Defining supply chain visibility is important. But in your visibility projects there will always be options about how to implement, and therefor questions about tradeoffs. Each option, or variant, has its own cost-benefit proposition. When it comes time to make a decision between the different paths to visibility, your life will be much easier if the options can be compared across common metrics. This should make intuitive sense for those who have been on project steering committees in the past. Comparing apples to oranges is frustrating and makes important decisions open to personal interpretation. By scorecarding the fundamental effectiveness of the visibility provided by each option, we help avoid this pitfall. We also enable ourselves to:
- Compare wildly different approaches in a common framework
- Explain to other persons why one approach is measurably superior to another approach
- Quantify trade-offs when no approach dominates all metrics
The Scorecard Document:
Explaining the Scorecard
What the scorecard tries to provide is evaluating any visibility initiative in terms of its expected visibility outputs in comparison to its cost & timeline inputs. This is a specialized kind of cost-benefit analysis. In order to keep the benefits part of the scorecard realistic and concrete, its suggested that the scorecard be tied to specific business decisions. Remember that no supply chain visibility system or process is inherently valuable. Only when it results in different and better decision outcomes is there a value-adding effect.
The scorecard looks at four categories of visibility effectiveness: Sensitivity, Accessibility, Intelligence, and Decision-Relevance. Let’s examine these individually:
Sensitivity: This category includes all measures relating to how effective a supply chain visibility process would be at capturing data as it occurs in the supply chain. A highly sensitive visibility process is one which very successfully captures supply chain data. The category of sensitivity decomposes into these kinds of metrics:
- Accuracy & Bias
- Depth of Detail
Accessibility: This category shows how integrated the visibility approach makes its data model. High accessibility implies that one start from any point, as any authorized user. It also implies that users can navigate from one object to another object in multiple paths and that such navigation is low-cost and fast. Although accessibility sounds more like theory than a hard metric, it can be well quantified. Look at the two data models below and then consider these example metrics of accessibility:
- Do all data objects connect?
- What is the min, average, median, and max node count between any two objects?
- What is the average effort to move through an intermediary node?
- What is the average time to move through an intermediary node?
The design on the right has more data elements, which would make it stronger in the Sensitivity category. But it is measurably less accessible: it has longer average connections between objects and making those connections is slower and more expensive.
Intelligence: The category “intelligence” refers to the effectiveness of the routines used to process data and render it into relevant information. In many ways, the intelligence behind a visibility solution is the hardest to measure. In general the intelligence of the visibility solution is related to these metrics:
- Ability to recognize an event or state as needing or not needing intervention
- Ease of updating from users, to improve the recognition of important business events
- The ability to learn or develop independently or through implied performance feedback
Decision-Relevance: This category is a measure of how well the visibility solution integrates into business decisions. The decision may be at very low levels and transactional in nature (like selecting from a list of approved vendors or ship-dates) or may be strategic and rare, such as when planning a logistics network after a merger or acquisition. The decision relevance category is fairly easy to quantify using these kinds of metrics:
- Is the visibility process or system required for the decision maker?
- Which party (visibility system or human decision maker) starts the process of making a decision?
- Does the visibility system offer one or more suggested actions?
- Can the visibility system execute any actions selected by the decision maker?
- Can the visibility system fully automate the decision?
Thinking in terms of “Fit”: Many projects to build or improve supply chain visibility get off-track because they focus on functionality as a benefit in itself. This is simply not an accurate understanding of why visibility adds value. Functionality or features of a visibility system or process are only valuable to the degree that they fit into the targeted business decision. As an example, if a visibility process delivers beautiful visualizations of the meta-data, such as by plotting flows of materials and capital onto a map, this is an interesting feature. But if the targeted business decisions don’t have use for the feature, then it’s not going to add value to the company. The degree to which a visibility process or system meets the targeted business needs is something I call its “fit %”. On the high end would be a 100% fit, where the application literally fully automates the decision at or above levels possible by a human being. At the other end is 0% fit, where the system cannot add anything meaningful to the decision maker’s process.
Together, the categories described above (sensitivity, accessibility, intelligence, and decision-relevance) are the appropriate sub-components for a visibility solution’s fit %. By sub-components, I mean that when a visibility option is not a good fit for business decision, its because one or more of these sub-components is not scoring well. I suggest that the fit % replace the typical “scope” category in the classic project management triangle: fit %, cost, and timeline.
Let me raise a quick point before leaving behind the scorecard. Keep in mind that any visibility project can have external considerations in addition to the fit %, timeline, and cost. For example, there may be two options for a visibility project in your company, but one of the options involves an external provider with bad financial health. The risk that the partner goes bankrupt and stops the project is NOT included in the visibility scorecard. This doesn’t mean that the financial health of the provider isn’t important, it just means that the scorecard is only targeted as specific visibility results achieved by the visibility solution. You can and should continue to look at the larger business environment for risks, synergies, conflicts of interests, etc.