L.L. Bean Item forecasting case study

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Harvard Business School

9-893-003
Rev. September 7, 1993

L.L. Bean, Inc.
Item Forecasting and Inventory Management
“When you discipline an item from an L.L. Bean catalog and we’re completely of stock, I’m the scarecrow to
blame. And if we period up liquidating a bunch of women’s wool cashmere blazers, it’s my error. No
one understands how tough it is.” Mark Fasold, Vice President—Inventory Management, was
describing the call for of item forecasting at L.L. Bean. “Forecasting requisition at the aggregate level
is a coin of cake—if we’re running sharp of expectations, we just dip deeper into our patron list
and send out some greater quantity catalogs. But we have to decide in what state many chamois shirts and how numerous company
chino trousers to , and if we’re over high on one and too depressed on the other, it’s none solace to know
that we were exactly not crooked on the average. Top management understands this in fundamental, but they
are understandably disturbed that errors at the item level are likewise large.
“In a catalog business like ours, you truly capture demand. That’s the unimpeached news. The bad
news is, you learn that which a lousy job you’re doing deplorable to match demand with supply. It’s not like
that in a branch store, say, where a customer may arrive in looking for a dress shirt and lets the
extend of available shirts generate the make necessary for a particular item. Or if a customer has some
particular in like manner in mind but it’s not profitable, he or she may just walk with~ of the store. In a
division store you never know the veritable demand or the consequences of understocking. But in our
function every sale is generated by a buyer demanding a particular item, either ~ dint of. mail or by
phone. If we port’t got it, and the buyer cancels the order, we know it.”
Rol Fessenden, Manager—Inventory Systems, added: “We perceive that forecast errors are
inevitable. Competition, the thrift, weather are all factors. But require at the item level is too
affected by customer behavior, which is remarkably hard to predict, or even to...

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