When discussing warehouse statistics many key performance indicators are mentioned. Stock movement, order duration times, utilization of equipment and personnel, error rates, and many more.
So what are the most important figures needed to run a warehouse?
Depending on the warehouse design and complexity there are at least a hand full of statistics that any manager needs.
Warehouse Statistics & KPIs to Know
To gain an easier overview let’s split the statistics into process-related topics
- Goods Received
- Material Review Board (MRB)
That allows us to see what warehouse statistics we need from each topic to see if our warehouse performs fast, accurately and cost-efficient.
Safety is the top priority in any Warehouse environment. A Safety Manager should be appointed to ensure all safety activities are in place and maintained daily. Continuous training should occur frequently. Statistics should be kept on any accidents that occur and accidents avoided. Proper signage has to be distributed in the facility to remind people of the importance of safety. OSHA requirements must be maintained and OSHA should audit your warehouse at least, yearly.
For most, warehouse picking is one of the most important processes. May it be case pick or split case picking, products need to be touched, put into the correct customer order in the right quantity while upholding product quality.
One of the warehouse statistics that comes to mind first is the order duration time. That is the time a customer creates an order, from being started at the warehouse, to until the order is finished and ready to be loaded onto a transport vehicle.
In many business models just in time is the word of the day, hence forcing a very short duration time for orders. The order duration can be seen measured by single orders, averages for days, tours, operators, etc.
Order Picking Warehouse Statistics
The next statistic that comes to mind is about picking itself. How many orders, order lines, pieces and how much volume got picked?
Many warehouses, especially automated picking systems, have contractual benchmarks that need to be reached. Achieving those benchmarks is never easy as usually those figures are designed for the best-case scenario and not “real life” operations.
These are the core frequencies of picking warehouse statistics:
These periods of measurement allow for a comprehensive overview of the picking performance and also help planning other processes such as goods in and replenishments to in order to run peak times for picking without running into out-of-stock situations at pick locations.
Slotting and re-slotting are placing SKUs in the warehouse based on usage/sales. The most frequent users in front of the warehouse to the least frequent in the back of the warehouse. Pareto’s 80/20 principle will assist in setting priorities. When demands change, and they will, you re-slot the warehouse.
As important as picking warehouse statistics are, they need to be seen hand in hand with the order profiling. So what is an order profile?
Order profiling is the average, minimum, and maximum values for order lines per order and pieces per line. The importance of these figures is sometimes neglected but they directly influence the picking operation’s output.
A warehouse is designed to deliver 2,000 order boxes per hour containing 26,000 lines holding 52,000 pieces. That would give us a profile of 13 lines per order and 2 pieces per line.
Now we see a change in the profile of lines, during a campaign, and we have 18 lines per order. The rest stays the same. Calculating that back we see that we know, under consideration that we can’t pick more than 52,000 pieces and 26,000 lines, our warehouse delivers a maximum of 1,444 order boxes per hour.
And that maximum will also be negatively impacted in an automated system as more lines per order box also mean that each order needs to visit more stations and therefore has longer time on conveyors before being completed.
As already mentioned in the above warehouse statistics, replenishment and picking are interlaced as replenishment demands peak shortly after picking peaks. Therefore, indicators for late replenishments and peak times are needed to control if replenishment is too slow for demand-driven and if so, how it can be optimized. This becomes crucial when using automated systems such as ASRS’s.
Considering SKUs in Warehouse Statistics
Stock Keeping Units (SKUs), products, have essential warehouse statistics that we need to extract information from, for example, to slot the warehouse.
The most important warehouse statistics measured in regards to SKUs is the ABC classification, and it needs to be seen in 3 different calculations based on
The importance to see all 3 different calculations comes from the fact that a product can be very high in a piece demand but low in a line demand. In that case, it is a case pick product and will need a different pick station and execution technology as a product that has high pieces and high lines indicator.
A product that has a high demand in lines and volume is tricky in an automated system as a high volume means that fewer pieces are in one stock transport unit. The impact of such is that the ASRS system responsible for the replenishment will have to execute more replenishment movements to keep the picking channel stocked up. And as that might not be the only product with high replenishment movements it becomes important to see where to slot those products so not to run out of capacity for the ASRS system.
Storage and picking locations provide some warehouse statistics that give an overview of the filling level, the location’s usage, and its errors. The higher the filling level, the more movements an automated system will need to execute to store and retrieve stock.
Receiving stock into the warehouse is a crucial operation and the key performance indicators for warehouse statistics are:
- Received Volume
- Customer Returns
- Missing / Broken Stock
- Return to Vendor stock
All figures should be able to be grouped by the source and also times to be able to identify peak times, quality issues with suppliers, and the return rate from certain sales areas.
When using a piece of automated system equipment, warehouse statistics becomes important, especially for maintenance operators as these statistics have to show downtimes, errors, and utilization of the equipment.
A modern conveyor system that relies on scanning barcodes should not have an error rate higher than 0.02% of the throughput driven on it. Many times when the error rate shoots up scanners got bent into the wrong angle. But there are also fancier sources of such peaks such as sunlight that comes in through windows and hits scanners, triggers or sensors at an angle where the equipment becomes incapacitated.
Operators in the warehouse, especially picking operators, are one of the most important keys to reaching performance benchmarks. Therefore, warehouse statistics that are operator-based are essential. Depending on the local labor laws, statistics should give indicators for error rates and picking times, whereby the picking times need to be seen in different calculations for orders, lines, pick, pieces and volume-based.
The Material Review Board’s (MRB)Role in Warehouse Statistics Management
Once a month, a cross-functional team, which must include Sales and Purchasing/Supply Chain and Finance representatives, should meet to review obsolete, non-moving, and slow-moving SKUs/materials/parts. This MRB team should recommend disposition to Top Management. They should also review Return to Vendors (RTV) materials to ensure non-conforming materials are returned to Suppliers.
This MRB effort monthly avoids having to lease additional space or use a Third Party Logistics (3PL) service provider to warehouse materials and aid in transportation management.