Inaccurate Forecasting and a Scenario Planning Alternative

Introduction

            Forecasting and scenario planning are two processes by which a business or enterprise attempts to predict their future (Wade, 2012).  Forecasting is the more scientific of two options.  Forecasts are based upon models of regression analytics (Dwivedi, Niranjan, & Sahu, 2013).  Forecasting considers dependent and independent variables and is considered a linear continuation of the present (Wade, 2012).  This may be where forecasts are flawed, though.  Forecasts are based upon mathematics and, thereby, will likely never be totally abandoned as a means of supporting strategic planning or change management.  They are based upon, however, statistics of what is currently known.  Scenario planning, by contrast, attempts to recognize a future of numerous possibilities that realistically may occur based the present (Wade, 2012).  It is more respectful of innovation in this way.  Scenario planning is more considerate of the realm of the possible or even the imagined versus what is strictly more scientifically likely.  Forecasting predicts a definitive future as regression analyses only have one dependent variable.  Scenario planning supports change management by recognizing the possibility of numerous realities or scenarios with the scope of these scenario being only limited by what is known in the present day.

            Unfortunately, there are some enterprises that can testify personally to the woes of being misled by forecasting.  Nintendo is one such company, and the consequences of their mispredictions are discussed below in this paper.

Inaccurate Forecasting the Nintendo 3DS

            Forecasts are based upon data analytics implemented as mathematical modeling and scripted programming.  Because of this, forecasts may be offered as often as the mathematical models or scripted analytics may be implemented.    It is very intricate to supply chain management (SCM), and supply chain managers regularly reforecast to accommodate for changes among variables as well as to ensure process improvement (Albarune & Habib, 2015).  Nintendo’s release of their 3DS is a cautionary tale concerning the misgivings of inaccurate forecasting that led to overcorrection during the SCM process.

            Between April, 2014, and March, 2015, Nintendo sold 8.73 million 3DS units globally (Lopez, 2017).  Nintendo released the base 3DS in 2011, but began the release 3DS XL in 2014 very likely because of success with the original model.  They were expecting that release of the 3DS XL would boost sales.  Nintendo forecasted in 2015 that approximately 7.6 million units—3DS and 3DS XL combined—to be sold in 2016.  By February, 2016, the predictive modeling had changed, and the forecast was modified (Lopez, 2017).  By March, 2016, Nintendo was now forecasting only 6.6 million units to be sold globally for that entire year, a million fewer than that predicted approximation a year prior.  The new modeling misled Nintendo; they sold 6.45 units in the U.S. and Japan alone.  The demand out-paced the supply, and according to Lopez (2017), Nintendo may still be facing shortages of the 3DS now because of conservative forecasting in 2016 and 2017.

            The Hanjin Shipping Co. of South Korea was the seventh largest shipping carrier in the world until they declared bankruptcy in August, 2016 (Kitroeff, 2016).  In terms of this company’s effect on Nintendo’s forecasting calculus for 2016, Hanjin was likely flailing long before they declared bankruptcy, and Nintendo 3DS sales started to suffer because of it.  It is believed that Hanjin’s demise negatively influenced Nintendo’s 3DS forecast (Lopez, 2017).  This bankruptcy would be considered a scenario, and even though Nintendo’s predictive analyses did reflect the decreased availability of shipping, the new forecast presumed that the demand for the 3DS would align with the slowed shipping, or so Lopez believed (2017).  This forecast was incorrect.

Considering Scenario Planning as an Alternative

            Valid scenario planning might require of think tank of individuals if for no other reason than to ensure as many as viable scenarios are considered as possible.  The think tank would be composed of stake holders in a continual dialogue discussing what-if scenarios.  Forces such as product innovation, diminishing demand, and the availability of suppliers would be regularly discussed.  The scenario planning think tank would be planning for the production of more innovative products than those currently on retailer shelves strictly to maintain the market share when the demand of current products diminishes.  The think tank would presume as a scenari9on that competitors were doing the same.  That think tank would also be considering the availability of external organization in the supply chain.  This scenario planning think tank would already be considering alternative shippers should Hanjin or any other shipper be no longer available.  The objective would be to have contingency plans in place as a result of the scenario planning to ensure seamless transition should one of those discussed what-ifs actually came to fruition.

 References

Albarune, A. R. B. & Habib, M.  (2015).  A study of forecasting practices in supply chain management.  International Journal of Supply Chain Management, 4(2), 55 – 61.

Dwivedi, A., Niranjan, M., & Sahu, K.  (2013).  A business intelligence technique for forecasting the automobile sales using adaptive intelligent systems (ANFIS and ANN).  International Journal of Computer Applications, 74(9), 7 – 13.

Kitroeff, N.  (2016).  Hanjin bankruptcy is the tip of the iceberg for flailing shippers.  Retrieved from https://www.latimes.com/business/la-fi-hanjin-shipping-industry-crisis-20160913-snap-story.html.

Lopez, E.  (2017).  How Nintendo’s forecasting miscues led to 3DS shortages.  Retrieved from https://www.supplychaindive.com/news/nintendo-shortage-3ds-supply-chain-failure/435662/.

Wade, W.  (2012).  Scenario Planning:  A Field Guide to the Future.  Hoboken, NJ:  Wiley & Sons.