Managing the merry-go-round

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As online sales soar so do returns, with three or four items in every ten sold heading back to the shelves – at what cost?


Penelope Ody

Penelope Ody

Retailing” – or so they used to say – “is the art of selling goods which don’t come back to customers who do.” Unfortunately in today’s digital world, those goods do keep coming back: a 40 per cent returns rate for women’s wear is not unusual, while reports from the US suggest that the average returns rate across all online sectors is more than a third and rising.

While some of those goods will be posted back to a distribution centre, a great many will simply be dropped off at the nearest relevant branch, regardless of whether the shop actually stocks that line or not. Stories of numerous flat screen TVs being returned to Tesco Metro branches may be urban myths, but Dino Rocos, operations director at John Lewis, has certainly recounted how one customer returned a three-seater sofa to her local Waitrose. Rather less dramatic are mountains of assorted garments which end up in shops that no longer stock that particular design. “It can be a significant problem towards the end of the season when stores already have broken ranges,” says Jason Shorrock, retail strategy director at JDA. “Across the estate there might be enough stock to cover the collection but the individual branches will only have disparate pieces that don’t form a complete outfit.”

Shorrock argues for IT systems that can efficiently collate information about store returns, match that with existing stock records and continue to offer a full range online, with each order fulfilled by despatches from several stores around the country. “We’re seeing interest from retailers who want the returns that have been brought back to branches used as ship-from-store stock to fulfil online orders,” he says. “Customer returns are a key issue for many retailers who need to optimise store systems for fulfilment.”

First published in Supply Chain Standard, March 2015.

First published in Supply Chain Standard, March 2015.

Real-time stock records, good in-store quality control for returned merchandise, and efficient in-store stock entry systems would all be needed – along with highly disciplined store processes so that items really are recorded accurately every time. Technically such models are quite possible – although whether customers would like an assortment of parcels for the same order potentially arriving at different times is another matter. No doubt logistics services providers could manage some consolidation if required.

Central to such scenarios is the vexed question of the “true cost to serve”. As Mark O’Hanlon, senior manager with consultants Kurt Salmon says: “Moving stock around is incredibly expensive, so it can be cheaper in the long run just to markdown the oddments or overstocks wherever they happen to be, rather than move goods up and down the country to meet demand.” Getting the stock allocation right to begin with is clearly crucial, but the best-laid plans can go awry when faced with 40 per cent returns from local online customers arriving at an already fully-stocked branch. Those plans can also be thrown by failure to pass returns data to all relevant departments.

Kevin Sterneckert, chief marketing officer at OrderDynamics, tells of one fashion retailer who was recording excellent sales of a rather expensive dress. “The buyer thought they were doing well so ordered more,” he says, “but there was a problem with a faulty zipper and actually 70 per cent of those dresses were being returned.” Because the merchandise department only took note of sales data, it took quite some time for the buyer to realise there was a problem and get it fixed. “There are discontinuities in information partly because it is in silos and also because there is simply too much of it for individuals to analyse,” he adds.

OrderDynamics is one of a growing number of IT specialists promoting the use of “big data”– clever number-crunching tools to wring much needed answers from information which retailers generally already hold but simply cannot analyse efficiently. Understanding that “true cost to serve” can involve data about the customer and his or her propensity to buy at full price, only at markdown, or to return goods. Add to that delivery, warehousing, staff time, packaging, logistics cost for delivering click-and-collect lines to store, repackaging and inspection for returns, and a raft of other factors and the big data number crunchers would probably come up with some rather depressing information about the impact on the bottom line.

Back in the late 1980s “direct product profitability” (DPP) was in fashion with regular conferences expounding the need for retailers to really understand just how much contribution each SKU was making. In those days the product costs focused on getting the goods to the shelf and selling them out. Today, a similar approach may have to include that “incredibly expensive” cost of moving stock around: such as between stores for click-and-collect fulfilment or from store-to-warehouse to return “returns” to central depots – not just once but perhaps several times for the same item – as well as staff time in pick-and-pack for ship-from-store and perhaps consolidation charges from logistics services providers to ensure that five parcels from different sources reach the customer in one delivery. If retailers are going to maintain profitability then perhaps the number crunchers may need to turn their attention to such calculations, sooner rather than later.

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