Inventory Optimization: Optimize your inventory and service level targets through the use of industry-leading multi-echelon stochastic inventory modeling and prescriptive analytics. Factor in demand and order behavior, volatility, lead times, replenishment minimums, and more to determine the optimal amount of inventory needed to meet or exceed your desired fill rates and product availability. Quantify your opportunity and business case for reducing lost sales, accelerating revenue, and expanding margin, and maximize your ROIC.
Service Level Strategy: Determine what mix of service levels to set across different portions of your portfolio to maximize the return on your working capital. Optimize not only inventory levels, but service targets and inventory levels at the same time, creating differentiated strategies for setting higher service where the business benefits the most, while reducing inventory investment in areas which are less efficient and contribute negatively to the bottom line while shifting capital away from the product availability which will drive the top line.
Inventory Deployment: Determine where to stock each item, and evaluate alternative deployment and fulfillment strategies from a total network, logistics, and cost to serve perspective, factoring in transportation, distribution, and inventory dynamics in a single holistic approach spanning both flow path and inventory optimization techniques. Model and evaluate slow-mover stocking consolidation, postponement and delayed differentiation, hub and spoke fulfillment, and other advanced stocking strategies. Quantify the business benefits of adoption before you make operational changes.
Inventory Optimization Technology Requirements Development: Determine and document the needs of your business and formulate requirements for selection and use of inventory target setting and optimization technology.
Inventory Optimization Technology Selection Support: Get the experts on your side before and during engagement with providers to select a new planning technology. Create RFPs, use cases and demo scripts, scoring criteria, and facilitate workshops and the overall selection project.
Inventory Optimization Technology Implementation and Transformation: Implement or re-configure your inventory optimization technology, from process design, to change management and PMO, to user acceptance testing and end-user training. Translate requirements into configuration specifications, create optimized solution architecture, implement functional and technical designs, test, tune, and go-live.
Inventory Optimization as a Service (IOaaS): Don’t want to procure or sustain multi-echelon inventory optimization technology or maintain the specialized resource skillset to refresh the process carefully? Chainalytics can provide inventory target setting as a Managed Analytics Service (MAS) through longer-term outsourcing arrangements.
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.