Barely a day goes by without our newspapers read on smartphones rather than in traditional print medium, of course reporting on how the Internet of Things (IoT) is set to change the way our fridges are stocked, or how a ride-sharing app is disrupting the age old and honorable profession of taxi-driving.
Our ubiquitous digital technology has become a medium to tell us how ubiquitous digital technology is revolutionizing consumer habits. But its only one side of the story.
For many companies, particularly those that produce consumer packaged goods (CPG), the ability to succeed in the digital era rests on their willingness to transform supply chains and manage deliveries in ways that meet the expectations of technologically-savvy, always on consumers. Supply chains may not make great copy for the mainstream press but they are where vast amounts of investment are being made as CPG companies rush to reinvent themselves as digital enterprises able to harness the power of Big Data and other transformative technologies.
In response to changing customer expectations, and the demand for omnichannel shopping experiences, supply chains are being reimagined as smart digital supply networks. Less linear and more sophisticated than a traditional chain, these networks are increasingly dynamic and able to sense and respond to customers real-time behavior.
The lifeblood of a smart digital supply network is its ability to manage master data.
In our whitepaper – Transforming CPG Supply Chains in the Digital Era, we discuss how analysis of master data is the starting point for any journey into a next generation, digitally enabled supply chain architecture.
We use the analogy that Big Data is the oil thats making digitally integrated business processes a reality. The IoT is the drilling rig that extracts the oil. Data analysis is the refinery that leads to new insights for improved demand and supply planning. With the arrival of more mobile devices and new data collection systems, this information can be captured and analyzed more frequently and with greater accuracy. Much of this newly available information is not point-of-sale but point-of-demand data. By analyzing that data, CPG companies can pivot from supply-led models to demand-driven ones. They can increasingly personalize the customer experience to the needs of the individual, and incentivize their customers to make purchases and request delivery in ways that maximize profit.
Take for example a scenario in which a consumer makes an online search via their smartphone. A company can use point-of-demand data to identify the individuals location and the product or category they are searching. A demand-driven supply network can sense an opportunity. It can incentive the consumer to follow several paths to purchase including buying the product at the manufacturers own store location, or that of a retail partner, or requesting delivery. Fulfillment can be managed directly or via a third party depending on product availability and inventory and the relative profitability of each option.
Of course the success of such an approach depends on the manufacturers ability to manage the data it captures and the insights it draws from it. Costing and pricing errors will generate inaccurate demand forecasting. Classification errors and missing item attributes will extract a high cost both in terms of missed sales opportunities and high rates of product returns, and the associated reductions in customer satisfaction.
Poor data quality will hinder a companys effectiveness much as dirty oil will inhibit an engines performance.