1) Agenda: It includes Contents of presentation. 2) Introduction: Introduces about LL Bean Inc. 3) Forecasting process: Forecasting process adopted by LL Bean for its products. 4) Timeline: Process from Catalog Forecasting to delivery to customers. 5) Problem statement: Problems in the case 6) Solutions of the case: Possible solutions

1. LL Bean Inc.
Item Forecasting and Inventory Management
Presented By:
Sahil
Shitize Trehan
Akshay Kumar
Gargi Malhotra

2. Agenda
• Introduction Of the company
• Forecasting Process
• Problem Statement
• Solutions of the case

3. Introduction
• L.L. Bean was founded in 1912 by Leon Leonwood Bean of
Greenwood.
• Leon obtained a list of non-resident Maine hunting license
holders and started a nationwide mail-order business.
• By 1991 L.L. Bean, Inc. was a major cataloguer manufacturer,
and retailer in the outdoor sporting specialty field.
• By 1991, 80% of all orders came in by telephone.
• Specializes in:
 Outdoor equipments (canoes, tents, camping gear)
 Outdoor and indoor Apparel
 Footwear
 Luggage and Bags

4. Introduction
• Major catalogs: Spring, Summer, Fall and Christmas
• Catalogs consists of 116 to 152 Pages and full catalog is being
circulated to Bean’s Regular customers.
• Classification of product line hierarchically:
Merchandise Groups Level 1
Demand Centers Level 2
Item Sequences Level 3
Items Level 4

5. Forecasting Process
List the Items
Rank the
items
Freezes the
forecast
Forecasting
error
Overage cost
and underage
cost
Salvage value Critical Ratio
Corresponding
error

6. Timeline
Oct-Jan
Preliminary
Forecast
Feb-May
Updating
Forecast
June
Print B/W
layout
July
To
Inventory
August
Delivery

7. Problem Statement
• Wide dispersion in forecast errors for Never out items and
New items.
• More Lead Time and inventory cost.
• More cost of Under stocking.
• Reduced Demand level.

8. Solutions: More Lead Time/
Inventory Cost
• Lead time is the time that elapses between the placing of an
order (either a purchase order or a production order issued to
the shop or the factory floor) and actually receiving the goods
ordered.
• Lead Time can be reduced by adopting :
o Just-In-Time
o Manufacturing Requirement Planning
o Cross Docking
o Point of Scale
o Have fewer vendors close by and build strong relationship with them to
shorten lead times and process a second order.

9. Solutions: More Forecasting Errors
• Forecast error is the difference between the actual or real and the
predicted value.
• There is wide dispersion in forecast errors for Never out items and
New items due to use of Naïve Forecasting.
• By using Exponential Smoothing we can smooth out variations and
resulting better forecast.
• Look at upcoming demand in the fast changing industry by using
qualitative forecasting methods not only at historical data alone.
o Delphi Method
o Conjoint Analysis
o Past Forecasting
o Customer Survey

10. Solutions: Low Demand Level
• There is lower Demand level for both New Product and
Existing Product.
• Collect actual and forecasted data for new items previously
introduced.
• Gather info on cost of sales, commissions provided, stock-outs
and back orders.
• Have sufficient buffer stock to avoid stock-out situation.
• Gather sales info of a new catalogue by comparing similar
items with competitors.
• Observe demand of existing products, once new products are
launched.

11. THANK YOU