Hello everyone, welcome to demand analytics. This is Dr. Yao Zhao. I'm a professor in Supply Chain Management from the Rutgers Business School. In this video, I like to tell you the story of AK MetalCrafters, a leading cookware manufacturer in North America. I'll tell you a crisis they faced during their new product introduction, and the results they achieved by demand analytics in resolving the crises. AK MetalCrafters was founded in 1960s, during the US steel age. They produce professional quality bonded cookware. And it is known for extraordinary properties and exemplary cooking performance. Their products are sought after by the world's top chefs, and passionate home cooks. Typically, demand is random and highly seasonal, thus an accurate demand forecast is critical to success. If the company can predict demand, the demand peaks and valleys accurately, it can avoid inventory shortage and customer backorders, that will make customers happy. It can also improve cost efficiency by reducing over time production, excessive inventories, and expedited shipping costs. Demand planning and forecasting is especially challenging in new product introduction because of the seasonal effects, and pricing and promotions. For example, AK launched a new top-line product, the 10-piece set in January 2011. The product started to gain momentum in the marketplace. To better promote the product, in September 2012, the retail price of the item was dropped from $949.98 to $799.95. This generated on average an 86% of lift at the point of sales. Unfortunately, the company didn't expect such a surge in demand of this item, and soon ran out of inventory. Customers were backordered by almost 3,000 sets during the busiest season. AK was at a risk of losing the market momentum for this item due to the upset customers, and the high cost of overtime production, and over stock. Suffering this huge loss, the company has determined to come up with the forecast for the next whole year, which is 2013. The factory can increase production during the summer months to meet the forecasted peak demand in the fourth quarter of the year. The requirement is that, the forecast should be sufficiently accurate, at least for three months forward. To allow enough time for sourcing, production, and shipping. Objective is to minimize backorders, and also to minimize production and inventory cost in the same time. Using demand analytics, the company built demand forecasting models based on factors such as time, price, seasonality and other factors. They identified the main drivers for demand and quantified their impact. Using the models, AK made the demand predictions for the whole year of 2013, and proved their accuracy. As you can see on the figure to your right, the gray dots are actual sales in units, the blue dots represent the model, and the red dots are the predictions made by the model. The accurate demand forecasts allow the companies to achieve their objectives of increasing revenue and reducing costs in the same time.