By Ross Valentine | Supply Demand Chain Executive
Clearly and compellingly articulating the risk factors, mechanisms and value of a supply chain is a core challenge that any product or logistics company faces. This is because supply chain management is a complicated business comprising any number of complex factors. These include internal challenges, such as the need to support product customization or personalization, meet aggressive product launch dates, and shorten product lifecycles, as well as external factors like tough geopolitical conditions, and mass globalization and exploitation.
Complicating matters further is that functions or departments within a company’s supply chain often operate in very distinct silos—such as design engineering, demand planning, procurement, manufacturing, logistics and others—that all measure success by very different criteria. Occasionally, silos may share common metrics and performance indicators. Yet more often, people within each silo do not collaborate, share or even communicate their metrics with others outside of their function.
Admittedly, advanced planning systems (APS), traditional supply chain control towers, data warehouses and single-instance enterprise resource planning (ERP) systems all provide platforms for cross-functional data integration and supply chain collaboration. The problem is that all of these systems are extremely process-oriented and, by themselves, fail to convey the story of the supply chain. More importantly, they fail to communicate the holistic value that the supply chain provides to end consumers. The systems mentioned above are, for the most part, transactional and tactical in nature—facilitating the sales and operations planning (S&OP) process, orchestrating the weekly forecast cycle or scheduling customer orders to the manufacturing line. On top of that, their presentation layers are designed to deliver reports, metrics, historical trends, or at most, dashboards.
What is needed beyond this, however, is a sense of what value the supply chain is actually delivering. Imagine, for example, a platform that was able to report: Customer on-time delivery (OTD) increased to 98 percent week-on-week because manufacturing operations and demand planning worked together to identify a persistent part shortage and engaged procurement to change to an alternative supplier—one which, according to the engineering team, was able to supply an alternative part that had much less engineering change order (ECO) activity, and therefore, less risk.
That is a story of value. It is also a story of cross-functional supply chain collaboration.
It is increasingly important for original equipment and contract manufacturers to provide their functional departments with the means to communicate and collaborate more effectively in order to deliver value in the supply chain, and ultimately, enable virtually any employee to articulate that value to the end customer.
Design engineering designs the products, demand planning generates forecasts, procurement sources the materials, manufacturing builds the products and logistics delivers them to the consumer. Each function within this overly simplistic supply chain model brings its own value. Meanwhile, various data platforms help orchestrate the flow of materials through the supply chain from a transactional or performance standpoint. But how do you articulate a compelling story of supply chain value?
First, it is necessary to step outside of functional silos. As most supply chain practitioners know, it is extremely difficult to generate an accurate demand forecast—ask any planner for a contract manufacturer that works with original equipment manufacturers (OEMs). If the demand planning function relies solely on forecasts received from the OEM, or the sales and marketing team, for example, then the result would likely be significant excess inventory. Forecasts need to be conditioned before they can accurately drive procurement of materials. Conditioning could consist of getting data points from manufacturing—or, more specifically, shipping via the shop floor, warehouse management system or ERP system—regarding historical shipments. This would provide a clearer grasp of how aligned the forecast is with historical patterns, assuming those patterns continue to provide an accurate predictor of future demand. This simple and perhaps more traditional approach to conditioning demand forecasts provides a basic example of cross-functional integration.
Pulling disparate data points from multiple functions into a common framework enables an overall assessment, which will lead to an insight that, in turn, provides a clear action for improvement. An example is assessing supply chain risk. Instead of looking at characteristics, such as sourcing, lead time or days of supply (DOS) separately, look at them collectively in a framework that comes up with an overall risk score. Not only does this help bubble up the parts, products and suppliers that contain multiple risk factors instead of just one, but it also synthesizes efforts between the functions instead of promoting less effective duplication.
Essential to articulating supply chain value is the task of generating a set of killer analytics that connect relevant cross-functional data points together. The key is understanding the relationships between those data points—what is statistically relevant, where are there causal relationships, where are trends and so on. Sometimes the least obvious data associations generate the greatest insights.
For an analytic to really gain momentum and importance, it needs to offer an improvement or incentive to multiple key stakeholders. Take an alternative sourcing analytic, for example. Wor,king with a part that has only one known supplier (i.e. a sole source) represents a significant risk in the supply chain. So, implementing an analytic that identifies the same form, fit and function part from a different but preferred supplier is hugely valuable to the supply chain since it helps mitigate risk. It also benefits procurement by offering the opportunity to use a higher leverage source. These qualities promote adoption of the analytic, and therefore, its success.
One reason supply chain companies neglect to properly implement analytics is that users do not understand what an analytic is telling them or what action needs to be taken. Supply chain management is complex enough. So, complicated, over-engineered or confusing analytics fail to promote action, or worse, they promote the wrong action. It is important to present analytics in a form that anyone can easily understand. The goal would be to enable anyone in the company to convey the value of the supply chain to customers.
Overwhelmingly, from personal experience, we found that C-level executives prefer analytics to be presented in visual formats that provide compelling insights, especially if those insights are proven and already delivered value. Further experience shows that most users relate to color coding to relate status—specifically reds, greens and yellows. So developing visualizations that apply this code can promote more clarity and actionable understanding.
In addition, to be useful, analytics must intuitively illustrate not only the supply chain’s current state, but also how it changed over time and how it may change going forward. In other words, time-phased visualizations are of particular value. Lastly, visualizations should entice users to explore the insights those visualizations display and lead them towards specific actions.
We think and communicate narratively. So, it is best to present the status and changes in a supply chain that way. This story is told, in part, by the user interface that integrates the analytics, insights and visualizations. This is the layer that organizes the information in a way that translates easily to the user. One interface once enabled a human resources representative to tell the story of a supply chain to the chief operating officer, demonstrating that, given the right tools, even non-supply chain practitioners can clearly communicate this complex story. This is a true acid test of successful analytics and user interface tools.
The key elements of the story should focus on where the supply chain is currently, how that status evolved and what the company is doing to improve that status. Typical dashboards and portal applications are not organized in this way. Conversely, they are often organized as a set of unrelated or, at best, closely related graphs or charts. They offer little or no narrative flow.
Jabil’s customer-centric teams are sharing new approaches to supply chain management with their customers, and communicating what value is being delivered and how. Jabil prides itself in a comprehensive and capable supply chain risk management platform. In the past year, some of the biggest brands in the world came through Jabil’s physical Control Tower in St. Petersburg, Fla. to learn the value Jabil delivers to their supply chain through its 170,000 storytellers.