become a shopping location, warehouse location, pickup location, return location, or a combination of these.
Decisions regarding which “from-to” permutations to support should be driven by the flexibility and growth versus
cost-to-serve analysis that was done as part of the network
design activity. Among the factors the analysis should consider, for example, are the costs associated with establishing
store locations and warehouses as sources of supply for
e-commerce sales. This requires some physical reconfigura-tion, employee retraining, and technology. Moreover, store
locations with in-store picking operations require warehouse management system (WMS) capabilities. Product
location information from store-floor and space-planning
systems can be integrated into in-store picking systems to
provide store associates with accurate location information.
3 Plan and execute localized assortments, and per- sonalize offers based on data-driven “personas.” An
assortment represents the variety, configuration, and range
of products that will be made available to a specific selling
location to maximize sales. In the past, this was based on
maximizing sales per square foot of physical retail space in
an attempt to maximize the return on assets, which include
physical assets, machinery, and inventory.
In order to provide assortments tailored to local tastes,
companies should augment their existing processes with
data-driven techniques. This means leveraging “big data.”
Big data is a general term that refers to the rapid growth in
volume, velocity, and variety of the world’s digital data. In
this particular context, it refers to “customer sentiment”
data that comes from various sources, such as Internet
reviews of products and services, Twitter, and Facebook.
Traditional rules-based computing techniques are not good
at processing and adapting to such data. The emerging
technique for processing this type of data is machine learn-
ing, which is based on algorithms that can recognize data
patterns, learn from the patterns, offer insights, and become
“smarter” over time, just as humans do. These techniques
currently are being employed to process location-specific
shopper data to develop shopper segments, or “personas,”
and then to recommend product assortments that maxi-
mize revenue from target consumer segments.
A product assortment that closely matches the desired
products and associated attributes for targeted shoppers at
a given location drives a high level of efficiency in the ful-
fillment process. Assortments that missed the mark in the
past would result in stockouts; in today’s world, they also
result in sourcing from an alternative, more expensive point
in the supply chain.
4 Employ a common demand planning and manage- ment process across all channels, and incorporate big
data and machine learning into that process. The term
“independent demand” refers to actual customer demand;
it is distinguished from “dependent demand,” which is
upstream in the supply chain and is calculated based on
inventory-replenishment rules. In an omnichannel world,
forecasts for independent demand must comprehend tra-
ditional in-store sales, online sales, and returns. These fore-
casts will drive all other plans across the supply chain—for
warehouses, transportation, store labor, and inventory. It’s
important to consider location-specific buying preferences
in omnichannel demand planning. In order to understand
location-specific demand, the information that a product
cannot be sourced from a local store, either because it is
out of stock or because it is not part of the local assortment,
must be fed back to the assortment
and demand planning processes.
Independent-demand forecasts
(as well as plans) at individual stock-
ing locations may be used to calcu-
late available-to-promise (ATP) for
use in fulfillment decisions. ATP
provides a time-phased view of
available inventory and resources;
this information is used to make
a promise to the customer that a
specific product will be delivered
by a specific date. In an omnichan-
nel environment, making sourcing
decisions to meet customer demand
is based on looking at ATP across
multiple locations. This is more
sophisticated than past approaches,
q Implement flexible, many-to-many relationships in the physical supply chain based on flexibility versus cost-to-serve
trade-offs.
w Enable stocking locations—whether they are warehouses, retail locations, or pickup points—to receive, pick, pack,
and ship single-item orders, and to likewise handle returns.
e Plan and execute localized assortments, and personalize offers based on data-driven “personas.”
r Employ a common demand planning and management process across all channels, and incorporate big data and
machine learning into that process.
t Synchronize distribution planning, warehouse management, transportation management, and store operations.
y Implement a distributed order and inventory management system to provide a single view of orders and inventory,
and to execute orders across assets.
u Deploy next-generation profit-, constraint-, and allocation-based available-to-promise (PCA ATP).
i Synchronize product returns with assortment, buying, and demand planning.
o Create a “learning loop” among fulfillment, planning, and strategy.
1) Use orchestration dashboards to automate policy management for strategy, planning, and execution.
= strategy and structure = planning = fulfillment = closed loop and automation
[FIGURE 4] KEY AREAS OF SUPPLY CHAIN FOCUS FOR
ENABLING PROFITABLE OMNICHANNEL COMMERCE
