Running a large capital program, especially one that deploys a significant part of the corporate annually budgeted spend can create a series of risks for the organization.  A well-thought-out program that includes clear alignment with strategy, leading data, and operational rigor can mitigate many of the risks a large program creates.

1. Strategic Alignment

Few capital programs will “bet the farm” such that if it’s unsuccessful the going concern status of a company is at risk.  However, one-time investment programs in things like power generation, or fiber deployment (a program that Google was famously unable to complete) can be “generational” investments that support future growth and resistance to competition.

This is why the most fundamental exercise is to confirm alignment with the company’s most significant strategic imperatives.  If this connection to strategy is fuzzy or is difficult to explain in simple terms, there is a limited chance, when this program is rolled out across the organization and its impacts are felt down the chain of command, that it will be supported by the leaders, middle managers, and frontline employees whose buy-in is required for success. 

Example:  an important north american TMT player initiated a major investment in upgrading pre-existing copper lines to fiber.  This would result in a more than 100x increase in connectivity speeds for many customers but could consume more than 50% of planned capex for the next 10-year period with additional strain on EBITDA until customers came online.

Without a clear picture (among other things) of the why (the need to expand product offerings to make meaningful increases in revenue-generating units in an oligopoly) and the when (a 10-year capital improvement program that will take the money and top-performing human resources from other programs), the team will never sign up for the work required to make it successful.

2. The Go-No-Go Decision

It’s unlikely that the C-Suite is going to write the program and implementation teams a check for a billion dollars. As such, this is the step ultimately where the most “governance” activity takes place.

There are several important success drivers here and the first is the ability to make a go-no-go decision.  The implication of this is that we have a steady supply of useful information that allows us to assess the use of capital in a leading way, not simply reporting lagging results. The simplest example is where two business cases are evaluated against one another where an option to only commit to one exists.

Back to the TMT company case:  as the owner of a ubiquitous copper footprint, the overall strategic objective is to upgrade a significant proportion of those customers to the latest technology.  What is not confirmed at the outset is the order of those upgrades.

Consider how an assessment might be done:  is the build itself less costly due to more aerial infrastructure (i.e. on poles instead of in the ground), is the community more friendly to the inevitable disruption the builds will cause, are the revenue expectations greater on a per unit basis; and are field forces are in close proximity to the build?

While it seems straightforward to start the aerial build in a friendly community with nearby field teams, other mitigating factors may emerge that make this a less attractive target for capital investment.  The ability to discern the key features of what will drive the most productive use of company resources and to make that decision prior to the deployment of a sizeable amount of capital is critical for success.

How can this be done? Large capital programs (as discussed above) must have clearly discernible outcomes against which success is measured. That will typically mean a large catchment area with a list of possible areas where investment will eventually be required. Having a consistent means of describing this customer area (aerial v. buried; friendly v. hostile; big spenders v. economy buyers) that can then be compared against one another is required.

One final point:  the success of this layer of decision-making means that other teams need to be engaged to ensure that they can provide the needed inputs in a timely fashion. A comprehensive means of identifying which markets will be targeted, a way to assess each against a pre-determined and agreed-upon set of criteria, and then the decision-makers armed with sufficient knowledge to understand the filtered opportunities and act in the company’s best interest, is a recipe for success.

3. The Harvest

As the third and final layer of this governance model, The Harvest represents where several key outcomes are expected.  These are:

●  an assessment of the quality of the team’s forecasting models

●  a framework for capturing and sharing lessons learned

●  detailed financial management which includes identifying opportunities for unit cost savings

●  Increasing levels of precision in spending and reporting which goes from annual to quarterly to monthly (and potentially daily)

Success is measured here by predictability, not over-delivery.  On the surface this seems counterintuitive:  is it not desirable to under-promise and over-deliver?  Not necessarily especially in light of the first key outcome above.  The ability to forecast accurately provides repeatability and the opportunity for automation. If a number of months are observed where delivery targets are met without concurrent deployment of capital or an important change in the delivery model, it’s just as likely that the next few months will see under-delivery.

The team needs to understand that at this stage being able to predict outcomes and share the results with the leadership team is far superior to erratic “win some lose some” outcomes.

This predictability also drives an appropriate competition for capital amongst interested stakeholders forcing decision-makers to choose the highest ROI opportunities (as can be calculated in a myriad of ways quantitatively with important qualitative factors to also be considered).

A useful way to look at this is through the lens of the Plan-Do-Check-Act (PDCA) model first identified by Walter A. Shewhart (d. 1967).  The model essentially drives continuous improvement based on a known set of criteria making it easy to improve existing processes and create new ones as needed.

As we get better at something, the criteria that applied at one time (e.g. aerial v. buried) now assume less significance in favor of other factors that became exposed over the course of the program. As processes continue to be improved, other opportunities emerge which can then be used to save costs or drive work to other more productive options.

A point about cost efficiency programs - whilst it’s true that companies are always looking for ways to save money, doing it well is another matter which requires greater than wishful thinking. If a program is able to generate savings in time, those savings need to be earmarked for something else that is not currently being done because resources are not available.  This means that a living wish list must exist to make the PDCA cycle worthwhile.  Otherwise, cost savings are little more than a shell game; and no one likes a shell game.

A robust capital governance program can be applied to initiatives of varying sizes and complexity. By ensuring that communication and transparency are key factors that remain top of mind for the C-Suite team and the program execution team, a positive result can be expected.