The massive online selling frenzy in China (Singles’ Day) and in US (Black Friday) are now behind us. The Christmas and post-Christmas shopping rush are still ahead of us. This year, like many before, we will witness the number of merchandise returns proportional in growth to the volume of sales. For all of you “in the know”, we are talking about reverse logistics – the “dark side” mix of art and science of the supply chain operations strategy.
If you are in supply chain management long enough, you’ve probably experienced many attempts at tackling this problem. And the moment you become comfortable with a solution, the sales and marketing people come up with yet another sales channel through which the customers can buy … just not the channel through which they can return an unwanted purchase. The era of easy buying and easy returning, also known as the omni-channel strategy, has led to an explosion of possible combinations of place, purchase and place-of-return. This, of course, has only increased the complexity of the problem for supply chain teams.
At a recent customer presentation, I was asked to elaborate on this topic. For lack of time, I pointed out that when supply chain planning and scheduling is optimized, manually or with the use of software, the paths of supply and paths of return should be planned and executed within the same optimization model. This ensures that the means of getting the product to the buyers are reused, in a continuous move, to return the unwanted product back into the value chain. Having a little bit more time to consider this subject now in blog form, I am now expanding the ad hoc answer I gave at that meeting. Let’s consider what measures and countermeasures are available to us to address reverse logistics:
The first measure: Never have anybody returning anything.
Yes. If you are like many companies I see, you are still under the spell of solving this problem with increased forecast accuracy. This involves installing a software forecasting tool of various complexity and beating up on the salespeople to be more realistic about the numbers they forecast in that system. We then promptly de-optimize this information by applying our demand knowledge and sprinkling the forecast fulfillment with constraints such as supply inputs, inventories, production/assembly, shipping, and so on. We call this part of the process “demand optimization” – just to inject a bit of confusion. No matter how sophisticated the process, the volume and cost of returns are not shrinking. So, we circle back to beating up on the salespeople and demanding more precision from them. Now imagine implementing the e-commerce channel. Suddenly you have nobody to blame, only another computer that analyzes past e-sales data and reads statistical tea leaves to extrapolate past performance into the future. Forecast (demand) optimization parameters can be modeled in the supply chain model to give us a certain level of understanding how to organize the reverse chain and keep costs low. Naturally, only if those salespeople gave us more realistic numbers.
The second popular measure: “Shape” demand.
We shape demand using the oldest of tricks in the supply game: We appeal to the human propensity to respond to incentives. In sales, “incentive” translates into “price discount”. Discounted price equals no return. Problem solved. So, adjacent to the demand optimization approach, we have the pricing optimization approach. In Fast Moving Consumer Goods (FMCG) industries, we’ve conditioned the customer to respond to predictable price movements at predictable intervals following the product landing on the shelf (real or electronic). Since then, price optimization has made it deep into many other industries and crossed the B2C boundary into the B2B realm. Price optimization parameters can be modeled in the supply chain model as a planning factor. We can plan multiple price-related scenarios and optimize the reverse logistics execution not only for typical seasonal peaks, but also for planned pricing events. Naturally, only if buyers react to the price incentives in a predictable and timely manner.
Since the problem of forecast accuracy and pricing optimization seem to have negligible impact on reducing the volume of returns, we need to accept this as the “new normal” and introduce countermeasures on the supply side.
First: Direct returns to the optimal place in your supply chain network. This will allow you to reuse your existing supply chain network and use existing logistics resources. A simple solution is to provide pre-printed shipping labels. While a clear advantage for supply chain operations, these labels might be disliked by the marketing people, as it makes it very easy for the buyer to return the product. Marketing people reason that a little difficulty in shipping something back makes the buyer more likely to keep their purchase. There is no impact on your supply chain optimization model from this step, as it smoothly reuses existing supply chain resources.
Second: Optimize the process of restoring the returns into the full-value inventory. A process workflow and work assignments can be done within the existing supply chain scheduling optimization model. Depending on the volume and nature of the returns, you can achieve efficiencies at assessment, cleaning, repairing, re-labeling, and re-packing workstations. The additional benefit is that with the optimization running, you can work out a recognition system for the workers, or work teams, who return the items to a resalable state faster than the system-managed baseline. This approach, taken from Fast Moving Consumer Goods (FMCG) industries, is applicable not only to industries functioning like FMCG – for instance, fashion, consumer electronics, so on – but to any industry.
Third: Optimize logistics to get the “recovered” item added to the existing stock management and shipment of new products. Your existing supply chain optimization model can see the returned product entering the re-fresh process and being counted as inventory in transit. This reduces the costs of replenishing known stocks across all stocking locations by avoiding new product orders where reconditioned products will do.
If you cannot beat the reverse logistics blues this season, try to outsmart it for the next. While merchandise returns can be considered a mind-boggling exercise in predicting the unpredictable, a few steps taken in the right direction can have a significantly positive impact on the bottom line. There is nothing better for you and your supply chain team than to be seen as a strategic partner in making the business more profitable.
What’s your take on reverse logistics this holiday season? Will retailers have the upper hand this year? Let me know in your comment below. I look forward to hearing what you think.