Supply chains across the globe need a better mousetrap to keep freight spend under control and efficiently manage freight. And, investment statistics reveal an increasing trend for more technology to enable multi-modal shipping and the application of big data analytics to increase scalability, flexibility, and demand-driven management decisions. According to a 2017 survey of supply chain investments, conducted by Gartner and reported by Supply Chain Quarterly, investment in analytics “is expected to grow to $22.8 billion by 2020 as executives become more cognizant of the importance of gaining sustainable value from big data analytical capabilities.” To continuously improve, shippers need to understand the challenges of managing supply chains without resilience, how analytics build flexibility into the process, and a few tips for the use of analytics to enable resilience.
The challenges of poor supply chain resilience contribute to a loss of visibility and poor responsiveness when circumstances change. The recent disruptions to the supply chain reflect the severe shortfalls that may occur when a back-up plan is unavailable. Moreover, failure to enable resilience will inevitably lead to these added problems:
The writing is on the proverbial wall, and shippers continue to face the worsening of the current crisis, notes Transport Topics:
“All over the world, drivers hauling goods across or between nations are running into locally enforced rules aimed at locking down the wider population to stop the virus from spreading.”
Clearly, the disruption will only exacerbate the problems and exploit the weaknesses in the supply chain, but a strategy to build resilience could help avoid those risks.
The crux of poor supply chain resilience lies in limited visibility, so more visibility will amount to better-informed decision making. Through added visibility into traditionally overlooked processes via big data analytics, shippers know what is and is not appropriate. Instead of basing decisions on assumption, analytics provide the hard facts and costs for all inefficiencies. They are the ultimate solution to an ever-evolving supply chain.
Since big data analytics provide more insight, shippers also have the advantage when making critical decisions, but their implications are broader than that. Descriptive analytics provide insight into what happened. Predictive analytics provide insight into what will happen. Prescriptive analytics reveal what needs to happen to reach that goal. It is a continuously moving process from past through future, and shippers that realize the potential of analytics can future-proof their supply chains. That is the base definition of resilience—enabling business continuity despite the challenges that may arise. So, analytics are the precursor to supply chain resilience.
It’s easy to claim analytics need to deployment, but what other steps can shippers take to improve use of analytics to enable responsiveness and success throughout times of disruption? The following list contains the best practices in applying analytics to improve responsiveness and create a self-propagating cycle of application of analytics and their effect on supply chain resilience.
Data in its raw form remains meaningless, and a disparate TMS solutions offer little insight. However, the culmination of advanced TMS functions and big data analytics open the door to continued scalability, flexibility, consistency, and data-driven decision making. Shippers that have not yet invested in analytics need to start thinking about how to increase resilience now—before their competitors do. Also, investment can be realized more easily through a TMS that includes analytics-driven functionalities, such as reporting and performance metrics to monitor and improve operational excellence.
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