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.
The profitability of a supermarket is largely determined by efficient product display within promotional
bins and shelf displays. Hence it can be argued that a sustainable competitive advantage can be
achieved if demand of particular products could be accurately forecasted to allow for a customized
The competitive retail landscape demands stores to maximize return from all possible avenues. This urge is further exacerbated by customer price-sensitivity and low customer loyalty. ABC retail too struggles with this problem and is exploring ways to improve its bottom-line. We, as a consultant, have come up with a proposal that would help ABC reduce cost without significant impact on revenue, thus improving the store’s profitability. As part of this proposal ABC is required to consolidate suppliers and enter long term contract with major players in order to exploit better deal.
Problem Description/Business Goal - As the fruit supplier to the hypermarket, we wish to match our
procurement with the fruit demand. This is important owing to the perishable nature of fruits. This
reduces over/under stocking. Also, by matching the fruit demand accurately, we can provide value-add
to the hypermarket and stay ahead of the competition.
Hypermarts frequently use promotions via mail-in-rebate coupons, bulk buy discount offers etc. to influence customers to purchase greater number of products from their stores. Keeping this in mind, the potential benefit to the Hypermart can be significantly increased if the right promotions are targeted to the right customers - more specifically, identifying a new customer as a potential high margin customer and targeting him/her with promotions related to high margin products for greater sales turnover of such products.
In a busy supermarket the number of checkout lanes is constant. Not all customers buy in large volumes. Some buy in small quantity but are forced to wait in long queues at the checkout counter. Our objective is to demarcate a few checkout lanes as "fast checkout lanes" which would exclusively serve these customers (who shop in low quantity), thereby lowering their waiting time. The challenge however is to predict an optimum number of fast checkout lanes, such that on one hand they are able to process these customers fast and on the other they do not remain empty.
Retailers spend a considerable amount of time, effort, and money to acquire a new customer. However once a customer has been acquired, the maximum value can only be derived if the customer becomes a repetitive buyer and his/her purchase amounts increase with time. Identifying which customer will qualify for a promotion is a key to this problem and our study makes an attempt to solve this issue. Our model predicts the future shopping basket value of a customer.
We have been hired by our client, a reputed FMCG conglomerate, Unilever as data mining consultants. Our client has a range of products in the Personal Care Category that comprises of soaps etc. One of the brands that our client happens to own is the Dove brand of soap. For the first time the client is formulating a Valentine mai-in-coupon scheme to be rolled out in the month of February (next year). The scheme has the following business objectives:
1) Understand the customer profile of those customers who buy Dove soap.
This project aims at accurately forecasting retail demand for women’s apparel in the United States, in order to help a large apparel exporter in China to reduce operating expenditure and rationalize capital expenditure in the future.