By Murray Wiseman (extracted from Chapter 16 of “Reliability-centered Knowledge”)

Introduction

Improvement concepts such as  “the maintenance dashboard”, “key performance indicators”, and “benchmarking of the best of breed ” resonate in the physical asset management community. They are the stock in trade of the maintenance management consultant. A far-sighted vision and a well-conceived strategy followed by a detailed implementation effort, will, we expect, transform the maintenance function into an ordered and controllable process.

 

With minor variations,  two schools of thought dominate the scores of philosophies that contend in the maintenance improvement marketplace. The symbolic “pyramid of excellence” (Figure 1), and the metaphoric “RCM house” (Figure 2) convey their respective, and somewhat conflicting, paths to “world class physical asset management”.

 

 

Figure 1 The Pyramid of Excellence[1]

Figure 2 The RCM "House"[2]

Order, rather than content, differentiate the two approaches. The former initializes its improvement cycle by establishing a suitable maintenance infrastructure (tiers one and two at the base of the pyramid) . The latter insists that we retain (for the present) our existing systems and structures, but, that we begin (the improvement process) by analyzing each significant physical asset’s functions, its failures, failure causes, effects, and consequences. Doing so will determine the appropriate maintenance requirements – the foundation (of the house of Figure 2). Proponents, of the “Pyramid of Excellence”, emphasize culture change as an explicit management process. The RCM camp contends that maintenance culture will adapt naturally with systematic RCM education and implementation. The former devotes attention to  effective planning and scheduling, while the latter focuses on developing, through RCM analysis, the proactive cyclic tasks (TBM and CBM) of the maintenance plan[3].

 

Summarizing, students of the “Pyramid” school defer reliability-centered maintenance (RCM) analysis to a future time by placing it up on the third tier. They see the processes (such as data management and planning) on tier 2 as pre-requisites for reliability analysis (RCM).  Advocates of the alternative point of view (the “House”), consign “systems” to the roof (the last element to be erected in an improvement plan), while positioning RCM analysis as the foundation. In this chapter we review software products such as Strategy Manager™ [4], Real-Time Production Intelligence™[5], and Real-Time Production Management™[6]  that seek to unify the two[7] approaches.

 

Extending the Maintenance Audit

Occasionally, corporate management elects to perform a “maintenance assessment” at one or more of its sites. Independent consultants conduct the audit, principally, by interviewing a cross-section of maintenance and operating staff, and, by reviewing various CMMS reports and budget documents. Sometimes the consultants map out flow diagrams that trace the business processes used in the maintenance department. Occasionally a consultant will observe and take notes during regular maintenance meetings. In all cases, to complete the audit, the consultant team delivers a final report evaluating the maintenance department’s performance relative to a set of benchmarks that characterize the “best” organizations. The report recommends projects and changes that will narrow the “gaps” to achieving excellence in each of the (ten) areas of Figure 1. The consultants propose a project priority sequence based on the value of the improvement and its ease of implementation.

 

New software enabled methodologies extend the reach of the occasional maintenance audit by offering continuous day-to-day performance visibility and control. To accomplish this function they integrate, (using O&MOpen[8] standards) with the CMMS, process computers, and other plant systems.

 

Physical asset management inputs, outputs, and control

Figure 3 Physical Asset Management input, outputs, and control

Figure 3 illustrates the two feedback “control” loops of physical asset management. Their ultimate output achieves the corporate vision. Each feedback arrow represents a management function:

 

1. To adjust maintenance policy in response to KPI achievement gaps.
2. To adjust KPI targets in response to vision achievement gaps

 

Arrow 3 represents the way that maintenance policy relates to the KPIs, and arrow 4 represents how the actual KPI’s achieve corporate vision. The physical asset manager strives to discover the intricate relationships governing how policy impacts KPIs. And, secondly, he seeks to know how achievement of the KPI targets will impact the balance sheet and the corporation’s societal responsibilities of custodianship. We might express the steps to world class performance as:

 

1. Start with a vision.
2. Set the target KPI’s that are needed to achieve the vision.
3. Set up the maintenance policy with the intent to achieve the KPI targets.
4. Execute the policy and measure the KPI’s and perform various types of age-exploration analysis.
5. Evaluate KPI target gaps and the results of age exploration. Based on that evaluation implement new policies or enforce current ones.
6. Evaluate performance relative to corporate vision. Add or alter KPI targets accordingly.

 

Note that the center block of Figure 3 specifies both KPIs and Age Exploration[9]. KPIs often summarize the results of a maintenance policy. They seldom direct us to specific policy changes regarding individual assets. On the other hand, age exploration analyses (for example, Pareto analyses) focus our attention on individual significant items whose collective performance governs the KPIs.

 

The foregoing implies that our maintenance management system (CMMS) must embody reliability-centered information (as outlined in Chapters 1, 2, and 3). Specifically, for each significant item, the five reliability-centered knowledge elements:

1. “What function was lost or compromised?”,
2. “How (full, partial, potential, functional failure)?”,
3. “Why?”,
4. “What happened?”, and
5. “How did it matter?”

will populate the database upon which the analyses will be performed. Furthermore, using our system, we establish the relationship between the  significant consequences of failure (knowledge element 5) and the KPIs that achieve the corporate vision. In practical terms, we use the performance management system to classify each incident (maintenance work order, or production log item) involving downtime, speed loss, or quality loss, as one of 11 to 19 of Table 2. Additionally, we document the five RCM knowledge elements that characterize each incident (see Figure 4).

Physical Asset Management Effectiveness Indicators (KPIs)[10]

 

Effectiveness KPIs classify productivity losses as: Downtime, Speed, and Quality losses.

Table 1

Theoretical production time 1
Valuable operating time 8	Losses
	Quality losses			Speed losses			Downtime losses
	MM	P	E	MM	P	E	MM	P	E

Two effectiveness KPI models

1. Production Economy
2. Dupont Analysis

Table 2 Model 1 Production Economy

Theoretical production time 1

Available production time 2

External losses 3 Unplanned: 1.      shortage of personnel 2.      shortage of materials (quantity, quality 3.      Environmental deals Planned: 1.      Modification, major mtce 2.      Limited need 3.    Social (policy not to produce weekends, holidays, etc

Gross operating time 4

Downtime 5

Net operating time 6

Speed losses 7

Valuable operating time 8

Quality losses 9

MM 14

P 15

MM 16

P 17

MM 18

P 19

Quality 11

Speed 12

Down time 13

Technical losses 10

MM=machine malfunctioning, P=process

 

Table 3 Model 2 Dupont (“planned production time” or “six big losses”) model

Theoretical production time 1
Planned production time = Available production time 2							Planned losses 3 -          Authorized breaks (pauses) during the working day -          Not working during weekends -          Scheduled stoppages for product changes -          Modifications and improvements to equipment (work financed under investments) -          Decreased production time due to lack of demand for the product -          Planned maintenance work (inspections, preventive maintenance, improvements) -          Problems in production planning -          Saturation of machines (upstream and downstream) -          Authorized shop floor meetings -          Classroom or ‘on the job’ training sessions for operators -          External power cuts -         Lack of raw material
Gross operating time 4					Downtime losses 5
Net operating time 6			Speed losses 7		Setup and adjustment Big loss 5	Equipment failure Big loss 6
Valuable operating time 8	Quality losses 9		Reduced speed Big loss 3	Idling and minor stops Big loss 4
	Reduced yield from startup Big loss 1	Defects in process Big loss 2

Choosing between model 1 and model 2

Of the two models (Production economy vs. Dupont analysis) Model 1 is more generally applicable. It is easier to allocate incidents to their causes (MM, P, or E) than to the “six big losses” of Table 3.

 

External losses are losses that cannot be altered by the production or maintenance team. Planned down time losses are down time losses that were planned. Note that planned down time losses (of Model 2) are specifically down time losses, whereas external losses (of Model 1), can be speed, quality, and downtime losses. For instance, speed losses because of environmental deals are external losses but are not planned down time losses. Similarly, quality losses that are caused by the raw materials are external losses but are not down time. Hence Model 1 discriminates more easily between losses controllable by maintenance (and operations) and those that are outside of its control, than does Model 2.

 

Furthermore external losses are not always planned. For instance, an external power cut or lack of raw materials is an external loss, but is not a planned down time loss. Hence, ‘available production time’ is different in the two models. Therefore the KPIs calculated in Table 4 will have different values depending on whether Model 1 or Model 2 is used. However, by leveraging the next generation of management software, we may,if required, convert the five RCM knowledge elements associated with each incident (from Model 1) into the six big losses (of Model 2).

 

Table 4 Productivity KPIs

Planning factor, PF	2/1
Availability factor, A	4/2
Performance factor, P	6/4
Quality factor, Q	8/6	or Quality level Quality spec Loss factor 1     2     3
Valuable operating time	8
OEE	8/2
Total OEE	8/1	(=OEExPF)
Theoretical cycle time	Determine by a neutral instance. Consult both production and maintenance. Do not include external losses. (imposed by legal/hygiene)	Time units /part

 

Table 5 Example: (using the Production Economy model definitions)

January to September	1	273 days = 6552 hours
Weekends 78 days, holidays 7 days, vacation 11 days = 96 days		273-96=177
No production on night shift		177 x 2 x 8 = 2816 hours
Breaks 1 hour /day		2816-177=2640
Lack of personnel/raw material estimated at 5%	2	.95x2640=2508
Downtime (unplanned) recorded was 551 hours	5	551

 

There are four production lines whose reference throughput and approved product for the period under study are given in Table 6.

Table 6

	Reference throughput kg/h	No. approved kg	Valuable operating time 8  hrs
Line 1	1500	720000	480
Line 2	750	334000	445
Line 3	900	160000	178
Line 4	680	36000	53
Total valuable operating time			1156

 

OEE KPI results

OEE	8/2	1156/2508
PF	2/1	2508/6552
Total OEE	8/1	1156/6552
Availability	4/2=(2-5)/2	(2508-551)/2508

 

Drilling down from the KPIs

Mastery of the processes 1, 2, 3, and 4 of Figure 3 imposes the greatest challenge upon maintenance performance management. The next generation of maintenance performance management software will dissect every KPI into its constituant incidences and knowledge elements (as in Figure 4).

 

Figure 4 A Performance Management system drills down from the KPI (for example, Quality Loss) to invoke analysis procedures that guide the physical asset manager to continuous policy improvement

Figure 4 illustrates that historical data (contained in plant systems) fuel reliability analyses such as Pareto, age-reliability relationships, and optimal CBM decision graphs[11]. Those methodologies steer us towards improved maintenance policies. The CMMS, the control system historian, CBM databases, and other plant systems feed information to the performance management system. The performance management system, in the hands of the physical asset manager, outputs continually improving physical asset management policies.  Today, the maintenance world hovers at the threshold of bridging two remaining gaps that impede “excellence” in asset performance management. They are:

 

1.    CMMS workorders do not yet record reliability-centered knowledge. (Chapters 1-3)

2.    The RCM knowledge base is not yet fully integrated with the CMMS, process historian, and CBM databases (Chapter 15)

 

With these final capabilities in hand, we may anticipate rewarding times ahead for physical asset management.

Do you have any comments on this article? If so, send them to murray@omdec.com