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David Inbar has more than 25 years of technology and business experience in enterprise data management, integration, analytics, and big data infrastructure. He has built and managed product and services teams in Europe and the US, focusing on solutions for customers’ toughest data problems.
Inbar is passionate about identifying and understanding customers’ business challenges and designing analytic solutions that deliver significant business advantages to customers. He holds an MBA and a Master’s degree in engineering.
Minimizing inventory levels while maximizing productivity is a critical yet cumbersome task for manufacturing firms.
A common inventory problem for manufacturers is maintaining sufficient levels of materials needed for production while not holding unnecessary stocks. The challenge for an inventory manager is compounded by a large number of components demanded from production simultaneously. For example, in the aerospace industry, a product may have tens of thousands of parts, while some may require a 6-12 month lead time. Missing one of these would cause a severe impact on the business.
The current Master Production Schedule function uses inputs from open Sales Orders, Purchase Orders, and Manufacturing Orders, which are known demand data. Many manufacturing companies need a more dynamic planning capability to analyze uncertain scenarios to make better decisions. Manufacturers also need the capability to analyze what-if scenarios.
In this talk, David Inbar will present a solution that allows manufacturers to perform planning using both known and forecasted demand. Forecasts can be manually entered (e.g., by the sales team) or automatically generated using AI/machine learning.
Target audience:
This talk is useful for manufacturing planners, supply chain managers, production managers, and executives in manufacturing companies.
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