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How to Use Data to Improve Supply Chain Operations

Innovation and pursuit of process excellence are nothing new for supply chain professionals. After all, supply chain management is often the backbone of an organization’s ability to cash in on the promises it makes to its customers. The efficiency of a company’s supply chain operations also has a significant effect on its bottom line, which is well illustrated by the fact that 57% of organizations perceive their supply chain as a competitive advantage, and 67% of the highest-ranking supply chain positions in organizations are held by top management or at C-level (Geodis 2017).

Golden Opportunities for the Supply Chain

There is no doubt that supply chain as a business function has a strong culture of operational excellence connected with it. However, even so the professionals cooking up tomorrow’s innovations – ones that will set new standards on how we the consumers expect to receive our online purchases, how the components of self-driving cars find their way to the assembly lines and how medicines are traced from raw materials to pharmacy shelves – face new challenges in putting their visions into action. The key in getting this done is – you guessed it – better utilization of data.

Data that is available in today’s business environment holds tremendous potential not just for supply chain and logistics operations but also for practically any other business function from sales and marketing to production.

The opportunities that clever use of data presents for supply chain leaders take on many forms and come calling for varying levels of sophistication. Simply combining data from different sources and visualizing it can provide surprising insights that have a significant business impact, but tapping into emerging areas like social media sentiment analysis or weather forecast data can transform the way companies perform some of the key tasks within their supply chain operations. As an example, consider a pharmaceutical company and if they would find it valuable to know in real time how many people in which geographical areas are talking about symptoms for a certain illness across the different social media channels. How about an airline being able to extract actionable insights from data on the probable long term weather patterns for all of their major flight destinations? In addition, internet of things (IoT), GPS positioning and many other technological developments that add to the volume, variety and velocity of data that is available for organizations are all playing into the hands of those companies that are prepared and able to utilize data to its full potential.

Challenges with Extracting Value from Data

Due to the well-recognized value data has in the modern economy, it has sometimes been referred to as the “oil of the 21st century”. While in this metaphor data analytics is the “combustion engine” that powers the digital world; simply having data is not enough to keep the pistons running. Much like with actual combustion engines, the shiny analytics tools and sophisticated algorithms that are in the center of the hype these days are useless for decision-making without accurate, reliable and timely data to run them.

Data quality is a real problem and not just for supply chains – in general, only 44% of organizations trust their data to make important business decisions while, on average, 33% of an organization’s data is perceived to be inaccurate by the C-level decision makers (Experian 2017). Therefore, data first needs to be refined and efficiently delivered for analytics to provide the desired value, which requires a versatile combination of integration and data management capabilities.

Another important aspect to note is that while the above numbers measure trust and perception instead of actual data quality, they are key indicators on how well investments in data analytics are being turned into real business value. After all, even the best and most reliable data points and metrics are meaningless unless they are somehow leveraged in decision-making. To address this challenge, data and analytics leaders need to address the perceptions of data quality, deliver tangible value and communicate it consistently to business decision makers.

Promoting Cooperation Within the Organization and Across the Partner Network

Supply chain operations have a strong history in being numbers-driven and embracing KPI metrics and analytical approach to improving performance which is exemplified by, for example, the Supply Chain Operations Reference model (SCOR). While not always perceived as sufficiently standardized, the methodical approach that supply chains tend to have creates an excellent foundation for cooperation between supply chain and data analytics professionals that can complement their respective skill sets by bringing together deep knowledge on business processes and advanced understanding of data modeling and analytics.

With 70% of companies describing their supply chains as very or extremely complex (Geodis 2017), it should be fair to say that this cooperation is needed for achieving the best possible results. Depending on the use case, partnering with other business functions such as procurement, production, finance or sales and marketing can also provide opportunities for innovation or, at the very least, relevant data to incorporate into supply chain analytics initiatives.

Partnering across business functions is important in supporting supply chain innovation through a data-driven approach, but external business partners such as customers, suppliers and 3rd party logistics providers should not be neglected either. Especially in the area of logistics, this kind of cooperation should have fertile soil to build on, with 92.9% of organizations stating that they are either very or somewhat willing to share data with 3PL and other solution providers (eft 2017). To highlight the importance of these relationships, 84% of organizations outsource at least some of their logistics handling to 3PL providers (Geodis 2017).

How to Organize your Data Operations in Alignment with Supply Chain Strategy

Improving the supply chain operations with a data-driven approach requires bringing together different skillsets to 1) obtain access to the relevant data, 2) bring the data together from the various siloes it currently resides in while ensuring data quality, 3) defining how the data should be used and how it will guide decision-making, and 4) running the analytics and delivering insights to the business decision makers in a timely manner and in a usable format.

Briefly described, doing this requires setting up the organization’s data operations in a way that supports the goals set in the supply chain strategy by engaging the relevant stakeholders to ensure that the right resources, skills and processes are in place. A key element for success is setting and communicating clear and meaningful targets for what good will look like and how the performance is developing against these targets. On this foundation, as data-turned-information proves its value and operations mature, the insights will begin to feed into adjusting and developing the supply chain strategy, thus enabling truly data-driven supply chain management.

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