Harshad Khatri

Trends in Manufacturing Operations: Leveraging Big Data across the Value Chain

Leveraging the vast amounts of data in the hybrid data ecosystem can transform the operational and industrial landscape. Three important factors determine the success of a big data initiative in operations.

by Harshad Khatri, January 2013

While a lot of the buzz around big data is externally focused—in areas such as social media, search engine marketing/search engine optimization, and sentiment analysis—utilizing the data to enhance and optimize operational capabilities has potential for far greater rewards. Leveraging the vast amounts of data residing in the hybrid data ecosystem has the potential to transform the operational and industrial landscape in ways that consumer landscape has been transformed by media, communications, and technology over the last decade.

A report from the National Bureau of Economic Research argues that gains from computing technology have flattened out over the past few years. While this may be true, these gains have focused on the consumer, communications, and technology sectors and not on operations and manufacturing. Extracting significant gains from a mature area poses some challenges, but given the large potential benefits, they are well worth the effort.

Dig for the data

For a typical manufacturing organization, materials account for 50 to 65 percent of costs. Conversion expenses in labor, energy, and land account for 15 to 30 percent of costs, and the rest of costs are in overhead. The focus of optimizing operations have been physical in nature, in areas such as lean, supply chain, and manufacturing excellence. These efforts are typically cost-driven, and the metrics that drive them are cost-focused—such as procurement costs, conversion costs, and inventory levels. Leveraging big data for operational benefits involves a deeper, wider, and most importantly, smarter analysis of the hybrid ecosystem. Data resides everywhere in an organization—in multiple ERP systems which are not necessarily integrated, CRM systems, enterprise data warehouses, cloud data, and thousands of disparate spreadsheets. Much data may also reside outside of the organization, across the value chain with partners on both the supply and sales side.

The data in the ecosystem can be characterized by the “four Vs”—volume, velocity, variability, and volatility. Focusing on these areas while managing large disparate data sets during the move away from ERP and spreadsheet-centric processing is the key to business value, as it gives decision makers access to meaningful analysis and discovery.

Focus on analysis

The goal is to develop a scalable platform for decision-making with speed scalability and extensibility. One of the key building blocks is to develop, maintain, and leverage benchmarking data (both internal and external) to drive value.

On the operational front, metrics are currently being captured relating to procurement, manufacturing, and conversion costs. These coupled with financial and statistical metrics—both market- and customer-focused—normalized to actionable decision drivers should be measured on an ongoing basis to drive decision-making and to develop incentives aligned with corporate goals.

Analysis of these datasets cannot be a “one size fits all” and should reflect the uniqueness of an organization and its IT environment—its industry, corporate strategy and goals, operational footprint, and data ecosystem. These data sets are being analyzed to drive decision-making aligned with corporate goals both at the executive and at operational levels.

Decision-making and operational execution

A key to setting up the supporting analytical infrastructure is driving execution aligned to change—external change in the industry and business environment as well as internal change in the corporation, business strategy, and the data ecosystem. While this is time consuming, it’s the most important factor to ensure the success of the analytics platform.

Executives at leading-edge manufacturing companies are leveraging big data to optimize operations on a near real-time basis. Key benefits include alignment with corporate strategies via proactive strategies in sync with the market, and tangible benefits such as lower procurement, conversion, and distribution costs. While the details of the approaches followed by these companies are tailored to their circumstances, the key principle they follow is the same: focus and capitalize on changes in the external and internal environment. Today, a handful of companies are leveraging big data for competitive advantage. In a few years, this is expected to be a price of entry.


Harshad Khatri is an Oracle Insight senior director, focused on customer strategy.