case study of marginal costing

• Marginal Costing As An Essential Tool For Decision Making...

Marginal Costing As An Essential Tool For Decision Making In A Manufacturing Company (A Case Study Of Rokana Company In Imo State)

This study was undertaken to empirically ascertain the role of marginal costing in decision  making of manufacturing firms with a particular reference to Rokana industries ltd. Imo State.  The study made use of primary data obtained through the administration of we/I structured  questionnaires. The collated data were analyzed using frequency distribution and ordinary least  square (OLS) Economic technique. The empirical results showed that there is significant  relationship between product pricing and marginal costing application. It was recommended  that manufacturing firms should base their output level and product pricing because it helps  them know how to optimize profit, react to signals from the market and help them retain share  of the market.

EPUNAM, J (2021). Marginal Costing As An Essential Tool For Decision Making In A Manufacturing Company (A Case Study Of Rokana Company In Imo State). Mouau.afribary.org: Retrieved Nov 17, 2024, from https://repository.mouau.edu.ng/work/view/marginal-costing-as-an-essential-tool-for-decision-making-in-a-manufacturing-company-a-case-study-of-rokana-company-in-imo-state-7-2

JANE, EPUNAM. "Marginal Costing As An Essential Tool For Decision Making In A Manufacturing Company (A Case Study Of Rokana Company In Imo State)" Mouau.afribary.org . Mouau.afribary.org, 22 Jun. 2021, https://repository.mouau.edu.ng/work/view/marginal-costing-as-an-essential-tool-for-decision-making-in-a-manufacturing-company-a-case-study-of-rokana-company-in-imo-state-7-2. Accessed 17 Nov. 2024.

JANE, EPUNAM. "Marginal Costing As An Essential Tool For Decision Making In A Manufacturing Company (A Case Study Of Rokana Company In Imo State)". Mouau.afribary.org , Mouau.afribary.org, 22 Jun. 2021. Web. 17 Nov. 2024. .

JANE, EPUNAM. "Marginal Costing As An Essential Tool For Decision Making In A Manufacturing Company (A Case Study Of Rokana Company In Imo State)" Mouau.afribary.org (2021). Accessed 17 Nov. 2024. https://repository.mouau.edu.ng/work/view/marginal-costing-as-an-essential-tool-for-decision-making-in-a-manufacturing-company-a-case-study-of-rokana-company-in-imo-state-7-2

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The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study

• Published: 22 December 2016
• Volume 45 , pages 849–873, ( 2018 )

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• Amit Agarwal   ORCID: orcid.org/0000-0002-3352-0227 1 &
• Benjamin Kickhöfer 2  

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Negative externalities cause inefficiencies in the allocation of capacities and resources in a transport system. Marginal social cost pricing allows to correct for these inefficiencies in a simulation environment and to derive real-world policy recommendations. In this context, it has been shown for analytical models considering more than one externality, that the correlation between the externalities needs to be taken into account. Typically, in order to avoid overpricing, this is performed by introducing correction factors which capture the correlation effect. However, the correlation structure between, say, emission and congestion externalities changes for every congested facility over time of day. This makes it close to impossible to calculate the factors analytically for large-scale systems. Hence, this paper presents a simulation-based approach to calculate and internalize the correct dynamic price levels for both externalities simultaneously. For a real-world case study, it is shown that the iterative calculation of prices based on cost estimates from the literature allows to identify the amplitude of the correlation between the two externalities under consideration: for the urban travelers of the case study, emission toll levels—without pricing congestion—turn out to be 4.0% too high in peak hours and 2.8% too high in off-peak hours. In contrary, congestion toll levels—without pricing emissions—are overestimated by 3.0% in peak hours and by 7.2% in off-peak hours. With a joint pricing policy of both externalities, the paper shows that the approach is capable to determine the amplitude of the necessary correction factors for large-scale systems. It also provides the corrected average toll levels per vehicle kilometer for peak and off-peak hours for the case study under consideration: again, for urban travelers, the correct price level for emission and congestion externalities amounts approximately to 38 \({EUR}ct/\rm {km}\) in peak hours and to 30 \({EUR}ct/\rm {km}\) in off-peak hours. These toll levels can be used to derive real-world pricing schemes. Finally, the economic assessment indicators for the joint pricing policy provided in the paper allow to compare other policies to this benchmark state of the transport system.

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Optimizing Traffic System Performance with Environmental Constraints: Tolls and/or Additional Delays

Simultaneous internalization of traffic congestion and noise exposure costs.

Environmental impacts of enlarging the market share of electric vehicles

‘Externality’ refers in this paper to ‘negative externality’ unless otherwise stated.

‘Multi-Agent Transport Simulation’, see www.matsim.org .

See Charypar and Nagel ( 2005 ) and Nagel et al. ( 2016 ), section “ Base case ”, for a more detailed description.

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The VTTS is defined as the individual willingness-to-pay for reducing the travel time by one hour. For linear utility functions, it is the ratio of the marginal utility of travel time and the marginal utility of money. The former is the sum of the disutility for traveling ( \(\beta _{trav,mode(q)}\) ) and the negative utility of time as a resource ( \(- \beta _{dur}\) ). Please note that the person-specific VTTS in MATSim can vary significantly with the time pressure which an individual experiences. This is because of the non-linear utility function for performing activities, influencing the actual value of ( \(\beta _{dur}\) ).

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The user benefits calculated from the utility of the last executed plan are not same as the user benefits calculated from the logsum over all plans of an agent. The latter (also sometimes called expected maximum utility) considers utility from heterogeneity in the choice set and is in theory the preferable figure for calculating user benefits in MATSim (see Kickhöfer and Nagel 2016a ). However, as the authors point out, the current MATSim implementation might, under certain conditions, yield biased choice sets. In consequence, the utility of the last executed plan is used in the present paper for economic analysis.

A recent study by Kickhöfer and Kern ( 2015 ) shows that the framework in principle allows for a similar classification in the case of emission costs. However, in the present study, only caused emission costs are considered and referred to as ‘emission costs’ from here on.

This result has been confirmed by two simulations with different random seeds, which are used to initialize the pseudo random number generator in MATSim. A different random seed will eventually result in different simulation outcomes. For an example of the effect of randomness on optimal supply in MATSim, see, e.g., Kaddoura et al. ( 2015a ).

Peak hours are identified as 07:00–10:00 and 15:00–18:00 considering total travel demand of all user groups in the BAU scenario.

For the visual presentation, a Gaussian distance weighting function is used to smooth emissions and delays throughout the area of Munich and surroundings. Uniform hexagonal cells of size 500 m are used for this purpose. The smoothing radius is assumed to be 500 m. For more information on the exact visualization procedure, please refer to Kickhöfer ( 2014 ).

All important pollutants are considered for pricing. For illustration purposes, the emission plot only shows \(NO_2\) .

In peak hours, the congestion pricing scheme and combined pricing scheme exhibit similar patterns.

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Acknowledgements

Part of the material from a preliminary version of the work has been presented at the 14th International Conference on Travel Behavior Research (IATBR) 2015 in Windsor, London. Important data was provided by the Municipality of Munich, more precisely by Kreisverwaltungsreferat München and Referat für Stadtplanung und Bauordnung München. The support given by DAAD (German Academic Exchange Service) to Amit Agarwal for his PhD studies at Technische Universität Berlin is greatly acknowledged. The authors also wish to thank Kai Nagel (Technische Univsersität Berlin) for his helpful comments and H. Schwandt and N. Paschedag at the Department of Mathematics (Technische Universität Berlin), for maintaining our computing clusters. Finally, the authors are grateful to anonymous reviewers for their valuable comments. The responsibility of any remaining errors stays with the authors.

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Agarwal, A., Kickhöfer, B. The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study. Transportation 45 , 849–873 (2018). https://doi.org/10.1007/s11116-016-9753-z

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Published : 22 December 2016

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DOI : https://doi.org/10.1007/s11116-016-9753-z

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