IndPress03_ch12

8/13/2019 IndPress03_ch12

1/8

Statistical Financial Optimization

This chapter covers a hybrid technique that blends statistical techniquesspecific to equipment maintenance with financial methodologies, therebyallowing for the most cost-effective solutions to be derived for a companysasset management policies. The concepts are based on quantifying maintain-ability and reliability calculations in financial terms. Some of the decisionareas in which this technique can be applied are:

Setting preventive maintenance inspection schedules

Age replacement policiesPreventive maintenance block replacement policiesCapital equipment replacement policiesEquipment overhaul policiesCritical spares stocking levelsRoutine spares stocking levels

The first five decision areas are related to maintenance policies and sched-ules. Even a company that has followed the evolution of maintenance improve-ment is still making these decisions intuitively, sometimes supplemented bysome RCM data. However, are the decisions being made based on the finan-cial impact they have on the total company? Not likely, because the reliabilityprinciples have not been financially considered in the RCM process. Althoughthe MTBF (Mean Time Between Failure) may be known from an RCM analy-sis, the following questions are still unanswered:

a. What is the cost to prevent the failure? (preventive maintenance laborand materials)

b. What is the cost when equipment fails? (repair costs and lost productioncosts)

189

12

Developing Performance Indicators for Managing Maintenance

Copyright 2005, Industrial Press, Inc., New York, NY


2/8

c. What is the correct number of spare parts to keep in stock to insure partavailability when required? (the holding costs, storage costs, storeslabor costs, compared to the downtime costs for the stock out)

d. For the routine spare parts need for the service, what is the reorder leveland reorder point? (holding costs, storage costs, stores labor costs, thediscounted prices for quantity orders, compared to the downtime costsfor the stock out)

It is in these areas that the RCM tools fall short. These areas also highlightthe reason why statistical financial optimization is implemented after TPM.

The optimization takes data from all parts of the organization (RCM frommaintenance and engineering, stores costs from inventory and procurement,downtime costs from operations, and overhead and labor costs from account-ing). Unless the organization has progressed through the levels described inthe decision tree, it is highly unlikely that it has the maturity and focus to uti-lize the optimization techniques.

It is beyond the scope of this text to detail the various formulas utilized inthe statistical optimization process; however, for reference, they are found inmost of the maintainability, reliability, and operational engineering textbooksavailable in most technical book stores in major cities.

There are also engineering software packages that perform the mathemat-ical calculations for the statistical financial optimization. However, it wouldnot be responsible for the author to recommend a specific package.

What are some of the indicators used to evaluate the effectiveness of thestatistical financial optimization process? The following are suggested.

1. Statistical Financial Optimization Implemented onPercentage of Critical Equipment Maintenance Tasks

This indicator examines the number of critical equipment maintenancetasks that are audited for financial effectiveness each year. It compares thisnumber to the total number of critical equipment maintenance tasks. Thismeasure indicates the level of tasks that are actually being financially opti-mized each year. It is important to review these decisions annually becausecost data, such as downtime (due to market changes), can vary periodically. Inaddition, parts costs increase, lead times change, and so forth. Annual analy-sis insures financial optimization.

190 Chapter 12




3/8

Number of Critical Equipment Maintenance Tasks Audited*Total Number of Critical Equipment Maintenance Tasks

This indicator can be derived by dividing the total number of critical equip-ment maintenance tasks audited by the total number of critical equipmentmaintenance tasks. The result should be expressed as a percentage. Thismeasure can be calculated annually and trended over a multi-year period.

StrengthsThe indicator is useful for insuring that the statistical financial optimiza-

tion program is closely monitored.

WeaknessesThe only major weakness is the availability of accurate data. The most dev-

astating mistake would be to guess at any of these numbers. If the data is notavailable, it is best to consider an alternative technique.

2. Statistical Financial Optimization Implementedon what Percentage of Critical Equipment

Major Spares Stocking Policies

This indicator examines the number of critical equipment major spareparts that are audited for financial effectiveness each year. It compares thisnumber to the total number of critical equipment spare parts. This measureindicates the level of parts that are actually being financially optimized eachyear. It is important to review these decisions annually because cost data,such as downtime (due to market changes), can vary periodically. In addition,parts costs increase, lead times change and so forth. Annual analysis insuresfinancial optimization.

Number of Critical Equipment Major Spare Parts Audited*Total Number of Critical Equipment Major Spare Parts

This indicator can be derived by dividing the total number of critical equip-ment maintenance tasks audited by the total number of critical equipmentmaintenance tasks. The result should be expressed as a percentage. This canbe calculated annually and trended over a multi-year period.

Statistical Financial Optimization 191




4/8


tion program for critical equipment major spare parts is closely monitored..



3. Statistical Financial Optimization Implemented onPercentage of Critical Equipment Routine Spare Parts

Stocking Policies

This indicator examines the number of critical equipment routine spareparts policies that are audited for financial effectiveness each year. It com-pares the number of critical equipment routine spare parts audited to the totalnumber of critical equipment routine spare parts. This measure indicates thepercentage of critical equipment routine spare parts that are actually beingfinancially optimized each year. It is important to review these decisions

annually because cost data, such as downtime (due to market changes), canvary periodically. In addition, parts costs increase, lead times change, and soforth. Annual analysis insures financial optimization.

Number of Critical Equipment Routine Spare Parts Policies Audited*Total Number of Critical Equipment Routine Spare Parts

This indicator can be derived by dividing the total number of critical equip-ment routine spare parts policies audited by the total number of critical equip-ment routine spare parts. The result should be expressed as a percentage. Thismeasure can be calculated annually and trended over a multi-year period.


tion program is closely monitored.



192 Chapter 12




5/8

4. Statistical Financial Optimization Savings GeneratedThrough Changes in Equipment Management Policies

This indicator calculates the total savings from all statistical financial opti-mization studies. These savings will help an organization focus on continuous-ly improving their equipment management policies, because the financialrewards are always highlighted. There is no real formula because the indica-tor merely totals all of the statistical financial optimization studies conductedthroughout the company.

The results should be calculated annually and trended over a multi-yearperiod.

StrengthsThe indicator is useful for insuring that the cost benefits of the statistical

financial optimization program are closely monitored..

WeaknessesThe only major weakness is the difficulty of collecting all of the data when

multiple groups are performing the analysis.

Typical Problems with Statistical Financial OptimizationThe techniques of statistical financial are valuable, but yet not utilized by

many organizations. Why? The following are the most common reasons.


Lack of Proper

CommunicationBetween Depts.

Lack of FO

Skills andTraining

Poor

FinancialData

Lack of

Production/ Process Data

Short -TermManagement

Focus

Lack ofManagement

Understanding

Lack ofFocusedEfforts

Lack ofEquipmentCosts Data

LowFinancial

OptimizationIndicators

Figure 12.1 Financial optimization indicator tree




6/8

1. Lack of Production and Process Data

In many companies today, the production and process data are not kept inenough detail. Even companies that have extensive distributed process controlsystems in place find that data about flow rates, operating speeds, and oper-ating efficiencies are not closely monitored and trended. In some cases, theactual sensors on the equipment are not working or even electrically connect-ed. In these cases, the accurate data required to study the impact of fallingflows and pressures on process is not available.

If the problem of falling efficiencies over time compared to the cost of lostthroughput was financially optimized, then the data would be invalid, as

would any decision to repair, replace, or overhaul the equipment. The produc-tion data play a critical role in most calculations. Without this data, there islittle chance of accurate results. The only solution is to dedicate the necessaryresources to accurately monitor and record the production or process data.

2. Lack of Equipment Costs Data

This data is the information typically found in the equipment history. Itwill contain the labor, material, and other costs associated with the mainte-nance of the equipment. If this data is not accurate, it indicates a problem withthe data collection discipline in the organization. As expressed earlier, if thedata collected is not accurate, then the analysis using the data will also beinaccurate. The only solution to the lack of equipment data is to go back andenforce the basics of data collection. The disciplines must be developed andenforced.

3. Poor Financial DataThe finance or accounting departments typically keep the relevant finan-

cial data. Some costs in this area include the original purchase price of theequipment, the current value of the equipment, the replacement value of theequipment, and possibly the cost of downtime or lost production. If this datais inaccurate or unavailable, then the financial optimization cannot be per-formed.

Another problem in organizations that have not matured to World-Classlevels is the lack of sharing data between departments. In companies wherethe value of data has not yet been understood by all departments, there havebeen financial departments that have refused to provide downtime costs or to

194 Chapter 12




7/8

fairly calculate the cost of lost production. In these companies, the education-al process needs to be developed to insure a clear understanding of the costsinvolved in equipment failures or inefficiencies.

If a company is at a level of maturity that it is utilizing statistical financialoptimization, then issues about not sharing data should never be a problem. If they are, the company is not ready to use the tools.

4. Lack of Focused Efforts

This problem arises when a company is just starting the statistical finan-cial optimization program. There are so many opportunities available, the

individuals who are performing the analysis simply pursue what they perceiveis the largest opportunity. This lack of discipline dilutes the resources and pro-duces fragmented results. It also creates problems because the total analysisskills have not been developed. As the analysts work on problems, they makeerrors because they are not communicating with each other. The mistakes goundetected and the savings from the analysis are less than optimum.

The solution here is to stay focused and work as a team until the analystsskills are fully developed and a complete plan for the analysis is also devel-oped.

5. Lack of Financial Optimization Skills and Training

Proper training in maintainability, reliability, and financial concepts mustbe provided anyone doing any of the statistical financial optimization analy-sis. If the skills are not properly developed, mistakes will be made and thesemistakes will cost the company substantial amounts because operational poli-

cies are being changed based on the erroneous analysis.

6. Lack of Management Understanding and Support

This problem is related to the previous one in that education is critical.This does not mean that upper management will need to go through the samedetailed technical training, although they could if they want to invest thetime. However, they do need training to the extent that they understand the

costs of statistical financial optimization and the benefits that will be achievedby properly using the tools. Unless this happens, the support required tochange policies is never developed and the program fails due to the lack of sup-port.





8/8

IndPress03_ch12

Documents

Transcript of IndPress03_ch12