Fiskars Corporation, which generated more than 1.1 billion in sales in 2013, is the second oldest incorporated entity in the world produces a variety of high quality products such as garden shears, pruners, hard tools, scissors, ratchet tools, screwdrivers and like. Business is highly seasonal and prices quite variable. About 10 percent to 15 percent of the annual revenue comes from one time promotions and 25 percent to 35 percent of its products are new every year. Quality is very important at Fiskars: its scissors were selected as the Official Net Cutting Scissors of the NCAA National Championship in 2014. Given the highly volatile demand environment, Fiskars Brands, Inc. a subsidiary of Fiskars Corporation located in Madison, Wisconsin needed to improve its forecasting process. It serves 2,000 customers ranging from large discounters to local craft stores providing about 2300 products. Fiskars Brands introduced a statistical based analysis in its forecasting process along with a web based business intelligence tool for reporting. It put much more emphasis on combination forecasts. Instead of asking members of the sales staff to provide their own forecasts, forecasts were sent to them and they were asked for their validation and refinement. Their inputs are most useful relative to additions, deletions, and promotions. Converting multiple forecasts into one number (forecasts from time-series techniques, sales input, and customer input) creates more accurate forecasts by product. Fiskar’s software has the ability to weight each input. It gives more weight to a statistical forecast for in line items, and inputs form the sales staff get much more weight for promoted products and new items. It also segments products by value and forecast ability so as to concentrate forecasting efforts on products that have the biggest impact on the business. High value items that are easier to forecast (stable demand with low forecast errors to date) tend to do well with the time-series techniques, and judgmental adjustments are made with caution. High value items that are difficult to forecast get top priority in t forecasting effort, and spark the need for collaboration with customers and suppliers. Much less attention is given to improving forecasts for low-value items for which there is some history and fairly steady demand. Finally, Fiskars instituted a Web-based program that gives the entire company visibility to forecast information in whatever for it needs. For example, Finance wants monthly, quarterly and yearly projections in dollars whereas Operations wants projections in units as well as accuracy measures. Everybody can track updated forecast information by customer, brand, and product.

A) What are the some of the core rationales used in the forecast decisions presented in the case about Fiskars Corporation?

B) How can Fiskars utilize exponential smoothing, naïve forecasting, seasonal patterns, and linear regression to attain a more concise forecast with minimum forecast errors? What are some of the negatives associated 2300with these forecasting methodologies?

C) Which kinds of time-series forecasting methodologies can be effectively used by Fiskars to gain competitive operational and strategic market share increases? Please share your rationales.