Predictive Analytics in Logistics

Business person hand holding financial statistics displayed on the tablet screen at office.

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Amidst intense competition in logistics with outsiders making big splashes in the pool, how can we prepare and keep pace with disruptive technologies?

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              Amazon’s advancement in the sales forecast model that transports goods way before the buyer clicks Buy has alerted every key supply chain player, i.e., anyone that doesn’t want to be replaced by Amazon, to the importance of predictive capacities. UPS, for example, recently began putting its massive database of more than 1 billion shipments per day to good use in estimating shipping volumes and customer requirements.

Predicting the future is a constant uphill battle in general, and particularly in logistics. Nonetheless, the longer the learning curve stays on the back burner the more to lose for snoozers as possibilities that spring from tech applications are limitless, it be network management, risk management, cargo load-route optimization all the way to fleet maintenance, all of which optimizes the utilization rate of existing resources with systematized streamline operations that are choreographed in real time.

According to A.T. Kearney Analysis, data forecasting will become the most important trend in Digital Supply Chain (DSC) for the next ten years ahead. AI will be embedded as instrumental in operational and asset management optimization, as well as other key areas. Thus, technological preparedness today is vital to how advancement takes shape tomorrow.

Organizational Modification

According to Gartner’s survey in 2017, three out of four supply chain executives acknowledge that present time operations are not in line with digital transformation plans. Unsurprisingly, as some still run attentive paper-based back offices for the status quo, never mind training on on the latest Cloud spreadsheets, with bureaucratic redundancy and computational limitations that hinder front line productivity and thereby a more difficult life than necessary for customers at large in this age of congestion. So what needs to happen internally prior to investing in the latest tech is digitalization on the enterprise level which can start simply be establishing a digital team to plan and implement the transformation with the deployment of basic technology such as GPS sensors and other IoT apparatuses required to operate predictive data.

Given slim margins in the logistics industry, big data that allows the entire supply chain picture to be grasped, i.e., Amazon’s, can significantly boost competitiveness and, most importantly, help staff understand the pivotal role of technology and thus embrace change and prepare for it. Many venues exist to help staff embrace the coming digital transformation through organized learning processes, e.g., seminars, events and training that provide user-friendly application fundamentals.

Clean Data is a must

In addition to data input and storage-ability, the quality of data is also very important in the predictive analytics process because decisions will be based upon it. According to the Deloitte survey in 2017, the significant technological barrier in logistics applications is upstream data quality and second is the unavailability of data which is due to incompatible recording systems and default settings at various entry points. Standard operating procedures (SOPs) and streamlined data management is thus of front and centre importance to development.

Given AI capability today that enhances data quality through data detecting-standardizing and approving prior to analytics input, it can also predict missing data from what should be there as well. By applying merely 10% of existing database, AI can already create the company’s remaining must-be database.

Invest in the right technology

With supportive data and systems in place, next on the table for the organization is investment in the right technology. The World Economic Forum estimates that data management technology related to analytics will cap a market value of over $8 billion. As self development is nearly impossible for the average logistics provider, acquiring a complete package from experienced startups with subject matter expertise could prove to be putting well spent money where the mouth is.

Last but most importantly, technological investment always involves the most valuable asset any organization could have remains the key success factor for efficiency. Human assets must have access to proper training in order to maximize the potential of the expensive tools at their disposal. And that requires investment as well. Progressive training is key.

Compiled by BLOG.SCGLogistics

References and photos http://transmetrics.eu, pixabay.com

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