Today, global supply chains are increasingly driven by big data analytics, which companies use to make decisions. This includes everything from sourcing raw materials to making and delivering products to promoting sales. In laymen’s terms, every supply chain is composed of four basic levers: source, make, move and sell. For companies, big data analytics can be crucial to the success of their supply chain system because it helps the different levers communicate and links the entire supply chain, resulting in minimized excess inventory and maximum profit.
For these reasons, companies large and small have been rushing to acquire big data analytics tools that will help make their supply chain seamless and their business profitable. What are the consequences for companies who don’t utilize big data analytics? For companies who want to implement big data analytics, what are the first steps? #LeadersatWork spoke with Nada Sanders, Distinguished Professor of Supply Chain Management at D’Amore-McKim and the author of Big Data Driven Supply Management (2014), about her research into big data analytics and global supply chain management. We wanted to know why companies should choose big data analytics that work for them, and where to start.
“When you’re looking at supply chain management, to make it work efficiently, it has to work as a system,” explained Sanders. “We have all these analytics applications for each of these levers, but the challenge is for them to work together to optimize the entire system.”
Companies like Amazon and Walmart are lauded for their innovative global supply chains, incorporating an integrated data driven analytics system that connects all of the company’s suppliers and systems so that each supplier knows how much inventory they need, when an order will be placed, how much inventory is in transit, and how much they need to source from vendors.
“The problem is, most companies are not these leaders,” said Sanders. “Most companies have yet to leverage big data analytics to transform their supply chains. For example, a company might be doing a multi-tier promotion to stimulate their sales efforts. However, if the coordination isn’t there with the operations and distribution levers, whether it’s at the stores or warehouses, the effort won’t work because customers will have to wait. We’ve seen this again and again- when the levers don’t communicate and treat the supply chain as a system, we will see failure.”
For companies who want to get started in big data analytics, the first step is to understand the business strategy. Each company has different competitive priorities and competes in different ways. Implementing a unified process that aligns strategy across the board is extremely important to make sure each sector is working in unison.
“For instance, while Walmart competes in cost, Apple competes in innovation,” explained Sanders. “They will have different relationships with their suppliers and their operations will be structured differently, so their algorithms will be different. Keeping the company’s strategy in mind is important so that when they align the different systems, they align to support goals of the business.”
Once a company has a clear strategy, the next step is to take inventory of its data to make sure the data is clean and trustworthy. Data can be dirty in a numbers of ways; for instance, New England was hit by a number of snowstorms this winter. If you looked at restaurant sales over the last five years and you noticed that this year’s winter sales were particularly poor in New England, you might not consider the storms that crushed commerce unless the snowstorms were recorded. Clean data take seasonal events into account, and notes events like promotions, among other things, which could lead to misinterpreted data if not recorded. There are countless other unrecorded items, measurement errors, or missing observations that contribute to dirty data. Data must be clean for analytics to provide good results. Otherwise the adage “Garbage in, garbage out.”
“Cleaning data doesn’t have the glamour, but it creates a solid foundation on which you can build,” said Sanders. “Once the data is clean, the majority of companies can do very well with basic analytics. When we look at the whole tool box of analytics, companies can utilize the basics like descriptive statistics, correlation analysis, and regression, which will lead to huge gains in profitability.”
Once a company masters basic analytics with clean data, they can choose whether to adopt the predictive analytics companies like Amazon, Walmart, and UPS have employed across their entire supply chain to maximize the benefits of coordinated processes.
How can today’s leaders encourage successful big data analytics implementation within their company? The first step is understanding the company’s strategy, because it shapes the entire supply chain process. Once this is understood, business leaders can ensure the culture and appropriate resources are available within the company to make significant changes in the way analytics are utilized so that the company’s supply chain is optimized.
Has your company employed big data analytics to optimize the supply chain? Leave your comments below.