Category Archives: Technology

Pros To Know (in the Supply and Demand Chain)

Supply & Demand Chain Executive recently published their annual Pros to Know for 2007, honoring supply chain leaders driving strategic transformation in the industry. In addition to Jason Busch, founder of Azul Partners and blogger extraordinaire of SpendMatters, whom I voted for in A Public Nomination, I’m happy to report that the two other bloggers I nominated were also named Pros to Know: Dave Stephens, founder of Coupa and author of Procurement Central [WayBackMachine], and David Bush, co-founder of Iasta and editor of eSourcing Forum [WayBackMachine]. In addition, two other prominent bloggers in the sourcing and procurement space also made the cut: Tim Minahan, marketing guru of Procuri (acquired by Ariba, acquired by SAP), and Charles Dominick, founder of Next Level Purchasing (now the Certitrek NLPA) and author of the Purchasing Certification Blog (now the Certitrek NLPA blog).

Furthermore, Supply & Demand Chain Executive also selected three other bloggers to their hall of fame: John F. Martin, author of the Building SaaS blog for his tireless promotion of On-Demand; Annrai O’Toole, CEO of Cape Clear Software (acquired by WorkDay) and author of the Clear Thinking blog for his efforts to cut through the clutter of the SOA (Service Oriented Architecture) space; and John Radko, strategist for B2B connectivity provider GXS and author of the On Demand B2B blog for his commitment to spreading the word about the power of connectivity On-Demand brings to B2B.

According to the article, “New media” are playing an important role in raising both the expertise of supply chain professionals and awareness of the increasingly strategic nature of Supply Chain in the enterprise. In the “blogosphere,” a network of Web logs, or “blogs,” authored by a growing number of industry veterans is playing its part by offering analysis of current events and trends, as well as a healthy dose of best practices. The blogs are sometimes serious, sometimes tongue-in-cheek, occasionally self-serving — the bloggers often are executives at solution providers, and other blogs are sponsored by solution providers — but almost always insightful and frequently entertaining. Supply & Demand Chain Executive recognizes the 2007 “Blogger” Pros to Know for their contribution to spreading the supply chain gospel.

It looks like Supply & Demand Chain Executive is finally catching on to the fact that it’s not the traditional media, but the new media that is driving the space. Maybe that’s why the publication has improved significantly over the past few months.

Sometimes it’s okay to get Rapt up in revenue

These days it seems like everyone is focussed on cost savings. This is not a bad thing, considering the vast majority of companies are not best-in-class, which means the vast majority of companies, on average, are probably spending too much on their purchases. But despite some vendor claims that revenue is, and will remain, flat, or that there’s nothing you can do about it since the market sets the price and constitutes the demand, this is not true.

We all understand that the fundamental goal of business is to make money, or profit, and we all learned the same calculation in our first business class: Profit = Revenue – Cost. This tells us that, as a business, there are two levers we can manipulate to increase profitability, Cost and Revenue. Now it’s true that we as sourcing and procurement professionals have a lot more control over cost then we do on revenue, but that does not mean our focus on cost should be myopic. We should also understand the revenue side of the equation and work with marketing on the pricing side of the equation, because neither the market price, the highest price marketing predicts they can get, nor the price at which demand (or consumption) is maximized is the optimal price.

If your goal is to maximize profit, the optimal price is the one where the profit equation is maximized, and this means this price is determined as much by cost as by revenue, and we all know that the cost for a product is not fixed – it depends upon the supplier we use (which determines a host of physical attributes such as quality, appeal, etc.) and, more importantly, the quantity we order. Generally speaking, the cost per unit will decline if we order more units, but this is usually only true to a certain point. Each supplier has a base capacity they can produce on their production lines during their regular hours of operation. To exceed this capacity they will have to add shifts, add lines, or both – which will increase the cost per unit. Or if your product requires a raw material in short supply, costs will increase as you try to divert supply away from your competitor, and there will be a point where you just will not be able to secure more material.

Is marketing, or if you’re big enough, product pricing, going to understand all of the factors that contribute to product cost – and, if so, are they going to understand the factors and inter-relationships as well as we do? Probably not. And that’s why sometimes we need to get Rapt up in revenue – to make sure that not only does the organization choose a price-point that theoretically achieves their profit, margin, or market-share goal (which, without our assistance will probably be based on cost-data that is only an approximation, and not necessarily a good one), but that the price-point is realistic and that the forecasted demand can be met in the intended time-window.

Furthermore, as the users of some of the most advanced analytic and business intelligence tools in the organization (spend analysis, cost modeling, and decision optimization, for example), we are much more likely to understand that our historical data alone is not necessarily sufficient or accurate enough to predict future demands, that different product features and price-points will have a considerable impact on actual sales, that costs can vary significantly by feature and demand level, and that the only way to analyze all of these variables and make the best pricing decision is to use a good decision support tool based on sophisticated analytics and optimization to model the different scenarios at different price points and obtain a true picture of feature – price point – demand level correlation.

And that’s why tomorrow I will introduce you to Rapt (acquired by Microsoft) a decision analytics and price optimization solution provider whose goal is to help companies maximize their revenue opportunities.

Procuri Spend Analysis

During my brief Chicago tour, I had a chance to sit down with Rod True of Procuri (acquired by Ariba, acquired by SAP), Senior Vice President and the former President and Founder of TrueSource, acquired by Procuri last year, and talk not only about Procuri’s TotalSpend solution, but about what Spend Analysis, Visibility, and Intelligence means to Procuri and where their solution is going.

Although I do believe that their tool is not yet a perfect “total” spend solution (but to be fair, I do not think any tool is – which is probably obvious from my recent posts on the spend visibility space), I also believe that with the acquisition of TrueSource, Procuri are just as close, if not closer, than any of the other big players in the space. The reason for this lies largely with Rod True and the team he built and the fact that they get that “spend intelligence” requires three major components to be successful: accurate visibility across all of the relevant data, analysis capabilities, and the ability to use the data for compliance initiatives.

To this end, TrueSource spent a great deal of time on ETL tools that could not only load data from a large number of data sources, but map such data into a plethora of out-of-the-box and custom categorization schemes and do so in such a way that duplicates are detected and dropped. (After all, if your data is no good, neither is your analysis.) Moreover, knowing that most companies still use old ERP or database systems where the best they can muster is a full database dump (for the last month / quarter / year), they have built their ETL tools in such a way that their spend warehouse can be incrementally updated from a full database dump at any time.

They have also built in a large number of reports (over 70) and standard reporting capabilities (through a custom report builder) to allow for role-based reporting and analysis, compliance & audit management, category management, and diversity management and built their warehousing capabilities to support just about any categorization you can desire. They also have role-based dashboards, category project management, and category intelligence built into the solution.

Furthermore, knowing that they could not possibly think of all of the things you might want to do with the data, they also support the fine-grained export of any set or subset of data or report that you might want to analyze in further detail.

And this is where I believe their one weakness lies. They have visibility down pat (and pride themselves on their ability to be able to quickly develop an automated cleansing, classification, and refresh for just about any data source you can imagine), they understand that the entire point of any spend effort is all about compliance – with diversity requirements, with reporting regulations, with business decisions, and with sourcing decisions (otherwise your “savings” might never be realized), but their analytics is limited to what you can do with their pre-defined reports and report builder. And although I have to admit that what they have is most likely more than enough for most of the users in an organization – executives, managers, and even average users – I am not convinced it will ultimately satisfy the emerging spend power users.

It is true that a power user can easily integrate into their cleansed data feed and extract just the data they want (and they told me that they are surprised at how fast their power users can get just the data they want for a custom report and build it in another tool), but I believe that the next generation of spend power users are going to want the ability to create their own custom views, reports, and analyses in the tool itself, versus on their desktop with a Microsoft Office or similar end-user tool.

However, you still need a centralized spend repository with complete, clean, categorized data for your analysis, reporting, and compliance management – and this solution is definitely a valid starting point from that perspective.

Wharton Nuggets (on Market Share, Entrepreneurship, and Grocery Purchasing Patterns)

Over the last couple of months, Knowledge @ Wharton has published three articles that caught my eye. (Well, more than three, but I felt that these three were worth blogging about.)

The first article that I am going to draw attention to is “The ‘Myth of Market Share’: Can Focusing Too Much on the Competition Harm Profitability?”. The article starts off by noting that it is a common practice of many companies to focus their attention on grabbing market share from their competitors, but such efforts can actually be detrimental to the firm’s profitability.

The reality is that even fifty years ago research indicated that competitive choices are often low-profit. Back in 1996, a study by Armstrong and Collopy analyzed data amassed by scholars to measure the level of competitor orientation of 20 major corporations for five nine year periods beginning in 1938 and ending in 1982 and found that competitive-oriented objectives were negatively correlated with ROI for the data. In other words, the more managers tried to be the biggest in their market, the more they harmed their own profitability. In contrast, companies whose only goal was profit maximization posted stronger returns on investment than the other firms.

The article ends by quoting Wharton Marketing Professor J. Scott Armstrong who says We’re not saying companies shouldn’t pay attention to their competitors; they might be doing reasonable things that you may also want to do … What we’re saying is that the objective should not be to try to beat your competitor. The objective should be profitability. In view of all the damage that occurs by focusing on market share, companies would be better off not measuring it. ‘Nuff said.

The next article that caught my eye was “Dos and Don’ts for Entrepreneurs, from Those Who Have Actually Done It”. As someone who spent the early part of their career working in a lot of start-ups, I know from extensive experience that most entrepreneur’s don’t have a clue what they’re doing. When you fail when you (a) have the best technology, (b) have more than enough money to do what you promised, or (c) have great talent across the board, or (d) have all three … something’s wrong … and it’s not with the employees.

Therefore, whenever someone who has a clue offers to share their advice, I thoroughly believe you should heed it. Tidbits you will take away from the article is that not every business idea needs venture capital, successful businesses solve a real problem (not a hypothetical one), disruptive technologies enable a start up to jump into a large, lucrative market where established leaders have become complacent, good entrepreneurs manage risk, and the KISS rule is always in full-force: get the prototype out as soon as possible, get feedback, and improve only where needed. Remember, a camera, mp3 player, personal organizer, web browser, etc. may be great, but sometimes you just need a phone.

The final article is “The ‘Traveling Salesman’ Goes Shopping: The Efficiency of Purchasing Patterns in the Grocery Store” about the application of the “Traveling Salesman Problem” to the study of the behavior of grocery shoppers.

Wharton marketing professor Peter S. Fader insists that the Traveling Salesman Problem (TSP), which seeks the shortest route available to a traveling salesman who has to visit a number of cities and then return home, closely resembles the problem faced by a typical grocery shopper who plans to purchase a certain list of items in the grocery store. To achieve the same efficiency as the salesman who meticulously plots his route, a shopper would need to know where products are located, and have a game plan on how to go about gathering the items on his list while covering as little distance as possible. However, the average shopper is quite inefficient.

What’s the goal of the research? To understand in-store behavior and how stores should place items to ( a) increase customer efficiency and ( b) increase sales. Does it affect your supply chain? Not really – unless you are in grocery retail, because more efficiency and better sales increase demand, which increases revenue, which should increase profit. But it’s still a very interesting article.

Embracing Complexity

Recently, Supply and Demand Chain Executive ran an article on “Embracing Complexity” that pointed out that supply networks that are becoming increasingly extended and complex; integration between companies and their trading partners is becoming deeper at the systems and process levels; and emerging technologies like radio frequency identification are producing ever-growing mountains of supply chain data and that these and other factors threaten to overwhelm the systems that companies rely on to monitor and manage their flows of goods and 20th century systems may be inhibiting companies from moving toward a 21st century supply chain.

In addition, it presented Lawrence Davis’, a senior fellow at NuTech Solutions (acquired by Netezza Corp), insights into problems with current supply chain technologies. In short, he believes that contemporary solutions do not allow companies to optimize at the appropriate level of aggregation and that companies should be able to use solutions to optimize across their sourcing and procurement, production and distribution processes all at the same time; that software solutions that optimize based on deterministic assumptions about how long it will take for any given process to be completed produce “perfect” schedules that do not allow for breakdowns of machinery, traffic jams, defective parts, and other real-world assumptions; and that stochastic simulations which employ embedded agents that follow the company’s business rules are required.

They got the problems right, but I’m not sure I agree with the proposed solutions. Here’s a short list of reasons why.

  1. Optimizing at the appropriate level of aggregation has always been a discipline-independent problem and we’ve always managed. It’s as much a process problem as a technology problem. It all comes down to using appropriate levels of abstraction that allow us to connect larger and larger problems. And it works. You don’t need to simultaneously optimize all of your categories and all of your lanes – a problem you can’t solve. You can optimize all of your buys using high-order freight approximations, then collectively optimize your freight costs and distribution network.
  2. Deterministic models can be used on approximations and ranges as well as precise models. Yes, the results are still “perfect ranges”, but you can capture most of the likely outcomes. Moreover, none of the technologies proposed will capture every exception and you’ll still need exception management.
  3. Stochastic simulations are a good methodology for determining what could go wrong, but the key is identifying a set of collaborative systems that can embed the company’s business rules – because, as I just said, the processes are as important, if not more so, than the technology.
  4. The technologies proposed – “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” are not silver bullets – just like the ERP was not the silver bullet you needed to manage your supply chain. They have their uses, but they are not that much better than today’s technologies, if they are better at all (as they all have their drawbacks).
  5. You’ll never be able to optimize everything. For that, you’d need a model that accounts for everything (and first of all, we can’t model the market), then you’d need an expensive High Performance Computing Cluster with hundreds (or thousands) of processors and a significant amount of memory, and finally you’d need an algorithm that can take advantage of the highly parallel machines – and you’ll quickly find that most of today’s optimization technologies, or at least the sound and complete ones, do not have efficient massively parallel implementations.

It’s true we still have a long way to go in supply chain, and that we do have to embrace technology, but we have to be careful of over-relying on new technologies, particularly those that have drawbacks as significant as the advantages they are being promoted for, to solve all of our problems. Although some things change, some things will stay the same – and the constant is that no matter what, we are going to need more brain power and good old fashioned human ingenuity to get to the 21st century supply chain.

One can wish it were otherwise, but as a technologist and former academic who could spend countless posts educating you not only on “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” but also on “fractal geometry” (the basis for NuTech’s logo), “chaotic dynamical systems”, and “complexity theory”, it’s not the case. Technology is just a tool – the real solutions will come from the brains who can identify the problems, identify the process solutions, and then put the appropriate technology in place to back it up.