How does data driven decision making transform Consumer Goods Industries performance

The consumer goods (CG) industry has long relied on a mix of internal data, like merchandise shipments, and external data, like point-of-sale retail figures and consumer demographics, to track industry trends. However, the streams of new, unstructured data generated by shoppers' digital footprints, customer reviews and social media mentions are redefining the way brands stay aware of consumer goods industry trends. Recognizing the diminishing value of traditional advertising in the digital era, company sees an opportunity to grow market share among millennial, Hispanic, tech-savvy and health-conscious consumers by appealing to their changing habits via big data and digital marketing. Customer experience has become an increasingly important part of the CG industry, and many companies are tapping into analytics to figure out their customers' likes, dislikes, wants and needs. Combining Social Media, E-Commerce & Analytics, the company’s Facebook store gains a wealth of information about its target consumer's behavior to drive future marketing strategy. Internal data such as stock and sell out at the retailer (or distributor) will be combined with external data such as consumer trends, macroeconomic conditions, climate and public holidays to formulate a forecast that will be more accurate than ever. The benefits will go far beyond sales as other departments such as marketing and supply chain can anticipate their next steps (e.g. promotional activity and factory demand respectively.) At the primary level of the value chain, analytics will allow supply chain and purchasing professionals to streamline their operations and increase profitability across portfolios. With predictive analytics, purchasing teams will be better equipped to use intelligence related to the external environment to forward-buy raw materials and packaging materials at their lowest possible prices – this itself can lead to millions of dollars of savings. Furthermore, supply chain teams can manage inventory at an unprecedented level of efficiency. Not only can they use the above-mentioned sales forecasting data to reduce inventory to the minimum level (and hence reduce wastage and slow-movers) but, in the unlikely event of out of stock, they can ensure that the stock is allocated to the most important retailer or distributor (e.g. according to strategic importance or profitability.) The finance and controlling departments will be at the center of implementing and governing analytics in a consumer goods company. With the proliferation data and advanced tools, finance teams will be in a much better position to forecast the P&L in accordance to external factors and customer behavior. Furthermore, root cause analysis will become far more effective as any unexpected variances would be easily pin-pointed to individual SKU's, brands, customers, or retail channels. The main benefit will be in the time spent on reporting vs. the more value-added task of helping management in devising strategy. Recruitment analytics will change the way hiring practices take place. With advanced data available through social media, recruitment will not only be limited to the resume of the candidate. Consumer goods companies will have data available on behaviour and recruiting channels of current top performers and will be able to use that information to hire future talent and ensure retention more effectively.[1]

ARI®, automotive market leader in North America, delivers fleet management services and solutions that improve our customers' operations and—ultimately—their bottom lines. Founded in 1948 by the Holman Automotive Group, ARI grown into the largest privately held vehicle fleet management company in the world. With 950,000 vehicles and 14,000 data points collected per vehicle, ARI data points are doubling every 14 months. Data volumes were increasing the time it took to run the reports and queries. ARI wanted to arm fleet managers with real time insights so they make decisions that optimized efficiency. Using an in-memory database ARI can quickly turn data into insight that help its customer optimize fleet efficiency. Data universes created within in memory database, integrate data from disparate systems. A majority of ARI’s employees use the system daily, and select customers run their own queries using the customer portal. ARI uses predictive analytics to spot trends and patterns among seemingly unrelated data points. Using In Memory Analytics, ARI achieves various business benefits: (i) Predictive Data Mining: Spotting trends and patterns ahead of time to be proactive rather than reactive. Recommending repair before a fault occurs. (ii) Benchmarking: Designing models that gather trends data on all vehicles to create baseline and then identifying outliers. Calculating a cost of ownership baseline across fleets with similar vehicles and identifying those that exceed baseline. (iii) Changing driver behavior: Finding correlations between events that seems unrelated at first glance and using that information to make recommendations. Discovering a link between fuel locations and accidents.

ConAgra Foods, Inc. is an American packaged foods company headquartered in Chicago, Illinois, USA. ConAgra makes and sells products under various brand names that are available in supermarkets, restaurants, and food service establishments. On October 1, 2015, ConAgra announced that it would cut about 1,500 jobs and relocate its headquarters to Chicago as part of a restructuring plan. To overcome business challenges, ConAgra desired to gain in the moment insight into the cost of material, acquire more accurate consumer purchase information to improve merchandising, forge closer ties with retailers. As a part of their business strategy, ConAgra[2] implemented In Memory technology to speed analysis, gathered new sources of data about consumer behavior and shared data driven insight with retailer. Do this, ConAgra[3] reaped the benefit of real time business with (i) expanded Option: ConAgra is able to analyze multiple “What If” scenarios when considering its strategies for purchasing commodities (ii) better merchandising: Using external data, such as consumer data provided by retailers, deliver real time insight into how to merchandise products more effectively and increase margins. (iii) closer relationship with retailers: Sharing data driven recommendations with retailers helps ConAgra build trust and become and indispensable partner.

Florida Crystals, headquartered at USA, is committed to a corporate eco-vision, which includes the practice of sustainable agriculture, along with various proactive efforts for protecting natural resources. In turn, they proudly offer consumers eco-friendly organic and natural sugars. Florida Crystals is America’s first and only producer of certified organic sugar, grown and harvested in the United States, and the first sugar certified CarbonFree® by Carbonfund.org. They also operate renewable energy facility which is the largest of its kind in North America and provides clean energy that powers their sugar operations along with tens of thousands of area homes. These efforts help them reduce their use of fossil fuels. Florida Crystal intended to standardize and simplify infrastructure and applications, create agile processes to facilitate growth through acquisitions, deliver data and enable analysis in real time. Florida decided to implement Big Data and deployed an in-memory suite of enterprise application to speed information delivery to business users; moveed mission critical business application to a managed cloud services with flexible capacity to support geographic expansion; used real time analytics to enable employees to make better operational decisions. Doing this, Florida Crystal[4] achieved benefits of real time business such as (i) self-service: Business users can process reports on demand without going through IT. (ii) faster analysis: Most business transactions are 50 percent to 500 percent quicker, so business users have more time to study data. (iii) improve flexibility to support growth: A scalable cloud infrastructure helps merger and acquisitions go more smoothly and reduces IT costs. (iv) new value creation: With access to real time data, Florida Crystals can focus more on value added work, such as improving inventory management, developing new formulations, product or packaging; or planning expansion into new markets. Total cost of ownership for computing and storage is down by an average of 30 percent. The decision making is improved by 500 percent faster as compared to market peers. A 2013 global survey of 400 large companies by Bain & Companies found that firms using advance analytics are more likely to be in the top quartile of financial performance within their industry.

Johnsonville Sausage is a sausage company headquartered in Sheboygan Falls, Wisconsin, USA. Founded in 1945, it is one of the largest sausage producers in the United States and the largest sausage brand by revenue in the United States. Johnsonville sausage is available in more than 35 countries. Privately owned, the company has approximately 1,400 employees. Johnsonville Sausage produces a variety of sausage products, including: brats, grillers, Italian sausage, smoked-cooked links, breakfast sausage in fully cooked and fresh varieties, chicken sausage, meatballs and summer sausage. Johnsonville’s had various business challenges in order to (i) optimize spending on trade promotion by measuring results and making adjustment in real time to maximize returns (ii) create an integrated and harmonized view of information across business functions (iii) enable decision makers to assess performance and plan for future. With Big Data, Johnsonvill implemented the solution like (i) modernizing existing business intelligence and data warehouse system with real time analytics, enabled by in-memory computing (ii) centralizing data in harmonized repository to track retail point-of-sale data and provide views into supply chain operation (iii) extending data visualization to mobile devices. Benefits of In-Memory Computing at Johnsonville was (i) improved query performance time (ii) positive executive response to tablet based visual data presentation (iii) ability to meet business objectives such as more accurate sales forecasts and marketing plans, optimized promotions, visibility into competitive postures, increased agility and responsiveness, and profitability. Johnsonville Sausage expects to gain more visibility into its operations by collecting and analyzing data in real time for all sources. Area targeted for improvement includes (i) trade promotion management and optimization: Track performance of retail promotions in real time, respond to market trend, measure results and optimize outcome. (ii) integrated data repositories: Provide sales, marketing, supply chain and other functions with a consistent view of key performance indicators (iii) mobile device dashboards: Deliver and update results quickly to encourage users to explore data trends on device such as tablets (iv) pricing: Analyze retail point of sales activities in real time to optimize prices and maximize revenue based on demand fluctuations and supply. (v) product launches: Collect data on new product sales to analyze sales trends and anticipate future performance (vi) supply chain management: Gain real time visibility into inventory, tracking movement from factory to store to consumers (vii) manufacturing operations: Analyze shop floor data to predict when machines will need maintenance.

Maple Leaf Foods Inc. (TSX: MFI) is a major Canadian consumer packaged meats company. Its head office is in Toronto. Maple Leaf Foods is the result of the 1991 merger between Canada Packers and Maple Leaf Mills. In 1989, the McLean family that had dominated Canada Packers since its founding announced its intention to sell its stake in the company. The controlling interest passed in 1990 to the British Hillsdown Holdings, which already owned Maple Leaf Mills, through a complex transaction in which Canada Packers purchased Maple Leaf Mills in exchange for its own shares. In 1991, the combined company was renamed Maple Leaf Foods. The firm thus included a large bread division, best known for the Dempster's brand (Canada's bestselling brand of bread). During restructuring efforts led by David Newton as CEO and Lewis Rose as CFO, it sold or closed most of its slaughterhouses. These measures were successful and the company returned to profitability. After being successfully revived, Maple Leaf Foods was purchased by Wallace McCain, formerly co-CEO of McCain Foods, who had been ousted by his brother and co-owner Harrison McCain, in 1995 along with the Ontario Teachers' Pension Plan. In 2002, the company purchased San Francisco-based Grace Baking Company. In 2003, the company purchased rival meat packer Schneider Foods. The company is also one of Canada's largest agribusinesses, owning poultry and hog farms across the country. The main slaughterhouse is located in Brandon, Manitoba. As the result of a series of divestitures culminating with the 2014 sale of the bakery division (Canada Bread Company), Maple Leaf today now only produces and sells packaged meats. Maple Leaf Food analyzed their business landscape and observed various actions were required to be taken on priority (i) real time information and analytics were required to optimize product pricing and enable other business performance insight (ii) data needed to be consolidated from 35 ERP systems (iii) processes needed to be standardized across the enterprise (iv) business users needed the ability to drill down into reports. To overcome this, Maple Leaf Food implemented the solution[5] like (i) consolidated systems and standardized processes (ii) in-memory technology to provide instantaneous analysis of desperate data (iii) analysis and visualization tools for hundreds of users. With implementation of Big Data, Maple Leaf Food[6] realized benefits of real time business (i) standardized view across enterprise: system consolidation and process standardization have led to improved operations through the use of in-memory analytics for management (ii) faster analysis speeds: business users are experiencing speeds that are 100 to 1,000 times faster than with traditional databases. (iii) Self-service: Users can now run their own ad hoc queries and reports (iv) real time pricing analysis: Maple Leaf will be able to price products based on the cost fluctuations of raw material by combining syndicated data with supply and demand from traditional systems. (v) future use cases: The Company is evaluating the use of in-memory analytics for recipe development, demand planning and trade promotion to help it further improve decisions and profitability.

Procter & Gamble Co., also known as P&G, is an American multinational consumer goods company headquartered in downtown Cincinnati, Ohio, United States, founded by William Procter and James Gamble, both from the United Kingdom. Its products include cleaning agents and personal care products. Prior to the sale of Pringles to the Kellogg Company, its product line also included foods and beverages. In 2014, P&G recorded $83.1 billion in sales. On August 1, 2014, P&G announced it was streamlining the company, dropping around 100 brands and concentrating on the remaining 65 brands, which produced 95% of the company's profits. A.G. Lafley, the company's chairman, president, and CEO until October 31, 2015, said the future P&G would be "a much simpler, much less complex company of leading brands that's easier to manage and operate". David Taylor became P&G CEO and President effective November 1, 2015. P&G examines its business program success and react more quickly to changing market conditions. P&G needed to clearly and easily understand its rapidly growing and vast amount of data, integrated vast amounts of structured and unstructured data across research and development, supply chain, customer-facing operations, and customer interactions, both from traditional data sources and new sources of online data. Now, P&G can load and integrate data faster and execute reliable analysis at scales that were previously not possible. (Rob, 2015)

References

[1] "Applying Advanced Analytics in Consumer Companies." Insights & Publications. McKinsey and Company, n.d. Web

[2] Noan, Bob, “ConAgra’s VP of Analytics on Big Data and career in retail”, video, Jan 9, 2014, http://goo.gl/PS1ozl

[3] Accenture, “Building an Analytics driven organization: Organizing, Governing, Sourcing and Growing Analytics Capabilities in CPG” pdf, Feb 19, 2014, http://goo.gl/KBFjnv

[4] Brynjolfsson, Eric, Lorin Hitt AND Kim Hikyun, “How does data driven decision making affect firm performance?” 2012, http://goo.gl/9VUMG6

[5] Yuhanna, Noel, Holger, Kisker, Ph.D, and David Murphy, “Case Study: Maple Leaf Foods relies on SAP HANA to enable faster business analytics”, Forrester Research, Oct 28, 2013, http://www.goo.gl/QGBfXt

[6] Schick, Shane, “Maple Leaf Food’s ERP System project: Acio Strategy” IT World Canada, April 18, 2011, http://www.goo.gl/8daaTX

[7] National Institute of Standards and Technology, “Real Time Data Analytics for smart manufacturing system project”, http://goo.gl/KZ9nzW