The correlation between time-to-market and profitability is well known. In fact, a report published by McKinsey claimed that on average, businesses lose 33 percent of profit by launching a project on-budget but behind schedule by six months. In comparison, the study found that launching a product on-time, but 50 percent over budget, only resulted in a 3.5 percent loss in profitability.
Now, due to the rise of the digital economy, the correlation between profitability and time-to-market is becoming stronger every day. Here are three ways that supply chains are playing a major role in determining who gets to market fastest:
Imagine two opposing supply chain networks. The first is centralized, with all aspects of production confined to one location with easy access to affordable labor and global logistics networks. Upon completion, new products are shipped across the world to different end-markets where they are ultimately consumed. This option is perceived as having the lowest risk, complexity and bill of materials (BOM) cost.
A distributed supply chain model, on the other hand, leverages multiple manufacturing centers located much closer to end-markets. Tighter proximity means faster time-to-market as well as enhanced flexibility to scale production based on local demand. Conventional wisdom states that the distributed model results in higher cost, but that perception is typically associated with BOM cost (cost before freight, duty and shipping time), not landed cost (cost including freight, duty, and shipping time) -- and the difference is all too often ignored when designing a supply chain network. The cost of inventory, for example, sits inside of shipping time which is not factored into the BOM cost of a product.
“By financing idle inventory sitting on a cargo ship in the middle of the Pacific ocean, you’re losing money,” says John Caltabiano, Jabil Vice President of Global Supply Chain.
“A centralized supply chain might produce a really low factory cost but it could be incredibly costly to land it.” For example, when producing a heavy physical product, it may be prudent to manage certain portions of the assembly in low-cost locales while performing the final assembly in a location closer to the end-market, where freight cost is much lower. “In the vast majority of cases, a three-day truck haul from Mexico to North America is preferable to a seven-week ocean transit from China to North America,” says Caltabiano. “If the customer needs that product in three days and requires an air shipment from China to North America, the costs are going to be very high.”
The cost of inventory represents just one of many variables that should be factored for when designing a supply chain network. Network optimization tools now allow decision makers to balance these competing elements and validate their assumptions faster than ever before. By creating networks that reduce time-to-market and optimize for lowest landed cost, businesses can hit their profitability targets.
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When brands create a new product there is understandably a heavy focus on industrial engineering and product management. Throughout the new product introduction (NPI) period engineers focus on form-fit, form-function and form-factor while product line managers narrow in on sales strategies and market trends. However, one of the big blind spots in many NPI periods is a curious lack of attention given to the sourcing, production and distribution of the product. Unfortunately, this omission can create major issues when the product reaches volume production.
“It’s not all that uncommon to see supply chain issues stop production lines,” says Ross Valentine, Jabil Senior Director of Product Line Management. “Sometimes it’s a part that has gone end-of-life, other times it is just poor quality from a supplier, but a lot of the issues that we run into could have been mitigated if the right people had been involved earlier in the design stage.”
The concept of injecting supply chain expertise into the NPI process is called design for supply chain and this principle is gaining mindshare due to its value creation and risk mitigation capabilities.
When launching a new product, it’s critical to get an early understanding of which parts within a product do and don’t have long lead times. This allows supply chain specialists to ensure that every part is always available when the production line requires it. To reduce lead times, a brand could introduce additional suppliers, swap suppliers or even replace parts with functional equivalents with higher availability. For the supply chain organization, getting a head-start is even more important when leveraging emerging technologies. For example, if a company planned to produce bacteria-powered clothing that expands or contracts in response to humidity levels, could they find a supplier? Does a supplier like that even exist today? Probably not. In this situation, a supply chain team would need as much time as possible to begin developing a supplier ecosystem that could support such an ambitious project.
After performing an initial network optimization exercise to enable faster time-to-market and lowest landed cost, supply chain teams then engage suppliers that can meet the agreed upon variables. For example, building a large part of the integration in Mexico (rather than China) may require establishing new relationships with sheet metal, plastics or packaging suppliers in Mexico. The domain expertise of manufacturing partners as well as design for supply chain tools make this process much easier. While these activities may seem like common sense, legacy organizational silos often impede cross-functional collaboration. Initially, this transition can be as simple as ensuring your supply chain team has access to some type of product structure during the design stage.
“Whether it’s a prototype bill of materials or just the back of a napkin, make sure to get it in front of your supply chain team as soon as possible so they can start analyzing the data and building an understanding of which suppliers are available,” says Caltabiano. “The ability to collaboratively work towards a common shared interest is what allows teams to bring a product to market on time, before the competition, without shutdown and stockout situations.”
As organizations mature their design for supply chain practices, they can begin utilizing tools like project comparisons, revision comparisons and bill of materials analysis. These stress tests allow for the identification of potential risks within the product plan and provide extra assurance throughout the NPI process.
A traditional forecast is essentially a prediction of how many products a brand thinks it will sell at specific time intervals in the future. However, since it’s impossible to predict the future with 100 percent certainty, the idea of an accurate forecast is, unfortunately, a fallacy. For example, a brand may predict that they will sell 1,000 units over the coming year (which may ultimately turn out to be very accurate), but might not have an understanding of how many products they will move on March 3rd of 2017 -- much less what featurization requirements (red buttons vs. green buttons) the product may have on that specific date.
This creates a problem because the available product to be sold on March 3rd is determined by a number that could have been loaded into the production schedule as far back as November of 2016 -- nearly five months earlier. In order to compensate for this uncertainty, companies over-plan, hold large buffer inventories and continually push and pull on the supply chain.
“The old model of placing a forecast on the system, driving demand against that forecast, and then expecting consumers to buy whatever you predicted 25 weeks ago just isn't going to work in today’s world,” says Caltabiano. “Demand changes, markets change, fashions change and the ability to react to those changes quickly is really what makes companies successful.”
In order to drive production schedules and product availability, many product brands and their manufacturing partners are turning to the demand-driven supply chain (DDSC), an advanced planning methodology.
The first characteristic of the DDSC is that it responds to consumer demand in a way that it is directly linked to the end-consumer. Rather than relying on a traditional forecast, a DDSC can see exactly what is being consumed and respond to it. A DDSC also uses advanced analytics, statistics and historical data to define the levels of inventory that are required in order to meet a defined service level. The goal of a DDSC is to hit a service level, not to drive an accurate forecast -- because as we established earlier, there is no such thing as an accurate forecast. This allows product brands to move with enhanced speed and flexibility.
In a world where there is no second place, eliminating redundancies, bottlenecks and waste in the supply chain can mean the difference between winning and losing. And product brands looking to get to market faster are rapidly building their competencies in network optimization, design for supply chain and demand-driven supply chain. For businesses that need to stay ahead of the speed of the market, strengthening in these areas can pay big dividends.