Benefits of logistics analytics in supply chain and logistics industry – part 2

In the first blog of this 2 partly blog series, we saw how big data analytics in supply chain helps enterprises manage logistics and make sense of their data with powerful techniques.

A recent survey found that more than one-third of C-level executives were engaged in serious conversations to choose Data Analytics consulting services, and 3 out of 10 already have an initiative in place to implement advance analytics.

So, why so many supply chain and logistics firms are eager to utilize logistics analytics?

Big data analytics in supply chain benefits

Analyzing customer data can generate useful insights on product placement, pricing strategy, cost & labor optimization, operational risk management, and better delivery.

Some of the key benefits of big data analytics are:

✓ Improved Productivity

✓ Higher Efficiency

✓ Edge over Competitors

✓ Ability to grab new Business Opportunities

✓ Being Future-ready

To fulfill orders quickly, Amazon links with manufacturers and tracks their inventory. It uses big data to choose warehouse closest to the vendor and/or customer, so as to reduce shipping cost by 10% to 40%.

A research study conducted across a wide range of companies found that adoption of logistics analytics helped them improve customer service and demand fulfilment of 10% or greater, better reaction time to supply chain issues, increase supply chain efficiency of 10% or higher, and drive greater integration across the supply chain.

While companies understand the value of big data consulting and analytics, they have yet to figure out the best way to use it to improve their overall business.

Applications of Big Data Analytics in Supply Chain and Logistics Industry

1. Procurement 

Machine learning and optimization algorithms can search for and analyze observed patterns and associations among various datasets and decisions made regarding inventory management. It can help you judge if you had made the right decision in the past and if not, what essential insights were missed.

2. Marketing

Data from various social media platforms, mobile apps and loyalty programs can be analyzed to get an idea of customer sentiment which can further be used to create a sales and marketing strategy as well as plan inventory for future.

3. Transportation

Big data analytics can be used to optimize the travel routes to minimize the cost of fuel and lower the risk of accident while delivering products on time.  Analysis of vast amount of data collected from routes preferences, traffic densities, weather conditions, type and size of the vehicle, etc. can give clues on ways to improve driving performance, identify & minimize risk areas and determine exact delivery time. Modulating transport operations accordingly, would be a win-win situation for both customers and businesses.

4. Warehouse operations 

Location, capacity and flexibility of warehouses have a significant impact on supply chain performance. Advanced analytics can analyze the performance data of existing warehouses and based on it, assess the performance of each new location. Or say, based on analysis of the stock of a product over a period, we can predict its demand.

Conclusion

Logistics analytics in supply chain can dramatically improve operations and maximize ROI. It becomes a lot easier to predict and work on customer requirements and hence enhances customer satisfaction and loyalty.