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