Failure to make a first-time delivery can cost up to 10 times the cost of the original delivery.

-- IMRG, Valuing Home Delivery

MetaPack Improves Carrier Efficiency, Reduces the Overall Cost To Serve and Increases First-Time Delivery Success by Allocating Carrier Services in Real-Time using its Intelligent Despatch Engine

The allocation engine selects, in real-time during the fulfillment process , the most cost effective and reliable carrier service based on specific carrier capabilities, retailer preferences, customer requirements and shipment attributes and locations -- including to over 200 destinations worldwide. MetaPack’s Intelligent Despatch allocation engine can automatically operate according to a wide range of criteria, meeting many different business objectives.

 

  • Eliminate manual data entry and human error
  • Automatically allocate to the most efficient carrier
  • Reduce delivery costs by leveraging carriers’ core competencies
  • Reduce operational costs by increasing warehouse and despatch efficiency
  • Minimise failed deliveries by assigning individual consignments to the best carrier for the job
  • Manage parcel volumes during peak periods to ensure business continuity and reduce risk
  • Retain your own carrier contracts or utilise the MetaPack relationships

 

Optimize Delivery Costs

Cost optimization compares carrier rates against parcel weight, number of parcels in a consignment, collection point and destination. This is the simplest allocation approach. The cost optimization can be improved by weighing cost according to carriers’ service standards.

 

Improve Customer Promise

Customer promise is improved by allocating the carrier service that can meet the agreed delivery date. This can be combined with operational optimization; for example a two day service reduced to one, or automated allocation to a carrier with a later cut-off time.

 

Instantly Adjust Carrier Preferences

The allocation engine has enormous scope at a regional as well as local level. Carriers can be switched on or off, or their preference roughly changed by street, town, city or region.

Case Studies