Short-term analysis emphasize on pricing strategy for the solution of meeting costs of asset management, condition-based maintenance and predictive maintenance. The solutions will focus on cutting staffing costs, vehicle expenses, supply costs, insurance costs and switching to cloud for computing needs. Giving unpaid time off to the senior employees will be used as an effective strategy for managing costs. Vehicle costs can impact the bottom line. By reducing capital costs of IT expenses the company will manage to minimize computing expenses CITATION Luk17 \l 1033 (Froeb, McCann, & Ward, 2017). The short-term strategy emphasize on earning above average profits by cutting insurance costs such as by performing annual review of insurance requirements. Premiums will be reduced by cutting deductibles. The coverage provided by different insurers differ so the best strategy is to choose the affordable one.
Direct price discrimination is an effective measure used for controlling costs by selling product at different prices to the customers. This will require that the firm identify demand of customers and use first-degree discrimination. The firm will examine the characteristics of the consumers and find producer surplus. This situation is known as perfect price discrimination in which seller will charge different prices depending on the characteristics of customers CITATION Luk17 \l 1033 (Froeb, McCann, & Ward, 2017). If the consumer surplus market power is $100, producer surplus is $100 and deadweight loss from market power is $10 the consumer surplus (competition) is $210. The firm will focus on driving consumer surplus to zero under perfection price discrimination. The strategy focused on reducing monthly fee from $500 to $300. If the costs of materials is $4000 and overhead charges are $3000 the company must be able to generate revenue of at least $7500. The theory suggests keeping profits more than the costs.
BIBLIOGRAPHY Froeb, L., Mccann, B. T., & Ward, M. R. (2017). Managerial Economics, 5th Edition. Cengage Learning.
Raju, K., Dr., & Gupta, S. (2018, June). Transforming Railroad Asset Management: Going Smart with Predictive Maintenance. Retrieved June 22, 2019, from https://www.tcs.com/content/dam/tcs/pdf/Industries/travel-and-hospitality/Transforming-Railroad-Asset-Management.pdf
Eisenschmidt, E., Reimig, S., Schirmers, L., & Stern, S. (2017, December). The Rail sector’s changing Maintenance Game. Retrieved June 22, 2019, from https://www.mckinsey.com/industries/travel-transport-and-logistics/our-insights/the-rail-sectors-changing-maintenance-game
Scully, P. (2017, March 21). New Report Indicates US$11 Billion Predictive Maintenance Market By 2022, Driven By IoT Technology And New Services. Retrieved June 23, 2019, from https://iot-analytics.com/report-us11-billion-predictive-maintenance-market-by-2022/
Tuzik, J. (2017, March 03). Big Data in Railroad Maintenance Planning: Evolving Science, Evolving Applications. Retrieved June 23, 2019, from http://interfacejournal.com/archives/1904
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