Is There a Difference Between Strategic Category Sourcing and Strategic Category Management?

And if there is, should there be?

Category Management is an approach to supply management where the range of products and services sourced by the Supply Management organization are broken down into discrete groups of similar or related products and services. The idea is that a systematic, disciplined approach is applied to the category which is treated as a business unit. Strategic Category Management is simply category management in a strategic context.

Category Sourcing is the process of sourcing a category designed to be treated as a business unit. It is generally treated as part of the category management process. Strategic Category Sourcing is category sourcing in a strategic context.

So, technically, there is a difference. But should there be?

Study after study has shown that, on average, 30% to 40% of negotiated savings never materialize. Why? Because most organizations treat category management and category sourcing as one in the same, and simply do the sourcing. In order to realize the full savings potential of category management, you can’t just focus on the sourcing. You also have to focus on the procurement, the logistics, the inventory management, and the accounts payable.

While strategic category sourcing can identify savings potential and value generation above and beyond regular strategic sourcing because similar products / services are often provided by the same suppliers who will offer greater volume discounts and / or who can customize the value added services to maximize profit potential for all parties, the savings are only realized if the sourcing strategy is followed through. For example, let’s say part of the strategy was to insure that stock was ordered just in time, but the warehouse decided to keep the old schedule and always maintain a buffer stock of 45 days when delivery only took 15 days. In this case, the value negotiated wouldn’t be delivered. Or, let’s say the supplier agreed to an additional discount of 10% off of all negotiated prices once 5 Million in orders had been placed (on an expected contract value of 10 Million over 3 years), but never actually deducted the discount on the invoices when the threshold was reached after 18 months. If a close eye wasn’t been kept on the total spend, there’s 500,000 down the drain.

In other words, for strategic category sourcing to deliver value, the strategy has to be followed through over the life-time of the award. Failure to do so will result in lost value. In other words, the category has to be continually monitored as part of a strategic category management effort for the value to be realized. And this means that while there is a difference, treating the processes as separate and just doing one or the other will result in lost value and strategic category sourcing and strategic category management should, for all intents and purposes, be treated as one and the same.

How Do You Define “Closed Loop” in the Indirect Supply Chain?

Yesterday, in reference to an article on 8 steps to a servitized supply chain that appeared last summer in the Supply Chain Quarterly, we asked what is a servitized supply chain? It was a good question that merited a good answer. However, if you read the article, which finished by noting that the most powerful benefits of this business model arise from integrated teams that can provide closed-loop feedback from the customer all the way back to the suppliers, you are led to another question. Namely, what does closed-loop really mean when you are talking about services, and, when you are sourcing such services, how do you define closed loop in the context of the indirect supply chain that provides the umbrella that services normally fall under?

In the direct space, a closed-loop supply chain is one where Original Equipment Manufacturers (OEMs) reintegrate their returned products into their own production network. The entire life-cycle, from cradle to grave, is effectively and efficiently managed to insure waste is minimized, value is maximized, and sustainability is achieved. A closed loop supply chain considers raw materials, production, distribution, warranty, returns, disassembly, and reclamation of raw materials. It designs for easy repair, reuse when possible, and disassembly / recycling when not. When properly designed, such a closed-loop supply chain maximizes value.

So what is the equivalent in the indirect space? For starters, it must be a supply chain that maximizes value over the life of the indirect supply chain. In addition, it must cover everything involved in the creation, production, delivery, and recovery of those services. Creation is rather straight-forward — it is the design of the services. Production is rather straight-forward — it is the creation of the materials and processes for the delivery of the services. Delivery is rather straight-forward — it is the distribution of the services to the end client. But what is recovery? In indirect, in addition to the reclamation and recycling of any materials produced for the purposes of delivering the services, it is the collection of feedback designed to improve the services in the next iteration.

For example, lets’s say the service is training on a new supply management solution you just purchased. In this services supply chain, the creation is the design of the curriculum; the production is the creation of the specific syllabi, texts, presentations, walkthroughs, videos, and guidebooks, etc.; the delivery is the in-person hands-on training course; and the recovery is the collection of any materials distributed for re-use and feedback on what was good about the course, what was not very effective, and what could be added or done differently in the future.

In other words, in the indirect space, the closed-loop is the creation, distribution, collection, and recollection of knowledge gained in order to increase the value delivered while improving the sustainability of the supply chain.

Do you agree?

What is a “Servitized” Supply Chain?

Last summer, the Supply Chain Quarterly published an article that defined “8 steps to a servitized supply chain”. Each of the 8 steps consisted of a supply chain best practice that you should be doing whether or not you desire a servitized supply-chain, or even care about services from a revenue perspective, as each of the 8 steps is something you should be doing even if you have a product-focussed supply chain. So why would you need, or want, a “servitized” supply chain and, more importantly, what is it?

According to the article, “servitization” is defined as bundled product-service packages that provide differentiated sources of value to customers, and, as a result, a “servitized” supply chain is one that supports such offerings and, ostentatiously, is different than a product-focussed supply chain. According to the authors, such a chain is more responsive and agile, can vary degrees of service outcomes to a differentiated customer base, and increases the probability of more profitable relationships between the Supply Management organization and the manufacturers with whom it does business.

At this point, I’m a little confused because, at least in the fast-moving Apparel and Consumer Electronics industry, a successful product-oriented supply chain is extremely responsive and agile (as orders for products in demand have to met quickly and orders for products not in demand have to be cut), offers various levels of product and warranty customization (where applicable) to user-defined tastes, and increases the probability of profitable relationships between the Supply Management organization and the manufacturers with whom it does business. This is because, in these industries, the product is the service, as McLuhan’s classic statement that the medium is the message, while not always true in today’s information age where you are hit with the same message across multiple mediums, is true in the consumer product industry. For many consumers, the products they buy define who they are and create the statements and messages they want to convey. As a result, when you create a product you are also creating a messaging service that your consumers can use to, indirectly, advertise who they are. So, in effect, your offering is a service as much as it is a product and the concept of a service supply chain being different is, well, a bit foreign.

Of course, if you are in the hardware industry and selling the same old nuts, bolts, and traditional C-section joists that you have been making for twenty years, then it’s probably the case that your supply chain is not very service oriented. In this case, if you “servitized” your supply chain and listened to your customers who want frames that are lighter (as steel prices are skyrocketing), stronger (as they want to build bigger), and faster to assemble (as labour is costly), you might come up with a solution akin to the iSpan Total Joist solution. To do this, you would have to become more responsive, offer various levels of product and services (including pre-fabricated kits for warehouses of pre-defined architectures and sizes), and, as an effect, increase your profitability as your customers pay more for the solutions they want (that save them time or raw material cost). But note that, even in this situation, your supply chain would still be oriented around a product — the only difference is that you would optimize the services offerings around that product.

So, in effect, a “servitized” supply chain is just one that is optimized for products and associated services, and, that, in effect, is just an “optimized” supply chain. And an “optimized” supply chain is one that creates collaborative teams across the supply, sales, and marketing functions to drive value. And we should call a spade a spade, instead of creating more unnecessary terminology.

Keep Your Big Data. Big Brains Will Win in the End.

I have to admit that I’m sick of all this hype about big data and how it is the answer to all our problems. As I’ve said again and again, there’s no such thing as big data in business. Relative to our ability to process it, data has always been big. And, in business, big has always been meaningless. Furthermore, in business, we’ve always been able to process as much data as we need to in reasonable amounts of time if we made good technology decisions.

And I’m even sicker of the fact that some people think we can replace science with math and processes with computer programs. We never could, and for the foreseeable future, where AI (artificial intelligence) will not be a reality, we can’t. Thinking like this is what causes economists to latch onto, and promote, financial policies that, seem good in theory but, in practice, result in economic collapse when taken to extremes.

The reality is that science can never be replaced by math and automated prediction. Not only is the author of this HBR blog post on “why data will never replace thinking” right when he says that it’s only by trying to come up with our stories (hypothesis) beforehand, then testing them, that we can reliably learn the lessons of our experiences — and our data, but it’s only by coming up with hypothesis, and putting plans into actions that we can beat the competition and gain market share in the global market. Look at the giants of industry today. Did Apple become the dominant first in the e-Music industry by letting Microsoft, Sony, Samsung, etc. develop their music players and music stores first, analyzing customer responses, and then introducing their offering? Or did they become the dominant force by using their brains to try and figure out what the market, and customers, were missing, using the best creative and engineering talent to design a solution, and then releasing that product on the market? It was the latter solution — the solution that required big brains that won the market. Similarly, Walmart became the biggest retailer not by asking consumers want they wanted, but by predicting what the average consumer really wanted — a one-stop department store that met most of their basic needs at low prices with a consistent product and service offering across each store for the mobile consumer.

This isn’t to say that data isn’t important, it is, just that it won’t solve all your problems and that, beyond a certain point, more data doesn’t help. Remember, statistically speaking, you only need 384 data points to have 95% confidence with a confidence interval of 5 on a population of 1,000,000. If you want a confidence interval of 3, you only need 1,066 data points, and if you want a confidence interval of 1, you only need 9,513. Beyond a certain point, more data doesn’t add much confidence and the only way you’re going to get more insight is to see it inside your head.

So keep your big data. I’ll use my brain instead. How about you?