Equipment Downtime Analysis - Trend, Pareto Analysis and Ranking

Equipment- Downtime- Analysis
Introduction
Every production process consists of a range of machineries integrated with each other in a manner
to produce an optimum finished product. In the present case, a similar flow process is being analysed
where in 8 machineries/ equipment downtime is taken into consideration for the past twenty six
months. The downtime trend has been analysed and efforts have been made to understand the
reason of such downtime. The data provided consists both downtime and slowdown, wherein the
major time has been devoted to downtime. Accordingly, the assignment shall focus on downtime
along with ranking of machineries/ equipment based on their performance for the last 26 months. The
assignment has been written on an assumption that all the equipment are equally efficient. Further the
details of the equipment is provided as under:
(a) ECSM;
(b) N crane and S Crane;
(c) HLW 1 & 2;
(d) APS;
(e) ASM;
(f) APM;
(g) WCSM
The structure of the report has been written in three part. The details of the part has been presented
as under:
(a) Part A: Analysis of trend of downtime of 8 equipment monthly and quarterwise;
(b) Part B; Analysis of downtime of machineries based on PARETO analysis;
(c) Part C: Analysis of downtime of equipment, ranking and find the reason for downtime for critical
machinery.

PART A

PART –A
Part A: Analysis of trend of downtime of 8 equipment monthly and quarterwise;
Under Part A, 8 equipment downtime has been analysed over the period of 26 months. On perusal of
the data it may be inferred that the equipment have performed worst in 2017 with recovery in 2018
and similar trend was witnessed in two months of 2019. Further, start down time has been used for
analysing the graph of downtime. The worst month of machine downtime has been observed for
December, 2017 which has seen a sudden spurge in downtime of machinery. The greatest contributor
of downtime in the month of December, 2017 was WCSM. The graph of downtime of machinery for
the twenty six months have been produced here-in-below:
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Jan
Feb
20
16
2017 2018 2019
0
200
400
600
800
1000
1200
APM
APS
ASM
ECSM
HLW 1 & 2
N Crane
S Crane
WCSM
Further, as observed in the graph that data for December, 2018 is blank implying that there has been
no downtime in the concerned month. Further, there has been a massive recovery in the downtime of
8 equipment for the last months of January and February. The data showing the total downtime of all
months has been presented as under:
Month Downtime
Dec 102
Total 102
2017
Jan 3656
Feb 3356
Mar 3487
Apr 3318
May 3502
Jun 3439
Jul 3915
Aug 3516
Sep 3858
Oct 3655
Nov 3459
Dec 7402
Total 46563
2018

Month Downtime
Jan 3868
Feb 3450
Mar 4462
Apr 5174
May 3844
Jun 3428
Jul 3648
Aug 3291
Sep 3265
Oct 3566
Nov 3214
Total 41209
2019
Jan 63
Feb 77
Total 140
Analysis – Quarter wise
Under Part A, 8 equipment downtime has been analysed over the period of 26 months. On perusal of
the data it may be inferred that the equipment have performed worst in 2017 with recovery in 2018
and similar trend was witnessed in two months of 2019. Further, start down time has been used for
analysing the graph of downtime. The worst month of machine downtime has been observed for
December, 2017 which has seen a sudden spurge in downtime of machinery. The greatest contributor
of downtime in the month of December, 2017 was WCSM. The graph of downtime of machinery for
the twenty six months have been produced here-in-below:
Qtr4 Qtr1 Qtr2 Qtr3 Qtr4 Qtr1 Qtr2 Qtr3 Qtr4 Qtr1
2016 2017 2018 2019
0
500
1000
1500
2000
2500
APM
APS
ASM
ECSM
HLW 1 & 2
N Crane
S Crane
WCSM
On perusal of the graph it may be inferred that the worst quarter for the production process had been
the Quarter 4 of 2017 which is in alignment with the monthly data. Further, the recovery can be seen
in the last two quarters of the production process i.e. Quarter 4 of 2018 and Quarter 1 of 2019. Thus,
it may be seen that company has made efforts to smoothen production process by putting efforts to
reduce the downtime of machineries.