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The Perils of Vertical Thinking
Different goals necessitate different training regimens. Running the Boston Marathon requires stamina and lower body muscular endurance, whereas doing a 5K race places a greater premium on speed and cardiovascular capacity. And of course, any sort of running event requires a completely different kind of fitness than a tennis match.
However, we don’t carry that mindset over to the business world. Irrespective of the industry we’re in, the type of products or services we provide, or the kind of customers we serve, our businesses are organized into pretty much the same kind of functional silos—sales, marketing, finance, product development, customer service, HR, IT, etc.
Dan MarkovitzOf course, there’s nothing inherently wrong with an organization built around functional silos. But that structure does have a real and significant consequence, because the siloed org chart profoundly shapes our thinking. It causes people to think more about what’s best for their department than about what’s best for their customer. Put another way, it makes people think “vertically” instead of “horizontally.”
Just like a fitness program needs to be oriented towards a specific goal—running a marathon, rehabbing a specific injury, playing better tennis—a fit organization orients around the customer and her needs. A fit organization thinks horizontally, not vertically.
The Perils of Vertical Thinking
Service quality suffers in siloed organizations. For example, in most companies, customer service departments are evaluated on call length. Shorter phone calls mean that the company needs fewer people to answer the phones—which means lower costs in the department. Other companies outsource customer service to India or some other low-wage country in order to reduce department costs, even if that results in lower levels of service.
Siloed organizations also tend to struggle with internecine battles caused by poorly aligned incentives. For example, one of the core measures of performance in a credit department is the number of “days sales outstanding,” or DSOs. This metric shows how long it takes customers to pay their bills. If the DSOs for a customer get too high, the credit department will put the customer on credit hold and refuse to ship merchandise. From a strictly financial perspective, this makes sense. But different types of customers may have different sales rates. Holding these two types of accounts to the same payment standards will inevitably result in slower sales and a frustrated sales force.
Horizontal orientation enables—even encourages—the company to optimize its activities for the benefit of the customer.
Siloed metrics such as DSOs destroy intra-company teamwork as well. The financial executive is measured and rewarded in part on reducing DSOs, which leads her to tighten credit. By contrast, the sales executive is measured and rewarded on increased sales volume, which leads him to create dating programs that increase the DSOs.
A Better Alternative: Horizontal Thinking
A fit organization focuses horizontally, toward the customer, resulting in higher quality, better service, faster response and happier customers. Going back to the athletic metaphor, this is equivalent to planning a workout regimen with a specific event in mind, rather than focusing on individual muscle groups without consideration for the ultimate training goal. Horizontal orientation enables—even encourages—the company to optimize its activities for the benefit of the customer, and not the department manager or VP.
A company that thinks horizontally considers the types of customers it serves, and breaks them down by their different needs. Each of these customer types has different product and service requirements, which can be best addressed by the creation of separate processes tailored to their needs.
Case Study: Asics
In 1992, Asics, the athletic footwear company, hired me to address a major problem. The U.S. subsidiary of this Japanese firm was on a four-year roll, nearly tripling revenue to $250 million, primarily by increasing volume in large chains like Foot Locker and FootAction. But ominously, sales through the specialty running channel suffered. Asics slipped from the top spot in this channel to No. 3. Although the sales volume from running specialty retailers only accounted for about 5% of the company’s business, these shops were critical to Asics’ brand image.
This distribution channel was abandoning Asics for competitors because the company wasn’t serving their particular needs. Policies, processes, and systems that worked for chains with 1,000 storefronts that ordered 100,000 pairs of shoes at once didn’t work for a single operator that ordered 72 pairs at a time.
Small Guys Have Special Needs
Big chain stores are sophisticated operations that manage their cash and inventory professionally. Foot Locker, for example, places all their orders before the season starts, schedules delivery throughout the season to refill their stocks, and strategically holds extra inventory at their distribution centers as needed. They pay their bills on time, and when they have a problem, their sales clout gets them fast attention.
Small running shops are entirely different. They don’t have the sophistication or the cash flow to purchase enough inventory, and they don’t pay their bills very well. As a result, they rely on vendors to carry enough inventory to “fill-in” their stock with overnight shipments, and hope they’re not on credit hold at that time.
In 1992, the Asics' vertically oriented organization was terrific at meeting the needs of large chains, and terrible at meeting the needs of the small guys—and that’s why the running shops fled to Saucony and Brooks. Those smaller competitors had less business with the giant chains, and could—or at least chose to—pay more attention to the running shops.
For Asics to address the unique needs of the running specialty shop, virtually every department in the company had to reconsider the way it operated and the way it measured performance. They needed to orient their service around these accounts.
How Asics Reconfigured Services
Sales: The sales department typically gave discounts to customers strictly based on volume. It was reluctant to give any other discounts, because senior leadership evaluated the sales team on the overall starting gross margin they maintained. Running specialty accounts couldn’t get those discounts because they couldn’t order enough products to meet the threshold. Asics created unique discount levels based on their smaller size (for example, 72 pairs of shoes, not 288 pairs). It developed specific sales programs and incentives for them if they would carry one additional SKU, or increased an order of a particular model from 24 pairs to 36. Finally, to help them with their cash flow, Asics gave them special terms and dating: instead of Net 60 days, it gave them Net 90 on advance orders, and Net 60 on fill-in orders.
Credit: One of the KPIs for the credit department was days sales outstanding (DSO). By any measure, most running specialty accounts are terrible credit risks compared to large chains or mass merchants, which led the credit department to frequently put these accounts on credit hold. Asics changed the threshold at which these accounts would be put on hold and allowed them to pay off their outstanding invoices more slowly than other accounts. The credit department even segregated these accounts when calculating overall DSO metrics.
Customer Service: Before the company started looking at the running specialty value stream, Asics used all the traditional customer service tracking metrics in evaluating the reps—length of call, sales dollars per incoming call, etc. Specialty running accounts are terrible for these metrics: Retailers take up a lot of the reps’ time, even if they’re just ordering one or two pairs of shoes. Asics formed a special customer service team that handled only running specialty stores. Reps on this team weren’t evaluated on average call length, removing the pressure on them and allowing them to provide the best possible service, irrespective of the sales dollars. And when they had free time, they would make outbound calls to the stores to check on inventory, place additional orders and deal with any other customer problems.
Purchasing: Like most companies (especially those that make size- and fashion-related products), the sales and finance teams were evaluated on their control of inventory levels, often leading the company to stock out of shoes that were in high demand. That made specialty retailers reluctant to rely on Asics too heavily, because the company often couldn’t provide fill-in orders. To allay their fears, Asics created a shoe bank each season that held extra stock of the “meat sizes” (8, 9, 10 and 11) of two core models. This bank was reserved for the exclusive use of the specialty running shops—no other class of retailer could poach from it. And critically, this inventory received special accounting status so that the sales and finance teams weren’t penalized for higher levels.
Shipping: The logistics team liked to bring in the new season’s shoes at one time and ship them at one time. This meant that specialty run shops received shoes at the same time as the big chains—which they hated, because the big chains discount shoes by $5 to $10, a discount that the running shops, with their thinner margins, couldn’t match. To help them compete better, Asics began shipping one key running model to these stores a month before the big chains got it. It was more work for the warehouse, but it gave specialty retailers one month of selling without major competition.
Metrics: To emphasize the new, horizontal focus on this specific customer, Asics abolished most of the internal departmental metrics related to them—and when they were maintained, like DSOs or inventory cost of the product in the shoe bank, they were calculated separately. The company also added an overall satisfaction metric for the running specialty program as a whole, and tracked sales through this distribution channel in aggregate, and the sales per storefront.
The results: Within one year, Asics recaptured the top position within this distribution channel, a position it held for the next 19 years.
Despite the advantages and intuitive logic of orienting around customer types, disbanding functional silos is a heavy lift for most companies. More than a century of business tradition has led to the primacy of vertical organization, making it exceedingly difficult to orient in any other way. But as the case of Asics shows, it’s possible to think horizontally, even in a vertical organization.
Monday Morning To-Do List
Here are some steps that will help you shift from a vertically oriented organization built around departments, to a horizontally oriented one built around the customer.
- List your major customer types and points of differentiation. Think about differences in size, geography and distribution channels. Consider how, when and where they use your products or services.
- Interview representative customers for each type. What do they value most? Why? Compare these answers across the different types you’ve identified.
- Examine everything you do in each major process through the filter of whether it serves the customer or your own internally focused metrics. What waste can you see? How can you shift focus from working vertically to working horizontally?
- Create three to five metrics that reflect what’s important for each customer type. (In general, these metrics will be holistic, and won’t tie into the metrics you use for your internal measurements.)
- Create a “value stream manager” for each customer type. Their responsibility is to advocate for the customer, not to the departmental head. Remember, you don’t have to go as far as Menlo Innovations or Aluminum Trailer and completely get rid of silos. Asics maintained its functional silos, but gave me authority and responsibility to advocate for one type of customer. Note that if your customer base doesn’t break naturally into customer types, you may benefit from creating value streams around product families. (For example, if you’re a manufacturer with production split among multiple plants, it may make sense to organize around product families.)
Dan Markovitz is president of Markovitz Consulting, a firm that helps organizations become faster, stronger and more agile through the application of lean principles to knowledge work. He is a faculty member at the Lean Enterprise Institute and teaches at the Stanford University Continuing Studies Program. This article is adapted from his forthcoming book, “Building the Fit Organization.”

We have all made decisions, whether in our personal or professional lives, based on imperfect information. How can we manage that risk and improve business outcomes? One answer is a statistical method called Monte Carlo simulation.
Monte Carlo simulation is a problem-solving technique used to approximate the probability of certain outcomes. This method simulates thousands of trial runs, using random values for each factor, based on predetermined probability distributions. Knowing the probability of certain outcomes provides better insight and leads to informed decision making. While based on statistical methods, with today’s software, Monte Carlo can be much easier to learn and apply to your business context.
Monte Carlo simulation is applicable to an array of scenarios, including:
- Business case development and ROI modeling
- Risk quantification associated with new product rollout and development costs
- Forecasting future sales based on uncertain market conditions
- Determining staffing requirements under various scenarios
- Estimating volume to produce based on downstream demand
- Estimating project completion date based on duration times for each activity
- Estimating the total cycle time for a process based on task durations for each step in the process
For example, let’s look at a core process within a paperboard manufacturing facility where paperboard is made from a pulp derived from wood chips. For obvious reasons, it is critical that wood chips are always available for the pulp mill.
Pulp consists of a mixture of both hardwood and softwood chips. As depicted in Figure 1, some chips are purchased and sent directly to storage, and others are produced at the facility. To produce wood chips, logs are first debarked and then ground into wood chips, which are then sent to storage. Prior to use in the pulp mill, they are run through a screening system to ensure the pulp mill gets the proper sized chips.
Figure 1: Paperboard Manufacturing

Conducting the Analysis in Three Steps
Step One: Define the input variables or assumptions
This could be the variables on a process map, or simply the input variables on a spreadsheet. We’ll need to determine the probability distribution for each. If historical data exists, most statistical software packages can identify the best-fitting distribution. If data does not exist, it may need to be collected or gathered through input from knowledgeable staff. Often, a triangular distribution works very well by simply asking for information around the best-case, typical-case and worst-case scenarios.
Step Two: Determine the forecasted metric
In this example, the key metric is the ratio of available chips to the daily demand of the pulp mill. Available chips, in this case, are those purchased, produced, and currently in storage as shown on the process map in Figure 1.
Step Three: Run the simulation
There are many software packages to run Monte Carlo simulation. The software used in this example is called Companion by Minitab. To ensure sufficient availability, the organization would like to have an average of 3 times the daily pulp mill demand for each type of chips, both hardwood and softwood, but no less than 2 days and no more than 4 days. Carrying too much inventory drives up cost and can degrade quality. Analyzing only the hardwood variety, Figure 2 shows the organization can expect an average supply of hardwood chips to be 3.9 times greater than the daily demand of the pulp mill and can expect to have and inventory of greater than 4 times the demand almost 40% of the time. Assuming an average hardwood pulp mill demand of 4200 tons per day (TPD) at $60 per ton, the pulp mill consumes about $252,000 per day of hardwood chips. Carrying inventory of more than the upper specification limit of 4 times the daily demand results in a significant opportunity for cost reduction. The question now becomes, “Where do we start with improving the process?”
Figure 2: Simulation Results

Improving the Process
The next phase in the process is called parameter optimization and refers to identifying the optimal settings on inputs that can be controlled. An extruding process, for example, may have to operate at no more than 2500F and no less than 1900F before scrap product is produced. In this example, there are three input factors that can be directly controlled: 1) purchased chips, 2) log throughput and 3) beginning inventories. All of these can be optimized simultaneously or one at a time to bring the ratio of available chips to daily pulp mill demand closer to the target of 3 days with less excess inventory.
To illustrate parameter optimization, one input factor was chosen. Since purchasing chips from the outside is expensive, this was the obvious first choice. Assuming there is a minimum of 200 TPD that must be purchased to maintain favorable pricing, but no more than 500 TPD purchased, Figure 3 shows the results of parameter optimization, keeping all other input factors constant.
Figure 3: Parameter Optimization Results

Figure 3 shows a decrease in excess inventory from almost 40% in Figure 2 to about 15% and an average inventory of hardwood chips of 3.5 times the daily demand of the pulp mill, resulting in a 60% reduction in excess inventory and a significant cost reduction.
We live in a world of uncertainty and virtually every business decision has some level of risk. Most decisions have a multitude of possibilities, but knowing, understanding, and quantifying this uncertainty increases the chance of favorable outcomes. For process improvement practitioners, Monte Carlo simulation is an important tool and method to reduce risk and facilitate those decisions grounded on data and evaluation.
Mark Sidote is a Principal with Firefly Consulting, a firm specializing in innovation and operational excellence. Mark has delivered multiple lean transformation, continuous improvement, and process redesign effort. He has significant experience in Design for Lean Six Sigma, Lean Transformation, and Lean Six Sigma deployment.





