Porters five forces model (Porter, 2008) is one of the most comprehensive, and linear performance price (LPP) (Newman and Krehbiel, 2007) indicators are another method used for determining whether less complex equipment and raw materials should be purchased based on their stakeholder demands.

One of the important variables that help in making the final decision for purchasing equipment is the estimated product purchasing price and lifespan cost. These values are imported into a projected final product pricing, that will give the company the estimated cost and projected profitability of the equipment. This estimation is at the core of the OTRME. The estimated cost versus the actual cost is based on a projected lifespan estimated cost versus the actual yearly costs. In our model, we call this the baseline cost per hour. It is significant since projected and estimated profitability are based on this figure.

Purchase Price of New Replacement Impact

In most models that we reviewed, the purchase price of a new piece of equipment is considered a major factor in the decision-making process. In fact, one extensive paper discusses this issue in great detail (Kärri, 2007). We, however, discount this concept, and consider the process of procuring a new replacement for old malfunctioning machinery a separate decision-making process that should not have any impact on the rationale to replace capex. If we were to discuss new technologies replacing old ones, then the cost of introducing a new technology would warrant such an important place in the decision-making process (Nair and Hopp, 1992). However, we only evaluate the impact a malfunctioning machine has on a process and decide when it is best to replace it. In the event of replacing old equipment with new due to the impact the frequency of repair has on the production process, the cost of the replacement machinery has no impact on the decision-making process for when to replace, it is only a factor for deciding what is the replacement make and model, and how much it costs to procure.

Maintenance Vs. Repair

We determine that maintenance is a standard procedure to keep equipment operational, as defined by Merriam Webster (2018b)“the upkeep of property or equipment” and repair “to restore by replacing a part or putting together what is torn or broken” (Merriam Webster, 2018c) is when a part of the equipment is damaged or must be replaced. Equipment undergoing maintenance is scheduled into a plant operations model, and preventative maintenance is a daily, sometimes pre-and post-operational procedure to maintain the equipment and general maintenance is performed according to a schedule when the equipment is “dissected” for a scheduled maintenance check (Colledani and Tolio, 2011).

Repair is when damaged equipment demands unscheduled stops and is determined by us as an irregularity. Many companies incorporate risk management for instigating contingency plans when equipment malfunction, these risk contingency costs are not included in any published equipment replacement model to date. Therefore, all current models do not include the use of replacements or substitutes that take over operations when a piece of equipment is in repair. This extra cost shortens the actual economic lifespan considerably.

Malfunction Ripple Effect (MRE)[edit]

The MRE is a value based on the overall costs a repair process creates in a system. When a machine is in repair downtime, either the machine is an isolated repair incident and the only effected process is the one being stopped, or, the product that must be processed might require the transfer of process to another machine. In this instance, the repair downtime will cause a ripple effecting other process for different products. This ripple must consider:

  1. the cost of stopping the flow of inventory for the production line with the malfunctioned machine;
  2. the cost of downtime of the machine;
  3. the cost of preparing and using another replacement machine;
  4. the cost on the flow of inventory of the replaced machines production line;

The total value we receive will be the overall cost to the system caused by the downtime.

The Frequency of Downtime (FD)[edit]

The FD is the variable that comes into force when calculating the full cost of repairs. Since maintenance is a scheduled operation, in most cases companies do not schedule replacement equipment when factoring standard and preventative maintenance. The downtime is an accepted norm of operation. With repairs, the downtime is not normal, and as such, most companies will immediately factor into their process a risk contingency plan. These risk plans include the use of a replacement to maintain the flow of work. The FD is critical in our model since it raises the CH significantly. For example, when a machine malfunctions once, and the repair takes a week, but the machine operates flawlessly for the rest of the year, the cost might be high, but the machine is not subject to replacement. If, however, the machine malfunctions ever few days and the downtime is above four hours each time, while the cost might be less than a month’s downtime, the disruptive effect, and the collateral costs, point to the machine being problematic and demands a replacement. Therefore, higher FD is more critical than the length of downtime, since it is an indicator that the equipment is failing to perform on an increasing basis.

The Disruptive Power of Repair (DPR)[edit]

The DPR has been downplayed and even overlooked in all models to date. Although it is a much-discussed issue in maintenance engineering research (Life Cycle Engineering, n.d.). When analyzing a production process, when a piece of machinery malfunctions, its “job” load must be distributed to another piece of equipment. If there is an identical machine, then the situation can be monitored for job balancing. If, however, there is no identical replacement, then the job must either be delayed or transferred to an alternative machine that can perform similar functions if that is possible. When viewing the first option, the less complex the machine, the easier it is to balance in, unless the job loads are such that balancing is not an option. With more complex machinery, for instance, a CNC machine (Akturk and Avci, 1996) that requires a jig or apparatus to hold the material for operation and software (code) that defines the automatic milling operation, then downtime to set up machinery must be factored into the overheads, and this can and usually does significantly increase the cost as well as damage the job balance. We also deemed it important to add the fact that repaired equipment performs less than pre-repaired, which reduces the quality of performance every time the machine is repaired (Chen et al., 2015). This is what we term, the disruptive power of repair and its estimation is complex due to the number of additional dependent variables that come into play when trying to transfer production from one line to another. DPR is given an important value, depending on the nature and complexity of the machine that is needed. The frequency of downtime with more complex systems is more important than the cost since each downtime has a ripple effect on the whole system as presented by the MRE.

The OTRME Model[edit]

The OTRME model comes into play once the device has been fully installed and is ready for use. The device begins its life cycle at this point, and its economic value is based on the income or savings it generates from its use. Productivity equipment, diagnostic devices, and operational tools are all income valued. This means that the tool is used in a procedure that is estimated and given an income value. The device used in the procedure is given a percentage of cost variance based on the projected usage of the device across all the procedures it might be used for.

OTRME is effective in all productivity tools and equipment since they can be directly related to an income source.

Maintenance tools and equipment are used to fix and maintain other equipment and property, and their cost is estimated by the frequency of use they have for every maintenance operation. The exact evaluation is complex since many tools are interchangeable and the OTRME model is best used for the more complex and dedicated devices, such as welding machines and full production machines such as a lathe or a CNC that might be used to manufacture replacement parts.

OTRME is infective when applying it to single-purpose tools such as hammers and spanners in maintenance procedures since they cannot be directly linked to a source of income, but rather make up a micro-detail of a macro map.

The Model is based on over 8 subsets of data that are arranged in a modeling system, but are adapted as equations for use in standard software such as Excel and Matlab. The final outcome of the model is:

Without time value of money

We allocate initial cost minus salvage value to each month, together with averaged monthly repair cost in the future, to get our monthly ownership cost.

RC = RFC+ MRC
F=∫_t^T 〖f(t)〗
MOC(w)=((IC –SV))/w+ RC*F/(w-t)

With time value of money at T:

MOC(w) =
〖IC*(r+1)〗^T/w     -     ( 〖SV*(r+1)〗^(T-w))/w    +     (RC*F)/w

DPRMM[edit]

The Disruptive Power of Repair Management Method (DPRMM)

DPRMM is a method devised to standardize an efficient approach to how a system will manage a repair process.

The DPR management method (DPRMM) was devised as an additional system to complement the OTRME, in which we both estimate and forecast when the equipment must be replaced as well as devise a method to optimize efficiency in how systems manage repair downtime effects, effectively lengthening the life cycle time of equipment by reducing the costs of machine downtime for repair.

The DPRMM allows us to look at how different companies manage the way they deal with downtime, and we propose an optimum method for managing downtime that will save over 50% of the expenditure that is currently being wasted by companies in the way they manage today.

DPR

The DPR has been downplayed and even overlooked in all models to date. Although it is a much-discussed issue in maintenance engineering research (Life Cycle Engineering, n.d.). When analyzing a production process, when a piece of machinery malfunctions, its “job” load must be distributed to another piece of equipment. If there is an identical machine, then the situation can be monitored for job balancing. If, however, there is no identical replacement, then the job must either be delayed or transferred to an alternative machine that can perform similar functions if that is possible. When viewing the first option, the less complex the machine, the easier it is to balance in, unless the job loads are such that balancing is not an option. With more complex machinery, for instance, a CNC machine (Akturk and Avci, 1996[2]) that requires a jig or apparatus to hold the material for operation and software (code) that defines the automatic milling operation, then downtime to set up machinery must be factored into the overheads, and this can and usually does significantly increase the cost as well as damage the job balance. We also deemed it important to add the fact that repaired equipment performs less than pre-repaired, which reduces the quality of performance every time the machine is repaired (Chen et al., 2015).[3] This is what we term, the disruptive power of repair and its estimation is complex due to the number of additional dependent variables that come into play when trying to transfer production from one line to another. DPR is given an important value, depending on the nature and complexity of the machine that is needed.

Determining the total effect of downtime

The first splash or the epicenter of the downtime is the malfunctioning equipment or machinery. This is allocated as the center of the ripple. The first wave that comes out of the center is the production units immediately affected by the downtime of the equipment. The second ripple is the number of cost centers that get involved in discussing the issue and finding a solution; the third ripple is the effect the repair process and the malfunction have on the management of the company. The final ripple is how the malfunction affects the inventory and supply chain process, and whether the downtime is going to impact the customer.

As you can now observe, the downtime for repair is not just how long it takes to repair a malfunctioning piece of equipment or machine; it is a whole matrix of interlinked causes and effect that contribute to the cost of repair, taking the cost from other important actions and transferring them to the repair process. This means that a system will not operate at 100% efficiency when downtime is present.

Naturally, the more important the machine or equipment within a process, such as a liner or vertical process that a malfunction will stop, the greater the impact of the repair on the system. We term this impact the disruptive power of repair. We term the number of times a machine breaks down as the frequency of downtime and the effect that each downtime has on a system as the downtime ripple effect. These three factors contribute to estimating the optimum time for replacing a malfunctioning piece of machinery, that could be could be causing the company much more damage than they realize.

Understanding the Frequency of Downtime

The FD is the variable that comes into force when calculating the full cost of repairs. Since maintenance is a scheduled operation, in most cases companies do not schedule replacement equipment when factoring standard and preventative maintenance. The downtime is an accepted norm of operation. With repairs, the downtime is not normal, and as such, most companies will immediately factor into their process a risk contingency plan. These risk plans include the use of a replacement to maintain the flow of work. The FD is critical in our model since it raises the CH significantly. For example, when a machine malfunctions once, and the repair takes a week, but the machine operates flawlessly for the rest of the year, the cost might be high, but the machine is not subject to replacement. If, however, the machine malfunctions ever few days and the downtime is above four hours each time, while the cost might be less than a month’s downtime, the disruptive effect, and the collateral costs, point to the machine being problematic and demands a replacement. Therefore, higher FD is more critical than the length of downtime, since it is an indicator that the equipment is failing to perform on an increasing basis.

Estimating the value of a company for DPRMM

There are a number of ways to estimate the value of a company, the value of a cost center and the value of a specific tasks manpower costs. However, the simple version is to ask a simple question: If your company just stood for a day, what would it cost?

This question can mislead since there are three kinds of companies, service, retail, and production. A service company only needs to factor in all its expenses, and in most cases does not have much capex. A retail company has a lot of inventory that is usually dynamic, so the correct way to estimate the inventory value would be to use the average cost of inventory method. A production company is much more complex to estimate since it has many hidden corners of inventory, such as maintenance, as well as a lot of capex and WiP. With these differences come either an easy estimation or a hard one, so no matter which type of company you have, you need to estimate the overall cost your business will spend when it is standing still for one day. This is what we call the daily cost of operation, and it tells us how much a company costs every hour.

The next variable that we need to estimate is the cost center costs; these are defined sectors within a company, such as sales, maintenance, administration, production, engineering, etc. Since we already have the overall cost of a company’s hour, all that is needed is to categorize costs centers and then allocate the resources to each category from within the companies defined cost per hour.

The final variable we will need to ascertain is the cost of the heads of cost centers, these are only hourly manpower rates, but necessary to estimate the time used per cost center head to deal with a situation. A cost center head is a person in charge of a specific task, and this requires that specific tasks be defined and allocate resources. So, the maintenance of an air filter would require specific resources; this would be defined as a cost center head for the maintenance of an air filter, which is the cost of the person dealing with the problem, without the resources used to solve the problem.

The variables are then assimilated into a system framework that generates the total cost of repair within the organization.

Model Testing[edit]

The Models were tested in three sites,

De La Rue Currency & Securities (Pvt), Colombo, Sri Lanka

Florian Engineering Tools, Wernigerode, Germany

Bet Shemesh Engines BSEL, Beth Shemesh, Israel

References[edit]

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Al-Chalabi, H.; Lundberg, J.; Ahmadi, A. & Jonsson, A. (2015) "Case Study: Model for Economic Lifetime of Drilling Machines in the Swedish Mining Industry." The Engineering Economist, 60(2), 138-154.

Asiedu, Y. & Gu, P. (1998) "Product life cycle cost analysis: State of the art review." International Journal of Production Research, 36(4), 883-908.

Bellman, R. (1955) "Equipment Replacement Policy." Journal of the Society for Industrial and Applied Mathematics, 3(3), 133-136.

Chen, X.; Xu, B.; Yang, Z.; Chen, F. & Meng, G. (2015) "Reliability Model for subsystems of CNC Machine Tools based on the Repair Degree", 6th International Conference on Manufacturing Science and Engineering. Guangzhou, China, 28-29 November.

Colledani, M. & Tolio, T. (2011) "Performance evaluation of transfer lines with general repair times and multiple failure modes." Annals of Operations Research, 182(1), 31-65.

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--- (2018c) Merriam-Webster.com, 2018c. Available online: https://www.merriam-webster.com/dictionary/repair [Accessed.

Nair, S. K. & Hopp, W. J. (1992) "A model for equipment replacement due to technological obsolescence." European Journal of Operational Research, 63(2), 207-221.

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