(This illustrative case study shows how SnapStrat can be applied to Portfolio Optimization)
How do we use our imperfect data to find the optimal mix and timing of projects to resource…
HAL was struggling with a strategic planning process that was mostly manual – collecting disparate business cases and determining priorities based on the decision criteria each functional area chose. Executives engaged SnapStrat because they knew they needed to be more data driven while balancing multiple criteria, but the tools and processes they were using would not support that improvement.
The Strategic Planning Process
To feed the predictive engine that drives recommendations, there were four strategies defined for this period:
- Increased price realization
- Store Growth
- New Customer Acquisition by key segment
- Increase in inventory turns (Supply Chain Optimization)
Then, four decision criteria were defined to focus on maximizing value based on their goals:
- ROI Maximized
- Strategic Alignment Maximized (how well a project enables the strategies above), and
- Risk Managed Appropriately
In the final part of the setup, all proposed projects for this planning period were represented in the system. Parameters for projects included measurements of budget, strategic alignment, resource requirements, ROI, risk, and others.
Once all key data was in place for this period’s decisions, the SnapStrat engine modeled a series of forward-looking scenarios, which allowed the team to understand the relative tradeoffs between the decision criteria. At that time, the team was able to compare projected results of the selected projects versus what would have been planned using their traditional strategic planning processes.
Compared to the results of their previous model, the results were striking. Optimizing for multiple criteria simultaneously showed a clear improvement in value generation, organizational efficiency, and strategic fit. Specific results included:
- Functional allocation was replaced with a best fit allocation. This improved predicted ROI from 10.5% to 11.9% and resulted in an expected improvement of $4.2M/yr within a $100M portfolio.
- Implementation delays were reduced due to resource contention. Implementation was pulled forward by an average on 1.8 months/project resulting in improved benefit realization of $1.8M.
- While the historic allocation methodology yielded 58% of projects aligned to one or more strategies. Using strategic alignment as one of the decision criteria raised strategic alignment to 90%.
- In addition, using confidence levels in ROI estimates from the leadership team, project risk was used to weight ROIs for the first time. This will become standard and more data driven over time and will drive continuous improvement of future decisions.
Key Lessons Learned
- These executives knew they were not being consistent in prioritizing projects across company functions. Formalizing the process allowed all functions to be consistently evaluated, driving significantly better results.
- Data inputs to the engine do not become perfect over night, but just ‘laying the pipes’ of digitizing these decisions yields immediate benefits and creates the path for continuous improvement.