With so many order picking options to choose from, selecting the best solution for your operation can be quite a challenge.
The order picking function in many operations screams for improvement. In most companies pickers spend up to 60% or more of their time walking. Reducing this walking time will increase productivity.
Such was the case at the Acme Catalog Co. Faced with pressure from his boss, Bob Smart, a newly hired director of distribution, embarked on a journey to improve picking productivity.
Bob knew that he needed to collect data to make an informed decision but felt he could not do this by himself. Experience had taught him that to accomplish this goal, he needed to do this with his staff, not to his staff.
So Bob formed a project team consisting of himself, the warehouse manager, the order picking supervisor, and an order picker.
Bob also knew that what they did not know, they did not know. To overcome this, he added a fulfillment consultant to his team.
The first step was to understand the current situation. As the team began to review the operation and collect data they saw that
- more than 7,000 SKUs were in the 30,000-sq.-ft. forward picking area.
- orders were batched by the computer system and sent to the picking floor.
- like that of most other direct-to-consumer operations, Acme’s order volume increased during the Christmas season. Off-peak it averaged 800 orders a day; during peak season it averaged 2,000 orders a day.
- the business had been growing at 22% per year.
- orders were picked discretely, one order at a time.
- orders were picked from a forward picking area.
- the average order had 2.3 lines.
- order picking productivity equaled to 67 lines per hour.
- order accuracy was 98.5% as calculated by returns and random sampling.
Armed with this newfound understanding, Bob began to read as much as possible about the different ways to pick orders. As he read, Bob began to feel he did not have a thorough understanding of his order picking operation; after all, he had never picked orders. He remembered that the best way to understand someone is to walk a mile in his shoes. The next day, Bob arrived at the order picking operation, paired up with a buddy, and picked orders for the day.
During the day Bob experienced numerous frustrations. The top three: items to be picked that were not in the picking location; items to be picked that had “no” location; and a lack of productivity and quality goals, so that at the end of the day, Bob had no idea if he’d performed well. These frustrations contributed to lost time and a decrease in picking productivity.
To ensure these were not isolated incidents, Bob and the team performed a study for a week. The study revealed they were not. Further analysis of the order picking productivity for each worker revealed wide swings and very low productivity for new workers.
Bob and the team decided that they would adopt a strategy of ongoing improvement. They would implement solutions after they had analyzed them, but continue to seek further improvements.
Removing the Order Productivity Constraints
Working with the warehouse manager and the order picking supervisor, Bob established goals and tracking systems for the number of stockouts and the number of items not in location and productivity and quality goals for new order pickers. Bob had learned in his previous company that what gets measured gets managed.
After a few months, the combination of these improvement programs increased order productivity by 11%. As an added benefit, the order pickers greatly appreciated the reduced number of items not in location and items with no location, removing two of their biggest job frustrations.
These improvements encouraged the team to press on. Eager to improve further, they looked for additional productivity gains.
Order-Completion or Fast-Pick Zone
Bob remembered visiting a warehouse that had set up a hot zone of its fast-moving items, an area it referred to as the order-completion zone. The manager there explained that the pickers could complete 60% of their orders in this condensed area, sort of like a warehouse within a warehouse.
Bob began to analyze the customer orders at Acme Catalog to see if this was the same in his operation. Sure enough, analysis of his customer orders disclosed that 64% of his orders could be completed with only 2,500 of the company’s 7,000 SKUs. Locating these items in a zone would reduce order picker walking and increase productivity by 12%. Implementing this would require labor and little or no capital investment. Bob and his crew established two zones, the order-completion zone and the area for all other items.
Bob was still concerned with the large percent of walking to pick orders. During his day picking orders, in order to save steps he’d taken a few orders at once. He noticed other order pickers doing the same. He wondered if the computer could be used to batch orders. If so, what how much would it increase productivity? What would the batch size be? What would the cart look like?
He remembered that 40% of orders were single-line orders. If he created a batch of single-line orders, these could be picked with fewer passes through the picking zones. He immediately implemented this.
Together the team and the workers decided on a batch size of nine, using a three-shelf cart with three orders on a shelf. The software was modified to create these batches for the order pickers. Now the computer created one batch of all single-line orders and the remaining orders in batches of 9.
After implementation, picking productivity increased by 20%.
Multiple Order Batch Sizes
As time passed, Bob and his team noticed that all the orders would not fit on the carts; in fact, some of the bigger orders required two carts. Examining the multiline orders, Bob learned which percentage of orders had two lines, which percentage had three, and which percentage had four or more — often appreciably more. Creating one batch of the multiline orders had improved productivity but created other problems, such as orders that did not fit on the carts.
As they looked at the orders that did not fit on the carts, the team observed very big items. They also noticed many carts that were using only 50% of their capacity. At this time they realized they needed more than one size batch. Without cubing every product, they identified the items that were too big to fit on the cart with eight other orders and created a batch of large orders with three orders per cart. They were able to flag these items to allow the computer to place them in a large-order batch.
If the computer software were capable of creating order batches by cube, other cart batch sizes could be created, further improving the productivity. But Acme’s software did not have this capability, and adding the cubing function would be too costly. Even so, this change increased productivity by 7%.
Put to Light
One day Bob read of the success of a company, Fred’s Widgets, that had implemented a put-to-light batch cart system. With this system, the previously printed batch sheet was translated into LED displays directing the order pickers to a location and indicating the quantity to pick. Lights on the cart shelves directed the pickers where to put the items they picked. Because pickers scanned a badge at the start of the pick, the system could generate order picking productivity reports. At Fred’s Widgets errors had decreased by 40% and training time was reduced from three days to one day.
Bob’s team calculated that a 40% reduction in errors would save Acme $100,000 annually. The reduction of training time and the labor reporting and measurement system would increase order picking productivity another 18%. The payback for this solution would take less than nine months. Bob immediately added this to his capital budget for next year.
Over a period of a few months, working continually to improve the order picking operation, the project team was able to increase order picking productivity by 50% with minimal capital expenditure. Bob and his team found through teamwork that by understanding the current operation and researching appropriate solutions to implement, they were able to achieve outstanding results. Although each company’s operation is different, many of the tried-and-true improvements Bob and his team at Acme Catalog implemented could no doubt improve picking productivity in your operation.
Wayne M. Teres is president of Framingham, MA-based operations consultancy Teres Consulting.
This article was published in 2006 and is frequently updated