Retail Has Transformed – Has Your Distribution Center?

It seems everything changes so rapidly these days with technology leading the way.  What is interesting, though, is how technological changes create a ripple (tsunami) effect in other industries.  Take retail as an example, there is no doubt online purchases have increased and the negative impact that has had on certain retailers is evident.  After all, when is the last time you went to the mall?  Chances are if you are over 18, you can’t remember.

OnLine Purchases Rising

Online purchases are on the rise as demonstrated in the chart above. The impact on some retailers has been bankruptcy filings and the closing of retail brick & mortar locations around the country; Aéropostale, JC Penney, and Sears just to name a few. http://time.com/money/4386499/retail-stores-closing-locations/

What’s the Difference?

Because so many purchases are now online, retailers are facing shipping smaller quantities of goods more frequently.  These shipments will go either direct to stores or direct to customers. Retailers must make accommodations for these changes and adjust their strategies in order to remain successful.  Those retailers who make the appropriate adjustments will have a higher chance to succeed.

So just how do you adjust your existing distribution centers to accommodate these changes? Shipping individual orders to customers or retail stores requires greater speed and accuracy. Distribution operations managers have realized that in order to achieve these greater speeds with more accuracy they must add high-speed conveyors, high-speed sortation systems, robotic palletizers, different picking & packing solutions, etc.

As distribution operations managers make these adjustments and choose the right equipment for their facilities, it also becomes very important to optimize the use of the chosen equipment.  Often, it is not until a state-of-the-art facility is up and running until management really understands how it works and how all of the complex parts and pieces come together.

What Can You Do About It?

This is where predictive modeling can be valuable.  As with any complex system, it is difficult to see and understand all the interdependent cause and effect relationships and overall system behavior.  For example, the tote size and number can affect high-speed conveyor performance, which in turn can affect the packing and shipping processes.

Enter predictive modeling.  A predictive simulation model of your DC can help you understand many aspects of system behavior including:

Order Mix:

  • What percent of orders use full pallets or full cartons?
  • What is the percent of single unit shipments?
  • What is the typical order size?
  • How many line numbers in each order?

Service Level Performance:

  • Do we need to offer overtime or use seasonal staffing to handle seasonal volume?
  • How do we balance the shipping docks to evenly load the work for the stores we service?
  • How do we balance the stores that each distribution center in the network services?
  • Do we have enough people or equipment to complete the day’s work?
  • Do we have items slotted correctly so that the fastest moving products are closest to the shipping doors?

Additionally, a predictive model can help you identify areas for improvement:

Picking strategy:

  • Single order picking or multi-order picking?
  • Order consolidation?
  • Should you use pick waves?

Which in turn will help you determine the design of your pick line. 

  • Straight line
  • Branch or pick zone
  • Serpentine line
  • Pick to conveyor
  • Pick to light
  • Automated conveyors or carousels

Wrapping Up

This shift in retail shopping behavior and delivery expectations is not likely to end anytime soon.  If anything, it will continue to become even more individualized and immediate.  Has anyone had a drone drop off a package yet?   It will be hard for retailers to keep with us overly demanding customers. Maximizing the performance of your DC’s, warehouses and delivery network will likely have to be part of the equation.