to the split between specialty and commodity grades would
result in increased profitability. Thus, our optimization
model was developed using projected demands, prices, and
costs, and it constrained the manufacturing plants to their
maximum achievable capacities. To understand the impact
of growing the new business segment we included deliveries
to a number of potential specialty customers at a projected
price per unit.
We developed a number of future scenarios
to compare against the original business plan,
which did not include growth in the specialty
segment. In addition to identifying significant cost savings by changing product sourcing and by changing the warehouse footprint,
we were able to show which customers would
be dropped for any given amount of growth in
the specialty sector. We also were able to determine the unit price that those customers would
need to pay in order to stay above the “cut line.” Those prices were then used by the sales organization as input during
the annual bidding events executed by major customers’
procurement departments.
During the scenario analysis, managers on the commodity
side of the business argued in favor of using the commodity
volume to absorb the plant’s fixed costs, thereby helping to
keep the cost per unit across all products at a reasonable level.
Because we were able to compute the TDC for each product
delivered to each customer, we were able to have frank, data-based discussions and were able to demonstrate that, within
the volume shifts we were suggesting, that approach was
irrelevant and would not improve profitability.
An example using TDC in the sales and operations planning process: Scenario analysis has become a critical part
of a good sales and operations planning (S&OP) process.
Often the S&OP process identifies short-term shortfalls of
product, and a company must choose which customers to
serve, and at what cost. One method used by most companies to address near-term supply shortfalls is to utilize
a premium freight service, like air or expedited trucking,
to curtail long lead times and deliver enough product to
last until adequate supply is once again available. But such
freight services are costly, and to make optimal decisions
about using premium freight because of capacity constraints in supply or manufacturing, it is necessary to have
accurate TDC information by product and customer.
In one case we know of, a large technology company that
sources most of its product from Asia into North America
and Europe uses weekly TDC cost calculations to manage
its premium freight spend. By using TDC to analyze the
trade-off of inventory and premium freight, the company
is able to more effectively assign manufacturing capacity to
the right customers and products. As a result, it has reduced
premium freight costs by more than 10 percent—all with
no negative impact on overall customer service.
A prediction is a forecast of what is expected to happen,
and as we all know, for many reasons forecasts
rarely (if ever) turn out to be truly accurate. To
minimize forecast error, we recommend inves-
tigating a number of different sensitivities
around the key assumptions in the model.
There is no single best answer in a strategic
network analysis. Rather, there are a number
of good solutions, each of which comes with
a certain level of risk. Our job as supply chain
professionals is to understand which sensitivities
to run and how to choose the “most good” scenario
that contains an appropriate level of downside risk if the
forecast should turn out to be wrong. Thus, we should ask
ourselves, “What if my demand forecast is off by ± 10 per-
cent?” or “What if the cost of a key raw material increases
or decreases?” and other, similar questions before making a
final decision.
LEVERAGE THE DATA
Every business wants to both satisfy its customers and
achieve the right level of profit on each and every sale.
Understanding the specific costs associated with each sale is
a key step in managing both the product portfolio and the
customer portfolio. Until recently it has been very difficult
to estimate the true delivered cost by product and customer, so businesses have been willing to make and work with
crude estimates, a practice that likely leads to decisions
based on inaccurate or incomplete data.
In today’s world, there is no need to settle for rough
approximations that may be far off the mark. The necessary
data are readily available, and improved analytics allows
companies to avoid the typical pitfalls associated with
calculating TDC. Following the recommendations in this
article will help companies make more accurate decisions
about what they should sell, to whom, and at what price,
thereby allowing them to leverage maximum competitive
advantage from their supply chain planning processes. c
TED SCHAEFER IS DIRECTOR OF LOGISTICS AND SUPPLY CHAIN
SERVICES AND ALAN KOSANSKY IS CO-FOUNDER AND PRESIDENT
OF THE SUPPLY CHAIN CONSULTING FIRM PROFIT POINT INC.
