Improving Demand Planning in Industrial Manufacturing.

Find out how several Industrial Manufacturing companies have been able to overcome disruptions and increase forecasting accuracy & service levels, while decreasing inventories & stockouts.

Manufacturing LP

A volatile environment

As a supply chain professional active in industrial manufacturing, you are confronted with increased supply chain risks, high uncertainty regarding demand, and scarcity of critical materials and components due to the global disruptions which are underway. Organizations struggle to anticipate change and manage constraints as these disruptions affect production capacity, supplier reliabilitysupplier reliability, and waste valuable resources. 


Traditional technology limits your capability to properly tackle these challenges, with complex Excel sheets, siloed information, and constant firefighting as a result. Poor forecast accuracy results in unsatisfied customers or increased working capital. 


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Benefits of AI in Demand Planning


It is time to put the market at the center of your planning processes and pro-actively manage the volatility of your demand. By combining human knowledge with AI, Garvis empowers planners to make fast and accurate decisions based on data, real-time insights, and risk profiles.  Typical benefits include: 

  • Forecast error drops by 30 to 40% 
  • Better decision-making: more stable production schedules and better alignment with retail network 
  • More accurate launch and planning of new products 
  • 4-5 days reduction in safety stock 
  • No implementation costs, immediate value (implement in one day, have results in a week, go live in a month) 
  • Higher job satisfaction, less firefighting and more time for productive work 

Key features for manufacturing organizations

  • Events library and user-machine (AI) collaborations allow faster and more accurate responses to disruptions
  • Demand Sensing to improve forecasting accuracy
  • Effective ramp-up of new products

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Put yourself in control of data.

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