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Supply chain
Think manufacturing closer to home is too costly? These digital tools might just change your mindÂ
Many executives recognize the value of local manufacturing but struggle to shake off the view that low-cost production is better for the bottom line. Exploring their options through simulations and competitive games can lead to better decision-making that boosts profit, fosters entrepreneurship, increases environmental sustainability, and strengthens local economies.
A fashion designer, fabric chemist, product developer, and marketing communicator combine forces to rethink apparel manufacturing. 
This is not the beginning of a tacky joke. It’s the start of an innovative way to design products that can support entrepreneurship. It’s also an opportunity to disrupt the fast-fashion model, which has long had a firm grip on the apparel industry. Rather than producing massive quantities of a wide variety of garments as cheaply as possible, selling them at increasing discounts, and then salvaging what cannot be sold — a model that is neither ecological nor profitable — we can invert it to produce precisely what the customer wants.
While people often assume that producing customized products will be costly, standard quantitative finance tools can uncover the financial value that will be unlocked when we bring the supply chain closer to home. That value means we earn higher profits even though the unit we sell costs more. 
The idea that avoiding overstocking saves enough money to justify paying a premium to postpone the decision about what to produce is not new. Already a decade ago, Lenos Trigeorgis and I made the case that it often turns out to be cheaper to manufacture at home when we add in the value of this reduction in mismatch costs. Avoiding these costs is a real option, allowing us to apply quantitative finance tools to estimate their value. 
Managers generally find these commonly accepted ideas to be intuitive and logical. However, many struggle to take action because the approach differs significantly from the prevailing low-cost mindset. Digital tools can help brave pioneers overcome these challenges and seize the value of these options — while also dramatically improving sustainability.
Let’s first review the basic real options from supply-chain compression, after which we will explore some of the follow-on options that emerge from combining a responsive supply chain with technology and science.
“They produce items based on orders sorted by time sensitivity. Those due immediately are produced first, and those with stable and predictable demand are produced at the end of the day with the lowest priority.”
The cost-differential frontier (CDF)
Our design team has two options for configuring the supply chain. One option involves sourcing materials globally to minimize costs, resulting in a per-unit cost of $10. Not bad for a product that sells for $100 at full price. The catch? The designer must commit to production nine months before it has accurate information about demand. A production plan made once demand is known makes it possible to increase production of items for which demand turns out to be high and to not produce items for which demand turns out to be low. 
The alternative supply-chain configuration organizes workers into small groups known as cells located near key markets. They produce items based on orders sorted by time sensitivity. Those due immediately are produced first, and those with stable and predictable demand are produced at the end of the day with the lowest priority.
The other configuration organizes workers into cells that are distributed around key markets. It allows production to order: The workers begin each production day by producing the most time-sensitive orders, with time-insensitive products with stable and predictable demand produced in the capacity that remains at the end of the day. It takes three months to train a worker for the cell, and the working conditions are good. Clearly, the cellular configuration yields a much more attractive supply chain on many fronts — but the per-unit cost at $20 is twice the cost of the low-cost supplier. This cost differential seems insurmountable at first glance, but is it? Let’s use the Cost-Differential Frontier (CDF) tool to explore the question in detail. A brief tutorial on the CDF is presented at the end of this article. 
Entering the company’s information, the CDF estimates that we should be willing to pay a premium of over 100% to eliminate demand-risk exposure, that is, design the supply chain so that the decision about what to produce can be postponed until demand is known. According to the CDF, local production to order at a 100% cost premium is a bargain. This does not mean instantaneously delivering, but — as happens in a restaurant where diners select their meal and then wait for it to be prepared — starting production based on orders. 
The CDF also shows that most of the option value is reaped by reducing the lead time enough to make it possible to observe demand before deciding what to produce. A relative lead time of one represents the longest lead time under consideration. Many managers expect that a modest reduction in lead time, say by 30% or 40%, will buy them most of the reduction in supply-demand mismatches. But reducing the lead time from one to 0.7 is only worth a cost premium of 12%, far from the 108% warranted for a full reduction of lead time. 
We note in passing that our analysis here is at the product level, whereas demand occurs at the item level. In apparel, we are dealing not only with whether the product itself is attractive to customers, but also with how demand occurs among sizes and colors. Even if the product-level demand volatility is relatively modest, it might be quite high at the item level.
Note as well that this tool only addresses demand-volatility exposure. We are not considering major supply-chain disruptions. The cost premium worth paying to reduce lead time can thus be considered a lower bound, with supply-chain disruptions and the resilience they require causing the warranted cost premium to be even higher than we estimate.
The CDF is a digital tool that allows us to factor in the hidden but unexpectedly large value of real options when designing supply chains. It demonstrates that it’s necessary to invest significantly more in products that are profitable but have fluctuating demand and don’t hold their value well after the demand period. Once our decision-makers have created a streamlined supply chain that can adapt to changes in demand for time-sensitive products, it opens exciting possibilities for entrepreneurial ventures.
“Rather than producing average products to stock, we are now able to respond to the needs of our specific customers — including the ability to customize and add services to the product. ”
Follow-on options
Having established that the real options created when we compress the supply chain so that we can postpone the decision about what to produce to compensate for the lower production cost at the distant supplier, we can now consider some exciting follow-on options that can redefine how we innovate, compete, and serve our client. Rather than producing average products to stock, we are now able to respond to the needs of our specific customers — including the ability to customize and add services to the product.
Opportunities to fully meet customer needs through customization
Imagine a customer who would like to purchase a dress. She contacts our company and provides a 3D body scan that she had made at a local shopping center. She meets (either in person or online) with a product designer. The product designer works with the customer to explore ways to make the dress particularly suitable for her with respect to color, style, fit, and finishings. The designer will study the particular person’s skin tones and suggest dress colors that would bring out the best in her coloring, also offering colors that are somewhat unusual. The designer will check the expected hair color, jewelry, and other accessories to make sure that everything fits together. The design process can even include a limited edition print from a local artist, emphasizing the uniqueness and one-of-a-kind nature of this dress. Isn’t this process too expensive for average customers?
But the design process goes beyond style to comfort. If the person tends to feel uncomfortable after a large meal, the designer can suggest making the waist flexible. The designer and customer can select an appropriate dress length, sleeve design, and neck shape for comfort and to bring out the best in the customer’s body shape. The fabric is chosen for comfort and temperature control. Coatings can be applied for functionalities like wind or moisture resistance. This organization makes it possible to match customer needs to new scientific and technological breakthroughs, such as new coatings — which then encourages scientific research. It also lets us deploy technologies such as 3D scanners that have been available since the 1990s, awaiting the right business case.
Once the details of the dress design have been worked out, the pattern is sent electronically to an automated cutting machine. The fabric is first dyed (only on the pieces that will be cut and assembled), then the kit of pieces is sent to the cell of workers that will assemble it.
While the dress that has been created will cost more than a cheap fast-fashion item, the overall price point can still fit the budget when we consider what is offered. The design and production of the dress can be compared to a trip to a hair salon, where design and execution combine in a service product that tends to have a higher price point than a fast-fashion garment. But there is also an exciting twist to pricing here. When our customer completed the dress order, she was asked how long she was willing to wait to receive the completed item. If she needed it within a week, she paid full price. If she could wait two weeks, she would receive a 20% discount. If she could wait up to six weeks, she would receive a 50% discount. Kits to be assembled are sorted according to priority. This scheduling flexibility makes it possible to produce time-sensitive orders first thing every day and then move down the schedule so that at the end of the day, the cell’s remaining capacity is used for standard, long-shelf-life products. Our digital tools allow us to choose the cell capacity that will be enough to cover all time-sensitive demand on most days — which means that the cell will typically have leftover capacity that can be used for standard products. The cost of the cell (labor and overhead) can be assigned to the high-margin products that require responsiveness, which means that in the leftover capacity we have created a low-cost supplier that is competitive anywhere around the globe. Just as an airline can use the same airplane to offer business and economy-class options to passengers, so our cell can meet the complex demand of our dress customer and also meet demand for standard products that can be purchased off the shelf.
The smooth production schedule yields work conditions that allow workers to focus on quality and serving the customer. It also allows us to distribute manufacturing very conveniently within the local economic community, strengthening this local economy by creating desirable jobs that allow workers to grow and develop.
Good for the bottom line and the planet
Choosing a distant supplier on the basis of a low per-unit landed cost is bad for sustainability. For products with a generous profit margin, it is profit-maximizing to produce substantially more than one expects to sell because the cost of stocking out is higher than the cost of producing an extra unit. As we compress the supply chain to be able to postpone the decision about what to produce until we have good information about demand at the item level rather than just at the product level, we reduce waste from overproduction. The situation where a retail customer loves the product and wants to purchase it in green, in a size medium, but what is available is red, in a size extra-large, and blue, in a size extra small, illustrates how we can stock out and have overstock even though our estimate of demand at the product level was pretty good. Assuming an extended supply chain, supply-chain optimization models typically suggest ordering two — even three — times median demand, with much of it remaining unsold. Unfortunately, unsold apparel often ends up in landfills, notoriously difficult to dispose of ecologically. Therefore, the carbon footprint is dramatically reduced just by producing what is needed and not producing what is not needed.
However, compressing the supply chain produces additional follow-on options that make circularity possible. Let’s go back to our customer with her dress, which has been optimized to make her look great and feel comfortable. She has now worn the dress many times and is in the mood for something new. She mails the dress back to us. We wash off the coatings, melt the fiber, and reuse the zippers, buttons, and other fastenings. The melted fiber can be respun into new fiber. Melting and respinning natural fibers has long been possible, with the constraint that the respun fiber is not as strong as was the original. But new coatings such as those from pulp-and-paper waste are being developed that can be used to make the respun fiber as strong as the original. We can thus completely reuse the material in the dress with almost no waste. 
“We use simulation and competitive games to give decision-makers a chance to explore follow-on options.”
Shifting the focus from low cost to value
When the real options created by compressing the supply chain so that we can produce what is actually needed are included in decision-making, profitability, innovation, and sustainability all increase in ways that encourage entrepreneurship and strengthen the local economic community. But it has turned out to be difficult for managers to move away from their myopic focus on low cost. The CDF is a digital tool designed to start the discussion of moving away from a low-cost focus. Digital tools make it easier for the brain to challenge this low-cost heuristic and explore richer thinking. Our Cost-Differential Frontier has helped managers to understand the real options. 
We use simulation and competitive games to give decision-makers a chance to explore follow-on options. Simulation allows us to expose managers to a situation that is sufficiently complex to provide insight into real life while being simple enough to be able to understand what is going on. As managers use these digital tools in their decision-making, they can improve profitability, competitiveness, innovation, sustainability, and the strength of their local economic community. And they are better able to use the wealth that real options represent to start successful companies with products that precisely meet the needs of their customers.
Brief tutorial on the CDF
The CDF is available at cdf-oplab.unil.ch. Use the button at the upper right-hand corner to Switch to Cost Premium. This version of the tool will tell us the premium worth paying to reduce the time between when we must decide what to produce and when we know what the demand is. 
Under Product Values in the upper left-hand corner, the price for our product remains $100. The long-lead-time cost is the per-unit landed cost at the distant supplier, which is $10. 
The residual value is the value that a unit retains of its original cost if it is not sold during the demand period. An item purchased from a distant supplier enters inventory valued at $10: If not sold during the demand period, the company has estimated that the part of this original $10 investment drops to $4. This occurs partly because of inventory holding costs, and partly because the price reduction required to clear it in the following period carries some risk of replacing sales at full price that would have occurred without clearance. Some units might even need to be scrapped at cost, so the residual value for some units could be less than zero. In general, the higher the residual value, the less-time sensitive the product is. 
The demand volatility describes the distribution of demand for the full relative lead time: How much is demand expected to vary if we have to decide what to produce nine months before we observe demand? The CDF gives some intuition concerning this value by asking the decision maker to compare the median demand to a demand peak. Demand is greater than the median half of the time. If a demand peak that is twice the median demand is experienced in one demand period in eight, that will be consistent with a demand volatility of 0.6. As the relative lead time decreases, the demand volatility will decrease following a square root rule: If we decrease the relative lead time to 0.4 (corresponding to a lead time of 9 months x 0.4 = 3.6 months), the volatility is estimated to drop to 0.6 x (0.4)^0.5 = 0.38. It is this reduction in volatility that produces the option value.
Recall that the design team can choose between two supply-chain configurations, one of which requires that production be committed nine months before good information about demand is available. This nine-month interval between committing production and observing demand is the longest lead time under consideration, so we will designate this as a relative lead time of one, and we will consider the lead times of other options as a percentage of this relative lead time. For example, a relative lead time of three months would be considered to have a relative lead time of 0.333. 
Use the button Add Curve to estimate the cost premium worth paying to obtain the reduction in demand-volatility exposure from reducing the relative lead time. The tool estimates that reducing the relative lead time enough that demand is known when the production decision is made is worth a premium of 108% — more than the actual 100% premium. It is also worth noting that — contrary to the general expectation that the option value lies in modest lead-time reduction — reducing the lead time to 0.4 is only worth a premium of 31%. The real options underlying incremental lead-time reduction tend to be much less valuable than those that come from postponing that production decision until demand is known.
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