Leverage OKRs to Achieve Breakthrough Portfolio and Investment Performance

Key Practices and Principles for the Demand Side of Portfolio Management

In previous installments of this series, jointly produced by Adaptivity and Workboards, we discussed high-level strategy realization processes and how implementing them with OKRs can dramatically increase success. The last installment focused on optimizing the supply/capacity side of portfolio management structures and processes. In this, the fifth installment, we’ll discuss practices and principles for achieving high-performance adaptive portfolio management focused on the demand side of portfolio management, followed by a discussion on optimizing the supply/demand matching function that assigns finite capacity to infinite demand.

In the last (fourth) installment, we discussed the imperative of a stable and measurable production capacity on the supply side of the portfolio equation and as an example of how not to do it, called out some all-too-common IT software development work-management anti-patterns like:

Aggregating large aggregations of high, low, and unknown value work into projects.

Time-slicing developers as fungible widgets shifting between multiple projects.

Optimizing to maximize utilization instead of efficiency and effectiveness (e.g., throughput, quality, and value).

Organizing “teams” around technical components or functional skills instead of cross-functional teams delivering customer value.

Pushing work into an overloaded delivery organization instead of letting teams pull work as capacity becomes available .

It bears repeating that traditional project management approaches depress value delivery in software development. In addition to messing up the efficiency and measurability of the delivery function, the project construct undermines value delivery on the demand side by delaying delivery of high-value features by bundling them with low-value or unvalued features. The opportunity cost of delaying high-value work items in the demand queue while awaiting completion of a project’s low-value items undermines aggregate portfolio returns.

Project to Product – A Shift in Mindset

There is a growing shift from “project” to “product” thinking. Among many differences, product thinking seeks a continuous flow of value delivered over time, embracing continuous learning from experiments, metrics, and feedback. Higher value features constantly displace lower value features as backlogs are dynamic and fluid. This contrasts with projects, which are characterized by defined-up-front inflexible requirements delivered in one or a few massive releases, and then the project and value delivery end.

Obviously, some types of products lend themselves better to incremental value release than others (e.g., a partially implemented medical device might not be valuable), but even big capital projects like bridges and ship construction can benefit from incremental delivery by prioritizing the riskiest or most variable components of the project as early as possible and embracing prototyping along with early and frequent integration. The astonishingly accelerated three-year development of the first large-scale-production hybrid automobile, the Toyota Prius, proved the efficacy of accelerated incremental and concurrent product development processes.

Defining and Shaping Portfolios

Organizations often struggle to design and delineate portfolios because it’s rarely possible to define portfolios that deliver every desirable goal with no tradeoffs. Usually, the overriding portfolio management goal is to maximize strategic and financial returns from portfolio investments. We want portfolios to support clarity of purpose and desired outcomes and be large enough to contain valuable and impactful work but also small enough to enable flexibility and transparency. Other goals for defining optimal portfolios are typically to:

1. Speed up and reduce the cost of investment decision-making.

2. Reduce unhelpful competition and conflict in decision-making.

3. Provide enough competition between initiative candidates to force higher returns through decomposition and tradeoffs.

4. Give stakeholder executives more autonomy and control of “their share of the pie.”

5. Enable efficient accountability and oversight.

As with any decision rife with tradeoffs, it’s helpful to clarify: “What are we optimizing for?” If leaders are misaligned on goals, the organization will spiral endlessly while attempting to define portfolios (and value streams, and org design, and team structures, and a bunch of other business and operating model elements).

Organizations often define portfolios within portfolios. First, we create portfolios at the top-level business units and P+Ls. Next, we create sub-portfolios under those to enable various functions and departments to have their own portfolios where there’s little benefit from putting those leaders and initiatives in competition for resources. Of course, large cross-cutting initiatives and certain types of portfolios can exist irrespective of the organizational structure.

Aligning Portfolios to Strategy

The responsibility and capability to align portfolios to strategic objectives lies primarily in the portfolio demand management function, with a governance component in the portfolio matching function. In previous installments we discussed the power of objectives and key results to provide strategic clarity, focus, and alignment to all parts of the organization as a key tool in your strategy deployment process. Nowhere is that more important than ensuring that the demand side of portfolio management embraces those objectives when assigning value to proposed initiatives and prioritizing them. Portfolio vision, value hypothesis, and portfolio objectives, ideally expressed as OKRs, provide value assignment and prioritization guidance to ensure strategic alignment.

Value assignment and prioritization rules should be explicit and appropriate for each portfolio. Each portfolio should have a portfolio vision stating what ideal future outcome(s) investments should produce. It should be supported by a value hypothesis that clarifies who/what is the focus of outcomes and what the impact of outcomes should be. Most of this derives from the company’s objectives and key results, and the value hypothesis should itself contain OKRs that would validate the hypothesis and guide investment decisions. Some portfolio value assignment and prioritization rules will be driven primarily by pragmatic financial metrics like cost savings and net present value (NPV). In contrast, others should reflect our expectation of strategic impacts like increasing brand value or exploiting new markets (more on this later).

Investment Sectors

We may assign each candidate initiative to an investment sector for large and complex portfolio scenarios. Investment sectors help us ensure that we are comparing like candidates (of a similar nature) to one another using metrics appropriate to assess candidates of that type. For example, organizations that use only IRR and NPV for prioritizing initiatives tend to starve innovation initiatives and long-term strategic investments.

Some typical investment sectors might include:

Cost-take-out

Growth

Innovation

Strategy advancement

Compliance

Brand and reputation

Short-term financial returns are the wrong measure of value for innovation and strategic impact-driven initiatives. While innovation initiatives can produce exponential benefits relative to investment, their success rate is typically quite low (one success out of five initiatives would be great). We’d describe this investment sector as having a high tolerance for variability of initiatives, with potential for extreme gains and modest losses. Contrast that with the cost-take-out sector, where a well-run business should expect VERY consistent and predictable successful outcomes from initiatives, delivering close to projections with relatively modest gains – a low tolerance for variability of results would be appropriate for initiatives in this sector. Our governance process should expect VERY high success rates for compliance initiatives, with modest (if any) direct hard financial benefit, but VERY high costs for failure.

It’s not hard to imagine how these different categories of benefits can cause consternation and confusion when comparing items of one type to items of another. Different investment types produce very different ranges of results, rates of success vs failure, and magnitude of benefit. Critically, investment sectors allow us to define appropriate value assignment and prioritization rules appropriate to each type of initiative. Candidate initiatives in the innovation sector should be evaluated almost entirely on their strategic impact, as defined by their ability to advance our strategic objectives as measured by our key results. Candidate initiatives in the cost-take-out sector aren’t usually strategic, so we’d evaluate their value using traditional financial metrics like NPV and IRR.

Define Rules for Assigning Value and Prioritization

Just like we can allocate more or less investment capacity to business unit A than to business unit B, we can set targets for a portfolio to invest appropriately in each sector, so we don’t starve innovation (for example).

Assign Each Initiative Candidate to a Sector

Prioritize Portfolio’s Initiatives within Each Sector. Consider Target Investment Levels

These illustrations show, first, several portfolios with their candidate initiatives color-coded by the investment sector. The second illustration shows the candidates from Portfolio 3 broken out into prioritized stack ranks by sector guided by investment targets for each sector as dictated in Portfolio 3 policies. The next step is to reintegrate all the candidates from each sector back into one portfolio backlog. Prioritizing all the items within their sectors and adhering to investment targets should speed up the prioritization process, but merging the items back to the portfolio backlog may still require tradeoff decisions between unlike candidates (e.g. “hard” vs “soft” benefits). Several techniques for supporting this process exist, such as “white elephant” prioritization, but in most cases it’s best accomplished in a process facilitated to gain consensus of stakeholders as exemplified in collaborative or participatory budgeting techniques.

Introducing Some Key Rules of Adaptive Portfolio Management

Each Portfolio Has ONE Intake System

Whether you call it a queue (order locked) or a backlog (order constantly changing), your portfolio intake process has ONE prioritized list of work items. All work items are subject to the same evaluation process and are compared against each other. Work is pulled from that list into development or production (WIP) ONLY as capacity becomes available.

Force-Rank All Initiative Candidates

Closely related to the above, no two items in a backlog or a queue are allowed to have the same priority. Yes, it can be hard to compare dissimilar things (soft benefits vs. hard benefits), but developing the skill to force rank all portfolio candidates is essential.

Treat Near Term Capacity as Fixed (and KNOW Your Capacity!)

MANY organizations violate this to their detriment. When capacity is fully allocated, but stakeholders demand “just one more” initiative or project must be committed to, it’s tempting to say: “We’ll just outsource to a vendor” or “We’ll source another fifty contractors” or some variant of “Our capacity is infinitely elastic.” In the short term, capacity is FIXED. Most organizations take months to add effective delivery capacity of most types. The more that effective delivery depends on human knowledge and onboarding, the more leaders tend to underestimate the time it will take to bring new capacity online to the expected level of efficacy. Leaders should grow their delivery capacity to whatever they need. Still, the portfolio process should ensure that initiatives cannot be assigned to new capacity until that delivery capacity is proven to be online and effective. That is, its capacity has been empirically demonstrated and measured.

Our previous installment, which focused on the capacity side of the portfolio equation, should have made this abundantly clear, but empirical observation of historical capacity, based on throughput, velocity, volume over time, etc., is the essential foundation of a mature portfolio process. Capacity statistics and portfolio scenario modeling are the scaffolding that demand management relies upon.

Operate with “Good Enough” Precision

People tend to conflate precision with accuracy; they are NOT related. Leaders or managers may increase precision to create a false perception of accuracy or to conceal a lack of confidence in the predictive utility of their production statistics. In a portfolio setting, this applies to metrics of delivery capacity, cost estimates for a proposed initiative, and, most frequently, the benefits a proposed initiative promises to deliver.

One of the many problems with excess precision is that increasing precision is expensive, whether that’s generating more detailed estimates, or in high manufacturing tolerances that are not fit to purpose for product usage. In estimating, if increased precision isn’t materially increasing accuracy (or worse, providing an illusion of accuracy), precision is pure waste, consuming time, dollars, and manpower to deliver. For every proposed initiative that is spending more time in an analysis state, there’s another potentially more valuable initiative waiting in the queue.

Good portfolio managers always subscribe to the maxim: “I would rather be roughly right than precisely wrong!” They also understand exactly how much precision and detail is required to proceed to the next decision point in prioritization and investment decision-making so they can maximize the flow of value and minimize waste. To this end, most mature portfolio processes embrace progressive analysis of proposed initiatives to avoid premature elaboration, to minimize analysis WIP, thus shortening cycle times and reducing waste. The goal is to identify the “dogs” as quickly as possible and eject them from the queue to focus scarce and expensive analysis and estimation capacity on candidates that are more likely to “make the grade.”

Progressive Analysis in Portfolio Intake

This example illustrates a multi-stage portfolio analysis process with an idea funnel and two analysis queues before candidate initiatives are promoted to the portfolio backlog. Both analysis queues are WIP-limited, so items must be deleted or promoted before a new item can be introduced for analysis. Each organization and portfolio will find its own best approach, but it is often useful for Stage 1 to produce a VERY rough cost estimate (e.g. ‘n’ number of team or production line months) because initiative sponsors will often kill off expensive initiative candidates quickly and cheaply based on their intuition of the relative value of their candidates. Stage 2 can develop a more formal benefits case for valuation and prioritization. The portfolio backlog, populated with the best candidates from the analysis process, becomes the citadel where constant and repeated scenario modeling occurs with initiative decomposition, refactoring, and reprioritization until the portfolio is optimized.

Use Allocations to Accelerate and Deconflict Prioritization

Resource Allocations are simply a portion of capacity (or resources) reserved or assigned to a subordinate portfolio by more senior executives. It is a cap on funds and delivery capacity that must be treated as a constraint. Allocation decisions must be made for initiative candidates within that portfolio and potentially among several portfolios and any subordinate portfolios, but at the level of the allocation and down, all competition is effectively internal.

Resource allocations at the top level usually track to the size, strategic importance, and growth potential of business units, product lines, P+Ls, geographies, etc. Shared cost centers, like IT and administrative functions, should have their own allocations for business as usual and internal improvement, while some of their discretionary capacity may be allocated among their stakeholders. Adjustment of top-level allocations typically occurs less frequently than the quarterly prioritization and funding processes that fund items in the working portfolios.

Example of resource allocations to business units and geographies of a global multi-market robot manufacturer:

In an organization that is early in its journey to a mature portfolio process, it’s usually advisable to bias towards more asset allocation assignments and more portfolios to deconflict decision-making. Asset allocations and smaller portfolios produce smaller groups of competing stakeholders and give them more perceived control of their “own” resources. Large numbers of dissimilar and less aligned stakeholders competing for the same pool of resources make implementing a transparent and high-functioning matching process more fraught. Once new rules and processes for assigning value and prioritizing demand queues have been mastered, the matching function may achieve higher portfolio returns by putting more stakeholders in competition with one another for resources more often.

Conclusion

The demand management side of portfolio management is complex and often politically fraught. We have learned the hard way that organizations that master the delivery capacity side of the portfolio equation achieve maturity and effectiveness in demand management much faster and with MUCH less angst and collateral damage than those who don’t. However poorly- or well-aligned, and however transparent and data-driven the organization is, those decisions will determine if the portfolio matching function will survive or thrive.

In the next installment, we will focus on more prescriptions for portfolio management effectiveness and delve into techniques and processes the matching function can leverage to maximize portfolio returns and provide adequate governance and controls to ensure the process has transparency and accountability.

This series provides a host of (hopefully) intriguing insights to help organizations realize their strategic objectives and get better outcomes from their portfolio management and asset allocation processes. Implementing all the processes, practices, systems, and cultural and behavioral changes to make strategy reality in your organization requires expertise and experience. Adaptivity employs some of the leading experts in the field.

We’d like to hear about your challenges and goals and help you succeed!

We can be reached at www.adaptivitygroup.com and www.workboard.com