Taxi Industry Forecasting Failure vs. Scenario Planning

Taxi Industry Forecasting Failure vs. Scenario Planning

By Ronald Wellman

Introduction

This blog will describe how the taxi industry failed while using traditional forecasting and how the use of scenario planning supports planning innovation for change. The blog starts with a brief background and timeline of the demise within the taxi industry. Next, the blog includes a description of scenario planning and how it supports innovation. This description is followed by a discussion of three major forces that affected the failure of the taxi industry and the rise of ride-sharing organizations. The blog concludes with a summary of how to best use scenario planning and the social impact for future change.

Source: (Coruscatem, 2019)

Taxi Industry Forecasting Failure vs. Scenario Planning

Background on the Taxi Industry

The taxi industry has had a major forecasting failure. Taxi driving in major cities has been regulated since the 1930s by limiting the number of licensing fees for the issuing of medallions for drivers (Goldstein, 2018). The forecasting failure runs deeper with a monopoly of greed starting at the government level by regulation and taxation. According to the Certify SpendSmart report, as recently as 2014, ride-hailing companies like Uber only had 8% market share compared to car rental companies at 55% and taxis at 37% (Goldstein, 2018).

According to the Taxi & Limousine Commission (TLC), in the first quarter of 2018, ride-hailing companies were at 70.5% of the market, while rental cars were at 23.5%, with taxis at only 6%. The New York taxi medallion business in 2014 valued each medallion at $1 million, which now are estimated at $170,000 (Goldstein, 2018). Other research suggests even lower amounts for the medallion values at the writing of this blog. Due to the drop in the value of medallions, many taxi companies that have taken business loans out to cover the cost of the taxi medallions filed bankruptcy. Some individuals like Doug Schifter, a 40-year old driver, blamed the New York government and the TLC for lack of responsibility. Unfortunately, Doug Shifter committed suicide in front of city hall; his reasoning for taking his life was because he blamed the government for making “a huge number of cars available” which caused him lost revenue (Gellafante, 2018). The number of taxis available for decades was typically 12,000-13,000, and on a Facebook post, Doug Schifter said ride-hailing vehicles had jumped to more than 100,000, which forced him to work 100-120 hours a week to survive.

Source: (Goldstein, 2018)

Scenario Planning Supports Innovation

The taxi industry, independent drivers, and the government used traditional forecasting methods in the execution of their business strategy and would have benefited from an innovative scenario-planning strategy. The industry was focused on budgeting, which had biased gaps in targets and sustainable performance which lacked a competitive strategy. Traditional forecasting methodology lags in geographical and industry data (Ellero, 2014). Furthermore, the forecasting strategy used constrained adaptability for innovation with technology, research and development, diversity, and growth.

Scenario planning uses imagination to speculate future horizons with mapping and is based in past performance; however, it is forward-looking to project the complex market uncertainty into the future. The once stable monopoly of the taxi industry was met with volatility and was caught proverbially sleeping at the wheel. Additionally, the taxi industry neglected to use any type of scenario planning strategy to cope with the volatility.

Unfortunately, since there is no methodology to predict the future, scenario planning supports innovation by planning for best and worse case scenarios that are most likely to occur (Wade, 2012; Wade, 2014). The Oxford scenario approach helps innovation with a best or worst-case scenario while considering the “what if” normative view. Traditional forecasting uses a combination of past and immediate transactional information intertwined with the stakeholders’ interpretation of indirect influences from other environments like the supply-chain, competition, and customers. The Oxford scenario planning uses the overlapping of inside and outside factors to better understand innovative plausible future outcomes (Ramirez, 2017). The Oxford scenario focuses on the ordering of best and worse case scenarios, in combination with, assigning a probability to future outcomes.

Forces that Impacted the Taxi Industry

Force 1

Government regulation played a large part in impacting the taxi industry by charging large fees and limiting the number of medallions, arguing that traffic flow and congestion were the reason not to clog streets with an excessive amount of vehicles. Government regulation created the monopoly, however, was caught sleeping behind the wheel when it came to innovation using the traditional forecasting method. Now the taxi industry as a whole is playing catchup trying to compete competitively and regulate the new innovative ride-sharing industry. The reverse innovation process and scenario planning of ride-sharing organizations like Uber averted direct government control of taxi medallions. A good example of government using scenario planning was in the Netherlands when Uber failed to get approval through changes in Dutch taxi law to operate its UberPop platform (Pelzer, 2019). In New York and numerous other cities, Uber’s innovation permitted unlicensed chauffeur drivers to connect with passengers and circumvent taxi use.

Force 2

Technology is a key factor, considering handheld GPS systems and cell phones have been around since the turn of the century. Granted that early technology was bulky bag phones and laptop computer sized devices; however, there was plenty of room in a taxi to house these devices. Fast forward to the present day, with more computing power in the palm of your hand or on your wrist with a smartwatch and cloud processing technology, which has taken ridesharing to new levels with increased convenience, security, speed, pricing, and availability. Uber's innovative mobile app allowed customers to book, pay, reserve, and even share rides at reduced rates in a platform economy using technology to bridge the socio-economic gap.

Force 3

Money and the monopolistic nature of the medallion system left taxies and government scrambling. One of the arguments for ridesharing is that more rural areas are now getting better service at a lower cost to customers. Taxis increased fares and poor service to urban areas discriminated against these customers. Drivers did not want to make trips outside the well-populated areas where the bulk of the business was; therefore, urban customers’ service suffered from low-quality service and increased prices. The opposite of that is happening with the ride-share innovation with better service and lower prices in these urban areas.

Summary

            Scenario planning in the ride-sharing industry continues to battle with the three forces mentioned above. In the United States, the socio impact of technology and availability to consumers helped fuel the platform. The deliberate alignment with the organizational vision and mission to the social benefits of service to an urban area, efficient mobility, safety, and non-discriminatory transportation were the contributing factors to the success of the ridesharing platform. Continued scenario planning using the alignment of technology with social impact will help ride-share organizations to obtain a sustainable future.

References

Bellafante, G. (2018). A driver’s suicide reveals the dark side of the gig economy. New York Times. Retrieved from https://www.nytimes.com/2018/02/06/nyregion/livery-driver-taxi-uber.html

Coruscate (2019). Taxi app development: Beat ride expansion, its strategies and cost to develop a taxi booking app. Courscate.com. Retrieved from

Ellero, A., & Pellegrini, P. (2014). Are traditional forecasting models suitable for hotels in Italian cities? International Journal of Contemporary Hospitality Management, 26(3), 383-400. doi:10.1108/IJCHM-02-2013-0107

Goldstein, M. (2018). Dislocation and its discontents: Ride-sharing’s impact on the taxi industry. Forbes. Retrieved from https://www.forbes.com/sites/michaelgoldstein/2018/06/08/uber-lyft-taxi-drivers/#78cd97e259f0

Pelzer, P., Frenken, K., & Boon, W. (2019). Institutional entrepreneurship in the platform economy: How Uber tried (and failed) to change the Dutch taxi law. Environmental Innovation and Societal Transitions, 13(1-12). doi:10.1016/j.eist.2019.02.003

Wade, W. (2012). Scenario planning: A field guide to the future. John Wiley & Sons.

Wade, W. (2014). Scenario planning–Thinking differently about future innovation. Globis Insights.  Retrieved from http://e.globis.jp/article/343