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1/7/04
Why
Does GKN Run Lean?
Compiled
by:
Erik Kruse, SCRC |
GKN Driveline North America’s (GKN DNA) primary
customers are automotive original equipment manufacturers
(OEM). An automotive OEM requires its suppliers
to ship the right materials, in the right quantity,
to the right place at the right time and at a competitive
price. That may seem like a basic request, but as
supply management staff at the GKN DNA’s Roxboro
and Alamance facilities can attest, it takes “lean”
operations to be successful in their business.
The driving force behind GKN DNA’s decision
to move toward lean was the result of the customers’
trend toward increasingly lean operations. The OEMs
want to better meet the demand of the end consumer
by reducing their own inventory levels and running
efficient operations. As such, they have very stringent
delivery requirements. In short, the margin of error
regarding on-time delivery in the industry is incredibly
small and the associated penalties are quite large.
This can be illustrated with some figures from GKN
DNA:
Small Margin for Error (1)
| • |
20-25
outbound trucks per day |
| • |
4
doors for loading outbound production parts. |
| • |
45-60
minute time window (specifically scheduled
times) for loading trucks |
Large Penalties (1)
| • |
A
missed delivery generally results in premium
outbound freight costs. For example, for a
mid-west customer destination, if the load
is shipped within approximately 8 hours of
the original scheduled time, then it can usually
be shipped expedited ground, which costs approximately
$1,000-$2,500 per occurrence. |
| • |
However,
using the same example, if the load is shipped
beyond approximately 8 hours of the original
scheduled time, then it will require an expedited
air shipment, which costs approximately $5,000
to $25,000 per occurrence. |
Given
the high cost of expediting a shipment, why would
GKN DNA do it? According to a presentation
by a GKN DNA representative at the SCRC semi-annual
meeting in the spring of 2003, one number says it
all (1):
| • |
In
2002, the typical OEM assembly line shut-down
fee was $41, 978 per minute. |
| • |
Not
to mention the impact on lost future sales
for the offending supplier (which is difficult
to quantify). |
Obviously, GKN DNA must be responsive enough to
maintain a high level of customer satisfaction
regarding on-time delivery. But while on-time
delivery is one thing, staying competitive on
price and quality is another. GKN DNA must ensure
that everything it buys and adds value to will
be sold to the OEM. In addition, they desire to
reduce the amount of working capital held up in
inventory. Being “lean” helps here,
as well.
GKN DNA typically receives from three days to
two weeks worth of “firm” release data
from the majority of its OEM customers. This “firm”
data, known as an “862” in electronic
data interchange (EDI) speak, can actually change
as late as 3 hours prior to dispatch.
GKN
DNA also receives “830’s,” which
can be used for short and medium range planning.
For instance, GKN DNA typically gets “non-firm”
forecasted release data from the “Big Three”
(GM, Ford, and DaimlerChrysler) for about six-months
out, and from Toyota and Honda for about two-months
out. But there can be substantial variances that
far in advance. 830s can vary drastically from
actual shipments, both by volume and by mix.
So as a safeguard, GKN DNA compares the OEM’s
forecast with one created by their own devices.
Currently, the division uses the economic and
financial information firm Global
Insight, Inc. Global Insight provides GKN
DNA with an independent forecast of domestic automotive
production on a high level. But data in that form
is not currently very useful to production, so
a forecaster at GKN DNA headquarters in Auburn
Hills, MI must quantify how much of that forecast
is likely to be the division’s business.
For this, the forecaster uses a combination of
quantitative and qualitative methods which take
into account data such as GKN DNA’s historical
ship rates and customer-specific model/platform
knowledge to arrive at a final GKN DNA volume
forecast. Then the data is divided up further
into the product mix forecast. The data resulting
from this analysis represents GKN DNA’s best
estimate, by part number, of future sales to its
customers.
That data then goes to the GKN DNA Master Production
Scheduler residing at the tier one plant in Roxboro,
NC. The scheduler uses the resulting forecast
as the source data for the master production schedule
(MPS), which is the basis for GKN DNA’s plan
for production, inventory, staffing, etc. Once
per month, the MPS sets the quantity of each end
item to be completed in each week of a rolling
90 day planning horizon. Within the first 60 days
of the planning horizon, OEM volume release data
varies by an average of around four percent. But
there is typically much more variance in the product
mix.
Beyond the 60 day horizon, a major driver of forecast
variance is caused by imperfections in the source
data (from Global Insight). For example, the forecast
is based partly on jobs per hour (productivity),
rather than total volume. The forecast therefore
does not take into account “unplanned”
plant shutdowns that frequently occur. Although
the forecast does a good job of predicting total
sales in the automotive industry, it is not designed
to predict the product mix at the driveshaft application
level. It also doesn’t handle demand “peaks”
or “valleys” very well. In short, it
is not intended to look at demand in the detail
that GKN requires.
Relying too heavily on inaccurate forecast data
could put the GKN DNA in a precarious position.
Owning obsolete raw materials – or worse
yet, obsolete finished goods – would be a
very costly mistake. But since it was unrealistic
that the average end consumer was going to tell
the OEM what they were going to buy, the forecast
data would likely remain imperfect. Thus, GKN
DNA decided to move toward “lean” operations.
Of course, that doesn’t happen overnight.
For more on this, see The
Road to Lean Manufacturing.
Reference:
(1) Greene, G. (July, 2003). Discussion with author.
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