Big Data: The Latest Rage in Supply Chain Management

A supply chain is rich with data and has a large cost component, making advanced analytics a strategic weapon.
Jason Meil and Regenia SandersNovember 23, 2015

Early uses of big data were concentrated in two areas: customer segmentation/marketing effectiveness, and financial services, particularly in trading. Recently, supply chain has become the “next big thing.”

Regenia Sanders

Regenia Sanders

Why? A company’s supply chain is rich with data, and it’s also a large cost component. Combined, those facts mean that advanced analytics can become a strategic weapon for optimizing the supply chain.

However, many companies can’t see the forest for the trees. They are optimizing, but not strategically. When applying data to supply chain, it’s critical to step back and look at what truly drives business value.

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“They’re Digging in the Wrong Place”

As every fan of “Raiders of the Lost Ark” knows, Indiana Jones found the Ark of the Covenant first. The Germans had far greater manpower and resources and they were more efficient, but they were competently digging a hole in the wrong place. The same goes for using big data in supply chain optimization. You could have the most efficient process in the world, but if you’re making the wrong amount of the wrong product, it will hurt your business.

The Supply Chain Operations Reference model (SCOR) — Plan, Source, Make, Deliver, Return, Enable — is the standard framework that most CFOs and COOs use to organize their supply chain and execute against it. Planning is often the hardest step but tends to have the greatest impact on cost, given the bullwhip effect.

Jason Meil

Jason Meil

We’ve found with our clients, again and again, that big data can have a measurable impact on driving greater accuracy in planning, ensuring that companies make the right amount of the right product. Advanced algorithms and machine learning can facilitate increased forecast accuracy across a company’s SKUs, which drives greater turns, less waste, less inventory, and fewer stock-outs, which leads to higher EBITDA, lower working capital, and greater competitiveness.

Optimizing for Fast-Changing Markets

Among other core business processes, supply chain can drive competitive advantage in an ever-changing, multi-channel, and global marketplace. Today, increased volatility in customer buying patterns and emerging markets makes it more difficult than ever to correctly anticipate where to focus.

Using advanced analytics to predict customer preferences and patterns, along with microeconomic and macroeconomic trends, leaders can prioritize the right levers to pull for supply chain advantage. If customers value convenience, it may behoove a company to look at its distribution network to optimize how quickly products get to customers. Similarly, if customers value quality, then investments in R&D, product lifecycle management, supplier relationship management, and manufacturing may be prioritized.

These supply chain decisions directly impact financial allocations. Making the best decision for the organization requires identifying and measuring key performance indicators (KPIs) directly related to key supply chain areas. Winning supply chains are:

  • Externally focused: Knowing the competition and when and where it is appropriate to partner with third-parties.
  • Agile and low-cost: Continuously baselining and benchmarking by using KPIs and key result areas (KRAs) to identify cost savings.
  • Lean: Finding operational efficiency improvements.

Using Data to Drive Efficiency

These new data-driven decisions have become game changers for many supply chain transformation efforts. To start, identify KPIs, which can be both leading and lagging financial indicators, with the greatest impact in these areas:

  • Reliability: The supply chain performance in delivering the right product, to the right place, at the right time, with the correct quality and documentation (e.g., delivery performance, fill rates, etc.)
  • Responsiveness: e.g, order lead times, demand understanding through forecast accuracy, etc.
  • Supply chain costs: e.g., cost of goods sold (COGS), cost of poor quality (COPQ)
  • Asset management efficiency: The effectiveness with which assets are managed to support demand satisfaction, including managing fixed and working capital (e.g., inventory turns, cash-to-cash cycle time, etc.)

Companies can also drive better forecasting and demand planning with data analytics using customer-insight, point-of-sale, and usage information to better predict customer needs and plan inventory at various levels within the supply chain (i.e., warehouses, stores, etc.).

Data analytics that combine tracked failure points during manufacturing with customer feedback improve product quality. For example, an electronics company achieved significant cost reduction by establishing supplier quality metrics, reducing the amount budgeted for warranty costs.

Supplier spend analytics can help rationalize supplier base and leverage economies of scale to get better pricing, increasing margin. Analyzing spend data to consolidate supplier base allowed a chemicals manufacturer to identify savings during a merger, while outsourcing warehouse operations to a third-party logistics provider reduced supply chain costs for a fashion retailer.

Analyzing product profitability can be useful in product rationalization to eliminate working capital costs of excess or obsolete inventory. The result can be improvements in forecasting and demand planning, as well as sales and operations planning, leading to significant inventory reduction.

There are many more practical applications for using data to drive both tactical and strategic supply chain decisions directly relating to financial performance. As the stewards of a company’s financial health, CFOs who apply data analytics to drive supply chain performance will create value.

Regenia Sanders leads the supply chain practice at management consulting firm SSA & Co. Jason Meil leads the firm’s advanced analytics and big data activities.