er, Executive Vice President of Marketing, Pier 1 Imports. “We needed a way of leveraging these multiple
sources to better match our customers to the products
they’re looking for.” With a solution that combined its
omni-channel retail data with machine learning, Pier 1
Imports could predict customer purchase patterns. “As
we learn the patterns in our data, it will help us personalize our website, and merchandise our stores with more
accurate and robust data-driven decision making,” says
Andrew Laudato, Senior Vice President and CIO of
Pier 1 Imports. While supply chain management isn’t
specifically mentioned, the case study provided some
interesting insight into how predictive analytics impact
Pier 1 Imports’ decision-making processes.
By understanding and forecasting customer demand, another major area of applying predictive analytics to supply chain management becomes relevant:
An article in SupplyChainDigest offers a solid
definition of inventory optimization: “…having the
right amount of inventory, in just the right places, to
meet customer service and revenue goals—but no
more than that.”
Inventory optimization helps reduce inventory distortion, a challenge that stems from out-of-stock and
overstock inventory situations. A recent study by IHL
Group, a global research and advisory firm specializing in technologies for the retail and hospitality industries, found that inventory distortion costs retailers nearly $1.1 trillion globally—$252 billion of that
happens in North America alone.
Traditional methods of optimizing inventory include adjusting inventory retroactively and reacting
to customer purchase habits that have already occurred. This method is further compounded by segmented IT systems stood up to meet isolated needs
that result in decentralized and short-sighted inventory decision-making processes. These traditional
methods result in significant issues throughout the
supply chain pipeline.
Southern States Cooperative, a U.S.-based agricul-
tural supply cooperative, has over 1,000 retail distri-
bution points. They recognized slow moving inven-
tory within their supply chain, although they didn’t
have the insight necessary to enable data-driven deci-
sion making around it.
For Southern States Cooperative, any inventory
that had no sales in the last 12 months or inventory
more than 12 months old is considered slow moving.
To address this issue, they implemented a multi-fac-eted project that leveraged data-mining, predictive
analytics and a transition to a centralized inventory
Even in the initial stages, the new reporting capabilities gave direct insight into the percentage of their current inventory that was slow moving:
• 34% of retail crop protectant products
• 33% of retail farm & home + animal health
• 30% of wholesale crop protection products
• 28% of wholesale farm & home + animal health
Aside from the reduction of current inventory while
maintaining sales volume, Southern States realized
other benefits of employing predictive analytics in its
inventory optimization strategy:
• Actionable data delivered to multiple operating
units to improve inventory management;
• Greater insight into seasonality, impacting timing
of delivery of seasonal products;
• More accurate sales forecasting, tied to inventory
levels, to ensure in-stock positions;
Cultural shift in use of information to improve the
business—fewer decisions based on gut feelings, more
based on the analysis of the business.
Predictive analytics will help you identify trends,
understand your customers’ purchase habits, predict purchase behavior, and drive strategic decision-making. If you’re not employing predictive analytics
in your supply chain management strategy, put away
your complicated spreadsheets and isolated databases
and start considering the transition today.
Gary Nakanelua is a Director at Blueprint Consulting Services,
a national technical consulting firm that connects strategy and
delivery. He helps clients successfully adopt a variety of technology solutions including the Hadoop ecosystem, machine learning, and cloud computing.