Shipping and logistics are extraordinarily complex. The two business functions generate a ton of shipping data. But managing data manually is too time-consuming. Instead, management and analysis of shipping and logistics data needs to be more straightforward. It needs a digital, automated process. That requires the use of data analytics, business intelligence, and advanced value-added service. Those functions maximize the real value of data. Being able to reduce transportation costs requires managers to focus on ways to boost overall return on investment (ROI). A focus on reducing total transportation costs will naturally lend itself to improved customer satisfaction, embracing intelligent application of data, and discovering new opportunities where professionals may employ automation to gain efficiency.
For example, Gartner reports "By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.
Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. AI and machine learning are critical realigning supply and the supply chain to new demand patterns."
That amounts to leveraging more data and shipping analytics-driven decision making to lower transportation costs through the current disruption and those that may arise in the future. While Gartner's report was specific to the pandemic, the impact is the same for all disruptions nonetheless. For that reason, here are five best practices to employ data, utilize data, and gain the prescriptive insights analytics platforms afford in the effort to reduce transportation costs.
By taking advantage of different shipping methods and analytical processes, it is possible to enhance transport productivity and reduce transportation costs. Bringing disparate data together into a more automated and streamlined approach makes every step in the shipping process faster, more sustainable, and easier to manage. Logisticians must also automate data entry and response for better results. Think about it.
Freight analytics has undergone some major transformations in recent years, mostly thanks to automation and digital tools. The shift to automated data collection, analysis, and distribution makes it easier to plan and automate everyday actions. These small improvements add up over time. And they yield excellent results to reduce transportation costs.
In addition to capturing data, it's critical to normalize all disparate shipping data to compare various shipping modes equally for scorecard purposes. Data normalization and standardization – the process in which data that may be listed in slightly different ways between two systems— is normalized into a single data point and, as a result, able to be analyzed effectively.
Normalized data will allow shippers to answer the following example questions, which are crucial to the optimizing freight management and to reduce transportation costs:
On the parcel side:
On the bulk side:
While these might seem like complex and time-consuming shipping data points to identify, a robust automation tool can help – but that tool must first be able to standardize and normalize data.
Everything involved in shipping and logistics can benefit from the utilization of real-time analytics and data. With the up-to-the-minute data, an automated system gives all parties equal footing. They can access critical data and information. Decision-making and problem-solving get simpler.
That makes it easier to reduce transportation costs in the long run as a shipping and freight analytics platform will provide actionable, prescriptive insights for your next best action. When you add the service component, i.e., people, to the equation, you then have experts at your disposal to help make sense of all the analysis of transportation and logistics data to further see potential for optimization and cost reduction strategies.
As an example, data and analytics that show opportunities for service level downgrades, consolidation opportunities, and address correction effectively consolidate overhead expenses through process improvements. In turn, that adds value by ensuring everything runs more smoothly.
Data is nearly meaningless in and of itself, but when that data is captured, normalized, standardized, and then presented through a platform that is user-friendly, and has the backing of expertise to guide you and make you comfortable with the insights the platform shows, truly, as a shipper, you are on your way to rooting out waste in your transportation network and reduce total costs.
A streamlined approach to communication and reliable end-to-end transparency will make management more effortless. When managers can see the overall flow and can easily share data, they reduce transportation costs. It's all about finding the low-hanging fruit to add value. And it encompasses working with the full network to find opportunities to add efficiency. That includes working across all modes and capturing data in all forms, including connection to partners' platforms or your platforms to feed partner data into the analytics platform either via API, csv files, EDI, and any other methods that aids in capturing partner data. Discussions around insights gained from partner data, such as your carriers or vendors, will only aid in seeing the whole picture, yielding prescriptive actionable insights that inform you of what to do next to reduce total costs.
When managing shipping modes, including freight and parcel moves, with the goal to reduce transportation costs, industries' success today comes down to real-time data capture. The more up-to-date the information, the easier it is to use.
Consider this. Managers and employees can view a dashboard in real-time. Using that information, they prioritize loads. They can lower overhead. Another example might include using data to detect anomalies within the data, which is similar to the idea of management by exception, to bring those matters to attention and correct them. One use case in this scenario might be the use of machine learning to gain insights into what's the typical cost per shipment, and when that moves beyond standard ranges, the system should flag the issue for correction or review. Of course, that's a subjective KPI that falls under the influence of peak season shipping demand and more. As such, it must continuously learn from the lifelong trail of data that powers it.
That's a big game-changer, especially in times of tight capacity or volatility. The insights gained in real-time aids shippers, and their frontline coordinators, make informed decisions and choices on the go and when major deviations occur. In turn, it contributes to an improved customer experience.
Intelligent applications must be actionable and empower everyone to make smarter decisions that proactively drive costs into retreat. Remember that any shipping and logistics company's primary goal is to protect its assets and maximize profit. One way to achieve this goal is to reduce transportation costs across the end-to-end supply chain. The trend of using data to gain actionable insights from analysis shows no signs of slowing down and is something freight managers and supply chain leaders must embrace and continuously prepare for. The most effective way to do so is with automation and digitalization.
It takes a skilled, intelligent, and experienced team to achieve success in the highly competitive market today. Therefore, be sure to visitIntelligent Audit onlineto get started today.
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