I learned a few things when I spent one winter in a distribution center in Canada. I learned what it was like to arrive for work in the dark and leave in the dark. I learned about the extra connector in Canadian cars that would allow the car to be plugged in to heat the engine block so the car would still start in sub-zero weather. I learned about how these winter warriors would get excited about the first days of 60°F weather, which seemed sultry in comparison to the long winters. Most importantly, I got to see first hand how simulation could be leveraged to get better visibility into a road to business success.
As we kicked off our project, the team I was working with analyzed every scrap of data we could get. We hired a third party consultant to focus exclusively on data analysis (looking at order profiles in the sales history). We designed the warehouse management system (WMS), created processes, and decided on a configuration. We checked in with senior leadership about process changes and got input and buy in that it would work. Finally, we took the warehouse management system WMS live.
Unfortunately, the new process slowed down many things. Nobody anticipated the impact the change would have until we executed the new process. In a nutshell, workers started warehouse replenishment at 4:00AM, picking would start at 7:00AM, and so on. We found out, with the new processes, the operators needed to do an hour of additional work to maintain the old volume—much slower than the old picking process. Despite all the data analysis we had done, we didn’t foresee the impact. If we’d had the ability to do simulation we probably would have. A truly powerful simulation tool provides visibility into the end-to-end processes and answer many important questions such as:
- How many pickers are needed to meet a certain picking volume?
- How many replenishers are needed to keep replenishing the bin locations?
- Where is the bottleneck?
- Which process among the multitudes of processes is causing the slowdown?
Every datacenter manager would love to see how the processes are performing under a given a set of variations. Consider the wealth if information that could be gleaned:
- Order profiles (i.e. What if I had a lot of 1Zs versus a lot of wholesale orders being shipped to other retail stores?) (
- Replenishments from receiving vs. replenishments from deep reserve
- Potential benefits of a sorter-based picking process
- Potential benefits of a pick-to-light picking process
- Potential benefits of an RF handheld-based picking process on batch picking
- The impact of seasonal changes
- Process effectiveness during high-volume scenarios (i.e. holidays, back to school, etc.)
- The impact of cross docking (with multiple scenarios including retail, wholesale, and e-commerce orders)
- The best avenues for delivering an omni-channel experience to customers
Simulation leads to beneficial insight and helps with load planning to ensure that products are efficiently picked and shipped. Of course, modeling the simulations can be a little tricky. It’s critical to consider all the pertinent factors and modeling every single variable and mimicking the real life scenario accurately with finesse is complicated.
The beauty of it, though, is that this work can be done during the system design process, which is usually done after a software vendor has been selected and the WMS deployment has been kicked off. Upgrading or replacing a WMS system provides a golden opportunity to put this sort of capability in place. Typically, the DC manager researches potential solutions, perhaps consults with others in the organization, and brings in a trusted advisor to help. The savviest companies involve key DC managers and supervisors to plot out the ideal processes. With this information, the software vendor can point to how to implement those processes considering minimal cost and maximum flexibility. The simulation step can easily be incorporated into that process. By doing that, DC managers can figure out the process that works best for their supply chain and address any unforeseen variables as they arise.
In the end, the Canadian client I mentioned above opted to add a voice picking process rather than redesign the whole system. In the end, they found a way to get back to their original efficiency.
Let us know in the comments section below how you think simulation tools might help your supply chain or distribution center.
ABOUT THE AUTHOR
Puga Sankara is the co-founder of Smart Gladiator LLC. Smart Gladiator designs, builds, and delivers market-leading mobile technology for retailers, distributors, and 3PL service providers. So far, Smart Gladiator Wearables have been used to ship, receive, and scan more than 50 million boxes. Users love them for the lightweight, easy-to-use soft overlay keyboard and video chatting ability, data collection ability etc. Puga is a supply chain technology professional with more than 17 years of experience in deploying capabilities in the logistics and supply chain domain. His prior roles involved managing complicated mission-critical programs driving revenue numbers, rolling out a multitude of capabilities involving more than a dozen systems, and managing a team of 30 to 50 personnel across multiple disciplines and departments in large corporations such as Hewlett Packard. He has deployed WMS for more than 30 distribution centers in his role as a senior manager with Manhattan Associates. He has also performed process analysis walk-throughs for more than 50 distribution centers for WMS process design and performance analysis review, optimizing processes for better productivity and visibility through the supply chain. Size of these DCs varied from 150,000 to 1.2 million SQFT. Puga Sankara has an MBA from Georgia Tech. He can be reached at firstname.lastname@example.org or visit the company at www.smartgladiator.com. Also follow him at www.pugasankara.com