Updated: Dec 22, 2019
Over the course of the past thirty years, companies have invested heavily in advanced decision tools for managing inventories. This investment has resulted in only modest improvements in inventory turns. One of the reasons for this is that there has been a countervailing trend – the number of choices that consumers have for just about anything has risen dramatically over this same time frame. And, not only has the number of product choices grown, but more recently the number of purchasing and delivery choices has also grown. There is a strong argument to be made that without this investment, companies would not have been able to significantly grow their variety of choices and thus remain competitive. Furthermore, without continued robust investment in the future, companies will not be able to continue to provide products and services that are tailored to the tastes of individual consumers.
This trend towards products and delivery services tailored to individual customers is what I call supply chains of one, and forms the essence of a customer-centric supply chain. It is a major driver behind supply chain segmentation and omni-channel transformation programs put in place by enterprises. Inability to invest in tailored choices, or falling behind the investment curve, has resulted in the demise of many retailers.
Moscow on the Hudson and Productivity
When I think of choice, I often think of Robin Williams in the movie Moscow on the Hudson. His character defects from the Soviet Union to the United States in the early 1980s and encounters choice for the first time. He goes to the grocery store for coffee and finds what to him is an unimaginable number of choices. This was the choice of coffee in 1982; it has obviously expanded dramatically since then. And, who would have thought then that you could create a juggernaut like Starbucks from something as mundane as coffee? This same choice explosion has spread across just about every product category. In many product categories, especially clothing, continual new product introduction contributes to and is overlaid on top of the increased variety.
Therefore, much of the investment has gone towards effectively managing inventory across an increasingly complex product profile. For example, if my overall volume is 100 and I have only one product, it’s relatively easy to manage; but if my volume is 100 and I have twenty products, managing becomes significantly more complex. How do I forecast the twenty products and how much of each should I stock and where? This growing variety also intersects with disruption theory, in which products with lesser performance capture lower ends of the market from established high performance players; the established players then have to add more variety to their product lines. For example, Dollar Shave Club and Harry’s have contributed to a 15+ percentage point drop in Gillette market share over the course of the past 8 years.
Productivity improvement has become a global conundrum, particularly in advanced economies. Productivity is simply output per unit of work per unit of time. If last year I produced 100 units with 10 units of work and I do the same this year, then my productivity improvement is zero percent. But what if last year my 100 units was comprised of five product variants and this year it is comprised of ten product variants? Did my productivity improve or stay the same? What if last year I provided two ways by which the consumer could attain my product (when, where, and how) and this year I provided four ways? For sure, the amount of work and skill involved increased. The units of variety have doubled. And, the units of variety are not limited to products – there is a lot more variety in services – how, where, and when the product is delivered to the consumer. All of this means that everyone is working much harder with much more complex supply chain decisions to provide more choice, but choice does not factor into productivity equations.
Let’s take a relatively mundane product like orange juice (I randomly chose this, without any idea what kind of variety it offered). A search on the term “orange juice” on the Walmart website yields 271 results. The same search on Amazon yields 684 results (a search using “coffee” as the keyword produces more than 20,000 results). The diagram at the right is the picture of page 1 of the 110 orange juice selections available from Kroger (for coffee, they offer 1497 selections). Now, let’s say that 30 years ago, there were a few orange juice providers, with each provider having a single variant. While the basic product has not changed dramatically, the number of variants and associated packaging has. Each of these variants must be managed across a common asset base, each with its own inventory profile, from raw materials to the shelf, and now directly to consumer homes. From a volume perspective, this supply chain may not be substantially more productive than it was 30 years ago, but from a variety perspective, it is magnitudes more productive.
Now, let’s also look at the ways by which consumers can attain their orange juice. Consumers can get orange juice from small format groceries, large groceries, big box retailers, convenience stores, cafeterias, vending machines, fast food and other restaurants, myriad pop-up locations, and of course, the internet. This creates a size-flavor-channel problem akin to the size-color-style problem in fashion retail. (In the case of Amazon (see How Does Amazon Do It?), they can increasingly put this problem back on their seller community).
In almost all industries, it’s not about getting the volume right. Typically, companies can forecast volumes with reasonable accuracy; for some industries, this information can be easily obtained from external data sources. The challenge comes in forecasting, planning, and executing at the mix level.
GDP and productivity growth are about raw power and economies of scale – if volume or revenue grows larger or faster, then it can be measured and adds to the pile. A large percentage of the productivity bump between 1995 and 2004 was attributed to computer manufacturing (much of this manufacturing is no longer performed in the United States; thus, future gains in computer performance would only partially help US overall productivity). The price-performance curve of computing equipment is a powerful statement of productivity and one that can be easily quantified (manufacturing and distribution creating ever-increasing power at lower prices).
Economies of Scope and Maslow's Hierarchy of Needs
However, while productivity focuses mostly on economies of scale, the challenge for companies to achieve greater volumes has been one of economies of scope, particularly in mature economies where the focus has been on achieving ever higher levels on the supply chain equivalent of Maslow’s hierarchy of needs.
Until economists figure out how to place a value on choice, all of this work will not show up in productivity numbers. For computers and automobiles, economists have figured out how to attribute faster speeds and more content to higher value (showing that for a given set of inputs much more value is being created; thus, even if inputs don’t decline, they are producing more value resulting in higher productivity numbers). For example, even though an assembly line today produces 200K units a year with 18 hours of labor per unit, just as it did five years ago, productivity is still higher because the value of the vehicle being produced is significantly higher, due to dramatically increased features, including reliability, electrical, mechanical, safety, and electronics.
But a gallon of orange juice is a gallon of orange juice, even if I can now buy twenty different kinds, when previously I could buy only two. In productivity calculations, there is no additional utility or performance to be gained by having twenty varieties of orange juice versus having two. Presumably, this might show up in consumers valuing variety and placing a higher price on it, which would show up in output numbers. Unfortunately, the complexity of managing supply chains (materials, production, distribution) for the increased variety has not allowed costs to be dropped at the same pace as if the same supply chains were managing single products.
There has been much written that all the investments in information technology and digital transformation have not resulted in much in terms of productivity improvements (the techno-optimists versus the techno-pessimists). The data supporting this assertion are indisputable. What is being missed is the larger point – that the framework for competition is now based on providing ever increasing personalized, individualized choice. Volume outputs are not increasing dramatically; likewise, labor, asset, and material inputs are not decreasing dramatically; thus, productivity is not shown to be increasing much. But the fact that these inputs are not increasing and are in fact decreasing marginally while solving a dramatically more complex demand problem – one of choice – is the direct result of increased investments in digital technologies. The other way to look at this is that if this level of choice were to be offered without digital technologies, the labor, asset, and material inputs would have had to increase dramatically. That this did not happen is a fine example of software eating the supply chain.