Cost Analysis Exercise
Row Labels
Jan xx
Feb xx
Mar xx
Apri xx
May xx
Jun xx
Jul xx
Aug xx
Sep xx
Oct xx
Nov xx
Dec xx
REPAIR & MAINTENANCE
Plant A
$0
$990
$0
$0
$0
$0
$0
$0
$0
$40
$0
$0
Plant B
$4,068
$25,005
$6,044
$19,204
$15,209
$9,068
$7,771
$12,743
$13,811
$19,892
$21,107
$6,504
Plant C
$0
$484
$180
$203
$521
$163
$363
$0
$0
$0
$0
$0
Plant D
$11,274
$948
$3,236
$542
$5,874
$2,593
$20,127
$11,125
$4,400
$9,074
$5,936
$5,628
Plant E
$0
$0
$6,955
$7,406
$4,719
$1,274
$1,656
$5,839
$4,866
$14,192
$0
$10,117
Plant F
$723
$0
$655
$855
$0
$0
$1,624
$1,799
$0
$0
$0
$1,973
Plant G
$2,739
$929
$3,238
$2,656
$2,442
$2,224
$0
$1,340
$4,699
$5,494
$948
$1,434
Plant H
$0
$0
$0
$860
$0
$0
$0
$0
$0
$0
$105
$0
Plant I
$0
$0
$0
$1,850
$40
$0
$0
$0
$0
$0
$0
$3,097
Plant J
$13,227
$399
$0
$1,368
$0
$0
$0
$0
$0
$10
$0
$0
Plant K
$1,808
$5,474
$586
$9,358
$2,802
$363
$40
$1,322
$10,009
$1,377
$1,658
$1,746
Plant L
$18,262
$3,132
$20,104
$52,293
$15,506
$10,740
$7,836
$25,002
$9,861
$16,767
$17,289
$8,705
Plant M
$1,404
$0
$505
($525)
$0
$777
$0
$0
$0
$0
$866
$0
Plant N
$897
$6,360
$1,807
$8,260
$6,977
$3,479
$0
$2,538
$1,764
$1,701
$2,187
$0
Plant O
$0
$796
$2,038
$948
$0
$228
$1,446
$0
$0
$0
$0
$0
Plant P
$423
$9,944
$4,163
$4,101
$10,949
$9,359
$644
$4,023
$2,691
$13,068
$3,074
$0
Plant Q
$3,361
$360
$4,335
$860
$3,778
$1,864
$294
$2,487
$6,621
$4,086
$5,582
$2,655
Plant R
$799
$292
$1,210
Plant S
$9,534
$4,774
$3,355
$6,599
$7,288
$3,780
$10
$8,082
$10,094
$3,693
$7,664
$2,232
Plant T
$1,119
$689
$0
$0
$2,046
$1,358
$0
$0
$4,458
$0
$116
$4,388
$1,210
$1,210
$2,420
Truck Maintenance Exercise - *2 Vehicles per Plant
Preventive Maintenance Normal Schedule
1. Every 90 Days - Visual Inspection Due : Cost $275 to $1000 normal per truck
2. Annually - Federal Inspection and Sticker: Cost $1000 per truck (Dec only)
3. Bi-Annually - Oil Change and Lube: Cost $2250 per truck
4. Monthly Tire Inspect Fee: $75 per truck
Questions:
1. Identify which plants are in-compliance with the normal schedule?
2. Analyze the data and give a minimum of 5 observations you discover
3. Based on your analysis, make a minimum of 5 recommendations to improve
the overall spend and compliance to schedule
4. Detail any data inconsistencies and make recommendations.
____________________________________________________
1. Compliance with the Normal Schedule:
o
Visual Inspection: Every 90 days, costing between $275 to $1000 per truck.
o
Federal Inspection: Annually in December, costing $1000 per truck.
o
Oil Change and Lube: Bi-annually, costing $2250 per truck.
o
Tire Inspection: Monthly, costing $75 per truck.
To determine compliance, we would expect to see expenses in these categories at the intervals
specified. For example, visual inspection costs should appear roughly every three months, and there
should be a significant expense in December for the federal inspection.
2. Observations:
o
Some plants, like Plant A, show very few expenses, which could indicate non-compliance
or an error in data recording.
o
Plant L has the highest expenses, which could suggest either a larger fleet, higher usage,
or inefficiencies in maintenance.
o
The negative value for Plant M in April might indicate a refund or credit was issued.
o
Inconsistencies in spending patterns across plants may indicate varying adherence to
the maintenance schedule.
o
The costs for Plant G in June are significantly higher than other months, which could
point to a major repair or overhaul.
3. Recommendations:
o
Standardize Reporting: Ensure all plants report maintenance costs consistently to
identify non-compliance and errors.
o
Review High Costs: Investigate any unusually high costs for potential inefficiencies or
overcharges.
o
Preventive Maintenance: Emphasize regular preventive maintenance to avoid costly
repairs and downtime.
o
Bulk Purchasing: Consider bulk purchasing of maintenance services or parts for all
plants to reduce costs.
o
Training: Provide training for plant managers on the importance of compliance to the
maintenance schedule to prevent breakdowns and unexpected expenses.
Great, with two vehicles per plant, we can now perform a detailed calculation for each
maintenance activity and check for compliance. Here’s the breakdown:
1. Visual Inspection: Every 90 days, costing between $275 to $1000 per truck.
o Expected cost per year:
2trucks* 4 times/year* (275 to 1000) USD/truck = 2,200 to 8,000 USD
2. Federal Inspection: Annually in December, costing $1000 per truck.
o Expected cost per year:
3. Oil Change and Lube: Bi-annually, costing $2250 per truck.
o Expected cost per year:
4. Tire Inspection: Monthly, costing $75 per truck.
o Expected cost per year:
Now, let’s analyze the data for each plant:
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Plant A: Shows a cost of $990 in February and $40 in October. This is below the
expected range for visual inspections and does not account for other maintenance
activities. Non-compliant.
Plant B: Shows consistent high costs throughout the year, which could indicate
compliance with all maintenance activities but needs further investigation for potential
overcharges. Potentially compliant.
Plant C: Minimal costs reported, likely non-compliant.
Plant D: Costs are reported almost every month, but there’s a significant spike in July.
This plant may be compliant, but the July expense should be reviewed.
Plant E: Costs are reported from March to August and in October and December, which
could indicate compliance with visual inspections and federal inspections. Potentially
compliant.
Plant F: Only reports costs in four months, likely non-compliant.
Plant G: Reports costs in most months but has a significant spike in June. Potentially
compliant but review June.
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
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Plant H: Only reports costs in April and November, likely non-compliant.
Plant I: Reports costs in April, May, and December, likely non-compliant.
Plant J: Reports costs in January, February, April, and October, likely non-compliant.
Plant K: Reports costs in all months except September and December, which could
indicate compliance with visual inspections and oil changes. Potentially compliant.
Plant L: Reports high costs in all months, which could indicate compliance with all
maintenance activities but should be reviewed for potential inefficiencies. Potentially
compliant.
Plant M: Reports costs in January, March, June, and November, likely non-compliant.
Plant N: Reports costs in all months except July and December, which could indicate
compliance with visual inspections and oil changes. Potentially compliant.
Plant O: Reports costs in February, March, April, and July, likely non-compliant.
Plant P: Reports costs in all months except December, which could indicate compliance
with visual inspections and oil changes. Potentially compliant.
Plant Q: Reports costs in all months except July, which could indicate compliance with
visual inspections and oil changes. Potentially compliant.
Plant R: Only reports costs in January, February, and March, likely non-compliant.
Plant S: Reports costs in all months, which could indicate compliance with all
maintenance activities. Potentially compliant.
Plant T: Reports costs in January, February, May, June, September, and December,
likely non-compliant.
Observations:



There is a wide variance in reported costs, suggesting inconsistent adherence to the
maintenance schedule.
Some plants show costs that align with the expected range for certain maintenance
activities, indicating potential compliance.
Other plants report minimal or no costs in certain months, suggesting non-compliance or
under-reporting.
Recommendations:





Conduct a thorough review of maintenance records at each plant to verify compliance.
Investigate any anomalies or significant deviations from expected costs.
Implement a standardized reporting system across all plants to ensure accurate tracking of
maintenance activities.
Provide additional training to plant managers on the importance of adhering to the
maintenance schedule.
Consider negotiating bulk maintenance contracts to reduce costs and ensure service
quality.
This analysis is based on the assumption that all plants have a similar number of trucks and
operate under similar conditions. Any deviations from this could affect the analysis. For a more
accurate assessment, individual plant data and operational context would be needed.