Congratulations, you’ve bought the software. Now what? Do you pick your best data analyst to go and build a model? Or do you focus on building an in-house center of excellence? Both of these options seem alright on the surface, but do they stand up to the realities of building and sustaining a supply chain design competency?
In my two and a half decades in the field of supply chain network design, there are two main issues I see time and again. First, building solid, trusted supply chain network models isn’t easy. Second, most companies struggle to maintain a team capable of creating confidence with the modeling software they’ve invested in.
The rise of bad models
The adoption of supply chain modeling tools has increased significantly over the last 20 years, and the tools themselves are better than they used to be. Along with the orders of magnitude improvements in hardware thanks to Moore’s Law, the embedded third-party solvers have made significant strides, putting more power in the modeler’s hands. This additional solving power is complemented by advanced data handling and visualization tools – all of which are contributing to the increase in usability of these applications. So why aren’t we seeing corresponding dramatic increases in confidence in the outputs of these models?
Unfortunately, as a byproduct of broader adoption, we’re also seeing a corresponding increase in “bad models.” My team at Chainalytics is increasingly brought in and asked to review models built by other consulting firms or an organization’s internal team. In a few of these cases, the issue was that management simply lacked the confidence to implement the recommended solution and needed a “gut check.” The majority of models, however, had deep flaws in the approach, model design, cost or constraint development, or key assumptions that caused errant answers that rightly failed to build confidence in the results.
In our review of these bad models, we’ve found that the overwhelming root cause is insufficient experience — either in the design of the model structure itself or with the techniques that support the broader end-to-end analysis needed for a specific requirement (e.g., transportation costing, capacity modeling, multi-period models, or inventory optimization). To be clear, these failures aren’t typically caused by the user simply not knowing the software or the tool not working properly; they’re caused by inexperience with applying these tools and techniques to real-world problems.
If your organization lacks confidence in its network models, look for a partner like Chainalytics who can help you get started the right way or walk alongside you in the journey.
Keeping your team together
The biggest hurdle in sustaining a team that can consistently deliver high-quality network analyses and answer “what if” questions is a critical mass of talent. Given the current demand for these data-driven skillsets, turnover within these teams can be distressingly high. It doesn’t help that people that are good at this type of work tend to be good at lots of things. They’re often pulled in many directions internally, promoted, or voluntarily leave for a competitive role at another company. In our experience, a sustainable team requires five or more people so it can withstand inevitable turnover, provide good mentoring, a meaningful career path, and a second, third, or even fourth set of eyes to ensure a well-developed model.
Large organizations – usually those with $15+ billion in revenue – can usually justify big enough teams and attract and build the expertise required to support doing good work. Furthermore, companies of this size can generate many complex business questions that quality modeling teams find engaging and rewarding. Companies like Coca-Cola, Cargill, and Starbucks have modeling teams of 10+ people, and they work hard to maintain them. We not only help firms this size build and keep their internal teams, but we also support them with specific project work. For instance, if there is a need that exceeds the team’s bandwidth or a unique modeling situation such as a merger or divestiture.
In contrast, companies under $15 billion or so in revenue don’t fare nearly as well. Teams at these companies are often just 1-3 people and often lack adequate peer review, mentoring, meaningful career paths, and competitive compensation – stifling their ability to build confidence in their analytics. Small, fragile teams can often struggle to attract talent in the first place and then are further challenged by inevitable departures or promotions. It’s not unusual for teams of this size to collapse, often due to attrition or a lack of management support, within the first year or two.
Furthermore, without stable peers or mentors to foster sound practices, these teams can create poor models and not even know it. This phenomenon has become known as the Dunning-Kruger Effect – “Many people…underperform simply because they don’t know that they could be doing better or [don’t know] what really great performance looks like.”
For companies that want ongoing supply chain network design capabilities without the challenges of adding and sustaining headcount, consider Chainalytics’ managed services program for Supply Chain Network Design. This unique approach combines your internal business experts with Chainalytics’ supply chain design experts and best-in-class technology capabilities to create a strong composite team.
It matters
Twenty-five years ago, supply chain models were used every one to three years to answer basic infrastructure questions like where the next warehouse should go, etc. Today, these tools are used far more frequently (weekly or monthly in some cases). They’re also applied to more planning areas like plant and line-level manufacturing decisions, inventory optimization including build-ahead strategies, and the deployment of private fleets.
Having confidence in your analytical teams and the models they build is one of the key governing constraints on our progress toward the vision of a near real-time “digital supply chain twin.”
Avoiding the pitfalls of bad modeling requires an often underestimated depth of knowledge and personnel. Reach out to us and find out how Chainalytics’ experience and know-how can help your company develop the capabilities it needs. Our combination of top supply chain talent, proven methodologies, and exclusive market intelligence consistently puts our clients ahead of the curve.
Steve Ellet leads the Supply Chain Design consulting practice at Chainalytics as Sr. Vice President. His team focuses on applying large-scale optimization and simulation models to answer strategic and tactical network questions such as facility site selection, capacity planning, network/facility rationalization, capital expense justification, M&A, and manufacturing planning.
