Home 〉Supply Chain Intelligence 〉Demand Planning Intelligence Consortium
Home 〉Supply Chain Intelligence 〉Demand Planning Intelligence Consortium
How DPIC works
Using a proprietary model-based benchmarking approach based on item-location level, final consensus forecast data, and transactional shipment data, Chainalytics maintains forecast accuracy and bias models which quantitatively capture the combined effect of specific demand, product, and network characteristics. Each company’s actual data is normalized and then benchmarked against these models to understand exactly where more accurate forecast predictions are achievable. With the help of data-visualizations and interactive analytical dashboards, DPIC also communicates these opportunities in simple to understand terms which can be shared beyond your demand planning team.
DPIC enables organizations globally to:
For more information about joining Chainalytics’ Demand Planning Intelligence Consortium or to schedule a live demo, please email us at dpic@chainalytics.com.
Chris concurrently serves as Chief Scientist for Chainalytics and the Executive Director of MIT’s Center for Transportation and Logistics. At Chainalytics, Chris pioneered the Freight Market Intelligence Consortium which he presently co-leads.
In his role as Executive Director of the Center for Transportation & Logistics (CTL) at the Massachusetts Institute of Technology, he is responsible for the planning and management of the research, education, and corporate outreach programs for the center to include the Supply Chain Exchange and the Master of Engineering in Logistics (MLOG) graduate program. He is also the founder of the MIT FreightLab – a research initiative that focuses on improving the way freight transportation is designed, procured, and managed. Prior to joining MIT, Chris held senior management positions in supply chain consulting, product development, and professional services at several companies, including Logistics.com, SABRE, and PTCG.
Chris holds a Ph.D. from the Massachusetts Institute of Technology in Transportation and Logistics Systems, a Master of Science in Civil Engineering from the University of Texas at Austin, and a Bachelor of Science in Civil Engineering from the Virginia Military Institute (VMI).
Inam leads the global Freight Market Intelligence Consortium (FMIC) at Chainalytics. In this role, he develops intelligence solutions for the transportation market using machine learning and data visualization techniques. FMIC offers unparalleled visibility to transportation rates and market data across the globe, making it the most powerful and trusted source of freight market intelligence.
Prior to joining Chainalytics, Inam led the engineering team at Transplace, a third party logistics provider, where he developed and innovated such service offerings as network optimization, site selection, procurement, and transportation optimization. These services helped many shippers balance the cost and service trade-offs across their large, complex networks.
Inam holds a Ph.D. from the University of Arkansas in Applied Operations Research, a Master of Science in Industrial Engineering and a Bachelor of Science in Industrial Engineering from Oklahoma State University.