We appear to be collecting more and more information about our buying habits through loyalty cards. We gather probably ten times more information on our customers’ needs than we ever did. And yet: Why is it that stock outs exist and retailers cannot predict when it might next be available? Why is it that there are perpetual sales as retailers try to shift unwanted stock? Why is it that we still cannot meet our fulfilment promises?
You will be familiar with techniques like just-in-time (JIT) manufacturing, inventory management, sales planning forecasting, off-system forecasting, pricing strategies and so forth and yet reality cannot match the prediction. We appear to struggle to get the right product to the right place at the right time, and in a world where customer loyalty is a thing of the past this spells trouble.
Unfortunately the examples are all around us – profit warnings, shop closures, another super sale extended for another week, you lucky buyer! However, what is fascinating in all of this concern is that neither manufacturer nor retailer has appeared to try and solve their challenges by increasing the flexibility and responsiveness of their supply (fulfilment) processes.
It is a fact that in fast-moving consumer goods (FMCG), the retailers have focused a lot of attention in getting the right products on the right shelves in the right time. Those that have got it right have captured market share and whether we like it or not supermarkets have superceded the high street shop.
Convenience corner stores will of course continue but only those that understand their customers and have efficient supply chains to meet demand. Once you have uncertainty of supply you will lose customers.
So how can one compete against the large superstores, how can we avoid the stock out and provide a special solution? The answer is a simple one:
Understand your customer and their needs and expectations.
Use both browsing patterns and buying patterns to forecast future demand.
Get your fulfilment right.
This last point is worthy of greater explanation. Fulfilment will vary from business to business, sector to sector. FMCG retailers have to get it to the shelf and to a lesser but growing extent to the delivery van to meet home shopping demands. Furniture retailers have to deliver to promise and have shop stock that attracts buyers – customer loyalty is strongly affected by the delivery experience.
High price clothing retailers have to have expensive stock and watch trends, lower price shops have to fill the shelves in much the same way as grocers. Fulfilment is therefore not a one-solution-fits all. It is in itself a unique experience and it is here that vast improvements can be made.
If your fulfilment processes do not meet your customers’ expectations then all the demand forecasting capability in the world will not solve issues with customer retention.
An excellent example of this, and a sad one because they went out of business and real lives were affected, was the fulfilment process of a chain of department stores. It involved home delivery of household furnishings, a free delivery service for the majority of customers. The delivery was forecast at the point of sale based on typical lead times for the items, six weeks beds, 12 weeks furniture, for example.
Nearer the date a phone call confirmed delivery and all appeared well. With the exception that nearly 40% of all stock was returned – most to be redelivered on a subsequent day, some because of missing items, too much because the customer no longer wanted the item. The warehouse doubled its call staff in a year to handle complaints, the transport staff had no control over its costs and spent all its time searching for missing items and there was a catalogue of failure.
It is easy to see why the chain went under, 40% returns is surely exceptional. However what is an acceptable level of returns, 5%, 10%? And the answer is of course everyone should be striving for first time in full delivery success. A challenge in this world of Internet home shopping, money back guarantees and free returns policies, but nevertheless a vision.
So how do we improve our demand forecasting by improving fulfilment; it seems that this is putting the cart before the horse. The answer is that by improving visibility, accountability and managing process we can move stock and purchases with greater accuracy thereby reducing stock outs, meeting demand and increasing customer care.
Let us take an example; you like to buy from a certain high street retailer, occasionally you buy off their website, but usually you like to touch and feel the products. On this occasion you go to the store and identify a shirt you like, however they do not have either the size or colour you want.
The salesperson keen to meet your needs goes on-line and discusses your delivery expectations. There is one on the way, or one in another store, or we can put it on order and get delivery in three days.
Critically they offer to deliver to you at your convenience, to your home, your work, your neighbour within a fixed short delivery window. Further more they book this at the point of sale and actually deliver on time. Sounds too good to be true, it needn’t be as the technology, systems and processes are available today.
The retailer has therefore sold you a product, but importantly knows where his pipeline stock is, diverting more to the point of demand, has increased visibility of what there is demand for, can make a fulfilment promise and keeps you as a satisfied customer. You in turn tell your friends of a great experience and your friends go and sample it for themselves. With assured fulfilment you can reduce pipeline inventory, reduce price reductions, increase your customer base and get out of today’s perpetual sales to shift stock cycles.
The way to improve fulfilment is to use modern distribution management solutions and deploy mobile computing out into your fleets. The return on your investment will exceed your expectations.
Chris Wright is managing director of Skillweb. T: 08700 707 077. E: firstname.lastname@example.org W: www.skillweb.co.uk