Leverage Big Data to Manage Product Proliferation
The noise around big data is deafening. Most of this is driven by a familiar question: "Is this going to solve any business problems within my organization?"
The fact of the matter is that data already exists, but it's hidden and underutilized. Leveraging this information can mean more than just a competitive advantage for today's customer-centric, product-focused companies. Companies need to shift from simply collecting and storing their data to searching for relationships between data points to reveal new insights.
Viewing data from multiple perspectives will reveal patterns that are oftentimes unexpected. By breaking down the silos that confine data in traditional storage methods, businesses can finally leverage the critical connections that reveal the relationships between different products and behaviors.
Optimizing product mix is a strategy that most people haven't associated with the surge in big data, yet this has the biggest impact on supply chain operations and makes up nearly 80 percent of manufacturing overhead. So, naturally, this is a challenge that deserves a solution. And as many companies offshore production and labor, the solution is more relevant than ever.
Companies that can get a good handle on the data around customer buying patterns will be more likely to outperform their competitors that do not. Leaders in supply chain management are turning to pattern-based analytics solutions to optimize their production initiatives.
Thousands of configurations and/or SKUs, data that exists in departmental silos, and a lack of alignment between customer demand and solutions management, operations, and sales are leading companies to seek out new methods for converting data into strategic insight. Companies like NCR have deployed pattern-based analytics solutions within their organizations in an effort to provide the best value to their customers at the least cost to the supply chain as a whole.
The cost of product variety on a salesperson's time alone is significant.
Consider the following: NCR sales representatives were spending an average of 10.5 percent of their time on solution definition, configuring and pricing the solutions, and then tracking deliveries. With an annual sales quota of $4 million, just 1 percent of time equates to $40,000 in potential revenue, so 10.5 percent is equivalent to $420,000 per sales representative. This quickly adds up: With a sales force of just 100, the opportunity cost is $42 million!
Product proliferation also has a negative impact on the supply chain. At NCR, it increased total overhead cost, and led to high inventory exposure, high supplier risk, high volatility, and a poor demand response. Factor in the effect on product management, and the impact on revenue is anywhere from 18 to 24 percent.
Implementing a pattern-based analytics solution at NCR brought these three departments together onto one solution set, which consolidated core product configurations, enhanced the performance of new product introductions, improved sales enablement, and gave the supply chain a better demand signal.
Why hasn't this been managed in the past? We've observed through conversations with VPs of supply chains, product managers, and sales operations executives that nobody really keeps up with the numbers of configurations within their organization. The automotive industry, for example, will brag about having the ability to manufacture a car in under three days…. Yet, if that car sits on the lot for eight months, what's the point of making so many? That's the conundrum that pattern-based analytics solutions tackle.
Traditional data management tools don't factor in parts, configurations, etc., based on customer buying patterns. And product mix is the single biggest driver of material cost, inventory, and sales velocity. At the same time, the key to growing—and keeping—business is to understand how to improve customer loyalty. In the information age, that becomes more difficult to master. Customers have easy access to information all the time, and as companies are finding, that information now encompasses feature choices for any and every product available. But customers don't often have such a rigid set of requirements—especially when it comes down to cost and availability.
By analyzing customer buying patterns in data, companies can improve the customer experience and increase profits. These patterns can reveal unexpected relationships and connections—information that will impact the bottom line. And isn't that what big data is for?
Radhika Subramanian is CEO of Emcien, a provider of pattern-based analytics solutions purpose-built for organizations in sectors such as manufacturing, distribution, retail, and law enforcement. In 1998, she cofounded Idmon, which was sold to Swissair Group in 2001. She has nearly 20 years of experience helping large organizations optimize their business processes.
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