Online retailers need greater automation in the warehouse to help meet delivery schedules during peak periods and to improve customer service, according to research for Conveyor Networks.
The survey by Sapio Research, covering more than 100 online retailers, found that 83 per cent believe that automation will drive future online retail growth.
Three quarters of the respondents said they were handling up to 50,000 orders per day, with this rising to 75,000 orders per day for some during peak periods, putting pressure on warehousing and delivery.
Some 45 per cent said a lack of staff to fulfil orders was a key hurdle to meeting delivery schedules while 42 per cent highlighted order errors.
Some 74 per cent of respondents have less than half of their warehouse management processes currently automated. And 49 per cent are planning to further reduce manual warehouse processes before their next peak spell.
The majority of online retailers said that customer expectations on delivery have risen significantly over the last five years, with 59 per cent admitting that meeting these expectations is the biggest challenge they face.
Almost three quarters of online retailers said that increased warehouse automation would help them improve their customer service capabilities. Just under half are planning to change their order fulfilment processes in the future with 49 per cent believing that increased automation will enable them to handle orders more quickly and efficiently during peak periods. In addition 41 per cent said it would help them to respond to orders more quickly; while 38 per cent said it would help reduce picking and packing errors.
David Carroll, managing director of Conveyor Networks said: ““By increasing automation – from mobile devices such as handheld scanners to help the pick process, to using a fully automated bagging line in packing –a range of slow, laborious and error prone manual processes in the warehouse can be made much more efficient. Retailers can meet delivery promises more effectively, process orders more efficiently during peak periods, and reduce the number of returns due to incorrect orders.”