This project aims at providing the best possible sales forecasts for our client, ‘Walmart’.
Walmart is currently facing stiff competition from E-commerce companies like Amazon. It is
commonly observed that customers visit the brick and mortar store to view the product and end
up buying at Amazon at cheaper rates. Individuals prefer doing their repeat purchases online
instead of visiting brick and mortar stores due to the associated convenience. This has led to year
on year declining sales trend of brick and mortar channels.
a. Market Introduction
Hsinchu 13 good market has been established in April, 2014, and opens every Saturday. Most of the customers are the households living nearby, so the market will hold events for family as well.
The market has their own promotion channel “Facebook fan page”. In January, 2016, the
accumulative page “likes” is up to 4,950 in over 1 years.
Since this is almost the most important and the only way to publish their news and
information to the customers. We wondering if there is any possible way to make good use
Our client is an automotive corporation (e.g. Suzuki) who sell automobiles and motorcycles in Taiwan. Our forecasting goal is to forecast the demand of automobile in 2015 of four of Taiwan’s largest cities: Taipei, New Taipei, Taichung, and Kaohsiung. The forecast results will be used to set promotional plans among regions and seasons, and measure amount of automobile to import in 2015. Potential business benefits include reducing costs (inventory costs and advertisement at lower sales seasons), and improving marketing strategies (targeting the right seasons).
The customer database contains a field called "MARITAL_STATUS". This is an important field for business. It can help the marketing department to segment the customers and target marketing and promotional initiatives accordingly.
• The stakeholder in this data mining project is Mine Sweeper Biscuits (MSB), a premium biscuit manufacturer based out of Denmark. While MSB has entered the Indian market through retail outlets, its sales have failed to take off due to the low product trial rate among Indian consumers.
Retailers face a formidable challenge of ensuring that they have optimum levels of inventory for goods that are perishable. This is because these goods have short shelf life without any salvage value and can hurt the profitability of the retailers significantly. It therefore becomes critical for the retailers to know accurate forecasts for perishable items such as fresh milk and yoghurt. These subclasses also drive footfall into the retail stores and hence it is important to maintain high levels of service for these products.
Managing inventory of perishable goods such as fruits and vegetables is a difficult task for big
retail stores as these items have a very short shelf life. If not managed properly, excessive stock
may result in loss of inventory, but at the same time under stocking may result in lost sales. In
this project we are trying to accurately forecast the demand for 4 SKUs - apple, banana, onion
The consumer products sector faces volatility in demand on a high scale and level of complexity, thereby posing challenges in the area of inventory management. Economic volatility and demand variability present challenges that simple models of demand forecasts are not equipped to handle. An important method of tacking demand variability is an effective way to improve the inventory control policy, which should be designed to smoothen stocking response to demand variation arising from the customers.