Disruptive innovation and bid data can address many challenges in logistics. Some of them are:

  1. The Last Mile of Shipping Can Be Quickened – The last mile of a supply chain is notoriously inefficient, costing up to 28% of the overall delivery cost of a package.
  2. Reliability Will Be More Transparent – As sensors become more prevalent in transportation vehicles, shipping, and throughout the supply chain, they can provide data enabling greater transparency than has ever been possible.
  3. Routes Will Be Optimized – If you underestimate how many vehicles a particular route or delivery will require, then you run the risk of giving customers a late shipment, which negatively affects your client relationships and brand image. Optimizing saves money and avoids late shipments.
  4.  Sensitive Goods Are Shipped With Higher Quality – Keeping perishables fresh has been a constant challenge for logistics companies. However, big data and the Internet of Things could give delivery drivers and managers a much better idea of how they can prevent costs due to perished goods. A temperature sensor inside the truck could alert the driver, and suggest alternate routes.
  5.  Automation of Warehouses and The Supply Chain – The ability to accurately predict demand in every DC, retailer, and customer is the holy grail of being able to deploy inventory where and when it is needed.
  6.  Better inventory deployment and labor management – For retail store managers, planning shifts to meet customer demand is a sensitive task- overstaffing kills profitability, and understaffing results in angry customers.  Planning has always been done based on history.  One retailer took into account the following additional data:
  • New delivery times
  • Local circumstances and holidays
  • Road construction
  • Weather forecasts

Big data and predictive analytics gives logistics companies the extra edge they need to overcome these obstacles. Sensors on delivery trucks, weather data, road maintenance data, fleet maintenance schedules, real time fleet status indicators, and personnel schedules can all be integrated into a system that looks at the past historical trends and gives advice accordingly.

References:

Swingle, K. (2017, September 25). Disruptive Innovation in Logistics. Retrieved from https://www.spartanwarehouse.com/blog/spartan-logistics-understanding-big-date-and-how-its-revolutionizing-logistics.

Questions:

  1. What challenges can be fixed with big data and disruptive innovations in logistics?
  2. How does big data help in better inventory deployment?
  3. How does big data improve reliability in transportation?