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Using Data to Improve Supply Chain Operations

Learn how to organize your data operations in alignment with supply chain strategy.

Forward-thinking supply chain professionals are looking to advanced technologies to streamline processes, improve accuracy, accelerate delivery and reduce costs. Cloud-based supply chain management tools, the Internet of Things (IoT), artificial intelligence (AI) and machine learning are expected to figure prominently in future supply chain operations. Today’s supply chains are increasingly complex, driving logistics operations to invest billions in systems to manage all the moving parts.

Complex supply chains generate more data, which companies can use to drive greater efficiency or engage in innovation that disrupts an entire industry—think Amazon. The prospect of using data to operate more efficiently and/or innovate is behind the impetus toward digital transformation that leaders across virtually every industry sector now pursue. At its core, digital transformation is about using technology and data to change the way business operates rather than just improving it.

Finding Transformative Opportunities in the Supply Chain

Supply chain professionals are eager to join their colleagues in other business units to drive digital transformation forward. The statistics that show robust demand for more advanced supply chain technology provide evidence of their willingness to set new standards of excellence by detecting fluctuations in demand earlier, handling raw materials more agilely and ultimately accelerating the delivery of products and services. But before they can fulfill their role as the team that delivers, supply chain organizations must figure out how to use data more effectively.

More data is coming in than ever before. It arrives from an array of sources, and it is presented in a variety of formats. The potential value of that data is huge for logistics and supply chain operations, but it’s also enormously valuable for other business units, including marketing, sales, production, etc. Algorithms can help supply chain professionals and their colleagues in other departments identify new opportunities to improve efficiency and innovate processes.

In the supply chain, data offers a number of ways to gain new insights that can have a big business impact. The techniques require different degrees of sophistication, but even the relatively simple exercise of putting data from different sources in the same space and producing a visual representation can suggest strategies for transformation. More sophisticated techniques might involve the use of social media sentiment, weather forecast data, etc., to anticipate conditions that signal an emerging demand spike.

For example, if a pharmaceutical company analyzed social media content and determined that people in specific geographical areas were discussing cold and flu symptoms, that could give them a heads-up that demand for products to treat those conditions are on the rise. As another example, airlines can analyze long-term weather patterns at their destination cities and gain insights that have a significant impact on logistics operations. The IoT is already providing incredibly valuable data to supply chains worldwide, and the companies that use it effectively are gaining an edge over their competition.

Generating Value from Data

Data is the new currency of business. But, just as crude oil, one of the 20th century’s valuable commodities, required significant refining to maximize its usefulness, data all by itself isn’t enough. To extract its value, business leaders need analytical tools and sophisticated algorithms to refine raw data into insights that can drive better business decision-making. They also need to be confident that they can trust their data.

According to a recent KPMG report, trust levels aren’t high among executives. The global tax and advisory firm surveyed nearly 2,200 senior executives worldwide and found that just over a third (35%) reported a “high level of trust” in their company’s data and analytics usage. The report also found that many organizations run parallel processes to test their analytics capabilities and the quality of the outcomes, replicating work manually because they don’t trust the accuracy or completeness of their data or the technology to glean insight from data.

Mistrust of data and analytics tools is not just a supply chain and logistics issue. Data quality is a real problem for many enterprises—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 of 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 and the challenges associated with integrating, cleansing and harmonizing data from multiple and diverse sources. They must 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 of being numbers-driven and embracing KPI metrics and analytical approaches 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 of 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 to achieve 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 equally vital is cooperation and collaboration among external business partners such as customers, suppliers and third-party logistics (3PL) providers. These organizations make up today’s expanding ecosystems and value chains which must eliminate data siloes and work together across organizational boundaries to identify and achieve opportunities for digital transformation.

Especially in the area of logistics, this kind of cooperation should have fertile soil upon which to build, with 92.9% of organizations stating that they are either very or somewhat willing to share data with 3PLs 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).

Getting There

Leveraging data to improve supply chain operations requires bringing together different skillsets to:

1) obtain access to the relevant data,

2) integrate the data from the various siloes in which it currently resides,

3) harmonize the data to ensure its quality,

4) define how the data should be used and how it will guide decision-making,

5) run the analytics and deliver the insights to 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 better utilizing data and measuring progress toward achieving these targets. On this foundation, as data-turned-information proves its value and operations mature, the insights gained will begin to feed into adjusting and developing the supply chain strategy, thus enabling truly data-driven supply chain management.

Greg Siefkin is enterprise sales director at Liaison Technologies, a provider of integration and data management solutions. He has more than 18 years of multi-level strategic sales experience in developing and maintaining C-level relationships with many Fortune 500 companies.

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