Analysis Of Work Order History

Summary: This paper presents an objective methodology for maintenance data analysis. The purposes of these analyses are to identify strengths and weaknesses in the maintenance management system, opportunities for improvements, and benchmark maintenance key elements against maintenance best practice. The paper contributes ideas on how to analyse large data sets and make useful determinations from an understanding of work order history as well as the

loaded PM strategy. This addresses two key traditional problems with modern CMMS: developing a reliability assessment from the work history and continuously improving the PM strategy in a

cost effective manner.

This paper presents a study of maintenance information obtained from for a site in the food industry. The study covers the analysis of failure modes in the work history and determination whether these modes were in control or further deteriorating. Failure modes and effects analysis (FMEA) is a comprehensive analysis capability to study failure modes determined from reactive maintenance work order history, the costs and equipment impacted, and the time trend in failure histories.

1. INTRODUCTION

The computerised maintenance management system (CMMS) was introduced in the 1970s to

bring control of the work order process. In developed companies data from work orders has been collected from some years and a statistically valid data set is now available for analysis of equipment condition and maintenance performance. This data can be analyzed to improve maintenance effectiveness and work management processes.

This paper describes a development which exploits the now data-rich environment pervading maintenance control in the modern organisation. The raw data for this work are the thousands of

work orders held in the maintenance history. These work orders need to be classified according to

failure mode, and in this work this is achieved by pattern recognition of key words and synonyms

in text fields such as the task description and work completed fields.

It is a complex process to accurately allocate multiple work orders to well defined failure modes that reflect the issues impacting on a complex facility. The process involves a number of iterations

and alternative methods in viewing the data. Even with the most meticulous attention to detail there can still ensue a small error in incorrect allocation or misunderstanding of the work order terms. However the overall allocations in this procedure are generally of a high enough accuracy to allow a statistically credible analysis of reliability trends.

2. ANALYSIS PROCESS

The process described in this paper is a text-based analysis of work order task descriptions whereby

key failure modes are identified. Hundreds and even thousands of work orders are processed very

quickly using a specialized search program within reliability software developed for this purpose.

Failure modes are identified as for example, CHAIN, V BELT, POWER and these modes summarise

classic types of problems such as worn chains, replaced V belts, distribution board faults etc. What we are looking for is the high frequency or high impact failure modes and the type of equipment which

is affected. The software identifies high frequency failing equipment and then tries to classify why the

equipment is failing.

In this work the term failure is quite broad – it is not just about a breakdown job, and the allocation of a

AMMJ 20th ANNIVERSARY YEAR

S A Safi, Covaris Pty Ltd A Paper from ICOMS 2007 (Australia)

Order History

failure mode is also executed when a planned and scheduled corrective maintenance job is detected. What we

are therefore classifying is the frequency of corrective work which can be addressed by: • Modifications to the service schedules

• Modifications to operating behaviour • Design/time-based replacement

The process to analyse maintenance data is based on the logic described below in Figure 1. At the heart of the

system is an understanding of the failure modes: why equipment fails. In this work the specification of a failure

mode is either in terms of a commonly used and well-understood component, eg BEARING or a damage mode that likewise is well understood, eg CRACKING. Due to the need to automate the processing of thousands of

work orders, the specification is necessarily kept simple to one or two words.

The reliability engineering analysis of the corrective maintenance work orders from a maintenance system

(independent of CMMS in use) has the methodology [1] given in the text below.

Figure 1 Logic of analysis

• A first cut of work orders is allocated to a set of primary failure modes derived on inspection from the work orders – hence this first cut process achieves the following:

o Specifies the primary failure modes

o Makes some bulk allocations of work orders to these failure modes

o Identifies that portion of the work order data set which will not be allocated to failure modes since the work is more to do with housekeeping, etc

• A second, finer cut is made where a primary failure mode with a very large grouping of work orders is broken out into a number of more specific failure modes, and the accuracy of the work order allocation is improved by a more detailed view of each registered failure mode (in the first cut, the thousands of work orders were viewed

and considered, when making the second cut the tens to hundreds of work orders allocated to a failure mode were considered)

• Each primary failure mode is then considered in detail and a set of very specific “secondary failure modes” is derived. The logic is that a PM work order is intended to address the failure mode and the operations within a PM are intended to address each of the secondary failure modes.

Advise on number of work orders, number of affected equipment, total cost and

total hours of WO’s for each

failure mode.

Advise on changes in MTBF for critical failure modes

Advise on time trends in the key failure modes and specify if failure modes are increasing or decreasing

over time

Group the equipment into common equipment types

Advise on key issues affecting particular equipment

within the group

Advise on design limitations in the equipment Advise on critical equipment

which have the most reactive work

Determine what are the key failure modes

Determine if key equipment are less reliable or more

reliable Select work orders which

are reactive in nature (breakdowns, ...)

Allocate work to characteristic failure

• The list of work orders allocated to each failure mode is then considered, and the effects of the

failure mode are specified by:

o The range of equipment affected by the failure mode – as determined by

the equipment associated with work orders allocated to the failure mode o The sum cost of work orders allocated to a failure mode - the software also handles the sum amount of labour hours.

o The Mean Time Between Failures MTBF for either failure modes

or specific equipment. MTBF is defined as:

Software called MaintSpeak has been developed to perform FMEA analysis [2].

2.1 RELIABILITY ENGINEERING REPORTS

The reliability engineering software has the capability to analyse maintenance data form various

commercial CMMS/EAM’s. Four main sections in this analysis are:

1. Failure modes – why equipment fails within the factory, based on corrective maintenance

work history. Time trends for failure modes are tracked using MTBF (mean time between failures) statistics.

2. Top failing equipment – which equipment requires the most corrective maintenance

work. Time trends for failing equipment are tracked using MTBF statistics.

3. Failure mode distributions – analysed for the critical types of machines as determined

from the failure trends.

4. Time trend the failure rate both seasonally throughout the calendar year and trends over years.

Failure modes are determined from the work history and tabulated from most frequent to least frequent. In some cases the failure modes which are dominant cost drivers may not be as frequent as the high

incidence items. Table 1 shows an example of the first few failure modes sorted by number of work orders for a site in food industry [3]. N work orders refers to the number of corrective maintenance

work orders stored for the failure mode and N Equipment refer to the number of impacting equipment and total cost is the cost of all work orders. The software can calculates the total cost and hours

associated with a specific failure mode. In this case labour hours were not provided in the data set

but total costs have been compiled.

No. Mode Name No Work Orders No Equipment Total Cost

1 CONVEYOR BELT 1485 485 586589.3

2 GENERAL 1068 411 413301.3

3 DRIVE 576 313 144243.7

4 BEARING 497 250 263588.8

Table 1 Failure modes sorted by N work orders [3]

The trends of the failure modes are investigated by reporting the time trend analysis of the MTBF calculated as the time between instances of corrective work order. This plot communicates whether the reliability associated with a failure mode is improving or reducing.

Figure 2 shows an example of failure history trend. The plot report data between 4/2003 and 9/2006 with the x axis reported in number of days from 4/2003. The vertical axis reports MTBF measured

in days. If the value of the plot is 2.0, then it means that there are two days between occurrences

of corrective work orders. If the value drops below 1.0 to say 0.5, then this means that there are on

average two corrective work orders created every day. The plots are called Duane plots in honour of the creator of the report type.

The filter Duane plot reports a success for the maintenance team providing all filter jobs are being

reported in the system. There is a recent correction down of the MTBF but otherwise this is an excellent result which communicates technical improvement.

Figure 2 Example Failure History Trends [2]

3. CASE STUDY

This section presents a study of maintenance information obtained from a site in food industry in February

2006 [4]. It covers maintenance improvement opportunities and reliability engineering trends obtained from

their CMMS work order history. Maintenance history data for the site was analysed for the period between

March 2001 to January 2006. There were 25,339 records in maintenance history database. The objective of

this report is to specify the key targets for maintenance improvement. 3.1 FAILURE MODES

The analysis identified 68 failure modes. Table 2 shows the top 20 failure modes determined from the CMMS work history, from most frequent to least frequent. 10876 work orders including breakdown and corrective

maintenance work orders were included in the analysis.

No. Mode Name N work orders N Equip

1 VALVES 985 162 2 SEALING 609 87 3 CONVEYOR 528 90 4 CLEANING/CIP 393 103 5 PACKER/INVOLVO 369 54 6 PUMP 362 102 7 FILLER 331 50 8 LEAKING 311 112 9 JAM 300 66 10 DRIVE 298 99 11 FLOW METERS/GAUGES/SENSORS 282 99 12 TANKS 276 86 13 VIDEO JET/PRINTING 276 59 14 BOX FORMING/CARTONS 247 42 15 BLENDER 181 26 16 LIGHTING 180 66 17 CUPS 169 32 18 BOTTLE 162 32 19 BUILDINGS 158 76 20 FINE TUNE 156 40

These work orders were processed and 9371 work orders were allocated to identified failure modes, which means that 86.2% of the work orders were allocated. Allocation of above 80% of the work

orders is required to ensure a statistically valid distribution.

Time trends in the key failure modes were tracked using MTBF statistics. Table 3 shows the trends

of the top four failure modes.

Generally Table 3 indicates that in recent times reliability associated with these failure modes has

increased, although there has been some drop. The sudden fall of MTBF plots can be due to either a poor level of recording early on or failure rates increasing with operational usage.

Conveyor Cleaning

Table 3 Top 4 Failure Modes MTBF

No. Equip ID Equip Desc N Modes N Instances First Date Last Date

1 1604 FILLER 1 45 743 9/10/2003 12/01/2006 2 1074 FILLER 2 35 303 21/07/2003 10/01/2006 3 1595 FILLER 3 41 247 10/01/2002 9/01/2006 4 1600 INVOLVO PACKER 1 31 195 20/02/2002 9/01/2006 5 2668 INVOLVO PACKER 2 34 190 23/08/2001 9/01/2006 6 2610 SLEEVER 24 177 2/07/2001 9/01/2006

7 1602 BLOW MOULDER No.1 32 170 6/08/2003 9/01/2006

8 1525 FILLER 4 39 167 7/01/2004 10/01/2006 9 1067 FILLER 5 34 163 28/04/2003 10/01/2006 10 1601 INVOLVO PACKER 3 32 162 2/04/2004 9/01/2006 11 1607 W&D PALLETISER 16 159 7/02/2004 9/01/2006 12 2667 FILLER 6 33 141 10/12/2001 11/01/2006 13 1650 BOTTLE CAPPER 21 132 7/10/2003 9/01/2006 14 1077 INVOLVO PACKER 4 34 132 2/07/2001 11/01/2006 15 1066 FILLER 7 34 131 3/12/2003 9/01/2006 16 2947 INVOLVO PACKER 5 27 118 9/03/2005 11/01/2006

17 1003 TRADE WASTE DAF PLANT 17 117 2/09/2003 3/01/2006 18 1647 BLOW MOULDER No.2 23 108 6/08/2003 9/01/2006

19 2918 FILLER 8 20 107 31/12/2004 11/01/2006

20 2611 FUJI SHRINK SLEEVER 15 94 20/04/2004 30/12/2005

3.2 TOP 20 EQUIPMENT

The equipment with the most issues at the site are shown in Table 4. N Modes refers to the number of different failure modes impacting the equipment, N instances refers to the number of corrective maintenance work orders

stored for the equipment and the dates refer to the first and last work order recorded in the data set analysed

for this item of equipment. Table 4 shows the top 20 failing equipment for this site.

Table 5 presents the failure history trend for two of the above equipment. This table plots the time based changes in MTBF for specific equipment. Table 5 has similar trend as shown in Table 3 which indicates that in

recent times reliability associated with these equipment has increased, although there has been some fall off. The sudden fall of MTBF plots can be due to either a poor level of recording early on or failure rates increasing with operational usage.

Table 5 Failure history trend for two equipment

We need to be careful with this result since we were advised on site that with changes in the clerical support to the maintenance department, not all work was being recorded into the CMMS. The change in clerical support to the maintenance department was around the time we observe the sudden drop in MTBF plats. If minor jobs associated with the equipment included above is being missed, then the reliability trends will kick up as shown in the period of this change.

We considered the fact that the key failure modes reported in previous section show similar improvement pattern. As a consequence we acknowledge the trends in improvement shown in this section but we recommend that the result be treated with caution.

3.3 FAILURE MODE DISTRIBUTIONS

Failure mode distributions were analysed for the critical types of machines as determined from the failure trends.

• Fillers • Packers

Fillers

• Filler 1 has obvious problems. Almost a third of these issues are associated with sealing. The other key

problems are lifters, something to do with bottles, heaters, valves, flow meters and knives. Based on the evidence to date this filler needs a complete rework of its PM strategy.

• Problems with the Filler 2 are cups and lidding devices. These would need to be checked for adequate set-up procedures, PM alignments and any design issues. The feedback on the work orders does not point to any

issues other than timing.

• There are labeller issues on the Filler 3, Filler 5 and Filler 6. These would need to be checked for design flaws since the work order feedback is poor and does not define the problem other than missing labels. Issues could

be the speed of the machine. • Filler 4 has a heater problem.

Equipment FILLER 1 FILLER 2 FILLER 3 FILLER 4 FILLER 5 FILLER 6 FILLER 7 FILLER 8 Total 743 303 247 167 163 141 131 107 No Failure Mode 1 FILLER 39 44 36 3 9 13 8 22 2 CONVEYOR 7 9 9 8 4 2 1 3 BEARING 14 4 2 4 2 7 VALVES 44 7 7 18 12 15 10 25 9 DRIVE 17 4 12 6 7 1 10 SEALING 214 53 21 14 12 15 10 25 13 FLOW METERS/SENSORS 39 9 4 8 4 3 3 14 PNEUMATICS 10 1 4 4 1 1 15 LIFTER 45 1 1 16 LEAKING 33 3 4 6 2 2 5 17 PUMP 4 9 1 3 1 1 4 1 22 PLC 2 1 12 1 24 LUBRICATION 4 3 3 1 1 2 6 2 25 FILTER 5 1 2 2 2 3 27 JAM 8 11 26 4 22 7 28 BOTTLE 45 29 CLEANING/CIP 44 6 34 6 13 12 7 3 31 HEATER 45 6 6 10 3 2 3 35 BLOCKAGE 18 1 5 2 1 38 CUPS 1 44 8 6 3 5 14 41 KNIVES 23 1 3 3 44 LID APPLICATOR 3 13 1 3 1 2 51 OVERLID 46 7 55 LABEL/LABELLER 19 13 18 56 FORMING 4 4 1 2 4 57 BOX FORMING 2 1 30

Table 6 Failure mode distributions for fillers Packers

• Packer 1 and 2 have obvious issues with forming which need to be investigated from both a set-up

and design issue. We would expect the box forming issues across all of the packers to be a function

of both set-up and PM issues.

• Fine tuning problems of packer 2 and 3 need to be considered as set up issues, although why Packer 1 is not an issue also needs to be evaluated

• Packer 1 has a video jet problem which we would expect to have been resolved by now.

• Packer 4 has a nozzle problem and its entire glueing system need investigation. In fact common across all the packers is a need to check the PM strategy for the glueing systems.

In general we would recommend a check of the PM strategies and set-up procedures for the case

packers.

4. CONCLUSION

The large data sets now available in the maintenance systems used by modern enterprises are offering a new resource to the reliability engineering. This paper presents a methodology to analyse maintenance data sets form a broad range of computerised maintenance management systems. The purposes of these analyses are to identify strengths and weaknesses in the maintenance management system and opportunities for improvements. The developed methodology to perform failure modes and effect analysis was described.

Easy to use and yet powerful software introduced to facilitate FMEA. It captures work orders from the CMMS

and allocates selected work to defined failure modes. It also specifies which equipment is affected by a selected

failure mode, what is the total cost of the work and what is the total amount of labour hours spent on work in this mode.

The results from the case study, show outcomes from the analysis and wide range of reports, which can be provided from analysing the computerised maintenance management system data sets were presented.

Although the examples used here are drawn from a database of 25,000 work orders, the author has experience

in analyzing data sets up to half a million work orders which presents new challenges in the data management environment. Equipment I N V O L V O PACKER 1 I N V O L V O PACKER 2 I N V O L V O PACKER 3 I N V O L V O PACKER 4 I N V O L V O PACKER 5 Dept 1005 1005 1003 1005 1005 Total 195 190 162 132 118 No Failure Mode 1 PACKER/INVOLVO 57 52 20 24 23 4 CONVEYOR 5 7 5 7 6 5 VALVES 4 2 1 1 5 7 DRIVE 3 7 3 5 11 10 FLOW METERS/GAUGES/SENSORS 6 1 6 4 3 11 PNEUMARICS 3 2 1 15 VIDEO JET/PRINTING 13 4 1 1 17 NOZZLE 7 3 3 10 4 20 FINE TUNE 7 12 35 4 7 21 JAM 2 2 5 2 1 22 HEATER 1 2 1 23 BOX FORMING/CARTONS 25 34 15 22 18 24 CYLINDER 6 1 26 GUIDES 2 3 3 1 27 BRAKE 2 4 1 2 1 28 GLUEING 12 6 9 10 4 29 HOSE 4 3 4 30 FORMING 12 13 4 3 2 33 BEARING 2 2 6 2 38 CLEANING/CIP 7 1 1 4 43 ELECTRICAL PROBLEM 1 1 1 1 44 CHANNELISER 22 1 45 GEARBOX 2 1

Table 7 Failure mode distributions for packers

ACKNOWLEDGEMENT

The authors acknowledge the input of work colleagues at Covaris Pty Ltd and the many clients and research

partners who have contributed to this work. REFERENCES

1. S. Safi, B. Bigdeli, Maintenance Engineering Bureau Using Maintenance Data to Drive Improvement, ICOMS-2005, Paper 43, Hobart (2005)

2. MaintSpeak, FMEA Analysis Software, Covaris Pty Ltd, 2005.

3. R. Platfoot, Report on Maintenance Improvement, Covaris Pty Ltd, Unpublished Internal Document for a food

industry company (2006).

4. S. Safi, R. A. Platfoot, Report on Maintenance Improvement, Covaris Pty Ltd, Unpublished Internal Document