Navigating change is a crucial competency for warehouses today, with technology advancements and consumer demand making it necessary to keep the pace or lose the race. Increasingly important in this equation is the use of Big Data to drive decision-making. However, as Deloitte explains, using Big Data to optimize the supply chain “requires selecting the relevant data, assuring a steady flow of accurate data, and translating the data into useful interpretations.” As such, it’s not only important for organizations such as warehouses to embrace Big Data but also to master the management and analysis of that data to inform decision-making.
Understanding Data Sources
Warehouses and other organizations in the supply chain generate large volumes of data spanning several categories. While not all organizations will utilize all data sources to the same extent, understanding the various sources of data and how they impact the supply chain is essential for optimization. Logistics Viewpoints provides a helpful breakdown of data sources relevant for supply chain optimization:
- Warehouse data – Storage capacity, equipment, personnel, and contracts.
- Inventory data – Volume, value, and distribution center allocation.
- Master product data – All products and their relationships to one another, such as shared attributes or physical characteristics.
- Production data – Production capacity and product portfolio.
- Volume data (or Logistics data) – The volume of product inbound or in transit, from plant to distribution center, stock transfers between facilities, or direct deliveries to customers.
- Financial data – Combined data including transportation, warehouse, production, and inventory data.
- Demand data – Including data from projections and forecasting, as well as actual historical data for analyzing trends.
- Qualitative data – Including soft factors such as customer service, lead times, and other business requirements.
While warehouses only generate a few elements of these varied data sets, nearly every data set will have some domino-effect impact on warehouse operations. Gathering and analyzing all supply chain-relevant data provides a broader, more in-depth picture of the overall supply chain, enabling warehouse operators to make better-informed decisions than by analyzing warehouse data alone.
Don’t Get Lost in the Forest
All of this data offers a big-picture overview, but warehouse operators and other supply chain executives should use caution to avoid getting caught in analysis-paralysis. Attempting to dissect and analyze every trend and data point can quickly lead you in circles.
Instead, use these available data sources for the big-picture insight, but also determine what data will truly drive business value. As Regenia Sanders and Jason Meil, Contributors to CFO.com, explain, “Planning is often the hardest step but tends to have the greatest impact on cost, given the bullwhip effect.” And Big Data is proven consistently to improve accuracy in planning, so use it to your advantage to fine-tune every facet of your operations.
Take Cues from Leading Enterprises
The University of San Francisco profiles the retail giant Walmart, noting the company’s $486 billion as of the end of the fiscal year ending in January 2015, marking an increase in $10 billion over 2014. Clearly, Walmart is doing something right, particularly considering the company is largely based on its low prices. Not only has Walmart mastered the traditional supply chain, but the company also makes investments in technology to capitalize on e-commerce and other trends. Doing all of these things precisely right is far more than mere chance.
Walmart employed a variety of tactics that culminated with one of the most efficient supply chains in existence, even earning recognition in Gartner’s top 20 supply chains for the past five years. A combination of vendor managed inventory (VMI), better cooperation and collaboration with suppliers, and interconnecting the various entities through technology for substantial efficiency gains – ultimately enabling the company to maintain the low prices on which it had built its brand in an economy otherwise pushing costs up across the board.
While Walmart’s success is not built exclusively on Big Data analytics, data played a critical role in powering predictions, projections, and innovative decision-making to realize the full possibilities that exist throughout the supply chain. Of course, not every company aims to be the next Walmart, but when you make smart use of data to drive innovation and optimize the supply chain, every company can realize its full potential.