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Big Data Strategy of Procter & Gamble: Turning Big Data into Big Value

Case Code: ITSY091
Case Length: 14 Pages
Period: 2010-2017
Pub Date: 2017
Teaching Note: Available
Price: Rs.600
Organization: P&G
Industry: Fast Moving Consumer Goods
Countries: US; Europe; Global
Themes: Big Data Strategy, Digital Transformation
Global Economic Impact of Coronavirus – Assessment and Mitigation (B)
Abstract Case Intro 1 Case Intro 2 Excerpts

Excerpts

About P&G

P&G, headquartered in Cincinnati, Ohio, marketed more than 300 products in over 180 countries. Its entire operations were classified into six categories – beauty, grooming, health care, snacks & pet care, fabric care & home care, and baby care & family care. For the year ended June 30, 2016, P&G had generated net sales of US$65.3 billion and net profit of US$9.93 billion..

Big data at P&G

At P&G, predictive analytics formed a vital component of all significant decisions which had a bearing on sales and margins (See Exhibit II). P&G started employing analytics in 1992 when it had an excess number of production units in USA and was required to cut down on the surplus production infrastructure and devise mechanisms to right-size its global supply chain. At that time, P&G’s analytics team worked on aspects like the North American Free Trade Agreement’s ...

Marketing

P&G made increasing use of Web 2.0 technologies to promote its products and augment their brand recall. An important medium was the factual accounts narrated by consumers about their product experiences. McDonald, who wanted P&G’s..

Decision Making

Data management at P&G was founded on three principles: Openness of data (making the same information available to all concerned employees), well-timed data (giving the data as soon as possible), and transmission of data (making the data available through various media)...

Product Development

By 2011, P&G was extensively using computer-based simulation to design and evaluate virtual replicas prior to their manufacture. Only when the virtual replicas satisfied the quality parameters did P&G progress to the real-world prototype phase...

Supply Chain

P&G started employing demand sensing software (DSS) worldwide to increase short-run estimate precision and reduce safety stock. DSS obtained the existent estimation data from the demand plan, synthesized it with the real-time information of every day indents and deliveries..

Results

In the early 2000s, P&G confronted the problem of rising R&D costs. It implemented the C+D program which leveraged InnoCentive, a web-based platform that invited experts to solve technical challenges that P&G was facing. By 2011, half of the new products had elements that had originated from outside the company, up from 15 percent in 2000. R&D efficiency at P&G was up at 60 percent, and R&D as a share of revenue had tumbled from 4.8 to 3.4 percent...

Challenges

P&G sales peaked at US$83.7 billion in 2012 before stagnating and then declining as the company began offloading brands. As of mid-2016, for 14 of the previous 17 quarters, P&G had been losing market share in half or more of its products...

Exhibits

Exhibit I: Key Financials of P&G (2012-2016)
Exhibit II: P&G Predictive Analytics
Exhibit III: P&G’s Business Sphere
Exhibit IV: P&G Heat Map
Exhibit V: P&G’s Simulated Store

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