Why Are Logistics Firms Not Ready For Big Data
The logistics industry is a complex mesh consisting transportation, inventory, warehousing, material handling, packaging and security that need to come together for actionable insight - and therefore it becomes important that this industry constantly experiement and deploy cutting-edge technologies such as big data and analytics to stay afloat.
Unfortunately, the transportation and logistics companies are slow to implement big data and business intelligence tools, according to a study by consultants PricewaterhouseCoopers. Only 19 percent of companies surveyed are using the technologies as part of their business processes.
The big challenges
According to the study, 35 percent of companies haven’t yet taken a look at big data and business intelligence and, of these companies, 70 percent don’t even plan to do so in future.
“Companies that are underestimating how new data analysis technologies are going to radically change the industry,” said Dietmar Pruemm, who heads PwC’s transport and logistics practice. “Who doesn’t address this, will be at a competitive disadvantage.”
According to a July 2014 Accenture report focused on Big Data and supply chain risk management, most organizations have a number of challenges when adopting and deploying a data analytics solution. It notes, while 97 percent of executives reported having an understanding of how big data analytics can benefit their supply chain, only 17 percent reported having already implemented analytics in one or more supply chain functions.
Back in 2004, the National Institute of Standards and Technology (NIST) published a report that found “manual data entry is widespread, even when machine sources are available; critical information is often manually reentered at many points in the chain” and that “interventions from purchasing clerks, order processors, and expediters are required to maintain supply-chain information flows.”
The researchers developed a model to quantify the cost of these integration issues, particularly for the automotive and electronics industries because they have very global and fragmented supply chains, and they concluded the following: “We estimate the total annual costs of inadequacies in supply chain infrastructures to be in excess of $5 billion in the automotive industry and almost $3.9 billion in the electronics industry.”
While that was over eight years ago, the situation hasn’t improved much. The huge ocean of data - what we generally call Big Data today — managing data quality has become extremely more difficult.
“The ever-changing global economy, fuel price instability, just-in-time requirement of customers, and the necessity of cost-effectiveness add to the complexity of the ecosystem. This, in addition to the huge scale of operations, makes it increasingly difficult for Logistics Service Providers to gain visibility across the supply chain and ensure efficient customer service,” said Mahesh AG is the CoE practice head for the cargo & Logistics practice at InterGlobe Technologies in his blog.
Therefore, it’s important for organizations to clearly define their goals and objectives before embarking on this endeavor.. He mentioned, “In order to have big data to analyze in the first place, companies must invest in the latest technologies, including state-of-the-art sensors and radio-frequency identification tags, which can build transparency and connections into the supply chain. It also becomes important to consider the type of business intelligence tool to implement in order to obtain the visibility that is needed to measure and monitor business across multiple workflows.”
Companies with an enterprise-wide solution are far more likely to generate a range of important supply chain benefits from their use of big data analytics with shortened order-to-delivery cycle times and improved cost to serve. Logistics companies need to ensure that big data analytics is embedded in supply chain operations to improve decision making across the organization, and hire people with a unique mix of analytics skills and knowledge of the business to produce actionable insights from big data.
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