Over the last three decades we’ve moved from maximizing resource utilization to operating demand-driven supply chains. So you can imagine my surprise when I heard a leading process manufacturer in a supply constrained environment, one that sells to large businesses, was considering returning to a fixed production schedule. The justification for the change – high demand variability and poor forecast accuracy – only furthered my disbelief.
Marketing hasn’t always had the best transparency for high ROI and organizations of all sizes have struggled to understand the impact marketing has on their sales and overall growth. But the insurgence of data analytics, and penetration in the marketing space, may soon render this ambiguity a thing of the past.
Has the ease of buying with just one click ever driven you to make a purchase on Amazon over a preferred website? The success of a product, website or app is dependent on an excellent UI/UX – Amazon thrives here – and increasingly, the same is true for the effectiveness of data analytic applications.
Innovative companies are cutting supply chain complexity and accelerating responsiveness using artificial intelligence. By applying AI and machine learning against vast sets of supply chain data to unearth insights into problems and performance, enterprises are augmenting knowledge-intensive areas such as supply chain planning to be more dynamic, flexible, and efficient.
Leveraging Cloud Computing for Distributed Analytics
on August 20, 2018
| By Anand Kapoor
|
Demand Analytics
Today’s enterprises operate at a global scale, managing multiple facilities and working with partners across several regions. For you, efficient operations is a necessity to remain competitive.
current_page_num+2: 9 -