Our objective was to evaluate the performance of the U.S. Postal Service’s Small Package Sorting System (SPSS) machines.
The continued growth of eCommerce and the package delivery market provides opportunities for the Postal Service to increase revenue. The Postal Service has directed resources and management attention toward building a world-class package platform to compete and gain business in the package delivery market. Part of this strategy includes purchasing package processing machines such as the SPSS to improve efficiency and meet demand.
The SPSS is an automated package sorter with five mail induction stations for employees to feed packages into the machine and 196 sortation bins. SPSS machines are expected to process 4,500 packages per machine hour (throughput goal) and 385 packages per employee workhour (productivity goal). The SPSS machine is designed to provide automated package sorting capability, alleviate existing processing capacity shortfalls, and reduce manual sorting and costs.
This is a follow-up to an earlier SPSS audit (Report Number NO-AR-18-002, dated November 29, 2017) that found the Postal Service, on average, nationally exceeded the throughput performance goal by 5 percent but was below the productivity goal by 17 percent.
Since that audit, the Postal Service has spent $52.6 million to purchase and deploy 11 additional SPSS machines. In total, the Postal Service has invested $187.2 million since FY 2014 to purchase 44 SPSS machines at 36 locations throughout the country. There were 41 SPSS machines in FY 2019 and the Postal Service added three more machines in FY 2020, but have no plans to add additional machines at this time.
We judgmentally selected sites for review based on FY 2018 and 2019 SPSS throughput and productivity data compared to goals and overtime usage. Specifically, we conducted site observations at one high performing site – the San Jose, CA, Processing and Distribution Center (P&DC) – and two lower performing sites – the Columbus, OH, P&DC and Indianapolis, IN, Mail Processing Annex (Annex). We also conducted interviews with management at two additional lower performing sites – the Denver, CO, and Akron, OH, P&DCs.
Our primary fieldwork was completed before the President of the United States issued the national emergency declaration concerning the novel coronavirus disease outbreak (COVID-19) on March 13, 2020. The results of this audit do not reflect any process and/or operational changes that may have occurred as a result of the pandemic.
SPSS machine performance nationally has decreased since our prior audit and both throughput and productivity performance goals are not being met. Specifically, SPSS machine performance of the 41 machines in use in FY 2019 showed:
- Twenty-eight machines (or about 68 percent) did not meet the throughput goal. On average, SPSS machines were 6 percent below the goal (or 269 packages processed per machine hour below the goal).
- Thirty-eight machines (or about 93 percent) did not meet the productivity goal. On average, SPSS machines were 28 percent below the goal (or 106 packages processed per employee workhour below the goal).
As of FY 2020 Quarter 2, SPSS performance for the 44 machines in use showed:
- Twenty-seven machines (or about 61 percent) did not meet the throughput goal. On average, SPSS machines were 6 percent below the goal (or 267 packages processed per machine hour below the goal).
- Forty-two machines (or about 95 percent) did not meet the productivity goal. On average, SPSS machines were 25 percent below the goal (or 95 packages processed per employee workhour below the goal).
During our review of lower performing sites, we determined the causes of lower throughput and productivity were due, at least in part, to insufficient management oversight and planning. Specifically, we found:
- Supervisors at the Indianapolis Annex were not present to initiate the start of SPSS machines at the beginning of the operation, leaving employees idle. Only supervisors and in-plant support personnel have access to start the machines.
- One SPSS machine at the Indianapolis Annex did not have the upgraded bulk conveyor belt installed, which caused packages to slide down the incline belt into the container unloading area.
- Not all mail induction stations at the Akron and Columbus P&DCs were used, despite sufficient mail volume and employee availability.
- Employees at the Columbus P&DC were not properly placing packages for the SPSS machine to scan.
- Employees at the Columbus and San Jose P&DCs were clocked into the incorrect operation, resulting in incorrect labor code usage for reporting workhours.
During our site visit to the San Jose P&DC, we identified best practices related to machine performance and management oversight that included:
- Daily meetings for supervisors to provide ongoing feedback to employees operating the SPSS machine and discuss opportunities for improvement.
- All five induction stations on the SPSS machine were used and employees were properly placing packages.
- Supervisors were consistently present during SPSS machine processing.
- Daily management team meetings which encouraged communication and ideas to improve SPSS productivity and throughput performance.
- Plant manager and management conducted quarterly meetings with P&DC employees to create a culture of accountability to ensure mail is processed efficiently.
We calculated the Postal Service could save about $9 million in labor costs annually by correcting the causes of low SPSS productivity nationally. However, due to contractual labor agreements and limitation on current staffing options, the Postal Service may not be able to realize all these savings. Improving SPSS performance will reduce costs, increase operational savings, and support the Postal Service’s package strategy.
We recommended management:
- Reiterate the operations user guide requiring supervisors to be present to initiate the timely start of SPSS machine processing.
- Identify facilities with SPSS machines experiencing issues with packages sliding down the belt incline and take corrective action as appropriate.
- Provide a standard work instruction to facilities to use available mail induction stations based on mail volume and employee availability.
- Provide standard work instructions to SPSS facilities and employees and reiterate the importance of properly placing packages into SPSS machines.
- Reiterate the management operating data system guidance requiring management to monitor mail processing productivity and ensure employees are correctly logged into the appropriate operation to accurately reflect SPSS productivity performance.
- Provide standard work instructions requiring supervisors conduct periodic meetings to provide ongoing feedback to employees operating the SPSS machine and discuss opportunities for improvement.