These are three different problems solved using the same decision modeling approach based on AHP (Analytical Hierarchy Process).
They all share the same characteristics in terms of needing to prioritize/select an optimal set of alternatives that deliver the maximum benefit given the objectives and anticipated outcome -- within the boundaries of known constraints like budget and resources.
Each of these examples had a goal, objectives (i.e. outcomes, some call this the decision criteria), and alternative solutions. Each alternative had risk, cost, and resources required in order to accomplish. Questions:
Which alternatives should we do in order to meet our objectives?
And what was the impact of our constraints on the choice of alternatives?
How did a shrinking budget, unavailable resources, and technical risk factors impact the selected portfolio set?
This model (above) was developed with an executive team who’s task it was to quickly reduce costs and improve efficiency of a large semiconductor equipment manufacturer. There were a number of alternatives from large scale outsourcing, offshore relocation of manufacturing operations, to radical new manufacturing methods and material procurement strategies. Which program would generate the fastest savings and have the maximum impact with the least disruption to the ongoing manufacturing operations? We used a brainstorming technique to capture the ideas in a mindmap during workshop sessions with the executive staff. This formed the basis for the decision model.
Using Pairwise analysis with the team we determined the relative priority of their Objectives, with respect to the Goal. This had the added affect of getting top management on the same page in terms of the key business drivers and execution strategy. For example, reducing installation and warranty costs and reducing manufacturing cycle time were their most important objectives. These would most influence the ranking of the alternative programs. These objectives were later built into individual executive performance plans.
Each alternative cost reduction program was ranked against each of the objectives. This gave us the relative priority of each program with respect to the goal. Using cost-benefit analysis we further prioritized the programs and found that three programs could deliver almost 80% of the benefit at about 2/3’s of the expected budget.
There are two more examples in the quicktime animation (above); one gives an example of a large software program and the other was a business strategy problem we were faced with when trying to rapidly grow a newly acquired business unit.
The software project is interesting in that they were late and needed to determine the minimum feature-set to get to market, by a certain date, while also satisfying customer needs. This lead to an incremental release strategy to hit release milestone dates. They faced a typical problem when late; what to through out and what to keep? Keeping the right stuff is the problem!
Each of these examples use the AHP concept to structure the problem and to conduct sensitivity analysis to make trade-offs based on changing priorities of the Objectives with large groups in order to gain shared/collective understanding.