What drives improved supply chain management decision making?

     

Supply chains today are governed by a vast number of unique factors that must all connect together in a way that makes sense. A company that rapidly expands its network of partners, for instance, must consider how visibility over information will be maintained. The technological capabilities of each partner as well as the processes governing information exchange must all be linked to ensure that stakeholders have the information they need. At the same time, organizations are facing increased demands from their customers, who want to know the status of their orders at any given time. 

This creates two major and competing pressure areas: Business need to make decisions faster, but they also need to ensure those decisions are accurate and result in valuable gains. The issue is exacerbated by the type of software that many supply chain management professionals rely on.

"Companies cannot effectively model the trade-offs of growth, profitability, supply chain cyclessupply chain visibility and community management such as procure to pay and inventory turns, and business operations complexity on a spreadsheet," wrote DataInformed contributor Lora Cecere. "As that complexity increases, most companies are unable to use supply chain systems to improve operating margin and inventory cycles."

It is important to realize that limited technology not only reduces visibility over the supply chain, it makes it difficult to unlock the potential of solutions such as data analytics. For example, Cecere noted that only 11 percent of organizations can sufficiently evaluate a "what-if analysis." Additionally, only 24 percent of companies can predict the impact of change on profitability. 

Analytics in the supply chain
It is no secret that many organizations are beginning to invest in more sophisticated business intelligence tools. As Gartner suggested in its 2013 Magic Quadrant for Business Intelligence and Analytics Platforms, organizations are shifting away from tools that provide only descriptive information. Instead, business leaders are increasingly adopting tools that can provide diagnostic analysis and identify relationships between different sets of information. This trend makes it important to consider features such as automated data discovery and integration.

Integration has been a pain point for many organizations, particularly those that rely heavily on legacy systems or do not have APIs available. However, it is important for companies to move forward with integration, as the capabilities of a fully integrated data ecosystem will provide much more value in highly collaborative business environments - whether data must be shared across departments or across entire partner ecosystems. Gartner identified several qualities that are essential for BI integration, including:

  • Utilize standardized security mechanisms, metadata, object models and query engines
  • Include robust search and publish functionality for all metadata
  • Provide programming and visual tools so developers can integrate solutions with IT systems and employees can integrate business processes
  • Offer functionality to discuss and share content for collaboration purposes

These features combined highlight a significant demand for accessibility. It is no longer enough to provide users with a table of numbers and raw information. The degree of integration and the number of difference sources available for collecting data mean that today's business analytics users will need to deal with infinitely higher volume than they have in years past. As Gartner suggested, this makes accessibility tools such as dashboards and visual reporting software a must-have for using analytics effectively.

Supply chain management implications
Given that analytics technology can provide significant value in the supply chain management arena, it is important for businesses to keep the above factors in mind when selecting the software that governs their supply chains. Particularly when considering large partner networks, potential solutions will need to account for a wide range of skill sets. An IT user may be able to generate his or her own reports and build custom queries. However, account management teams would need graphical interfaces and intuitive tools that make it easy to see relationships between data.

ITBusinessEdge contributor Kachina Shaw recently highlighted the analytics skills gap, noting that only 38 percent of employees have the knowledge to use data for decision making. This is partially because many organizations are venturing into new territory as they push their analytics initiatives forward. One of the key problems here is that business leaders are not likely to wait long for investments to return value.

This suggests a growing need for some degree of analytics expertise among all levels of the organization for IT departments to provide tools that enable all stakeholders to effectively leverage data-driven decision making.

"If IT provides the tools deemed necessary for data analytics success, but the workforce hasn't the skills or the capacity for critical thinking to use those tools effectively, the investment is wasted, and IT will be blamed for not providing value," Shaw wrote.

While it may not be the sole responsibility of IT to enable analytics success, it will become more important for organizations to encourage end-user buy-in and incorporate analytics into their cultures sooner rather than later.

Supply chain visibility and access to real time data is the key to SCM sans disruption, learn more from Lightwell: