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There is a lot of industry buzz about big data but few people can enunciate what it is and what benefits it will deliver. The Grocery Manufacturers’ Association (GMA) defines big data as:
Data that appears in high volume, velocity, or variety, and encompasses billions of data points that can be analyzed to microtarget marketing data based on individual buying patterns.
How does that differ from all of the work the industry has done to leverage transaction and shopper loyalty data? The difference is in the higher volume, faster velocity, and greater variety of forms that are the new sources of data in the “Big Data Pool”. The data streams that flood the pool come in three basic types:
Transaction Data
Most CPG companies have experience collecting, analyzing, and leveraging information from transaction data. Transaction data includes information on inventory movement, point-of-sale data (POS), and financial transactions. Most of this data comes in structured formats, like electronic data interchange documents (EDI), and flows into large, enterprise resource planning (ERP) systems.
This information is typically used to plan, manage, and report to all areas of the business. It is the basis for forecasting used to build the production and deployment plans and monitoring financials. Transaction data is also the foundation for most collaboration discussions between manufacturers and retailers.
System-Generated Data
A wide variety of data is generated by various hardware and software systems throughout the CPG supply chain. The volume and velocity of system-generated data is increasing as the industry moves to activity-based tracking and leverages more automation.
This type of data is typically used to improve processes and productivity, as well as provide a detailed “chain of custody” as product makes its journey through the supply chain, enabling manufacturers and retailers to identify opportunities to reduce costs and improve speed and service.
The newest form of system-generated data is generated from GPS enabled consumers. Companies are using this data to interact with a consumer when they come in range – in a city that has a store, or in an aisle that has a product that is of interest to the consumer.
People-Generated Data
The industry has been monitoring consumer behavior for years using shopper loyalty cards. Loyalty data inputs transactions into sophisticated models to understand consumer behavior. With widespread smartphone usage consumers have more than a trail of purchases to communicate their feeling—they have a digital voice, a voice that is growing in volume and power.
Consumers’ online discussions can cause rapid spikes in sales; manufacturers and retailers must monitor trends to anticipate these spikes and adjust forecasts accordingly. The volume, velocity, and variety of unstructured data is beyond the scope of anything that the industry has previously seen, and will create challenges for manufacturers, retailers and distributors.
Black Hole or White Light?
The task of collecting, analyzing, and utilizing all of this data is daunting, and the ROI is uncertain. The IBM/Kantar Retail Global CPG study of more than 350 top CPG executives showed that 74% of the leading CPG manufacturers use analytical data to improve decision-making in sales, compared with 37% of lower performing manufacturers.1
The 2013 Supply Chain Big Data Report surveyed supply chain executives and found that 86% expect big data to have a “reasonable” to “game changing” impact on their companies. More than 27% of these companies are currently integrating Big Data. Of these, more than two-thirds expect an ROI in 12 months.2
This research indicates that executives are working hard to make big data a competitive strength. Challenges still remain however; this same group of supply chain executives indicated that integration, understanding what data to use, and gathering external data remain obstacles to overcome.
GMA, in partnership with Deloitte, completed a comprehensive study on how CPG companies can use big data to improve operations. This study developed five conclusions and recommendations:
1. Analytical foundation: Few companies have the analytical foundation in place necessary to leverage the new data sources.
2. Rapid-fire pace: The rapid-fire pace of digital and technological innovation requires data and analytics competency; leading analytics will separate the winners.
3. Exponential disruption: The CPG industry is moving from linear change to exponential disruption. Navigating the rapid innovation will require the effective and efficient use of big data analytics.
4. Business context required: Big data will be used to facilitate better decision making through cross-functional business planning and execution. Big data has the potential to enable both internal and external collaboration to yield top and bottom line growth.
5. Cultural shift: The pace of innovation has led to a culture of experimentation. Companies who develop cultures that embrace data and develop analytical maturity will be able to navigate and capitalize on this new era of innovation.3
Walk Before We Run
The IBM/Kantar studies show that successful retailers are using analytics to understand consumer behavior and drive assortment, planning, price, and promotion strategy. Successful manufacturers are using analytics to drive more factual conversations with retailers.
Discussions of big data include volume, velocity, and variety but, there is another key element – veracity. Data, big or small, is useless unless the quality is good and multiple data sources can be smoothly integrated. Data accuracy is key for big data’s successful use. Nine leading companies collaborated to develop “A 5-Point Best Practice Process to Improve Product Data Accuracy” to drive the data quality initiative.
5-Point Process
1. Adhere to foundational attributes: GTIN, UPC, brand, net content, unit of measure, etc. and change the item if any of these attributes change
2. Clearly identify ownership and accountability of attributes with written process control and validation
3. Appoint a single group or individual that is accountable for shepherding and gathering item attributes from owners
4. Ensure that all new items are measured off a stable production environment
5. Communicate production measurements internally and externally4
Consumer to Factory
With the rise of empowered consumers, the CPG industry is moving from a “factory-to-shelf” to a “consumer-to-factory” view of supply chains. This focus on the consumer provides the business a case for big data. To be competitive, manufacturers and retailers will need to merge information from social media, marketing, retail sales, supply chain, and manufacturing. This pool of data will provide a complete picture and support rapid, flexible movement of product based on real time trends.
3PLs Must Master Big Data
Third-party logistics providers (3PLs) must become the fulcrum point for big data, balancing consumer demand against factory production and distribution. In this new world, 3PLs must have warehouse management systems (WMS) that integrate inbound and outbound activity, act as expert translators of master data information, handle order flows from truckloads to eaches, and provide real-time information to all parties to facilitate collaboration. Synthesizing this data and taking purposeful action based on insights gained will be crucial to companies’ future relevancy and success.
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