Using Lean Six Sigma to Improve Retail Point-of-Sale System
Client: Manufacturer and retailer of award-winning dining room houseware and tabletop products
Industry: Information Technology
Service: Lean Six Sigma
- The client’s retail point-of-sale system could no longer support the quickly growing sales volume
- Suffered from slow response times and intermittent downtime
- Used Lean Six Sigma tools to identify root causes of bandwidth problems and server downtime
- Developed hardware reconfiguration and process changes to permanently eliminate issues
- Saved hundreds of thousands of dollars in new hardware to upgrade its communication network and point-of-sale registers
- Dramatically improved customer service levels in the retail locations
The point-of-sale (POS) system of this fast-growing manufacturer of uniquely designed housewares had not kept up with the company’s growth. The company sells its products through Bloomingdale’s, Saks Fifth Avenue and Neiman-Marcus. These stores were experiencing slow server response times, intermittent downtime, and a database that had become corrupted on multiple occasions.
In addition to long delays at checkout, clerks were unable to locate out-of-stock items at another store, and customers had difficulty returning products to a store different from the one where they were purchased. The expensive solution from the communications and hardware vendors was to add more bandwidth to improve response times, replace PC-based POS registers with faster machines, and limit the number of fields and records being replicated in the database, at a cost of hundreds of thousand of dollars.
Faced with such a large, unplanned expense, the CIO turned to the Lean Six Sigma consultants from Guidon who already helped make dramatic process improvements in the company’s purchasing, sales, and order fulfillment departments. They formed a team of people from the retail stores, the help desk, and IT, as well as experts from the firm’s communications and software vendors.
To better understand the problem with the POS system, the team used a variety of problem-solving tools including cause-and-effect analysis, fishbone diagrams, comparative analysis, and process mapping. Identifying common cause variations and special cause variations helped the team focus on the right causes before looking for solutions to the problems.
Beginning with the bandwidth issue, the team measured server response times. Following a thorough analysis, they discovered that the response time variability had nothing to do with bandwidth. Several of the servers were sending out previously undetected noise that was eating up capacity. After making several hardware adjustments, response times improved dramatically.
The analysis also revealed some structural problems with the data being replicated. The team found that if they parsed the sales data differently, the replication process proceeded much more efficiently and quickly. These two changes solved the bandwidth problem without any new capital spending.
Next, the team looked at server downtime. Typically when a server went down, everybody dropped what they were doing and did whatever they could to get it running again without investigating why it had crashed in the first place. Once the team mapped out the server response activity, they could clearly see all of the wasted activity and non-value-added work. They discovered that too many people were making changes to the servers and databases without sharing that information, causing one configuration change to interfere with another. The team established standard processes for such updates that would prevent problems from repeating.
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