Logistics case study: Analysing data to cost cut


Questions about Data

1.What can be done to improve the business process i.e appropriate tech implementation?

2. What is the industry growth rate and is there potential to run a successful logistics business?

3. How can one minimize and anticipate unexpected/unforeseen circumstances?

4. Is it possible to improve supply chain visibility?


Originally a military-dominated industry, many enterprise businesses recognized and took advantage of the logistics industry in the early 1960s after world war II. Most logistics companies begin with the best intentions to achieve successful and sustainable supply chain cost management whilst maintaining existing structures but somehow the biggest challenge remains to create cost-saving solutions entailing activities such as filling orders, storage, and warehousing, distribution of products, production planning, and customer service.

One such business sought help from Quantar seeking to have these questions addressed: What could they do to improve their business process? How could they improve their supply chain visibility? Based on the available data, what is the average logistic business growth rate?

It’s noteworthy to point out that some logistic businesses have a veteran monopoly in the industry due to circumstances such as being early pioneers. However, there is massive potential in the industry and the client was curious if machine learning would be able to shed some light on this.

Data Collection

The data uploaded to Quantar’s Data science software consisted of the various logistic fulfillment stages such as booking details, GPS information, origin and destination of products, driver details, planned ETA, customer and supplier details. These metrics were uploaded over a product’s logistic lifecycle.

The Solution

Through Quantar’s NLP (Natural Language Program), the client was able to obtain a great understanding of their business model within the logistics industry. Most importantly, the data showcased how to seal loopholes that caused massive spikes in business operating expenses in addition to enhancing and standardizing the customer experience across all geographies, channels, and touch-points. Quantar’s proprietary software provided the client with full visibility on all aspects of the supply chain’s workflow that enabled the client to appropriately plan/realign the businesses’ future operations and strategy to reduce cost and anticipate the future direction of the industry.

AI/Machine Learning and Logistics

According to freightwaves, the size of the global logistics industry ranges from $8 trillion to $12 trillion annually. Machine learning recognizes various patterns in supply chain data which quickly helps in pinpointing the foremost influential factors which may be pivotal to the success or failure of any logistics business. Quantar’s data science helps businesses in the logistics sector digitize logistic plans through machine learning which predicts unforeseen circumstances (via anticipatory logistics), which further reduce the chances of any mishappening during the delivery of goods, enhances planning and scheduling, working with more accuracy and efficiency, thus streamlining the business processes efficiently. 

Undoubtedly, artificial intelligence is expanding its application into almost every industry, with the logistics industry not being excluded. By 2021, Tractica estimates that thesale of logistics robots will be approximately $22.4 billion with AI software revenues alone reaching near $100 billion globally. Contact Quantar to harness the power of machine learning today!

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