By Ann Grackin
Apr 20, 2010
|The holy grail of consumer packaged goods (CPG) companies (or any manufacturer, for that matter) is to become more responsive to customer demand while reducing inventory. Learn how new solutions such as Demand Sensing, or what we call near-term planning, now make that possible.
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Terra on a Tear
Terra on a Tear
For decades, CPG companies created long-term forecasts,
with levels of granularity in months and maybe weeks. Conquering the mountain of inventory and
learning to be responsive have always been challenging for CPG companies.
Demand variability, in practice, is usually addressed with inventory. There was
little opportunity to be more responsive and also keep inventories low. Though
progress has been made over the years, inventory levels for most CPG products
can be from 1 to 6 months!
It's a bit of a myth,
though, that inventory equates to customer service, since the question always
arises, exactly which product does the customer demand? It may be the one
you don't have in stock. In
addition, this is a costly approach. Months of inventory, stuffed into
warehouses, impact margin, and can be very inconvenient with perishable or
end-of-life goods. End-of-life, or
after-sell-date inventory, is not visible, missing that last chance to earn
revenue, and may ultimately be trashed, which can cost millions.
But no more! Terra Technology has been on a tear, grinding down the level of
inventory for some of the most admired brands in the world. We recently talked
with the company and learned how they have evolved. Some exciting news has come
out of this Connecticut-based company, including a European presence to support
their global CPG client base.
So what is Terra doing that traditional forecasting software
firms are not?
Traditional forecasting packages and approaches were just
not architected to sense this 'near term' demand, sorting through the layers of
data, crossed signals and huge volumes of POS. Nor were they designed to
determine whether there is existing inventory, in the plethora of warehouses,
which can be used to economically fulfill that demand--now.
So, why don't traditional forecasting solutions deliver
this? For a number of reasons. Firstly
forecasting packages are not designed to be "responsive." That is, they are
designed to create a forecast. They do not then analyze short term demand
(changes in forecast, new orders, nuances in demand) and based on inventory
positions (forecasting packages are not actually looking at real inventory)
create a new plan and execute it. In
other words, forecasting systems are not designed as execution systems.
Terra Technology has dedicated
itself to addressing this very type of challenge. And, as it turns out, that is a very good
place to be. The company has grown, even in this down economy, by getting it
right, and adding value to even some of the most supply chain sophisticated and
progressive companies out there.
P&G is one case in point. Without sharing too much of
P&G's data, they credit their use of Terra's Demand Sensing solution for helping to reduce
forecast errors by around 40%! And the subsequent inventory saving of ~10%.
Demand is a very hard number to derive when you are selling
into a rich network of channel partners and retailers. Many organizations interpret withdrawal of
inventory from a warehouse as a signal to replenish, when it may actually be
just moving inventory to the back of a store. In addition, POS data can be inaccurate, late, and so prolific that it
is difficult to interpret. So interpreting these factors and translating them
into what is a real demand signal is the work. Figure 2 is a simplified version of the span of information and the
audience for the solution. In other
words, traditional forecasting systems are not designed to sense demand.
Multi-echelon View of Demand and Supply
Sitting between various data sources--warehouse inventory,
in-store inventories, plant inventory and other inventory stores, the demand
sensing engine can determine demand and update the forecast daily based
on meaningful events. Then everyone executes to the most current, accurate
numbers, not to something agreed to a month before. This requires new planning system algorithms
that take into account not only seasonal trends from previous years, but also orders
received yesterday. There are a lot of
nuances in interpreting demand, and a system has to learn them. In
addition, with a more accurate product fulfillment schedule, this can be used
to improve transportation schedules as well.
The users of such data are the
manufacturer, the wholesaler and probably the retailer itself. Although it may be purchased by the brand
company, everybody wants that great data!
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