Situation This case study is about the struggle that Santa Clause has to deal with in order to keep its promise to give every child the toys he or she deserve. Children all year long have been sending Santa Claus letters to request toys for Christmas. The main problem for Santa is to figure out what the children want for Christmas and get all ready for delivery before Christmas. Santa has noticed that children request has become over the years more various and flexible which make it harder for Santa to keep its promise and deliver children their wishes.
It’s the busiest time of year for North Pole Workshops. Production is in high gear, and the elves are on overtime in the sprint toward Christmas. But an unexpected spike in demand for one toy may leave children around the world disappointed on Christmas morning, whether they‘ve been naughty or nice. At the same time, another toy’s popularity threatens to plummet, leaving Santa and his elves faced with the prospect of millions of unloved playthings left in the warehouse. This is the third time in three years that Santa’s elves have been caught off guard by a toy’s sudden surge in popularity.
Earlier in the season, even just a month ago, it would have been possible to find capacity, but now every line is running full tilt. Obviously Santa like the old days when everything used to be simple; wooden blocks, a train set, a doll… One of the demands on hand is more than a million SKUs. The investment in software doesn’t seem to be helping and Santa can’t help thinking that one of these days they are not going to be able to do it. For Santa and his staff not to disappoint the children, they have to find a way to improve its response to shifts in demand. Discussion
The main concern for Santa is what he should do to avoid this situation. There are many ways to predict the trend in the market. M. Eric Johnson, the director of the Glassmeyer/McNamee Center for Digital Strategies at Dartmouth’s Tuck School of Business who advises Santa to stop reacting to fads and start creating them. Market research is necessary, but it is a mistake to rely on focus groups for kids. Nor do even the best technologies for capturing early demand indicators work reliably for toys. Instead, Johnson recommends advertising, tie-ins (with some cautions), and controlled scarcity with variety (as used with Beanie Babies).
He also points out the rolling mix strategy used to good effect by Mattel and Spanish retailer Zara. I think the best way for Santa to manage the situation is use a better forecasting method. Forecast is a well informed, educated decision. In forecasting and modeling you take various input parameters and other factors to create a well informed decision. However, just because you make an informed decision or guess, it doesn’t mean that you will be correct. Since forecasting deals with using past data, it would be hard to account for unforeseen circumstances that may arise.
According to businessdictionary. com, forecasting is, “a planning tool which helps management in its attempts to cope with the uncertainty of the future (“forecasting,” n. d. ) As well, Kros (2009) suggests that, “the heart of forecasting is “good” guessing and the best guess is an educated guess. ” Part of forecasting deals with formulating equations to make models from past data. The other part is based on logic and common sense that managers obtain through experience. Combining the two parts help managers make more confident decisions.
Using a program, the manager should be able to figure out when more stock of “x” should be ordered, question why the inventory of product ”x” may be high during this or that season, or relate the output to other external factors (i. e. socio-economic or behavioral) that could make sales fluctuate. In forecasting, there is always that possibility of that choice not being the right answer. But also it might depend on the type of forecasting method use. Some methods work better in some circumstance. According to Jain, Chaman L (2000), there are three models of business forecasting systems.
In the time-series model, data simply is projected forward based on an established method such as the moving average, the simple average, exponential smoothing, decomposition, and Box-Jenkins. Each of these methods applies various formulas to the same basic premise: data patterns from the recent past will continue more or less unabated into the future. To conduct a forecast using the time-series model, one need only plug available historical data into the formulas established by one or more of the above methods. Obviously, the time-series model is the most useful means for orecasting when the relevant historical data reveals smooth and stable patterns. Even when unforeseen events occur, the time-series model may still be useful, providing those events can be accounted for. The second forecasting model is cause-and-effect. In this model, one assumes a cause, or driver of activity, that determines an outcome. For instance, a company may assume that, for a particular data set, the cause is an investment in new technology, and the effect is sales. This model requires the historical data not only of the factor with which one is concerned (in this case, sales), but also of that factor’s determined cause.
It is assumed, of course, that the cause-and-effect relationship is relatively stable and easily quantifiable. The third primary forecasting model is known as the judgmental model. In this case, one attempts to produce a forecast where there is no useful historical data. A company might choose to use the judgmental model when it attempts to project sales for a brand new product, or when market conditions have qualitatively changed, rendering previous data obsolete. In addition, according to the Journal of Business Forecasting Methods & Systems (2000), this method is best use when most of the sales come from a relatively few customers.
To compensate the absence of historical data, alternative data can be collected through experts in the field, prospective customers, trade groups, business partners, or any other relevant source of information. Since the toy market is very flexible, Santa must start early his forecasting and keep it update regularly. He can also start the production of since the beginning of the year base on the periodical forecasting and can increment, stop or reduce the production of each toy. References Jain, Chaman L (2000).
Which Forecasting Model Should We Use? Journal of Business Forecasting Methods & Systems. Fall 2000 Forecasting (n. d. ) Retrieved from http://www. businessdictionary. com/definition/forecasting. html Kros, J. (2009). Spreadsheet Modeling for Business Decisions: (2nd edition). Kendall Hunt Publishing Company, Dubuque, IA ManyWorlds http://manyworlds. com/exploreCO. aspx? coid=CO12120512391517 Safavi, Alex (2000). Choosing the Right Forecasting Software and System: Journal of Business Forecasting Methods & Systems. Fall 2000.