Technology and Business Decisions

Technology and Business Decisions – by Rehtafeht Johannus Lihp

 

The accelerated pace of business in the global market place coupled with the compounding wealth of data has made it imperative for firms to utilize the latest processing technologies and the tools developed in management science.  Management Science provides predictive modeling and analysis tools to help firms make strategic and informed complex decisions on how to maneuver their way through the rapidly changing market environment.  Simulations are run with data collected by the firm and used to predict how scenarios will change and develop over time and how different choices at different times will create different opportunities or losses.  These simulations can predict the best decisions for your firm to make and can predict the likelihood of various decisions possible for other firms to make (Adler, 2002).

It is possible to paint a relatively reliable picture of the overall market environment with the right data and the right data processing software and hardware (Fahlman, 1983).  The firm Amazon, has a wealth of data that it collects and utilizes to predict and better serve the behaviors of their customers.  This data grows exponentially.  Human brain power and processing speed are not viable options to make use of all of the data a firm is able to collect.  A human may not even know what data is useful and what is a waste of brain power to take into account.  Artificial Intelligence, or AI, is being developed and utilized to process and make sense of all of the data.

Proprietary intellectual property, strategic vision, reliable and highly motivated workforces and economies of scale are not enough for firms to continue to succeed in the new global market.  Big data and predictive analytics combine the forces of computers’ and humans’ reasoning and decision making skills.

For managers looking to gain an advantage on competitors, we see opportunities today to do the following:

  1. Find the strategic edge. In assessing past organizational forecasts, home in on areas where improving subjective predictions can really move the needle.
  2. Run prediction tournaments. Discover the best forecasting methods by encouraging competition, experimentation, and innovation among teams.
  3. Model the experts in your midst. Identify the people internally who have demonstrated superior insights into key business areas, and leverage their wisdom using simple linear models.
  4. Experiment with artificial intelligence. Go beyond simple linear models. Use deep neural nets in limited task domains to outperform human experts.
  5. Change the way the organization operates. Promote an exploratory culture that continually looks for better ways to combine the capabilities of humans and machines (Schoemaker, 2017).

 

References:

Adler, R. (2002). System and Method for Modeling and Analyzing Strategic Business Decisions 

United States patent application publication, Adler ,Pub. No.: US 2002/016958 A1, (https://docs.google.com/viewer?url=patentimages.storage.googleapis.com/pdfs/US20020169658.pdf)

Fahlman, S.E., Hinton, G.E., Sejnowski, T.J. (1983). Massively Parallel Architectures for Al: NETL, Thistle, and Boltzmann Machines, Proceedings of the AAAI-83 conference Washington D.C. August 1983., (https://www.researchgate.net/profile/Terrence_Sejnowski/publication/221605073_Massively_Parallel_Architectures_for_AI_NETL_Thistle_and_Boltzmann_Machines/links/54a4b0170cf257a636072712.pdf)

Schoemaker, P, Tetlock, P, (2017). Build a More Intelligent Enterprise, MIT Sloan review, (https://mackinstitute.wharton.upenn.edu/2017/building-a-more-intelligent-enterprise/)

 

 

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