Author Archive

–

Integrated information for policy-managed decisions

MM: It seems that as your solution evolves to include WiFi Max networks and 3G phones—such as the iPhone—these mobile Internet connected devices become points of control of an entire industry, almost like the channel changer for a TV; it’s becoming the control system for these very sophisticated applications.

MB: I think of the world of mobile devices as a great way to give freedom to people who otherwise have to be slaves to the careful tending of systems and so forth. In that sense, they’re very freeing.

If you take the kinds of monitoring and management application that people want as a business intelligence solution and simply display it to them on a mobile device, you’re not going to be doing them any favors. You’re just changing the location at which they have to do a piece of work, where they look at a screen, make a business decision and so forth. It might give them some location freedom, but there’s a lot more potential out there for the activity you have to do, from the mobile perspective—to be a higher level of monitoring. You automate the decision-making at the lower level.

Today, let’s say you’re looking at a sales margin inventory kind of report. You say, “Gee. Here’s a product that I have very low inventory of, and I happen to be selling a lot of it. Gee. It’s selling at high margins. I guess I should reorder that.”

Of course, the system should just reorder that for you.

Today, people struggle just to get all that information on one line. So they can see that the problem is actually there. The next generation of systems will be ones directed at business rules that will help people automate the solutions. It’s what we call “operational business intelligence,” where triggers and tripwires and things of that sort can notice characteristics of the data in the enterprise, and can take actions.

Then from their favorite mobile device, people can make sure that the decision-making that’s happening for them is not going off the rails for some unforeseen reason. Instead of having to switch every switch on the train, you just have to see that the trains are all moving in a reasonable way.

I think the future will lead to integrated information properly displayed for human decision-making, to support of that human decision-making.

MM: And eventually, I guess, we get into policy-managed processes that basically report back to you that, “Hey. I did this. Is that okay?”

MB: Once you have integrated information, the sky is the limit with what you can do with it. Integrating the information and presenting it in a reasonable model for people has been the bottleneck and remains the bottleneck today.

MM: Well, that sounds like a great place to conclude. Thanks very much.

–

The fact that your client is basically a browser means that you don’t have a bunch of fat clients to manage and synchronize and update and all that kind of stuff.

That basically allows you to bring a strategic capability to users at a highly disruptive cost. “Disruptive,” here being the classic definition of Clayton Christiansen in “Innovator’s Dilemma,” and seeing what’s next. He describes it as a “good enough solution at a significantly lower—if not almost free—cost.”

MB: Yes. What we’re doing is intended to eventually disrupt the typical development process of systems integrators building large-scale data warehousing systems and doing custom-designed data schemas and so forth.

Value-chain optimization

MM: In the spirit of value-chain optimization, the idea of giving tools—not just tools, but capabilities—to the people who understand the data and who can effect an operational or a tactical decision in real time as they run their business… can you provide a couple of forward-looking comments? Or what I call “future proofs,” in terms of how you see this rolling out or evolving over the next 2 or 3 years?

MB: Well, I can certainly tell you that the sophistication of these applications will be growing. I’d say over the last 10 years—certainly 6 or 7 years ago—there was a lot of talk about data mining systems, for example. But every data mining system I saw was a tool for an analyst to use.

I think this kind of advanced analytic technology is going to show up for the end users, and it’s going to be a feature of a product. It’s not going to be data mining. It’s going to show up in a way that’s meaningful to a business decision maker—as more data for them to look at and take action upon. It’s not going to be packaged as some sort of fancy data analysis tool.

I think that is certainly what our focus would be on—leveraging that kind of technology. Customers do want the capabilities that kind of technology can bring, but they don’t want it packaged for use just by data analysts.

–

Hidden costs

–

MM: There was another dimension that you introduced. You kind of suggested a little bit in terms of needing to understand the behavior of a logistics supply chain—or in this case, a transportation value chain. In classic economics, according to the work of Ronald Coase in his book, “Theory of the Firm,” he would refer to these as “transaction costs.” Transaction costs was his way—as a theorist and economist—to describe all of the handoffs. The communication, interactions and handoffs—as well as the delays associated with getting a business process completed.

So you were really calling attention to the fact that there were all these other hidden costs—almost like opportunity costs. A percentage of the truck that wasn’t fully loaded, and the amount of time it was sitting some place.

MB: Or the inability to ship something at a certain time, for lack of availability of capacity, and so forth.

Solving many of those problems, honestly, is easy for people once you give them access to the information.

MM: Right. Because it’s their data.

MB: Yes. It’s their data. The big headache here is integrating it from multiple systems. Representing it in a uniform way for people, getting it in the form they need, and in front of the eyes of the people that have to take action on it.

In that sense, solving the transportation and logistics problem is not just a matter of some computer-science oriented thing. It’s just as much — or more — of the basics of data display and information integration.

That said, those practices have until now been far too costly and far too complex for many companies to acquire. So, that’s what we’re going after and trying to make far more cost-effective.

–

–

Nickels and dimes

MM: That was one of the things that really came through in your talk, Mike. First of all, you were approaching these applications that you call “diagonal applications,” really almost as a value chain optimization suite. So you’re looking not just at one business, but rather at how to optimize an entire value chain—irrespective of your location in that value chain.

MB:  I guess that’s a way to look at it. There is a collection of these Diagonal BI applications. We’ve tried to package a number of them in modules that can be sold to particular industries.

MM: There was another thing that was remarkable in your presentation. That was the ability for using all of these kinds of hidden charges in the trucking area. There were some terms that you used, but they referred to—basically—”hidden” markups.

MB: Yes. That actually brings me full circle to the point I started to make at the beginning of the interview here. That was about “tools versus solutions.”

When I said Oco is a provider of business intelligence solutions, well—every business intelligence provider will tell you they’re providing solutions. The question is “solutions for whom?”

If you’re a data analyst, then a data analyst tool is a solution to your problem. At Oco we’re trying to provide a solution to business users for a transportation cost minimization problem—as our example here. That application goes to the eyes of the business user—not to the eyes of a data analyst. It’s intended for use directly by the people that are in the trenches who need that information. That’s why I stress that it’s a solution.

MM: The nickels and dimes, to use a metaphor. Right?

MB: Yes. Well, because it really adds up. That’s the problem. This is part of the reason why summarized data cubes and so forth have given way to, customers saying, “I need to be able to drill down to the actual data.”

In a summarized data cube, you would just roll up all the accessorial charges noted above. If instead you can actually see what’s happening at the individual bills-of-lading of the trucks, you can spot many of the problems and identify the carriers charging more than others, and so forth—even though the line-haul charge which is the advertised cost of the shipping, is the same.

MM: I refer to these as “carbon monoxide expense items.” Carbon monoxide constitutes an odorless gas that you can’t see, touch or smell. But you know you have it because you have a headache. And if you’re in a cave, you know the canary dies.

MB: Yes. These are, in some sense, ways for people to slide charges in on you.

–

–

Problem of transportation logistics

MM: Not just trucks, but what’s on the pallet and how many pallets get organized by what truck.

MB: That’s right. And how many stops it takes and so forth.

This brings me back to what we mean by a “Diagonal,” BI application.

To build an application that really helps address the problem of transportation logistics, or the truck shipping of goods, you have to embed a lot of industry understanding and knowledge of trucking into the application. So it requires information specific to the business problem of shipping goods by truck, but it’s not specific to any particular industry.

You don’t really care whether you’re shipping machinery or consumer packaged goods or clothing. These applications cut across industries, but not all industries. Obviously, financial services people aren’t shipping goods around by truck, and for the most part, shipping is just not a part of their primary value proposition. Similarly, higher education is not a truck-oriented industry. But any manufacturing company, whether in the food segment, the clothing segment, the toy segment, the industrial products segment, etc., all have a similar trucking problem to solve.

Another example is any company that makes or sells something that typically has sales margin and profitability issues. The companies really want to understand what products are selling at good profit margins. They want to be assured that the inventory they carry, relative to sales rate, is in balance.

Sales margins and profitability issues cut across industries that have goods to buy and sell—but obviously these aren’t applicable to government or higher education. It’s not like a database system because it doesn’t apply across all industries.

These diagonal types of applications are important because they add high value for their customers. They typically save companies thousands and thousands of dollars all the time, or even millions, for large companies. So they are applications that can command high price points, because they really deliver great savings and a very attractive return.

But also, they’re applications that—because they can be sold across many industries—have a pretty large base of prospective customers—larger than vertical-market applications that are targeting a very narrow perspective. They are very attractive from a business standpoint.

Diagonal applications also work very synergistically with SaaS deployments. That was one of the things that I emphasized in the talk I gave at SaaScon. The reason there are companies like Oco and obviously other new market entrants in this space is because of this synergy.

When you build a system for a particular business problem, transportation logistics, let’s say, then the structure of the database of information that’s needed to support it is not specific to that particular customer. It’s a database that’s designed to support transportation logistics.

As a result, you can get great economy of scale in the deployment of that system by creating a SaaS multi-tenant deployment of that database. All the customers sharing that infrastructure are trying to solve the same kind of transportation and logistics problem against a database of similar structure.

This works a lot better than the ASP models of a decade ago. Back then, custom data warehouses would be designed for each business. If you tried to aggregate those together, you’d get a whole bunch of totally different databases. In some sense, they were too customized. You’re not going to get common behavior by putting them together.

–

–

Transportation logistics at Welch’s

MM: In the case of Welch’s, you talked about what again?

MB: That one was transportation logistics. Trucking, basically. They have to ship their goods on trucks from their plants to their customers’ warehouses. It’s a somewhat complex world out there in trucking. You have some customers that handle customer pick-up and drive their own truck to your factory. Other times, you have to schedule and hire trucks from a variety of carriers that deliver your products to the market.

Optimizing shipments for consumer packaged goods companies, like Welch’s, can save a lot of money… A meaningful fraction of their revenue is spent on transportation costs. Companies can optimize that by assuring your trailers’ utilization is high, or by comparing accessorial charges which are the extra charges like fuel surcharges, costs for unloading of goods by the driver at customer locations, trailer storage, and so forth.

They can optimize these across carriers to see if any carrier’s charges are out of line by analyzing shipment patterns to see where load-leveling may be out of balance. By having carriers report actual time of delivery to your customers through a Web-based tool, you can analyze on-time delivery performance, and company to have the capabilities of larger competitors at a fraction of the cost through these SaaS Web-based solutions.

–

–

Emergence of diagonal applications

MM: Would it be fair to characterize that these vertical applications tend to be not necessarily transaction systems, but rather analytic systems?

MB:  I do not believe that is exclusively true. But I do think more of them tend to be like that.

MM: Predominantly true?

MB: For example, in industrial manufacturing… Industrial manufacturers sell—in many cases—equipment used by other people. So they have the service and maintenance applications associated with after-market service of the equipment. That’s not analytical.

Yes, they’re very interested in analyzing why these machines are all failing or what the quality issues are, but they are also scheduling the repair cycles.

MM: Yes. The operational systems, then.

MB: Yes. There are some operational ones. But I would tend to agree with you that a lot of them do have the tendency to be analytic.

That brings me to the issue of what I call Diagonal BI. The term was coined by our CEO Bill Copacino. So, diagonal—if it’s not horizontal and it’s not vertical—what is it? Diagonal is the word we chose to define things that live across some industries, but not across all industries.

These are applications or analytical solutions that are definitely not specific to a particular industry, but they represent common functionality that’s needed across many industries.

–

–

Cycle times

MM: Isn’t another dimension of supply-chain theory or supply-chain strategy, cycle time and defect rate? How quickly things move through? And how many times I have a defect or a rework at various parts?

MB: Clearly a focus on product quality and rapid processes—manufacturing and replenishment cycle times—are foundational capabilities today. Companies often lack visibility, quality and cycle time metrics across the organization. These metrics are often not visible to senior management, suppliers, and other key stakeholders in a company. The data may be buried in a Manufacturing Execution System (MES) or an isolated system that tracks quality, perhaps even on a spreadsheet. One of our solutions targets quality and operations reporting, and it can make these metrics or dashboards visible to all interested parties.

Consider managing inventories across the extended supply chain—from suppliers, through one’s own supply chain through to your customers’ inventory levels. The largest players have developed sophisticated systems to have visibility of inventory across the channel or extended supply chain. The options for the mid-sized players are much more limited and often they have not been able to afford these systems. However, Web-based SaaS BI solutions, such as Oco’s offerings, now level the playing field and make these capabilities available to mid-sized companies.

MM: In your presentation at SaaScon, you had used an example of—I believe it was—Welch’s, as I recall?

MB: Yes. Welch’s is one of our customers.

MM: You were describing the notion of a vertical business intelligence and the notion of a horizontal business intelligence. Then you coined a new term called, “diagonal,” business intelligence. Could you just give us a quick reprise of vertical-horizontal and then the new neologism of “diagonal?”

Diagonal Business Intelligence

MB: Yes. Sure. This is the business school concept—vertical and horizontal markets.

A horizontal market is a solution designed for a specific business function or application area—such as a business intelligence software product. A horizontal market is one that can be used across industries (or across several industries).

MM: Databases. Web content management systems.

MB: That’s right.

In many cases, HR packages, for example. They’re not particularly industry-specialized. . They don’t have any inherent industry-specific requirements built into them.

Now, in many cases, in order to use them effectively, a company that purchases one of these packages has to build in or configure in that domain knowledge or best practices themselves.

MM: In fact, they really instantiate a database or tool with a digital business model. Or at least the logic of their business model.

MB: That’s right. And there is substantial cost involved in doing that. .

Vertical applications involve solutions that are really specialized to particular industries. In the retail industry, you might have size assortment planning for clothing. It’s absolutely specific. Not just for retailers, but for clothing retailers.

Or in the financial services area, a really good example is anti-money-laundering kinds of activities. These things are very specialized to a particular industry and add tremendous value. But the number of places that you can sell such a software product is a lot smaller than one of these horizontal solutions that you can sell across many industries.

–

–

Weakest links of a supply chain

MM: Isn’t the other idea of a supply chain the notion of constraints or constraint theory? That is, the supply chain is as efficient as its weakest link.

MB: Well, certainly if you use the kind of lean inventory-management strategies where you’re trying to minimize the inventory that you’re carrying—then, yes. You have to have a lot of trust that the inventory is going to be replenished rapidly by the party on the other side, and these Web-based SaaS solutions provide visibility that dramatically increases this trust because each player can see what is happening and also dramatically reduces the risk of failure.

MM: Isn’t that in fact reality of today’s economy? Virtually, if you’re not lean, you’re carrying a whole bunch of inventory or raw materials on your income statement. So inherently you’re seeing all of that as, “Gee—not on my financials.”

MB: In fact, that is one of the things our solutions target. We have specific dashboards and alerts that identify for each of your many, many thousand of items, which ones are at risk of being out of stock or of having excess stock. They are solutions that allow you to run lean without getting into trouble.

–

–

Core concepts

MM: Let’s shift now into the conversation and hopefully extended discussion of digital supply chains and how they parallel to a high degree physical supply chains. Would you just give us a quick reprise of the core concepts or the core ideas of a supply chain? Then start to correlate that to digital and physical versions?

First, supply chains start with the idea that there are multiple business entities or operations that are part of an end-to-end process of transforming raw materials or IP into some sort of tangible good or service at the end, that a consumer ends up buying.

MB: Part of the complexity of managing a supply chain is that the number of these parties is not small.

If you are a company that buys things from two other companies, you do not have a big and complex supply-chain problem.

But many companies buy many thousand items from a large number of suppliers and in turn sell to hundreds or thousands of customers—these companies have very complex planning and execution issues and can benefit from new analytic tools.

Beyond analytics, collaboration with your supply chain partners, sharing of information and allowing views by your suppliers into the inventory levels of their products in your facilities, or views by your customers into the status of your shipments to them, can dramatically reduce your joint costs, improve product availability and increase customer service.

Web-based access to these analytics and collaboration applications by supply chain partners is a big advantage of SaaS solutions.

–