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Case Studies > Procter & Gamble Implements Terra Technology's Demand Sensing for Improved Forecast Accuracy

Procter & Gamble Implements Terra Technology's Demand Sensing for Improved Forecast Accuracy

Technology Category
  • Analytics & Modeling - Data-as-a-Service
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
  • Consumer Goods
  • Retail
Applicable Functions
  • Sales & Marketing
Use Cases
  • Demand Planning & Forecasting
  • Inventory Management
  • Predictive Replenishment
  • Supply Chain Visibility
Services
  • Data Science Services
  • Software Design & Engineering Services
  • System Integration
The Challenge
Procter & Gamble (P&G) faced significant challenges in accurately forecasting short-term demand for their consumer products. Their existing 24-month forecast provided a good overview for monthly or weekly production, but it was insufficient for the immediate needs of supply chain planning and manufacturing teams. These teams required a short-term forecast to plan production effectively and avoid 'fire-fighting' practices. P&G needed a solution that could provide accurate short-term demand forecasts to ensure agility and flexibility in manufacturing, especially for products with very short production and order lead times. The company explored various solutions but found that most big software companies lacked the agility to meet their specific demand sensing needs. Terra Technology's Real-Time Forecasting, later known as Demand Sensing (DS), emerged as a promising solution due to its specialized focus on consumer packaged goods (CPG) demand planning and forecasting.
About The Customer
Procter & Gamble (P&G) is a global consumer goods company known for its extensive portfolio of brands, including Always, Dash, Dreft, Duracell, Gillette, Head&Shoulders, Pantene, Pampers, Pringles, Swiffer, and Vicks. With approximately 135,000 employees working in over 80 countries, P&G's products touch the lives of people in 180 countries daily. The company's mission is to offer trusted, quality brands designed to improve the lives of consumers worldwide. P&G is committed to innovation and constantly seeks IT tools to enhance its business processes and organization. Terra Technology's Demand Sensing (DS) solution aligns with P&G's mission by providing accurate short-term demand forecasts, helping the company maintain high service levels and improve operational efficiency.
The Solution
P&G implemented Terra Technology's Demand Sensing (DS) solution to create accurate short-term demand forecasts for finished products. The DS solution uses data from P&G's existing SAP APO system, including daily shipments and open order data, to generate a new forecast every day. This automated process eliminates the need for manual data entry and adjustments, allowing P&G to respond quickly to changes in demand. The pilot project for DS began in July 2005 with hair care products manufactured in France and sold in Germany and Switzerland. By June 2010, DS was used for 90% of P&G's product categories in Western Europe and the Americas. The success of the DS implementation led to the development of Terra Technology's Multi-Enterprise Demand Sensing (MDS) solution, which incorporates additional data sources such as Point-of-Sale (POS) data and retailer forecasts. MDS was first implemented in North America in August 2009 and is being piloted in Western Europe. P&G plans to roll out DS and MDS to Central and Eastern Europe and explore implementation in Asia, despite the region's unique challenges.
Operational Impact
  • The DS solution has significantly improved forecast accuracy, with a 32% improvement in Western Europe and a 40% improvement in North America.
  • Safety stock levels have been reduced by an average of 10%, minimizing the need for excess inventory.
  • The automated forecasting process has eliminated the need for manual data entry and adjustments, freeing up time for value-added tasks.
Quantitative Benefit
  • Forecast accuracy improved by 32% in Western Europe.
  • Forecast accuracy improved by 40% in North America.
  • Safety stock levels reduced by 10% on average.

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