Optimal pricing. Once the desired
level of demand is attracted, it can be
further managed through optimal pricing.
Optimal pricing balances both margin and
volume so that transaction profitability is
maximized amid business constraints
such as production and distribution limitations. Determining the optimal pricing
level requires a good understanding and
quantification of the underlying demand
for goods and services and of the profitability of any given transaction. At the
portfolio level, optimal pricing, implemented based on multivariate constraint
optimization techniques, allows driving
some segments for volume and others for
margin growth, aligned with the business
strategy. Profitability improvements as a
result of applying these techniques can
reach 2 percent to 5 percent of revenue.
Once demand is understood and the
right level to maximize the profitability of
sales transactions is attracted, it is time
to analyze how demand triggers other
activities in the company. In traditional
systems, demand only affects decisions
at the nearest stock locations and their
replenishment through outbound logistics. However, demand signals from the
point of sale can also be taken into account on upstream stages of the supply
chain to more accurately decide how to
allocate stock across the system, which
needs to be modeled holistically.
A NA L Y T I C S
SUPPLY CHAIN
Strategic network. Whether it is after
inorganic growth, mergers and acquisitions, moving sourcing to low-cost countries or resourcing transport providers,
footprint and flow path restructuring is a
crucial activity for supply chain managers. The most common optimization applications are establishing where to get
raw materials, what to produce where,
how much and where to store it, who to
deliver to, and what assets are required
across the whole network. However,
real life presents interesting modeling
and implementation challenges.
Examples of these include convincing stakeholders of the need to holistically optimize the end-to-end supply chain,
considerations for production scheduling, demand sensing across all network
tiers, non-linear costs for warehousing
and transport, non-linear relationships
between the quality of raw materials
and the quality of finished goods, the integration of less quantifiable and known
elements such as competition into models, and the double objectives of costs
and CO2 emissions. Typical network optimization projects save 5 percent to 10
percent, but higher benefits – up to 20
percent – are not unprecedented.
Supply chain operations. Supply
chains are inherently dynamic because
of the uncertainties of customer demand,
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