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ead the Analytics in Action, Procter & Gamble Uses Business Analytics to Redesign its supply chain then describes how analytics was used to drive efficiency in Procter & Gamble’s North American supply chain. Which part of the article showed the Descriptive analytics? Predictive analytics? Prescriptive analytics?

ead the Analytics in Action, Procter & Gamble Uses Business Analytics to Redesign its supply chain then describes how analytics was used to drive efficiency in Procter & Gamble’s North American supply chain. Which part of the article showed the Descriptive analytics? Predictive analytics? Prescriptive analytics?

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
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Read the Analytics in Action, Procter & Gamble Uses Business Analytics to Redesign its supply chain then describes how analytics was used to drive efficiency in Procter & Gamble’s North American supply chain.

Which part of the article showed the Descriptive analytics?

Predictive analytics?

Prescriptive analytics?

ANALYTICS (in) ACTION PROCTER & GAMBLE USES BUSINESS ANALYTICS TO REDESIGN ITS SUPPLY CHAIN Consumer goods giant Procter & Gamble (P&G), the maker of such well-known brands as Tide, Olay, Crest, embarked on a effort known as Strengthening Global Effectiveness. A major piece of that effort was the North American Supply Chain Study, whose purpose was to make the supply chain in North America as efficient as and efficiency are critical to the company's profitabil- possible, while ensuring that customer service require- Bounty, and Pampers, sells its products in over 180 countries around the world. Supply chain coordination ity. After many years of acquisitions and growth, P&G ments were met. SJ. Camm, T. Chorman, F. Dill, J. Evans, D. Sweeney, and G. Wegryn, "Blending OR/MS, Judgment and GIS: Restructuring P&G's Supply Chain," Interfaces 27, no. 1 (1997): 83-97. A team of P&G analysts and managers partnered with a group of analytics faculty at the University of Cincinnati to create a system to help managers redesign the supply effort in North America. The fundamental cast product family demand by three-digit zip code for ten years into the future. This future demand was then input, along with projected freight and other relevant questions to be answered were: (1) Which plants should make which product families? (2) Where should the dis- tribution centers be located? (3) Which plants should serve which distribution centers? (4) Which custom- costs, into an interactive optimization model, that min- imized cost subject to service constraints. The suite of analytical models was aggregated into a single system that could be run quickly on a laptop computer. P&G ers should be served by each distribution center? The team's approach utilized all three categories of business analytics: descriptive, predictive, and prescriptive. At the start of the study, data had to be collected from all aspects of the supply chain. These included demand by product family, fixed and variable pro- duction costs by plant, and freight costs and handling charges at the distribution centers. Data queries and descriptive statistics were utilized to acquire and bet- ter understand the current supply chain data. Data vi- sualization, in the form of a geographic information system, allowed the proposed solutions to be displayed supply chain. The system of models was later utilized on a map for more intuitive interpretation by manage- ment. Because the supply chain had to be redesigned for the future, predictive analytics was used to fore- product category managers made over a thousand runs of the system before reaching consensus on a small set of alternative designs. Each proposed design in this selected set was then subjected to a risk analysis using computer simulation, ultimately leading to a single go- forward design. The chosen redesign of the supply chain was imple- mented over time and led to a documented savings in ex- cess of $250 million per year in P&G's North American to streamline the supply chains in Europe and Asia, and P&G has become a world leader in the use of analytics in supply chain management.
Transcribed Image Text:ANALYTICS (in) ACTION PROCTER & GAMBLE USES BUSINESS ANALYTICS TO REDESIGN ITS SUPPLY CHAIN Consumer goods giant Procter & Gamble (P&G), the maker of such well-known brands as Tide, Olay, Crest, embarked on a effort known as Strengthening Global Effectiveness. A major piece of that effort was the North American Supply Chain Study, whose purpose was to make the supply chain in North America as efficient as and efficiency are critical to the company's profitabil- possible, while ensuring that customer service require- Bounty, and Pampers, sells its products in over 180 countries around the world. Supply chain coordination ity. After many years of acquisitions and growth, P&G ments were met. SJ. Camm, T. Chorman, F. Dill, J. Evans, D. Sweeney, and G. Wegryn, "Blending OR/MS, Judgment and GIS: Restructuring P&G's Supply Chain," Interfaces 27, no. 1 (1997): 83-97. A team of P&G analysts and managers partnered with a group of analytics faculty at the University of Cincinnati to create a system to help managers redesign the supply effort in North America. The fundamental cast product family demand by three-digit zip code for ten years into the future. This future demand was then input, along with projected freight and other relevant questions to be answered were: (1) Which plants should make which product families? (2) Where should the dis- tribution centers be located? (3) Which plants should serve which distribution centers? (4) Which custom- costs, into an interactive optimization model, that min- imized cost subject to service constraints. The suite of analytical models was aggregated into a single system that could be run quickly on a laptop computer. P&G ers should be served by each distribution center? The team's approach utilized all three categories of business analytics: descriptive, predictive, and prescriptive. At the start of the study, data had to be collected from all aspects of the supply chain. These included demand by product family, fixed and variable pro- duction costs by plant, and freight costs and handling charges at the distribution centers. Data queries and descriptive statistics were utilized to acquire and bet- ter understand the current supply chain data. Data vi- sualization, in the form of a geographic information system, allowed the proposed solutions to be displayed supply chain. The system of models was later utilized on a map for more intuitive interpretation by manage- ment. Because the supply chain had to be redesigned for the future, predictive analytics was used to fore- product category managers made over a thousand runs of the system before reaching consensus on a small set of alternative designs. Each proposed design in this selected set was then subjected to a risk analysis using computer simulation, ultimately leading to a single go- forward design. The chosen redesign of the supply chain was imple- mented over time and led to a documented savings in ex- cess of $250 million per year in P&G's North American to streamline the supply chains in Europe and Asia, and P&G has become a world leader in the use of analytics in supply chain management.
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