Guest Commentary

In this latest guest post, Douglas Macbeth of the University of Southampton School of Management argues that western economies can no longer afford to pursue a strategy of relentless offshoring - the costs and the risks are far too high...

Let us start with a few propositions which, in my experience, describe the majority of our manufacturing organisations – as well as many others.

• Procurement isn’t well understood at board level;

• There are not enough CPOs who are able to influence board direction and strategy;

• Short-term financial myopia drives decisions;

• Manufacturing is no longer seen as core to western economies – services are more important.

For many years, western companies have been moving away from manufacturing and have chased the world to find the latest low-cost country from which to source everything from materials to complete products.

But why has this happened?

To some extent there is a certain logic – labour and social costs are initially much lower in developing countries. But does anyone really know how to calculate the total cost of acquisition (far less the total life-cycle cost) of anything? And if not, what data is the decision maker using? While the concepts might be easy to grasp, extracting meaningful data out of ERP or traditional accounting systems is enormously difficult. Add multiple divisions and legacy information systems, and the quest for the Holy Grail looks simple in comparison.

Add to this the fact that the proportion of total cost which is accounted for by labour tends to be very low, and it is slightly puzzling why the trend for low-cost sourcing is so prevalent.

The reality is that the difficulties of offshoring are now well recognised. There is often a shortage of appropriate skills in the target location; infrastructures for physical logistics and legal structures to conduct western-style business transactions may be in short supply; time zones, culture, behaviour and attitudes are likely to require careful consideration and it will often be necessary to pay the costs of ex-patriot managers to help in the start up phases at least.

The alternative to the latter is to train up locals – a form of technology transfer which can create competition much quicker than you would like. And while some companies try and limit this by only transferring some of their capabilities, the same supplier might be building up skills across multiple orders. Who, apart from the supplier managers (and in some cases their governments), would have any view over the whole supply chain to see this pattern?

When we add to this the experiences we have just been through with the global financial system meltdown and we have the makings of a real catastrophe which will challenge the perceived wisdom of offshoring.

One major lesson for me from the banking crisis is that not enough people saw how interconnected the world’s financial systems were. Equally, no one had the appetite to perform a proper due diligence and risk assessment on the nature of the assets that were supposedly underpinning the whole house of cards.

However, before we criticise the bankers too much, how many of us can define our extended networks of suppliers and customers and have done a detailed assessment of where the critical risks are located and what mitigations are needed?

Wwe seem to be in the midst of a perfect storm. Some organisations are replacing bank lending to suppliers with their own financial support just to keep transactions moving, there are issues around currency fluctuations which are difficult to hedge against and the recent threat to business credit insurance threatens to further restrict the fluidity of supply chains. Without trust – or at least, insurance – how can any trade function, especially across international borders?

In addition, while there is talk about avoiding the threat of protectionism in international trade, the levels of taxpayer investment, and therefore future taxation, is at mind-blowing levels. It is no surprise that politicians are trying to control the effects of their investments to derive local benefits.

In the midst of all this, the environmental message seems to be getting heard more clearly. One of the features of this, however, will be measurement and concerns about carbon footprints and the true costs of transportation.

The opportunity for procurement to take centre stage here is clear – no other function has the potential to contribute so much. Risk assessment has always been part of the procurement process, but now we have to extend its horizons beyond the suppliers we are directly contracting with and into our extended networks more explicitly. We also need to be involved in the redesign of products to meet the challenges of extended life, reuse and repurposing that the green agenda will drive.

The fundamental need is to restructure supply chains to support these networks, which might still be international in part rather than simply chasing headline price reductions. It might also be necessary to repatriate some activities closer to customers to reduce the risks and costs of international transportation – companies might have a mixed model with different supply solutions for different channels of customer service, for example.

So, this article started by focusing on manufacturing rather than services. Surely we must by now recognise that the reliance of an economy on invisibles is inherently flawed – we must rebuild a balanced portfolio of activities. Of course we still need an effective and reliable financial services sector but we also depend on goods producers, transportation providers, energy and water providers to live our normal lives.

While some of the information and entertainment industries may be less concerned with some of these aspects since their dependence on physical location is less critical, for the rest, physical location must be a mix of close to source and close to consumer. And let us do that in a more considered way, informed by a vision of a more interdependent future.

And while I’m not suggesting that we should head for a state interventionist system (although that seems to be what is happening) rather, we need to redefine and then persuade our societies’ stakeholders that we need a more enlightened model which recognises and can work with interconnectedness and diversity in a dynamic and entrepreneurial way.

Are procurement leaders up to the challenge?

Professor Douglas Macbeth is director of business development, MSc global supply chain management and supply chain research, as well as professor of purchasing and supply chain management, at the University of Southampton School of Management.

Site Selection – Part 3 – Match Your Network to Your Business Strategy

However, Gillette's theoretical analysis ended up taking a backseat to a practical consideration: The company was locked in to significant lease commitments with its current warehouses, which made it prohibitively expensive to just pack up and leave. So the question became: How can Gillette deliver much better customer service without changing its physical infrastructure?

"The goal was, at a minimum, to have a warehouse on the East Coast that carried all of our products," Knabe says. Ultimately, Gillette ended up keeping both its Massachusetts and Tennessee DCs, but what changed was how they functioned in terms of what products they carried and who they shipped to. Both warehouses now stock all Gillette products.

So far, so good. Gillette discovered it could improve its customer service without having to invest in new infrastructure. However, as Knabe discovered, carrying all products in both warehouses would have significantly increased inventory levels, which was a no-no. To get past this potential sticking point, the company conducted a statistical safety stock analysis to optimize its distribution network. Gillette made some process changes to set its safety stock targets, which made it possible to hold inventory constant while improving customer service.

"Your distribution network should be a function of what your business strategy is," Knabe emphasizes. "If your business strategy is to be the low-cost provider, you set up one kind of a network. Wal-Mart, for example, sets up its distribution network to be as cost efficient as possible. If your business strategy is to be as responsive as possible, you set up a different network. For Boston Scientific, a maker of surgical equipment, it's not about the cost of its distribution network, it's about having the right product at the right place instantly."

In the end, by adhering to best practices in configuring its distribution network, Gillette was able to maximize its use of truckload shipments while improving its on-time deliveries to its customers. As a result, its goal of "excellent customer service at least cost" became a reality.

HOW MUCH IS TOO MUCH?

So how do you know if you're spending too much on your distribution network? Using the Site Selector index of the most logistics-friendly cities, location consulting firm The Boyd Company developed a comparative cost model that identifies how much it costs, on average, to operate a warehouse in the top 50 markets.10

Boyd's comparative model focuses on a hypothetical 350,000-square-foot warehouse employing 150 nonexempt workers. This hypothetical warehouse serves a national distribution network that delivers products to 10 destination cities. Not surprisingly, New York City is the most expensive city in which to own a warehouse, in terms of annual operating costs, which Boyd estimates to be $15.8 million. Of the cities studied, the least expensive is Mobile, Alabama, at $10.4 million.

The most expensive city in which to lease a warehouse is San Francisco ($14.5 million), while Mobile again ranks as the least expensive ($9 million). Overall trends play out pretty much as you'd expect: Cities in the Southeast tend to be the least expensive, those in the Northeast and on the West Coast are the most expensive, and the Midwest places in the middle.

Boyd also looks at a hypothetical outbound shipment model that assumes a volume of freight in 30,000-pound truckload shipments costing $1.46 per mile to move. This model indicates that it costs the most to serve a national market from Portland, Oregon ($4.1 million), while the most economical city for outbound shipments is St. Louis, Missouri ($2.4 million).

According to Jack Boyd, principal of The Boyd Company, companies now prefer to build their own warehouses rather than lease them. The trend today is also toward building fewer but larger facilities, often including nonwarehousing corporate functions within the buildings to save on costs. In effect, this involves moving white-collar workers into blue-collar locations. You're locating to a warehouse where real estate costs $5 per square foot versus the $20 or more per square foot you would pay in an office building, Boyd points out. "Staffing requirements for warehouses have been elevated over the years as companies become more information technology intensive," Boyd explains. "There are greater labor and skill set demands, and it does require more labor cost analysis as part of the mix in terms of where these warehouses should be located."

Planning and Forecasting 5 – The First Shall Be First

Not surprisingly, technology has a lot to do with defining best practices within IBM's supply chain planning processes. By integrating demand fulfillment capabilities with its enterprise resource planning (ERP) system, IBM can schedule an order throughout its supply chain within milliseconds, says Joe DiPrima, manager of supply chain planning and optimization with the ISC group. "That is a best practice because you can have the best planning tools m the world, but if you can't pull the data into the planning tools with integrity so that people trust the data and know that it's current, you're not going to use the planning tool," DiPrima observes. It took a while to convince people that the planning tools were accurate, he admits, but the sheer speed of the forecasts has won over those skeptics who were still relying on their trusty spreadsheets.

Enterprise resource planning (ERP) software ties together manufacturing, sales, distribution, and finance by collecting data from each area and using it to plan a company's resource use—everything from employees to raw materials.

IBM used to manually schedule orders, which became a problem when the company began to dread the arrival of unexpected orders. In normal circumstances, getting new business is good news, but IBM's visibility into its supply lines was less than ideal. There was a fear within some quarters that a new order would divert supply from a high-priority customer that hadn't actually placed its order yet but was expected to. "We didn't want to schedule a lower-priority customer in the hopes that a high-priority order would come in," DiPrima remembers.

To get past that mindset, IBM has done away with those manual processes and replaced them with new processes and new tools based on streamlining the order receipt to delivery time. In the past, order entry to delivery could take anywhere from 15 to 20 days; that process is now down to 5 to 10 days.

How did IBM pull that off? As DiPrima explains, the company instituted a business policy of first in, first out (FIFO). "Orders are now scheduled FIFO. If a customer wants supply, they need to get their orders in first. Very simple. We have exception processes that we invoke occasionally, but if a product is deemed to be FIFO—and over 95 percent of our products are FIFO—they're scheduled first in, first out."

Additionally, IBM has enabled direct shipment to customers from suppliers as they've gone global. "We've outsourced manufacturing to China, Eastern Europe, and Mexico," DiPrima observes, "and as a result, we've enabled these companies to direct ship on behalf of IBM. It looks like an identical order whether we ship it to the customer from our warehouse or whether the manufacturer ships it." This postponement strategy includes some subtle back-office processes such as enabling the outsourcers to print invoices with the IBM logo. The goal, DiPrima says, is to postpone the building of the product until an order is received from a customer.

"From a demand planning standpoint," he continues, "we used to have to be able to forecast each end item a customer would buy." That was no small task since IBM had tens of thousands of end items. "If a customer wanted to buy a standard ThinkPad, but with his corporate logo on the start-up screen, that was a new model number. So while we might only have 300 or 400 core models, it would turn into tens of thousands of models when we actually built them. We used to forecast demand that way, and it was extremely difficult to do. It was never accurate. We would always be chasing and remixing supply from what we had forecast to what actually got ordered."

IBM's solution was to move to a sales building block model, based on a best practice known as attach rate planning. "We have tens of thousands of components and tens of thousands of end items," DiPrima states, "but if you look at the sales building blocks, we only have several hundred to a couple thousand of those. So we find the pinch-point in the development of a product by asking: Where can I have the fewest planning items in the plan, not only because it's easier, but also because I'll get all the advantages of risk pooling by doing it at that level? So we went to a forecast attach rate approach."

IBM's forecasting accuracy at the sales building block level is 80 to 90 percent, a marked improvement from the 50 to 60 percent accuracy it had when it was planning at the end item level. "We always knew how many units in aggregate we would sell, but where we would get it wrong was in trying to figure out the mix," he says. "Now that we know what the percentage mixes are, the planning process is a lot simpler."

Another best practice at IBM has been moving from a monthly planning cycle to a weekly S&OP process. "We also have an ad hoc process running daily to share our demands, including orders, with our suppliers via the web, so they can respond back to us with their capabilities every day," DiPrima explains. "We used to only share that information with a supplier once a week. Now they see it every day, which is critical when you're trying to bring your order and delivery cycle times down below 10 days. We're a lot more collaborative today with our suppliers. Our supply chain is not limited to what happens within the four walls of manufacturing, or even inside of IBM. We extend it out to our suppliers, and even our suppliers' suppliers, so we can have Tier 2 visibility as well."

A HAPPY ENDING

Improving its supply chain visibility has proven to be the key to Cisco Systems' rebound from its forecasting nightmares, which were described at the beginning of these articles. The company's turnaround began with a dramatic paring back of suppliers (from 1,300 down to 600) and the concurrent outsourcing of logistics, subassembly manufacturing, and materials management. All suppliers and distributors can now tap into the same supply chain network, dubbed eHub, and as a result everybody has access to the same forecasts and is working off the same demand assumptions.

Not only does eHub save Cisco millions of dollars by eliminating paper-based purchase orders and invoices, but it also has improved on-time shipment performance. And by applying "analytical rigor" to its supply chain plan, the company can make better decisions sooner in the process, such as what to do if a key supplier can't meet its commitments. By optimizing its supply chain plan, "we find you can remove emotions and bias from decision-making processes," explains Jim Miller, Cisco's vice president of manufacturing operations. "Supply chain has become a science now."

Planning and Forecasting 4 – The Truth Plays Out

As Campbell's Soup learned, no matter how capable and experienced its planners are, their plan is only as good as the information that feeds it. The big "a-ha!" moment at Campbell's Soup came when the S&OP process illustrated exactly how broken many of the company's processes were throughout the organization—from finance to commercialization to label design, custom pack planning, and transportation. S&OP provides a heightened level of transparency to the extent that, over time, as Mastroianni puts it, "the truth plays out." By bringing all of Campbell's business plans into a single, integrated set of plans—the end game of an S&OP initiative—the company was ultimately able to fix a dozen or more major processes.

Sales and operations planning (S&OP) aligns all of a company's business plans (customers, sales and marketing, research and development, production, sourcing, and financial) into a single, integrated set of plans. The end goal is a plan that more accurately forecasts supply and demand.

For instance, Campbell's has improved by as much as 50 percent the weekly accuracy of the item-level signals sent to its manufacturing plants, which resulted in an immediate benefit: The company can now better plan how many trucks it needs to replenish its distribution centers with product. That increased level of accuracy has also paid off by reducing how often Campbell's has to use expedited shipping to make up for not having the right products at its customers at the right time.

Taking it a step further, Campbell's has leveraged its precision of accuracy to provide improved visibility to its warehouses and manufacturing plants. The company has used its long-range planning capabilities to prebuy transportation with some of its carriers. It's also used those forecasts for labor management, specifically in determining when to add extra crews to its warehouses and when to cut back.

There's one last benefit to the best practices Campbell's uses for its supply chain planning: "It makes me sleep real good at night," Mastroianni says. "It's no fun getting your head handed to you."

END-TO-END INTEGRATION

The key to Campbell's S&OP program was being able to integrate all of those different departments and processes into one central plan, and that strategy can be applied in any company in any industry. At computer giant IBM Corp., for instance, integration is not only a key best practice for the company, it's included in the very name of its supply chain organization, the Integrated Supply Chain (ISC).

In 2003, IBM completed an end-to-end integration project that connects all of its business processes and supporting systems into the ISC, an organization employing 19,000 people at more than 50 locations worldwide. The ISC comprises manufacturing, procurement, logistics, distribution, customer ordering, and planning and scheduling— the whole nine yards of supply chain processes.

"There are many factors in supply chain planning," observes Rich Hume, vice president of operations and strategy with the ISC. "Every proposed idea or change at IBM must meet certain criteria. Initiatives must improve customer satisfaction, increase the flexibility of the supply chain, improve economics, and improve functional excellence. Proposals must be executable and include measurable economic results."

Most of IBM's supply chain planning is done internally, involving such departments as logistics, fulfillment, manufacturing, and manufacturing engineering, as well as functional experts in the company's business consulting and business transformation groups.

"In other companies, these professionals are typically aligned with corporate functions like procurement or logistics," Hume notes. "Having them in one organization allows us to take advantage of their expertise within each function, while also benefiting from their integration across the supply chain."

Planning - Part 3 - Soup and S&OP

So how does a company overcome the inherent bias that seems to trip up even the best-laid plans? When Mike Mastroianni joined Campbell Soup Co. in 2001, he saw many of the same cultural inhibitors to good forecasts that had stymied Cisco's planners. Brought in to oversee a sales and operations planning (S&OP) initiative at the world's leading soupmaker, he found a supply chain that had become complacent, focused too much on managing internal costs and not enough on customer service.

"For Campbell's, like a lot of companies, manufacturing was king," explains Mastroianni, vice president of North American planning and operations support. Manufacturing was in a position to second-guess the forecasts, thanks largely to the fact that some people had worked in that department for 30 years and had a historical perspective on how the market fluctuated. Mastroianni's mission, however, was to realign the supply chain to facilitate the introduction of new products. "We had become complacent," he says, and to turn things around, forecast accuracy had to get a lot better.

The average error rate of forecasts in the consumer packaged goods industry is about 50 percent, but Campbell's wasn't going to get too far if it merely maintained the status quo. "We decided to focus in on forecast accuracy, which meant we had to change the behavior of bias," Mastroianni explains. "People used to get their heads handed to them" for missing their numbers, so they tended to over-forecast. As a result, they drove inventories up, as well as the costs of obsolescence, warehousing, expedited shipping, and everything else that was affected by overly optimistic forecasts.

How is a forecast created? No, they're not made up out of the thin air, as some wags have observed. Campbell's, like many other companies, uses a traditional S&OP consensus process, which triangulates between sales, marketing, and demand planning. These three groups get together to agree on a number. That forecast number ultimately ends up going to the general manager for endorsement.

"Instead of aiming for a single demand figure, progressive companies have turned to forecasting a range of potential outcomes," explains Yossi Shefli, director of the MIT Center for Transportation & Logistics. "They estimate the likely range of future demand, and use the low end and high end to guide contracting terms and contingency plans." The goal of this range forecasting is to get companies to widen their planning horizons.

Even after consensus planning, though, the odds are pretty good that a company is not going to hit that number, which makes it all the more important that a system of open and ongoing dialogue is in place.

NO TIME LIKE THE REAL TIME

One element driving Campbell's need for better forecasts is its collaborative planning, forecasting, and replenishment (CPFR) efforts with key retail customers. "We were forecasting at a very high level, based on history," Mastroianni says, but to get to a truly collaborative relationship with its customers, the company had to be able to restate its history more frequently than once a month. Because CPFR requires manufacturers and retailers to share point-of-sale data over the Internet in real time, inaccurate forecasts only hasten the distillation of bad information.

"What fuels S&OP is facts," he observes. That meant Campbell's needed to put Key Performance Indicators (KPIs) in place to hold people accountable, as well as measure improvements in forecast accuracy. Mastroianni's team turned to a real-time forecasting tool capable of creating daily, short-term forecasts with 52 weeks of live data. Being able to forecast in real time allows Campbell's to track patterns that used to go undetected. The system might say, for instance, "Forget about the order today as it relates to your forecast. You need to be thinking about the next seven to fourteen days because, based on this current pattern, your next month is going to look like this," he explains. "Or it might say, 'You're holding on to a forecast that just isn't going to happen. So let it go, and produce to this lower number.'"

At National Semiconductor, the production group meets with the demand planning group weekly to review the forecast. "We gauge the effectiveness of forecasting at a high level rather than on each of our 15,000 chips," notes Si Gutierrez. "We also look at how we're scheduling orders compared to how customers requested them and fix any mismatches." Like Campbell's, National Semiconductor looks at a number of KPIs (e.g., how close the company's production matches up with the forecast) and then analyzes the difference between forecast and performance.

National's supply chain planning starts with an annual plan, and once that's in place, the staff looks at forecasting for each month, planning six months ahead, Gutierrez explains. "Sometimes we're surprised. Something we thought would do just okay goes like gangbusters. So we monitor the plan weekly and can revamp it weekly. Each day, we plan factory starts based on what happened the previous day. This allows us to maximize customer service and optimize inventory to maintain customer service levels."

To be continued tomorrow....

Planning and Forecasting - Part 2 - A Bias Against Smart Planning

Cisco's supply chain planning suffered from a common malady that afflicts many companies—bias. It's a pattern of behavior within a company where different departments focus on their own individual priorities, often disregarding the overall health of the company in favor of propping up their own fiefdoms. A good supply chain plan will fail every time, for instance, if employees are being given incentives to avoid stock-outs, and as a result keep building up the safety stock. Because employees are not being penalized for making too much—in some companies, the only unpardonable sin is to be caught short—the importance of the overall supply chain plan ends up taking a backseat to the size of one's weekly paycheck. When it comes to protecting and keeping their jobs, employees learned long ago that management will rarely punish those who tell them what they want to hear.

In Cisco's case, forecasting growth had been the right answer for more than 10 years, so it seemed the most natural thing in the world to keep going forward, even when it started to look like the boom days were over.

"There's a growth bias built into the business of forecasting," explains Ajay Shah, a former director of Solectron Corp., one of Cisco's major suppliers and one of the companies that got caught up in the undertow when too many unwanted electronics products started to flood the marketplace. "People see a shortage and intuitively they forecast higher." That kind of growth bias leads to the unwritten rule of forecasting demand that says, "Err on the side of needing more, not less."

Forecasts need to make sense, adds Si Gutierrez, vice president of central planning and production control with chipmaker National Semiconductor Corp. A big part of forecasting at National involves an analysis of general economic conditions. He uses the cell phone industry as an example: "If the forecast says we'll need 20 percent more chips, we ask, 'Does that make sense, given current market conditions?' Everyone can agree that's a reasonable expectation for total market growth. The challenge comes in meeting with major players in the industry. Everyone wants to win and everyone's planning for success, so they add 30 percent. But not everyone wins. If you add up all the players in the industry, you might double a realistic forecast," he explains.

Ultimately, in the wake of the economic downturn in 2001, Cisco ended up with far more products than it could ever sell. How much more? The company wrote off $2.2 billion worth of unsaleable, unusable inventory and reported a $2.6 billion quarterly loss. Although Cisco had gained the reputation of being the supply chain poster child for the New Economy, it reacted to the supply chain glitch in a typically Old Economy fashion: The company laid off 8,500 employees.

More to come tomorrow...

Planning and Forecasting - Part 1 - Headed for the Future

Every supply chain program, good or bad, launches from a plan. It's the ability to forecast and analyze product demand, consumer buying patterns, and economic trends that separates the winners from the losers. In reality, any kind of a forecast is going to involve the mystical black arts of predicting the future, a process that inevitably will result in some errors even under the best circumstances. It's not an issue of what happens if a forecast goes wrong—it's more an issue of by how much.

Although the history of supply chain management is fairly recent, it includes some notoriously bad plans—plans so far off the mark that they've become legendary in the "what were they thinking of?" category. The bigger the company, the more spectacular are its supply chain glitches since the ripple effects can extend well past the four walls of the company to include suppliers and customers.

The main reason companies struggle with their forecasts is the fickleness of the marketplace. Try as hard as they might—and they've been at it for centuries—manufacturers and retailers still haven't been able to consistently figure out exactly how much of something consumers are going to buy. Accurately forecasting product demand is probably the single most important—and most challenging—measure of a company's supply chain proficiency. Improving forecast accuracy has gotten a lot of attention, but as meteorologists have always known, you can be right most of the time, but it's the one time you're wrong that gets a lot of people upset.

When analyst firm AMR Research Inc. studied forecast accuracy at several dozen manufacturers, it turned out— not surprisingly—that errors are very much a fact of life within the supply chain. Forecast errors at bulk chemical producers, for instance, range from 10 percent to 24 percent, for a median error rate of 11 percent. That's actually pretty good, though, since consumer goods companies get it wrong from 14 percent to 40 percent of the time, or an average 26 percent error rate. Consider that for a minute: One time out of every four the forecast is wrong. It's even worse in the high-tech arena. The error rate ranges from an outstanding 4 percent to a horrific 45 percent rate (with a median rate of 28 percent). That's right—at some high-tech companies, they're getting it wrong nearly half of the time.

Supply chain planning coordinates assets to optimize the delivery of goods, services, and information from supplier to customer, balancing supply and demand. Supply chain planning solutions allow companies to create what-if scenarios that weigh real-time demand commitments when developing forecasts.

Case in point: A few years ago, Cisco Systems Inc. had a royal doozy of a glitch, centered squarely on the failure of its supply chain plan. As the leading manufacturer of networking routers and switches, Cisco was one of the most influential companies driving the dot-com boom of the late 1990s. In the spring of 2001, Cisco was riding as high as any high-tech company had ever ridden, having reported a profit for 40 quarters in a row. With a culture that literally knew nothing but growth, naturally enough Cisco's planning systems—which were considered state of the art—kept forecasting more of the same.

Unfortunately, the inevitable bursting of the dot-com bubble happened to coincide with a severe slump in the telecom industry, both of which had a direct impact on Cisco's business. The decade-long uptick had finally peaked, and demand for Cisco's products began to slow. Problem was, the company's supply chain didn't seem to recognize "make less this month than we did last month" as a viable plan. Instead, the planners kept following the system's advice to "make more."

Think about what kind of havoc that can play, not only on Cisco's system inventory but on that of its suppliers as well. Cisco had helped popularize the concept of virtual manufacturing, meaning that outsourced (or contract) suppliers were building the routers and switches and then shipping them direct to Cisco's customers. Now, all of a sudden, Cisco's customers didn't want or need any more networking equipment—in fact, they already had too much. But Cisco's supply chain plan kept steadily insisting, "make more." The most important test of a supply chain plan is accuracy, and it became clear that Cisco was flunking that test.

More on this topic to come....