Palm Scenario Planning Failure

The new Palm in Stephen Curry’s Hand (McCracken, 2018).

Introduction

This blog will discuss the scenario planning failure of Palm brand Personal Digital Assistant (PDA). The blog begins with a discussion of why and how organizations have scenario planning failures and what recent research suggests for improving success and efficiencies in the scenario plan.  Then the blog will include a detailed explanation of the failed scenario plan of the Palm PDA. Next, the blog focuses on the impacts and the relevancy to the organization followed by the forces that contributed to the innovation’s failure. Last, the blog features a summary highlighting the key points of the Palm scenario planning failure.

When Plans Go Wrong

Organizations are increasingly using the scenario planning approach to predict future situations and to make strategic plans based on those predictions. The reasoning behind the use of scenario planning is to help reduce failures and the level of uncertainties and to provide alternative thinking used in the decision-making process (Ratcliffe, 2000). Scenario planning can be time-consuming; specifically, the construction of contextual storytelling using what-if techniques of mapping, creativity, or Delphi techniques for decision-making and strategic planning are time-intensive. Daszynska-Zygadlo (2012) defines scenario planning in three components:

1. identifying future combinations of uncertainties and emergent technology;
2. interpreting relevant past and current events for scenarios; and
3. developing logics for scenario moves and the interplay of predetermined elements and uncertainties.   

Paul's (2016) research suggests scenario planning needs to be aligned with a risk failure mode effect and analysis (REMEA) based in project management to efficiently eliminate the element of uncertainty in the detection method of risk.

Impact Example

In 1992 the Palm, Personal Digital Assistant (PDA) was created by Jeff Hawkins and was called the Casio Zoomer, designed to compete against Apple’s Newton PDA. In 1996 USRobotics (purchased by 3Com in 1997) released the Palm PDA at $299 (McCracken, 2018). For those who remember, it was small and easy to use and allowed users to add cameras or phone capabilities. Palm’s scenario planning was similar to Microsoft’s with licensing its operating systems to third-parties like Sony. In the early 2000’s, Palm introduced Treo650 and was an industry leader with 20,000 apps including games to spreadsheets (McCracken, 2018). The move to running the Windows operating system proved to be poor scenario planning because of Windows' low customer ratings at that time.

Furthermore, in 2007 Palm introduced Foleo, a small laptop, for Treo users to increase screen size and to add a keyboard. Foleo, however, never shipped, and Palm later promised a Foleo II in the future that never shipped and never reached the market. During all of the Foleo debacle, Palm released a new smartphone with a slide-out QWERTY keyboard and a new operating system called WebOS in 2009. The new WebOS was initially only available on the Sprint network which was no threat to iPhone or Android competition (McCracken, 2018). By 2010 the lackluster Palm brand was sold to HP, and in 2013, HP sold the WebOS to LG which adapted the technology for smart TVs. In 2014 TCL purchased the right to use the Palm brand, and in 2018, created a startup using the Palm brand powered by Android in an attempt to rejuvenate a comeback of a small (3.3-inch screen) handheld device using celebrity marketing by Golden State Warrior Stephen Curry. The Verge labels the Palm devices as tiny, strange, quirky and having low-end specs (Welch, 2018). Despite numerous attempts, the Palm devices have failed at scenario planning.

Relevant and Why

The relevancy of the Palm scenario planning failure was pivotal at the adoption of the WebOS, which was only initially available on the Sprint network. At what point in the scenario plan did a Sprint-only network come up? Palm’s strategic plan may have been to dominate Sprint sales; however, the scenario plan failed to align with it. In 2004 Palm was dominating the market, and by 2009 became no threat to iPhone or Android by isolating itself in the small Sprint network. As previously mentioned, Paul's (2016) research showed increased success when scenario planning used REMEA. The scenario plan effect and analysis with the WebOS and Sprint-only network failed to assess the iPhone and Android cellphone competition and growth.

Forces

The technical forces mentioned above of WebOS and isolation to the Sprint network drastically reduced the success and ultimate failure of the Palm brand and device. Furthermore, the social aspect of customers’ wants and desires of a larger screen and easier-use keyboard were ignored. Instead, Palm tried to create the Foleo and Foleo II mini laptop—complicating matters—and never released either product to advance societal perceptions. An additional influence was money. After multiple attempts for R&D on the Palm and Foleo with no release or product sales, the funds had run out, and the organization had to sell.

Summary

Scenario planning must include RFMEA to align the scope and project goals with the real-world consequences of cost, schedule, and quality in uncertain conditions. Palm failed to correctly scenario plan after initial success in 2004 when it introduced Foleo products with the WebOS exclusively on the Sprint network. After being sold several times, Palm brand has struggled to make a comeback, or obtain a sustainable competitive advantage. Issues of relevance to competition, combined with financial forces, have misaligned the scenario plan and overall product strategies leading to failed product sales and loss of market share.

References

Boehner, K., DePaula, R., Dourish, P., & Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275-291. doi:10.1016/j.ijhcs.2006.11.016

Daszyńska-Żygadło, K. (2012). Scenario planning and real options analysis in integrated risk management process. Universitatis Mariae Curie-Skłodowska. Sectio H. Oeconomia, 46(4 (XLVI)), 75-84. Retrieved from https://pdfs.semanticscholar.org/bf85/86c19e2d7f56d30873d72629b914a6dcb331.pdf

McCracken, H. (2018). Palm’s progress: The rise, fall-andrebirth-of a legendary brand. FastCompany.com. Retrieved from https://www.fastcompany.com/90246716/palms-progress-the-rise-fall-and-rebirth-of-a-legendary-brand

Paul, V. K., & Basu, C. (2016). Scenario Planning and Risk Failure Mode Effect and Analysis (RFMEA) based Management. Journal of Construction Engineering and Project Management, 6(2), 24-29. doi:10.6106/JCEPM.2016.6.2.024

Ratcliffe, J. (2000). Scenario building: a suitable method for strategic property planning?. Property management, 18(2), 127-144. Retrieved from https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1015&context=futuresacart

Welch, C. (2018). Alleged new Palm smartphone is tiny, strange, and has low-end specs. TheVerge.com. Retrieved from https://www.theverge.com/circuitbreaker/2018/8/9/17672068/new-palm-android-pepito-smartphone-photos-specs-leak

Mysterious Innovation

Mysterious Innovation

Introduction

Innovations are found in new and mysterious ways. In this blog, I will look at three different ways innovation is at times discovered through:

1. Serendipity
2. Error
3. Exaptation

I will give the definitions of the three terms above, followed by scholarly definitions, and a real-world example of the phenomenon. Finally, the blog will end with a summary.

• Serendipity

My definition of serendipity is when something happens by chance or is an unexpected result or consequence of another action. For example, Charles Goodyear accidentally charred some rubber, and it formed a leather-like elastic rim that serendipitously weatherproofed the rubber. Fink (2017) points out serendipity does not have an antonym. Johansson (2012) defines serendipity as the fortunate development of events when organizations stress the importance of making a quest for discoveries by accident and sagacity in research. Serendipity is the intersection of different cultures, industries, and disciplines. For example, when Bill Gates realized Microsoft Windows had a serious memory flaw in the operating system, he considered abandoning the operating system. Later Bill Gates and members of his team meet with IBM to help fix the problem but to no avail. Subsequently, those same IBM employees were on a business trip at an after-work hour’s party. The IBM employees met other non-employees who joked about how to fix the problem. Within hours after sitting down to try to resolve the problem with the jokesters’ recommendations, the IBM employees solved the problem (Johansson, 2012) which forever changed Microsoft’s future.

• Error

We all have heard of trial and error. Generally speaking, this is how innovation occurs, usually with more errors opposed to instant innovation on the first or second try. Errors trigger subsequent corrections and potential improvements by researchers and innovators. What comes first—errors or innovation? Arguably errors typically come first and during the innovation process. The integration of errors and failure (intentional or non-intentional) are the drivers and diffusion of organizational innovation through knowledge, social systems, and organizational structure (Kister, 2019). A classic example of an error was in 1956 when Wilson Greatbatch was building a heart rhythm recording device and installed the wrong resistor. The machine produced a heart-like (lub-dub) sounding rhythm. Thus, an innovative new pacemaker was discovered. Previously, pacemakers were the size of TVs; Greatbatch’s device was two cubic inches, and now more than half of a million of the life-saving devices are implanted yearly (Donnelly, 2012).

• Exaptation

There are very few methodological and epistemological criteria used to identify and analyze exaptations. The use of exaptations in the field of social sciences remains latent (Andriani and Carihnani, 2012). The application of exaptation in technology and innovation is defined as the characterization by a creation mechanism with new functions (Lane, 2011). I would characterize exaptation as entrepreneurship. Entrepreneurs must use creativity with existing resources in the creation of mechanisms with acts of exaptation.

The heuristic mechanism of exaptation diverges from the mainstream rationalist paradigm for problem-solving (De Sordi, 2019). Entrepreneurial exaptation is the need for actions and mechanisms to exercise a new function, product, or service. An example of an exaptation in technology is the use of data analysis for customers’ purchases. British retailer Tesco, a new entrant in the insurance market, used intensive data analysis by monitoring the evolution and relationship of customers to know the insurance requirements of its customers beforehand (Peppers & Rogers, 2011). Exaptation can and should be used throughout all areas of an organization and not limited to the core business and R&D. Exaptation mechanisms can spur additional innovation (Andriani & Carignani, 2012).

Summary

This blog defined and discussed the way innovation can happen with modern-day examples. There is no innovation without failure. Innovation doesn’t happen without action. Organizations must plan to innovate and draw inferences for innovation to occur. Innovation comes from internal knowledge and resources, reflective analysis, and perseverance with critical thinking. Often during that process, innovation intentionally, or unintentionally, occurs in serendipity, error, or by exaptation. 

References

Andriani, P., & Carignani, G. (2012). Exaptation and modular systems. In EURAM Annual Conference, Rotterdam School of Management, Erasmus University, Rotterdam (NL), 6th–8th June.

De Sordi, J. O., Reed, E. N., Meireles, M., Hashimoto, M., & Rigato, C. (2019). Exaptation in management: Beyond technological innovations. European Business Review, 31(1), 64-91. doi:http://dx.doi.org.proxy.cecybrary.com/10.1108/EBR-01-2018-0020

Donnelly, T. (2012). 9 Brilliant inventions made by mistake. Inc.com. Retrieved from https://www.inc.com/tim-donnelly/brilliant-failures/9-inventions-made-by-mistake.html

Fink, T. M. A., Reeves, M., Palma, R., & Farr, R. S. (2017). Serendipity and strategy in rapid innovation. Nature communications, 8(1), 2002. doi:10.1038/s41467-017-02042-w

Johansson, F. (2012). When success is born out of serendipity. Harv. Bus. Rev, 18, 22. Retrieved from https://hbr.org/2012/10/when-success-is-born-out-of-serendipity

Kister, A. (2019). Error Monitoring as an Organisational Innovation in Public Hospital Activity. Economics & Sociology, 12(4), 213–227. https://doi-org.proxy.cecybrary.com/10.14254/2071-789X.2019/12-4/13

Lane, D. A. 2010. “Innovazione e distretti industriali”. In Processi di innovazione e sviluppo locale. Teorie e politiche, Edited by: Russo, M. 57–68. Roma: Donzelli.

Peppers, D., & Rogers, M. (2011). Managing customer relationships: A strategic framework. Hoboken, NJ: John Wiley & Sons.

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

Socio-Technical Plan

Introduction  

This post will define, describe, and critically evaluate the sociotechnical plan from the paper “Affectability in educational technologies: A socio-technical perspective for design” by Hayashi & Baranauskas (2013). The purpose of the post is to examine the impact of the introduction of new technology on social cultures.

Define

Hayashi & Baranauskas (2013) define a socio-technical plan of how the integration of digital technology (laptops) can potentially help formal and informal learning in an elementary public school in the city of Campinas, in Sao Paulo, Brazil. The study showed the use of technology impacted feelings, values, and the culture of both teachers and students. Embedding technology into learning will affect the entire organization (school); furthermore, socio-technical plans increase the scope and perspectives of informal, formal, and technical aspects of the learning settings. Stein (2012) argues the socio-technical innovation is the new paradigm of innovation by reversing the traditional top-down logic to a bottom-up logic. Marcel de Arruda Torresa's (2017) research shows the new paradigm shift from focusing on economic growth to focusing on a holistic approach to well-being that emerges new economic models, production systems, and wellness ideas as strategies that overcome traditional barriers. The compound layers of a socio-technological plan merge technology, time, and space, with experiences—inside and outside the classroom or organization.

Describe

The Hayashi (2013) socio-technical plan used OLPC (One Laptop Per Child) at the public school in Brazil. The ages of the children ranged from 6-14 years old. Four cases contributed to the understanding of how the socio-technical plan was able to contribute to more meaningful practices. Hayashi (2013) discussed how cognitive models of traditional paradigms are transforming. Research by Boehner et al. (2007) suggested a cognitive socio-technical plan will enhance cognition over rational thought. On the macro side of the study, all teachers and some other employees—such as the principal, pedagogue, janitor, cook, and library attendant—participated in the study. The data were collected through workshops and other activities at the school, including regular classes, along with informal interviews. Reliability was reinforced by pictures, videos, and field notes (Hayashi, 2013).

The technological artifacts (the XO laptops) case results included:

1. Transforming homework assignments;
2. Integrating the school in interdisciplinary activities;
3. Using XO laptops inside and outside the school’s walls; and
4. Incorporating student volunteers.

Transforming homework assignments allowed students to use laptops with internet browsers and wireless internet connections which saved time from using traditional libraries and walking to computer labs. Other advantages included teachers’ involvement in emotional-management strategies and avoidance of negative, emotionally charged events from doing homework at home (Xu, 2005). The emotional responses from pupils gave teachers and students a better understanding especially when faced with difficulties in assignments (Hayashi, 2013).

Integrating the school’s interdisciplinary activities resulted in scenarios, one of which was “students and consumption at home.” The students’ consumption of food at home scenario involved a process described below:

1.       Students took pictures of products (including nutrition labels) and advertisements.

2.       Students then studied the differences in comparisons of nutrition facts.

3.       Teachers-then initiated discussions. For example, an English as a second language teacher asked the students to capture English words on these pictures to help discuss and better understand words.

4.       Finally, teachers shared results of these discussions, and frequently described students’ attitudes as “happiness” and “sense of accomplishment” with higher motivation, and students with interdisciplinary issues were more proactive with the technology and helped fellow students with increased engagement (Hayashi, 2013).

The use of the laptops—both inside and outside the school—increased students’ pride, and they were more outgoing in discussing the technology with bystanders. On a trip to the park, students took pictures and short videos of animals and made notes with the laptops. Students demonstrated increased values of ownership, happiness, and engagement. Hayashi’s (2013) research indicated the younger students had higher responses of valence and arousal.

Student volunteers, aka “student monitors,” were needed due to the increased challenges and responses from the technology. Student monitors met every two weeks, and students expressed feelings and understandings of the program. Hayashi (2013) noted that the emotional and affective responses and outcomes transcended technological, formal, and informal categories and impacted the students’ lives with valuable learning.

Evaluate – 

In this section, you will evaluate the plan reviewed in this article. You might consider covering both pros and cons about the plan, or you provide examples of successes and/or failures using the plan.

The technology increased engagement between students, as well as, between students and teachers and allowed increased emotional strategies, both in and out of the classroom. The technology helped motivate and give everyone involved a sense of accomplishment. Even students with interdisciplinary issues had positive engagement and emotional responses. One negative issue noted in the study was with a disabled student being more challenged; however, the experience helped her learn more patience and understanding of other people’s needs. The study pointed out technical issues with plugging in the laptops in the classroom and not having enough outlets, lagging computers, internet issues, and operational systems with different interaction models. Informal issues of parents and some teachers not being comfortable with the technology existed. Formal issues of laptop theft prohibited students from taking home the laptops. 

The integration of new technology will always come with some issues especially when technology is completely new, and systems are not in place to better manage use, distribution, time-management, and support systems. Despite some cons of the introduction of the technology, which is to be expected, the positive affective and emotional aspects contributed to increased productivity, satisfaction, and integrated learning opportunities (Hayashi, 2013).

Summary

In summary, the research showed the sooner technology can be introduced in learning with a holistic approach, the better it can simulate contemporary world applications. The socio-technological plan reduced the negative aspects with increased positive emotional and affective aspects demonstrated in the four case scenarios. Furthermore, combining the different learning places (home, school, field trips) stimulated intentional learning and motivation and decreased interdisciplinary issues.

References

Boehner, K., DePaula, R., Dourish, P., & Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275–291.  doi:10.1016/j.ijhcs.2006.11.016

Hayashi, E. S., & Baranauskas, M. C. (2013). Affectability in educational technologies: A socio-technical perspective for design. Journal of Educational Technology & Society, 16(1), 57–68.

Marcel de Arruda Torresa, P. (2017). Design for socio-technical Innovation: A proposed model to design the change. The Design Journal, 20(sup1), S3035-S3046. doi:10.1080/14606925.2017.1352811

Stein, J. (2012). Bottom-up and top-down innovation: Create an innovative company. New York. Simply Innovate.

Xu, J. (2005). Homework emotion management reported by high school students. School Community Journal, 15(2), 21-36

One Infamous Prediction

One Infamous Prediction

Introduction

The blog begins by discussing the definitions and distinctions of forecasting and predictions in an innovation environment. The business innovation context discussed is Nikola Tesla’s prediction of wireless charging and the wireless transmission of power. Tesla was in the process of developing the technology, and the project was shut down for two major factors. One force was that the technology was new and was not readily available nor developed. The second force was that he ran out of funding, and the wireless tower he was in the process of constructing was demolished. In hindsight, had Tesla created a smaller version of the invention, his prediction could have come true during his life instead of 100 years later. If Tesla was able to build a small-scale production model, he would have been able to forecast the large scale and commercial production of wireless electricity from past sales.

Innovation with Forecasting and Prediction

Innovation with forecasting and predictions both involve analysis. Forecasting uses expert judgment and statistical methods of past events to estimate future production or events. An example, of forecasting, is a Rule-Based Forecasting (RBF) method that uses forecasting expertise and domain knowledge judgment to apply rules and develop extrapolations (Armstrong, 2001). Prediction is analysis by experts who model over a timeline in a prototypical point of view to predicting future events. Tesla used his analysis of the new electrical AC-current in the ether and applied it to the experimentation of prototypes towers to extrapolate new wireless products. The Delphi prediction method uses a panel of experts with statistical aggregation in multiple rounds (Goodarzi et al., 2018). Predictions use “applied science” and connect sets of variables to predict unknown values and inferences (Simon, 2001). Forecasting is typically utilized with more accuracy for short-term scenarios that include low uncertainty, stability, domain knowledge, and trends. Prediction is better suited for uncertainty, low domain knowledge, little or no historical information, and outcomes that are ten or twenty years in the future. In contrast, prediction accuracy lies in not just measuring data as in forecasting but constructing statistical models to explain phenomena to generate measurable predictions.

Wireless Charging

Nikola Tesla, a Serbian-American inventor/engineer, born to an orthodox priest, is credited for over 300 patents and numerous predictions like automated cars, drones, and other cutting-edge technology. I will be focusing on one of his renowned inventions and experiments involving near field electromagnetic (EM) transmission. Recently wireless charging for mobile phones has become a viable option for many people. New automobiles are also offering wireless charges as features, and the aftermarket of products for wireless charging is growing. Nikola Tesla discussed experiments with alternate currents of high frequency and the application methods before the American Institute of Electrical Engineers, Columbia College, N.Y. (Tesla, 1891). He discussed his scientific observations, which verified the ether present in our atmosphere as the medium of an electric phenomenon using induction coils that he created to discharge high tension currents. He predicted employing a commercial coil to power lights and potentially other devices across great distances.

Tesla’s Magnifying Transmitter was his vision of revolutionizing the world with free power, transportation, communication, and even weather control. His transmitter was on the cutting-edge of sending electricity wirelessly in a time when wires were an innovation. The Tesla Tower, known as the Wardenclyffe Tower (Magnifying Transmitter) was built by Tesla in the early 1900’s (see figure 1).

Figure 1. Wardenclyffe Tower

Source: Tesla Memorial Society of New York (2019)

The Wardenclyffe Tower was demolished in 1917 and never became fully functional. Financial difficulties for Telsa caused the sale of the property that housed his tower, for commercial development. (Tesla, 2002). The possibilities of the tower could have been endless had it become functional.

Force 1

The technology was a force that prohibited the development of this tower. Telsa was on the cutting edge of new innovative hardware that did not yet exist, and thus, the ability to manufacture the parts needed for this hardware was limited. The first decade of 1900 was ripe with new inventions such as the washing machine and the model T-car. Additionally, the first human-crewed controlled flight by the Wright brothers was yet another technological break-through of this decade. Electronics was in its infancy and prohibited Tesla from building the inventions promptly. It also led to increased costs, the second force.

Force 2

Finances, coupled with the increased costs with the slow production of equipment, delayed Tesla from producing the tower and developing it more quickly. The slow and costly production led Tesla not to be able to produce revenue from the device in a timely manner. The lack of profits and financial support led to the foreclosure of the property where the tower resided, followed by its demolition. The Tesla Memorial Society of New York (2019) quotes J.P. Morgan, financier of the Tesla Broadcasting system, “How can we get money from the electricity which Tesla is supplying to every part of the world?” Tesla’s vision was for free power, and when Morgan heard that, he cut the funds to the tower, and it was never completed.

Summary

J.P. Morgan got his answer; the wireless technology has been commercialized successfully to make money. Tesla’s prediction was never actually accomplished in his lifetime; however, he was correct, and we benefit from wireless charging today. Some interesting facts about the story from the Tesla Science Center at Wardenclyffe website are that the property was never developed. The original red brick laboratory building still stands today. In 2017, ground-penetrating radar confirmed hundreds of feet of tunnels beneath the structure, and the reason for these tunnels remains a mystery. The Telsa Science Center at Wardenclyffe, a non-profit organization, now owns the property and hopes to restore it to develop a science and technology center with a museum. Additionally, the property is also listed on the National Register of Historic Places.

References

Armstrong, J. S., Adya, M., & Collopy, F. (2001). Rule-based forecasting: Using judgment in time-series extrapolation. In Principles of Forecasting (pp. 259-282). Springer, Boston, MA.

Goodarzi, Z., Abbasi, E., & Farhadian, H. (2018). Achieving consensus deal with methodological issues in the Delphi technique. International Journal of Agricultural Management and Development, 8(2), 219-230. Retrieved from http://ijamad.iaurasht.ac.ir/article_540498_d4bd6133361312bb4c273242368de1ee.pdf

Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289-310. doi:10.1214/10-STS330

SIMON, H. A. (2001). Science seeks parsimony, not simplicity: Searching for pattern in phenomena. In Simplicity, Inference and Modelling: Keeping it Sophisticatedly Simple 32–72. Cambridge Univ. Press

Tesla, N. (1891). Experiments with alternate currents of very high frequency and their application to methods of artificial illumination. Transactions of the American Institute of Electrical Engineers, 8(1), 266-319. Retrieved from http://www.tfcbooks.com/tesla/1891-05-20.htm

Tesla, N. (2002). Nikola Tesla on His Work with Alternating Currents and Their Application to Wireless Telegraphy, Telephony, and Transmission of Power: An Extended Interview. 21st Century

Tesla Memorial Society of New York. Wardenclyff Tower. (2019) Retrieved from https://www.teslasociety.com/teslatower.htm

Scenario Planning versus Traditional Forecasting

Scenario Planning versus Traditional Forecasting

Introduction

The blog will compare and contrast scenario planning versus traditional forecasting. First, an explanation of scenario planning, along with its advantages and disadvantages, will be presented, followed by an explanation of traditional forecasting and its advantages and disadvantages to differentiate between the two concepts. Next, a summary and conclusion will follow on how to best apply scenario planning and traditional forecasting.

Scenario Planning

Organizational leaders must direct their organizations to a sustainable competitive advantage and must plan and implement business strategies five to ten years in the future. If there is one thing for certain about the future, it is that the future is not absolute; consequently, no one can predict it with absolute certainty. There is no methodology to see the future; therefore, leaders must plan for different educated scenarios that would be the most likely to occur or to emerge (Wade, 2012; Wade 2014). The scenario planning strategy is advantageous to strengthen the ability to cope strategically with volatility and uncertainty strategically. The Oxford scenario approach uses the probabilistic approaches of best- or worst-case scenarios, or a normative (what the future should look like) view. 

The Oxford approach focuses on immediate business transactions and environments beyond direct influence. For example, the immediate environment of the supply chain, competition, customers, and stakeholders. Both scenario planning and traditional forecasting follow no exact cookie-cutter method. Scenario planning differentiates from traditional forecasting by involving a cross-section of inside and outside management to invest time and involve planning processes to understand plausible future versus probable future (Ramirez, 2017). Traditional forecasting focuses on the quantitative probability of future outcomes, and the Oxford scenario assigns a probability to plausible future outcomes.

For this reason, the Oxford scenario planning approach has an advantage over the traditional planning approach by relating challenges to a larger contextual system thinking organizational framework. The contextual environment includes, but is not limited to, international finance, commerce, legislation, exchange rates, energy prices, technology, social values, geopolitical trends, demographics, and macroeconomic conditions. Transactional environments include, but are not limited to, employees, customers, suppliers, investors, NGOs, lobbies, regulators, and competitors. 

The strengths of scenario planning are that it combines a broad range of contextual environments with transactional environments and then assigns a probability factor with unique combinations of organizational elements to those plausible outcomes. The primary weakness of scenario planning is that it is an iterative process that typically requires additional insights with more rounds of iteration. Furthermore, it requires organizations to define what is plausible and suitably obtainable. In juxtaposition, traditional forecasting focuses on past data analysis and assigns a higher probability of plausible outcomes. Finally, scenario planning may benefit from quantum computing, AI (artificial intelligence), and machine learning with neuro-networks to better assign suitability to plausible and obtainable outcomes in the future to increase accuracy over traditional forecasting

Traditional Forecasting

Traditional forecasting mostly uses qualitative data analysis with less than 10% of traditional forecasting using the qualitative methods approach (Treiblmaier, 2015). Treiblmaier (2015) notes some exceptions with a mixed-methods approach taking into account socio-economic, technological, and market developments. An advantage of traditional forecasting is that frequently management judgments are used for adjustments to improve accuracy as a form of triangulation.   Traditional forecasting methodology advantages in research by Burger et al. (2001) showed simple traditional forecasting models outperform more complex ones in tourism demand forecasting. Ellero (2014) suggests that traditional forecasting models based on historical data are suitable for many markets, and the comparison of different forecasting models is needed to adjust for geography with differencing data parameters. Geographical and lag of industry data are a disadvantage of traditional forecasting. Another disadvantage is that industry gaps in research exist between the practitioner and academic research to produce successful outcomes. Decision-makers need to focus on data generation, range, time, and the target will help decision-makers triangulate to adjust and refine results for recent and significant market changes and the Bullwhip effect.

 Traditional forecasting strength lies in its simplicity with a qualitative data analysis approach, especially when combined with management judgments that have years of experience in industries and organizations. Decision-making is both an art and a science, and knowing the past trends and how the industry, competitors, customers, and employees have responded in the past can offer great insight to the organization's ability to accurately find a suitable forecast. Transactional environment experience and knowledge will help leadership deduce a strategic action plan to potentially use mergers and acquisitions or partnerships for future endeavors to increase market share and competitive advantage. 

The weakness of traditional forecasting is that it is prone to bias, disruptive technology, underestimation of environmental factors, lack of creativity, and strategic diversification. When contrasted with multiple rounds of scenario planning and potential machine learning, traditional forecasting could be left behind with an over-simplistic model. Traditional forecasting also is not utilizing an RBV (Resource Based View) to fully utilize internal expertise in forecasting. Traditional forecasting is at the mercy of upper management for the decision-making process which could amplify biases and conflict with culture, thus sabotaging innovation and change.

Summary

Both scenario planning and traditional forecasting have their strengths and weaknesses and potentially have strategic advantages depending on the organization, industry, and external environments. I recommend organizations first define their vision and mission and then evaluate which process will align with the goals and help differentiate the organization from the competition. Additionally, the organizational strategy might need to be slightly adjusted to open up strategic conditions for collaborative strategies depending upon the results. I would suggest organizations use a combination of both traditional forecasting and scenario forecasting to create a sustainable competitive advantage consistent with the board and executive team. My recommendation is to first start with the big picture scenario planning because it is easily aligned with the board and vision of the organization (Ramirez, 2017). I then recommend also incorporating multiple traditional forecasting methods to help get short-term wins that align with a long-term strategy. 

References

Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting—a case study of Durban, South Africa. Tourism management, 22(4), 403-409. doi:10.1016/S0261-5177(00)00068-6

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

Ramirez, R., Churchhouse, S., Hoffman, J., & Palermo, A. (2017). Using scenario planning to reshape strategy. MIT Sloan Management Review, 58(4), 31-37. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1916720878?accountid=144789

Treiblmaier, H. (2015). A classification framework for supply chain forecasting literature. Acta Technica Corviniensis - Bulletin of Engineering, 8(1), 49-52. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1646396272?accountid=144789

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

Accidental Vulcanized Rubber

Introduction

This blog is about innovation by accident. The invention is vulcanized rubber, which is also contested by Goodyear as not an accident. Goodyear maintains the hot stove incident that created weatherproof rubber or vulcanization held meaning for Charles Goodyear because “his mind was prepared to draw an inference.”  Goodyear’s meaning of the preceding quote is that people who apply themselves most perseveringly to a subject are intentionally drawing an inference—not experiencing a revelation as the result of an accident.  This blog will go into the details of how and what Charles Goodyear went through and how his perseverance, innovation, and relentless pursuit of innovation lead him to success. Furthermore, the blog will detail another example of Thomas Adams’s accidental invention in rubber to try to compete with Goodyear that lead to modern-day chewing gum.

Vulcanized Rubber

Often innovation happens by mistake. I am reminded of the saying of a broken clock: “Even a broken clock is right twice a day.” Sometimes when organizations have processes and systems in place to promote and encourage innovation, the results can be different than what is anticipated. In the case of Charles Goodyear, he started in the rubber business as a recovering bankrupt hardware merchant in Philadelphia in 1834. Goodyear tried to sell a new valve he invented for rubber life preservers to no avail. The early 1830s were dubbed “rubber fever,” and the demand for Brazilian waterproof gum increased. The problem with the rubber was it melted in the heat and froze in the cold, and this outraged the public. There were massive returns, costing investors to lose millions, and everyone thought the rubber business was over in America. Creativity researchers Reiter-Palmon and Robinson (2009) list the creative problem-solving process to include:

1. Problem construction,

2. Idea generation,

3. Idea evaluation, and

4. Idea implementation and monitoring.

After speaking with Roxbury India Rubber Co., Goodyear was put in prison for his debt upon his return to Philadelphia. While in prison, Goodyear began experiments with rubber working on a batch of raw rubber with his wife’s rolling pin that she brought him. Goodyear tried items like magnesia powder and quicklime to solve the melting issues. Once released from prison, he started a laboratory in his kitchen and earned a medal for his progress at a New York trade show. He decorated samples with paint, gilded and embossed them. Kaufman and Beghetto (2009) found the creative process termed “little-c” when non-experts are creative and work through solutions for simple innovations.

It was not until Goodyear ran short of rubber and used nitric acid to remove the paint and other coatings that made the piece turn black, and he threw it away. Several days later he remembered how the nitric acid made the rubber smooth and dry and better than anything else he had previously created. In the financial panic of 1837, Goodyear was destitute and camped his family in an abandoned rubber factory living on fish they caught in Staten Island harbor. After some time, Goodyear found new backing for a government contract of 150 mailbags to be manufactured with the nitric acid process. However, the warm weather melted the bags, and the rubber was still problematic.

Finally, in 1839, Goodyear experienced a breakthrough when he was showing off his latest formula. He accidentally threw his hands up in the air in excitement, and the piece of rubber fell on a hot potbelly stove.  Goodyear expected it to melt like before, but instead, the rubber charred like leather with an elastic rim. So by serendipity, weatherproof rubber was born. Gilson and Madjar (2011) define radical creativity when the creation of ideas or processes distinctly are markedly different from what exists. Goodyear went on to success using a steam-heating process (vulcanization) with shirred goods that he rushed into production, and rubber rose to worldwide success. Goodyear made flags, jewelry, ships and sails, calling cards, musical instruments, rubber hats-vests-ties, and even had his autobiography printed and bound with rubber. Because Goodyear continued to experiment with his never-ending rubber creations, he potentially missed delegated manufacturing revenues.

Yes, people wanted the waterproofing qualities of rubber, but what was the best use of these materials for profit? Lindgren's (2016) research showed two successful predominant strategies for startups; one was experimentation and innovation. A second approach was to have a business strategy and goals guiding the development activities rather than customer feedback. Goodyear was doing just that.

Another innovation in the late 1800s involving rubber was from an inventor by the name of Thomas Adams Sr. Adams changed the rubber industry through his experiment with making rubber from a local tree, Maniklara Chicle, as a cheaper alternative to Goodyear’s Brazilian rubber. The Ancient Greeks and Egyptians used several forms of chewing gum. The big change came when Adams made Chicle chewing gum, which was far superior to paraffin wax gums at the time. Adams then went on to market it in small gumballs wrapped in colorful paper and added flavors. He is known as the father of modern-day chewing gum. The difference in Adams’s business model versus Goodyear’s business model is that Adams established his manufacturing and grew his business with patented machines used to manufacture the product.

Conclusion

Creativity is a manifestation of consciousness with psychosocial and cultural influences that endeavor beyond the inventor’s cognitive processes and accumulated knowledge (Sereboff, 2015). Goodyear and Adams used their creativity to manifest their inventions. Goodyear figured out by mistake that heat would vulcanize a sulfur-based rubber, which revolutionized the industry. Similarly, Adams was trying to find a cheaper rubber to compete with Goodyear and discovered a new use from the chicle experimentation. He later capitalized on it by innovation in manufacturing and mass production.

References

Gilson, L. L., & Madjar, N. (2011). Radical and incremental creativity: Antecedents and processes. Psychology of Aesthetics, Creativity, and the Arts, 5, 21. doi:10.1037/a0017863.

Kaufman, J. C., & Beghetto, R. A. (2009). Beyond big and little: The four c model of creativity. Review of General Psychology, 13, 1-12. doi:10.1037/a0013688.

Lindgren, E., & Münch, J. (2016). Raising the odds of success: the current state of experimentation in product development. Information and Software Technology, 77, 80-91. doi: 10.1016/j.infsof.2016.04.008

Reiter-Palmon, R., & Robinson, E. (2009). Problem identification and construction: What do we know, what is the future? Psychology of Aesthetics, Creativity, and the Arts, 3, 43-47. doi:10.1037/a0014629.

Sereboff, J. L. (2015). Invention: A creative manifestation of consciousness (Order No. 3721305). Available from ProQuest Central; ProQuest Dissertations & Theses Global. (1727757121). Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1727757121?accountid=144789