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Demand Variety And Planning

Demand Variety And Planning

Demand Variability and Planning

Danone uses machine learning to predict demand variability and planning. The new capability improved its forecasting process and led to more efficient planning between different functions, such as marketing and sales. It has led to a 20% reduction in forecast error and a 30% reduction in lost sales. Unilever uses AI to influence operations by predicting outcomes and improving efficiency levels to optimise output. Applying this approach Unilever has saved almost 3 Million $ and has driven a 1-3% increase in productivity. L’Oréal, one of the world’s largest beauty products manufacturers, which sells over seven billion products a year, uses information from various sources to predict changing customer demands, anticipate trends, and optimize sales. These sources include:
  • social media
  • data gathered at point-of-sale, such as reception, collection, and inventory
  • data points such as weather and financial markets indicators.
Organizations are using machine learning to predict changes in consumer demand as closely as possible. This means they can make the necessary changes to production schedules and raw material procurement. The improved accuracy leads up to a 65% reduction in lost sales due to inventory out-of-stock situations and warehousing costs decrease around 10 to 40%.

What are the benefits of demand forecasting?

  • Demand forecasting helps organizations optimize their supply chain, sales, and marketing operations and prevent from having an excessive amount of goods in stock or out-of-stock situations:
  • Improving accuracy by time: Machine learning algorithms learn from existing data and make better predictions for sales, profits and customers demands .
  • Increased customer satisfaction: Stock outs reduce customer satisfaction while being available with your product anytime boosts customer satisfaction. Thus it improves brand perception and increasing customer loyalty.
  • Improved markdown/discount optimization: Cash-in-stock is a common situation for retail businesses. In this situation, certain products stay unsold longer than expected. This causes higher than expected inventory costs and increases the risk for these products to go out of fashion or become obsolete, thereby losing their value. In these scenarios, organizations sell their products with reduced margins. With accurate demand forecasting, such scenarios can be minimized.
  • Improved manpower planning: Demand forecasting for the full year can support HR departments to make efficient trade-offs between the part/full-time employee mix, optimizing costs and HR effectiveness.
  • Overall efficiency: Accurate demand forecasts help teams focus on strategic issues rather than firefighting to reduce/increase stocks and headcount to manage unexpected demand fluctuations.

Demand Planning-Targeted Deliverables

  • Historical Data interpretation:

Utilizing and rendering raw data for the complete supply chain starting from the import process till delivering the items to the end user eg.(customers demographic data , sales data , imported items , marketing ,warehousing,…) into value which helps to make better strategic decisions.

  • Storing and Warehousing Optimization:

Optimizing the warehouse area related to the orders demand and imported orders according to historical data eg.(warehouse size(m2) , type of stored items , number of stored items , suppliers, imported items dates ,sold items , sold items dates , orders demands , the date customers need for ordered items,…… ), which helps in Reduction in lost sales due to inventory out-of-stock situations and warehousing costs.

  • Prediction Models:

Prediction models for sales, profits and customers demands according to historical data.