Analysis Of Financial And Operational Performance Of A Busy Transport Fleet

Operational Changes for Improved Financial Performance

As stated by Wilke, Majumdar and Ochieng (2014) in the operations management the total quality management has been seen with the application of the concepts such as “total quality management or just in time”. These concepts have been seen to be improving the operational performance and significantly contributing to the financial performance. Several types of the empirical evidence have supported the effect of the operational performance is context dependent. The findings have further revealed that both “accounting- and market-based financial performances” are closely related to the effectiveness of SCM. The supply chain integration and performance drivers are seen with the sourcing strategy, “SC integration, and external relationships play critical roles to improve firm-level financial performance”. The supply chain alignment and integration with the IT infrastructure and SC relationship is important for realising the full financial advantage and effective SCM (Rajapakshe et al. 2017).

A cost centre is identified as the department in which the cost is allocated. The term includes the departments which does not produce directly however incur cost to the business. This is considered when the manager and the employees of the cost centre are not accountable for the investment decisions and profitability and only responsible for some of the costs. The two main types of the cost centres are seen with production cost centres and service cost centres. The production cost centres are seen with the place where the products are processed or manufactured. The service cost centres are considered with the services provided to the other cost centres (Zhao 2014).

Early learnings have been seen with the strong connection among the cost advantage and organization performance. The firms specific margin in the cost competency is relative to their rivals, low manufacturing cost and improved performance. The firm’s performance with the trade performance is depicted with the basis of “sales return, yield, return on investment, output, Market split and the manufactured goods growth”. There have been several studies which is able to examine the relationship between the competitive edge and financial performance is depicted with the “higher performance, unique advantage without better performance and superior performance without unique advantage” (Hazen et al. 2016).

As discussed by Qin, Nembhard and Barnes (2015), It is seen that trimming customer service cost and boosting of customer satisfaction is challenging in the best of times. Mangers often view service efficiency and customer satisfaction to be incompatible with the goals. Three main practices to strike an optimal balance in the service operations is recognized with the segment service levels, striving for consistency over several budget cycles and sharing of accountability and continually look for efficiencies (Pedraza-Martinez and Van Wassenhove 2016).

Relationship Between Operational and Financial Performance

The study aims to identify and explore the KPIs in terms of vehicle fill and miles per store journey. The discussion of the KPIs will be explained in terms of explaining the each KPI individually, identify the various types of the data in terms of the plan vs actual performance. It has further identified the drivers of the performance and back it up.

The impact of the operations on financial performance in the given case is considered with the times vehicle fill and miles per store journey. The different types of the parameters used in the evaluation has been seen to be improving the operational performance and significantly contributing to the financial performance. Several types of the empirical evidence have supported the effect of the operational performance is context dependent. The findings have further revealed that both “accounting- and market-based financial performances” are closely related to the effectiveness of SCM. This consideration will be considered with the impact of the selected KPI in the financial estimation. It is further considered that the cost centre is identified is associated with the respective KPI, i.e. the volume fill and miles per Journey. The relevant discussion of the study on the consideration of the cost centres has been able to state on the different aspects where the products are processed or manufactured. In this case, the total volume is considered. The service cost centres are considered with the services provided to the other cost centres. On addition to this, the various types of the discourse of the study related to the selected KPI has been able to state on the firm’s specific margin in the cost competency is relative to their rivals, low manufacturing cost and improved performance. The firm’s performance with the trade performance is depicted with the basis of “sales return, yield, return on investment, output, Market split and the manufactured goods growth”. Some of the different types of the other considerations of the report has further highlighted on the techniques which are seen with the trimming customer service cost and boosting of customer satisfaction is challenging in the best of times. Mangers often view service efficiency and customer satisfaction to be incompatible with the goals (Kou and Yu 2015).

The various types of the other aspects of the appraisal is further seen to be considered with the identify and explore the KPIs in terms of vehicle fill and miles per store journey. The discussion of the KPIs will be explained in terms of explaining the each KPI individually, identify the various types of the data in terms of the plan vs actual performance. It has further identified the drivers of the performance and consider the various types of the other relevant data to estimate the overall contribution of the operations techniques in financing (Choi, Cheng and Zhao 2016).

Key Performance Indicators for Operational Performance

The volume fill KPI is defined as the total quantity of the petrol which had been filled in the trailer. The various types of the data available for this parameter is able to state on the planned and actual amount of the litres of petrol which were filled in the trailers. The different aspects of the description of the KPI data is further able to estimate the cost required in filling petrol in the vehicle.

The miles per journey is seen to be defined with the total distance which the trailer able to travel before it required to be refilled.

The different types of the interpretations made as per the planned and the actual fill of the tanker is understood to be depict that there was significant mismatch in the week one itself. This has been seen to be evident with planned volume fill of 1574 litres and actual fill of 1439. This is seen to be depicted with a total variation of 8.6%. in addition to this, in week 41 and 42 there is a discrepancy of the 21.3% and 18.7%. Despite of the positive increase there has been a significant increase in the cost of the volume of petrol required for refilling of the trailer in terms of the planned and the actual results. Some of the different types of the variations in the price of the trailers are considered with the various types of the negative trends, as per the depiction of this data, despite of the positive data there is significant scope of the improvement in he planned and the actual results.  The depictions of this data have been able to state that there has been a high risk of the increasing nature of the cost in the planned and the actual results (Hübner, Holzapfel and Kuhn 2015).

Figure: Difference in planned and actual Volume Fill

(Source: As created by the author)

Wk	Vehicle Fill
Plan	Actual	var %
1	1,574	1,439	-8.6%
2	1,502	1,562	4.0%
3	1,560	1,593	2.1%
4	1,424	1,574	10.6%
5	1,506	1,531	1.7%
6	1,530	1,590	3.9%
7	1,444	1,571	8.8%
8	1,518	1,564	3.0%
9	1,556	1,552	-0.2%
10	1,472	1,552	5.4%
11	1,502	1,561	4.0%
12	1,529	1,538	0.6%
13	1,490	1,512	1.5%
14	1,542	1,534	-0.5%
15	1,514	1,588	4.9%
16	1,478	1,678	13.5%
17	1,471	1,571	6.8%
18	1,511	1,586	5.0%
19	1,559	1559	0.0%
20	1,590	1630	2.5%
21	1,548	1613	4.2%
22	1,587	1565	-1.4%
23	1,534	1623	5.9%
24	1,583	1597	0.9%
25	1,575	1612	2.4%
26	1,604	1610	0.4%
27	1,588	1623	2.3%
28	1,505	1640	8.9%
29	1,617	1598	-1.2%
30	1,536	1519	-1.1%
31	1,585	1624	2.5%
32	1,584	1570	-0.9%
33	1,552	1610	3.7%
34	1,575	1575	0.0%
35	1,619	1604	-0.9%
36	1,545	1590	2.9%
37	1,526	1573	3.1%
38	1,564	1524	-2.6%
39	1,558	1552	-0.4%
40	1,561	1543	-1.2%
41	1,300	1576	21.3%
42	1,300	1543	18.7%
43	1,300	1465	12.7%
44	1,594	1468	-7.9%
45	1,551	1507	-2.9%
46	1,551	1489	-4.0%
47	1,551	1458	-6.0%
48	1,551	1459	-6.0%
49	1,551	1485	-4.3%
50	1,551	1495	-3.6%
51	1,606	1544	-3.9%
52	1,515	1501	-0.9%
YTD	1,510	1,558	3.2%

The various types of the drivers of the performance is realized to be based on the depictions which are seen to be made with the total number of the vehicles which needs to be considered for refill. The depictions of the factors for the volume fill is also related to the total amount of the distance which is travelled by the individual vehicles. It needs to be further understood that the significant distance travelled along with the mileage of the individual vehicles is able to be backed up with the types of the petrol which is filled in the vehicles. This factor depicts whether the volume petrol filled is normal or premium in nature, based on this the vehicles volume fill will be able to be determined (Heizer and Render 2014).

Cost Centers and Their Impact on Financial Performance

The present KPI of the volume fill variation as per the real and planned can be improved by ensuring that the vehicles are allotted in the appropriate location as per the procurement needs. There needs to be several types of the initiative taken which will be further able to ensure that the vehicles are refiled only while on the trip and not going to distant gas stations which will increase the time taken in the deployment of the individual orders and cause negative variations in the volume fill (Hitt, Xu and Carnes 2016).

There is a significant number of the depictions which shows that week 49 to 53 is represented with the high amount of positive difference between the miles travelled per journey. The important interpretations of the planned and the actual performance has shown how the planned Miles per Journey has increased in the first four weeks.  This is evident with 10.8% variance in week 1 19% in week 2, 8.5% in week 3 and 8.2% in week 4. The interpretation of the different types of the negative variations in the trends is further seen to be evident with the 7.6% change in week 47, 20.6% in week 48, 26.1% in week 49, 29.1% in week 50, 14.3% in week 51 and 11.1% in week 52 (Bochtis and Sørensen 2014).

Figure: Difference in the Miles per Journey

(Source: As created by the author)

Wk	Miles per Store Journey
Plan	Actual	var %
1	132.7	147.0	10.8%
2	133.9	155.3	16.0%
3	133.7	145.1	8.5%
4	133.7	144.7	8.2%
5	133.7	116.1	-13.2%
6	133.8	127.3	-4.9%
7	133.8	127.1	-5.0%
8	133.5	141.8	6.3%
9	134.0	145.7	8.8%
10	134.0	136.8	2.1%
11	134.1	128.7	-4.0%
12	133.8	138.5	3.5%
13	134.0	140.4	4.8%
14	133.9	137.6	2.7%
15	133.8	138.6	3.6%
16	134.2	123.7	-7.8%
17	133.6	138.0	3.3%
18	134.0	135.5	1.1%
19	133.8	129.2	-3.4%
20	133.9	131.7	-1.6%
21	133.8	123.8	-7.4%
22	133.8	121.5	-9.2%
23	134.1	126.6	-5.6%
24	133.8	119.1	-11.0%
25	133.8	127.3	-4.9%
26	133.9	126.1	-5.8%
27	133.8	129.3	-3.4%
28	134.1	130.7	-2.5%
29	133.8	134.1	0.2%
30	133.9	128.3	-4.1%
31	133.8	130.1	-2.8%
32	133.9	124.7	-6.9%
33	133.6	135.6	1.5%
34	133.7	124.4	-7.0%
35	133.8	128.6	-3.9%
36	133.9	135.0	0.9%
37	133.8	134.0	0.1%
38	133.8	125.0	-6.6%
39	133.6	127.0	-5.0%
40	133.9	135.0	0.9%
41	134.5	124.3	-7.6%
42	134.4	125.4	-6.7%
43	134.5	118.0	-12.3%
44	133.8	135.0	0.9%
45	133.7	122.0	-8.8%
46	133.7	131.0	-2.0%
47	133.9	144.0	7.6%
48	133.7	161.1	20.6%
49	122.9	155.0	26.1%
50	122.7	158.4	29.1%
51	125.1	143.0	14.3%
52	133.5	148.3	11.1%
YTD	133.1	133.9	0.5%

The main drivers for the performance are based on the factors such as condition of the vehicles, capacity of fuel of the tanker and kilometres driven per litre of fuel. This will be able to determine that main reasons for the variations in the planned and the actual variations in the obtained data. It needs to be further understood that the driver of the operational performance is directly relevant to the financial impacts. This is seen to be evident with higher variation in the planned and the actual find will have more negative impact in increasing transportation costs. The company needs to ensure operational efficacy in the fleet system so that it is able to incorporate a sound financial plan (Li et al. 2016). Some of the various types of the other drivers to the performance is for this factor needs to be seen with the additional routes which the vehicles need to go through. In case the vehicles need to cover stoppages in distant location to deliver good then there is a higher change of variations in the planned and the actual data obtained. This will also significantly increase the financial burden of the company (Venkat et al. 2015).

Conclusion

The main recommendations to improve the selected KPI needs to be based on several initiatives which will ensure improved maintenance of the vehicles to improve the operational efficiency. Some of the basic initiates needs to be taken in form of checking the tire pressure at least once a month. Under-inflated tires burn more fuel. The company needs to also ensure that the vehicle is not idle for more than a minute during the journey. In addition to this, changing the air filter at least the set number of times outlined in the owner’s manual will ensure that the vehicle is able to sustain a better mileage even in dusty conditions. Having a regular engine check-up needs to be made mandatory for all the vehciles in the fleet.  Installing an onboard trip computer will ensure “Instant fuel economy” setting. Watching this gauge, the company will be able to keep the fuel consumption as low as possible. The companies such as new “Ford Fusion Hybrid has a leafy graphic display that sprouts leaf’s each time a user reach a fuel economy milestone” (Anand and Gray 2017).

The paragon hours have been depicted to be significantly less in the first week is seen to be significantly less with negative increase of 17.6%. However, in the second week onwards there has been a linear increase in the paragon hours utilization rate. This is depicted to be evident with the increase of 3.6% in week 2, 6.7% in week 3, 5.0% in week 4, 7.8% in week 5, 6.7% in week 6 and 7.9% in week 7. The increase nature of the paragon hours utilization percentage has been depicted to be conducive for the increase in the overall quality of the services of the company. It needs to be further determined that the various types of the decline in the week 29, week 30, week 31, week 32 and week 33 has been evident with -3.3%, 5.6%, 7.8% and 4.4%.

Figure: Difference in the Miles per Journey

(Source: As created by the author)

Paragon Hours % Utilisation
Plan	Actual	var %
90%	74%	-17.6%
90%	93%	3.6%
90%	96%	6.7%
90%	95%	5.0%
90%	97%	7.8%
90%	96%	6.7%
90%	97%	7.9%
90%	96%	7.0%
90%	97%	7.8%
90%	97%	7.8%
90%	95%	5.3%
90%	96%	6.7%
90%	96%	6.7%
90%	94%	4.4%
90%	92%	2.2%
90%	94%	4.5%
90%	93%	3.3%
90%	94%	4.4%
90%	94%	4.4%
90%	98%	8.9%
90%	93%	3.3%
90%	92%	2.2%
90%	92%	2.2%
90%	91%	1.1%
90%	93%	3.3%
90%	92%	2.2%
90%	91%	1.1%
90%	90%	0.0%
90%	87%	-3.3%
90%	87%	-3.3%
90%	85%	-5.6%
90%	83%	-7.8%
90%	86%	-4.4%
90%	95%	5.3%
90%	94%	4.4%
90%	92%	2.2%
90%	90%	0.0%
90%	97%	8.0%
90%	93%	3.7%
90%	92.3%	2.5%
90%	95.4%	6.0%
90%	95.2%	5.8%
90%	97.5%	8.3%
90%	96.7%	7.4%
90%	93.7%	4.2%
90%	94.7%	5.2%
90%	92.9%	3.2%
90%	94.6%	5.1%
90%	98.0%	8.9%
90%	92.3%	2.5%
90%	91.1%	1.3%
90%	91.6%	1.8%
90%	93%	3.3%

The condition for the identification of the performance is related to the various types of the drivers which are associated quality factors of the organization. It needs to be further decided that the utilisation of the paragon hours is directly related to the efficiency of the manufacturing units. Therefore, in case there is any problem with the maintenance for the manufacturing unit then it will hamper the quality of the products.

The main recommendation for the company should be depicted with the various types of the importance which should be considered with maintaining the consistency in the KPI for Paragon Hours percentage Utilisation.

Conclusion

The findings of the discussion on the impact of operations on financial performance have stated that the “accounting- and market-based financial performances” are closely related to the effectiveness of SCM. The supply chain integration and performance drivers are seen with the sourcing strategy, “SC integration, and external relationships play critical roles to improve firm-level financial performance”. The supply chain alignment and integration with the IT infrastructure and SC relationship is important for realising the full financial advantage and effective SCM. The firm’s performance with the trade performance is depicted with the basis of “sales return, yield, return on investment, output, Market split and the manufactured goods growth”. Three main practices to strike an optimal balance in the service operations has been identified with the segment service levels, striving for consistency over several budget cycles and sharing of accountability and continually look for efficiencies.

The different types of the interpretations made as per the planned and the actual fill of the tanker is understood to be depict that there was significant mismatch in the week one itself. This has been seen to be evident with planned volume fill of 1574 litres and actual fill of 1439. The various types of the drivers of the performance is realized to be based on the depictions which are seen to be made with the total number of the vehicles which needs to be considered for refill. The depictions of the factors for the volume fill is also related to the total amount of the distance which has been travelled by the individual vehicles. In terms of the second KPI has highlighted on the important interpretations of the planned and the actual performance which has shown how the planned Miles per Journey has increased in the first four weeks.  This is evident with 10.8% variance in week 1 19% in week 2, 8.5% in week 3 and 8.2% in week 4. The various types of the drivers of the performance for the Volume Fill KPI is realized to be based on the depictions which are seen to be made with the total number of the vehicles which needs to be considered for refill.

The depictions of the factors for the volume fill is also related to the total amount of the distance which is travelled by the individual vehicles. The present KPI of the volume fill variation as per the real and planned can be improved by ensuring that the vehicles are allotted in the appropriate location as per the procurement needs. There needs to be several types of the initiative taken which will be further able to ensure that the vehicles are refiled only while on the trip and not going to distant gas stations which will increase the time taken in the deployment of the individual orders and cause negative variations in the volume fill. The drivers of the performance for the mileage per store journey is interpreted with factors such as condition of the vehicles, capacity of fuel of the tanker and kilometres driven per litre of fuel. This will be able to determine that main reasons for the variations in the planned and the actual variations in the obtained data. It needs to be further understood that the driver of the operational performance is directly relevant to the financial impacts. As per the findings it can be stated that the in the second week onwards there has been a linear increase in the paragon hours utilization rate. This is depicted to be evident with the increase of 3.6% in week 2, 6.7% in week 3, 5.0% in week 4, 7.8% in week 5, 6.7% in week 6 and 7.9% in week 7.

References

Anand, G. and Gray, J. V. (2017) ‘Strategy and organization research in operations management’, Journal of Operations Management, 53–56, pp. 1–8. doi: 10.1016/j.jom.2017.09.001.

Bochtis, D. D. and Sørensen, C. G. (2014) ‘Special Issue: Operations management’, Biosystems Engineering, p. 1. doi: 10.1016/j.biosystemseng.2014.02.011.

Choi, T. M., Cheng, T. C. E. and Zhao, X. (2016) ‘Multi-Methodological Research in Operations Management’, Production and Operations Management, 25(3), pp. 379–389. doi: 10.1111/poms.12534.

Hazen, B. T., Skipper, J. B., Boone, C. A. and Hill, R. R. (2016) ‘Back in business: operations research in support of big data analytics for operations and supply chain management’, Annals of Operations Research, pp. 1–11. doi: 10.1007/s10479-016-2226-0.

Heizer, J. and Render, B. (2014) ‘Operations Management. Sustainability and Supply Chain Management’, in Operations Management. Sustainability and Supply Chain Management, p. 255.

Hitt, M. A., Xu, K. and Carnes, C. M. (2016) ‘Resource based theory in operations management research’, Journal of Operations Management, 41, pp. 77–94. doi: 10.1016/j.jom.2015.11.002.

Hübner, A., Holzapfel, A. and Kuhn, H. (2015) ‘Operations management in multi-channel retailing: an exploratory study’, Operations Management Research, 8(3–4), pp. 84–100. doi: 10.1007/s12063-015-0101-9.

Kou, G. and Yu, L. (2015) ‘Intelligent knowledge management in operations research’, Annals of Operations Research. doi: 10.1007/s10479-015-1985-3.

Li, F., Nucciarelli, A., Roden, S. and Graham, G. (2016) ‘How smart cities transform operations models: a new research agenda for operations management in the digital economy’, Production Planning & Control, 27(6), pp. 514–528. doi: 10.1080/09537287.2016.1147096.

Pedraza-Martinez, A. J. and Van Wassenhove, L. N. (2016) ‘Empirically grounded research in humanitarian operations management: The way forward’, Journal of Operations Management, pp. 1–10. doi: 10.1016/j.jom.2016.06.003.

Qin, R., Nembhard, D. A. and Barnes, W. L. (2015) ‘Workforce flexibility in operations management’, Surveys in Operations Research and Management Science, pp. 19–33. doi: 10.1016/j.sorms.2015.04.001.

Rajapakshe, T. K., Vakharia, A. J., Wang, L. and Yenipazarli, A. (2017) ‘Sustainable operations’, in The Routledge Companion to Production and Operations Management, pp. 276–290. doi: 10.4324/9781315687803.

Venkat, A., Kekre, S., Hegde, G. G., Shang, J. and Campbell, T. P. (2015) ‘Strategic Management of Operations in the Emergency Department’, Production and Operations Management, 24(11), pp. 1706–1723. doi: 10.1111/poms.12346.

Wilke, S., Majumdar, A. and Ochieng, W. Y. (2014) ‘Airport surface operations: A holistic framework for operations modeling and risk management’, Safety Science, 63, pp. 18–33. doi: 10.1016/j.ssci.2013.10.015.

Zhao, S. (2014) ‘Analyzing and Evaluating Critically Tesco’s Current Operations Management’, Journal of Management and Sustainability, 4(4). doi: 10.5539/jms.v4n4p184.