Geographic Analytics: How HP Visualizes its Supply Chain
January 25, 2013
When HP launched its biggest supply chain transformation in history in 2010, it was evident that the challenging multi-billion year-to-year savings targets would be difficult
to reach in the traditional way. This transformation required new approaches to deliver substantial improvements across businesses, targeting cost reductions as well as structural streamlining in the form of partner, network and site consolidations. To meet these challenges, HP had to enhance its analysis toolbox to deliver supply chain projects faster and with better alignment across businesses and regions. (For more on the transformation initiative, see sidebar on “HP’s Transformation Challenge.”)
One of the successful approaches developed to enhance our supply chain analytics capabilities is what we called “Geographic Analytics” (GA), a visualization technique that we describe more fully below. GA works. It reduced the time required for network optimization projects by up to 50 percent. In addition, projects driven by this approach were often better supported by the business groups. The reason: Executives became involved much earlier than they would have in traditional, purely data-driven supply chain analysis work.
Although designed to support supply chain network optimization, Geographic Analytics has applications far beyond its original purpose. We received major interest in this technique from groups both inside and outside of HP, including from sales and after-sales organizations, enterprise risk management functions, and from supplier relationship managers. Each of these groups has a regular need for visualizing and analyzing location data for a variety of purposes ranging from studying after-sales networks to risk management of the supplier base.
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When HP launched its biggest supply chain transformation in history in 2010, it was evident that the challenging multi-billion year-to-year savings targets would be difficult to reach in the traditional way. This transformation required new approaches to deliver substantial improvements across businesses, targeting cost reductions as well as structural streamlining in the form of partner, network and site consolidations. To meet these challenges, HP had to enhance its analysis toolbox to deliver supply chain projects faster and with better alignment across businesses and regions. (For more on the transformation initiative, see sidebar on “HP’s Transformation Challenge.”)
One of the successful approaches developed to enhance our supply chain analytics capabilities is what we called “Geographic Analytics” (GA), a visualization technique that we describe more fully below. GA works. It reduced the time required for network optimization projects by up to 50 percent. In addition, projects driven by this approach were often better supported by the business groups. The reason: Executives became involved much earlier than they would have in traditional, purely data-driven supply chain analysis work.
Although designed to support supply chain network optimization, Geographic Analytics has applications far beyond its original purpose. We received major interest in this technique from groups both inside and outside of HP, including from sales and after-sales organizations, enterprise risk management functions, and from supplier relationship managers. Each of these groups has a regular need for visualizing and analyzing location data for a variety of purposes ranging from studying after-sales networks to risk management of the supplier base.
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