ORION High Level Strategic Planning provides tools to permit a different and much more comprehensive approach to the analysis of the options that confront any large business, a new level of interactivity with the strategic plan, and a degree of mechanisation of the plan unattainable with previous methods.
Strategic plans range from the simple single choice, take it or leave it, to a complex swirl of options, interactions and constraints, and up to now the only tools on offer suited the evaluation of single choices well, while giving a false sense of determinism when used on more complex planning.
Almost by definition, planning in a complex environment will involve tentative information, otherwise it wouldn't be complex. Up to now, the tentative information was kept in people's heads, because there was no place for it in the formalised plan. These new tools handle tentative information simply and efficiently, greatly increasing the scope and effectiveness of the mechanised aspects of the plan.
The High Level Strategic Planning tools sit on top of the ORION Knowledge Network system, providing a direct representation of analytic language elements without the mysteries of programming a computer.
The system is well suited to graphical interaction by planners, as all elements of the plan including its logic have a simple intuitive visual representation.
The most dramatic difference is that the strategic plan should link into operations, and change in the operational environment should impact on the strategic plan - possible when knowledge as uncommitted structure is used at all levels in the organisation.
There are two ways to go about Strategic Planning:
|Work out exactly what your current position is, then look at ways to move from that position in some new direction - "We are here, how could we do better". This strategy is both conservative and severely limiting.|
|Work out where you would like to be, evaluate the advantages, then work out if it is possible to get there from here - "The light on the hill". This strategy can be visionary, but without a detailed road map of how to get from here to there, can be disastrous.|
Of course, any worthwhile plan is a mixture of both of these approaches, but the two differ in the way that constraints are handled in the transition. What both methods require is the ability to change the structure of the plan.
"Knowledge is structure" - knowledge provides a structure which is capable of self-modification.
The currently available Strategic Planning tools are based around spreadsheets or Financial Modellers, tools that have a fixed structure, and are intended for analysis where every aspect is known to some specific value. The spreadsheet approach, with its simple concepts for calculation and its fixed representation of time by cells, was developed specifically to mechanise the analysis of deterministic cashflows, where a purchase happens on a particular date, and the rent is paid every month.
There have been various grafts onto the basic structure of a matrix-based calculation, backwards calculation for one, but the cell-based natural order calculation has remained unchanged - that is, there is no dynamic assessment of how to calculate during the calculation - the flow of calculation has been determined before any calculation has been made. These tools have dominated in the strategic planning area, mainly because there was no viable easy to use alternative which could handle the fluidity and variability essential in effective strategic planning.
The options that confront most large businesses have a much more complex interacting structure, where the successful outcome of the plan depends on what set of things to do, what to do when, will the resources be available. These different aspects of the plan influence each other, pushing each other around in time, or closing off alternatives, destroying the simple cell-based notions of the existing tools. The larger the organisation, the more likely it is to find that many alternatives "constrain each other out" by reasons of market dominance or lack of capital or over-gearing.
A type of plan which exemplifies all of the aspects where deterministic planning is inappropriate is the planning of a major acquisition, where planning proceeds simultaneously with changing business requirements, a rapidly altering technology fit, competitor actions and a volatile stock of excess management skill that could be sunk into the acquisition. Such plans have a swirl of interactions and alternatives, a swirl that up to now could not be mechanised in any useful way.
ORION High Level Strategic Planning uses a knowledge network approach to allow a much more realistic description of the plan, including a variety of methods and outcomes in the plan, not just the cost of a single course of action. Alternatives at multiple levels can be embedded, allowing the Strategic Planning team to model the complexity of the real situation, and the Strategic Planning Manager to assess the potential outcomes from the intended actions and inputs.
The plan can span from describing the concept, through the fleshing out of details, to detailed planning of the execution. A formalised plan that contains the core concepts driving the process can be very useful when you are "up to your armpits in alligators".
The basis of the strategic plan is an activity or event, with a potential start date, possible limited duration, predecessor activities and resource input and output. A significant difference from the spreadsheet approach becomes apparent when values are specified for these things. Ranges of values are used, and logical connections can be added between any of the elements of the plan. This allows the plan to be simple where simplicity is appropriate, and complex where necessary to describe the real situation. The knowledge network plan extends to cover much more of the total planning function, planning that up to now was done on paper or in people's heads. It is the greater coverage that allows better information to be available from working on and developing the plan.
One difficulty with plans only on paper is that inconsistencies creep in. If you look at one aspect, "We could do this or that". Look at other aspects, come up with alternatives that may very well be inconsistent with planning in the first area. During the working out of the plan, decisions can be made which close off alternatives other people were relying on to avoid problems, alternatives that existed only in their heads because the plan could not accommodate or represent them. Mechanisation of more of the plan makes things explicit, helps to eliminate inconsistencies and shows the effects on future alternatives that current decisions have. Just the fact that more of the plan is explicit allows more people to contribute to the plan and make it better.
The knowledge network plan will need more thinking about, but that forethought will pay off in avoiding dead ends and assessing costs of alternatives, or leaving decisions to be made later, when better information will be available - all elements of the knowledge based plan. A significant weakness of cell-based analysis is the need to spell out everything in precise detail - unrealistic in a plan where developments unfold and unintended consequences occur as you go along.
Another important difference is that the planning system is supported by a knowledge network modelling tool called ORION. Activities, resources, costs, durations are expressed in terms of interacting operators in a network. The planner can ignore the way knowledge about the plan is represented while building the simpler aspects of the plan, and for many plans 90% of the plan construction can be done using facilities similar to those found in Financial Modellers. The difference is that ORION can hold 100% of the plan, because it is not restricted by a matrix or other crude and simplistic data structure used in conventional financial modelling tools.
The more complex aspects of the plan are easily expressed in terms of constraints using the ORION Textual or Drawing Editors. The Drawing Editor in particular provides the ability to point at elements of the plan, and connect them through assemblages of analytic operators to form constraints.
Some Constraint Reasoning tools can only move from one consistent state to another. This would be fairly useless for Strategic Planning, except for the first cut. As the plan unfolds, you often need to jump to a new state inconsistent with the present one - from a foothill to the to the top of the mountain - or even more critically, respond rapidly and flexibly to unforeseen consequences, consequences which may seriously disrupt your initial plan.
ORION can support the decision-making needed to determine what new state you should move to, and then maintain Constraint Reasoning on that new state.
With the greater expressive power of a knowledge network, it becomes worthwhile to explore the real constraints on the organisation at the start, and model them in the plan. These constraints up to now have stayed in people's heads, and their judgements have been used to weed out inappropriate choices. By formalising them, others can contribute, perhaps showing that an option dismissed out of hand is not only possible, but the best available, because of changes to the organisation's technology base or mode of operation.
At the end of this exploratory phase, there should be a much better appreciation of the dynamics of the organisation, both by the strategic planning team and the decision makers. In particular, the "cut losses and get out" criteria are worth building into the plan. Just as the plan does more, the planning team has to move up a notch in handling the psychology surrounding the plan, and propagating it into the organisation.
If an alternative is not yet sure of its existence, and it has a zero in its range of duration, it observes any changes taking place on its Start and Finish pins.
If the possible duration between the Start and Finish values becomes less than its minimum real duration, the alternative switches its duration to zero and sends FALSE out of its Logical Control pin. If connected through an XOR to another alternative, the other alternative will be forced to turn on and exist.
Other influences on the existence of a tentative activity can be exerted by cost, duration, and use of resources. A constraint on total cost can feed back to force the duration to zero, or a constraint on some other resource, including total risk, may force the duration to zero.
The resource usage for the tentative activity will be booked before the activity is known to exist, the zero in the duration range signalling that the usage is tentative.
This way, the logic in the rest of the model can operate on the possibility of the alternative existing, rather than wait for a hard decision about its existence. With many alternatives being switched on and off, the interactions can become quite complex and require careful examination to find the best tradeoffs.
Interaction Of Cashflow and Activities
With most planning tools, a cashflow can be experimented with while activities are fixed in time, or activities can be moved about with primitive cashflow output. Orion Strategic Planning allows you to manipulate activities and observe cashflow, or constrain cashflow and observe the effect on activities as the cashflow pushes them around. Other planning constraints such as NPV may be added to the plan, coming into effect only when the variability reduces.
NPV is a simple way of determining the relative worth of a project by reducing the cashflow to a single number. For an investment in a project, NPV is calculated on a cashflow which will typically look like the diagram, where all the variability has been stripped from the project by making firm assumptions about where everything will occur, and the cashflow in each period.
It is more useful to allow some variability to remain in the scenario, to get bounds on the possible NPV to aid in making other related decisions. The resulting cashflow diagram shows variability of magnitude and risk of slipping. Without these being present, the person who interprets the NPV must look to other measures to assess the risk.
With a range for the NPV, it is then simple to fit a knob to constrain the range, and have this feed back into the decision-making process. The minimum NPV may be a nonlinear function of the amount to be invested, making it a rate of return which is variable depending on amount to be invested. Other factors which may be included are time to payback and risk. The structure used to evaluate NPV is undirected so changes to amounts per period or interest rates can ripple in either direction through the structure. By twiddling the knob in combination with other controls, some alternatives will be forced out of contention.
An advantage of this approach is that corporate management can set a minimum NPV figure, and this will feed back into the investment model, constraining cashflows and occurrences.
With some other metrics also acting as constraints (risk versus time, maximum exposure) and interacting with each other, the people developing the model are "directed" along the corporate path, and are much less likely to produce a good NPV figure based on invalid assumptions, assumptions that had to be made when there was no way of carrying variability forward in the analysis.
The environment that a knowledge network system can provide is exceptionally flexible, because all of the knowledge about the plan is continually "live", and can interact with the user, as well as grow and change with user interaction.
The user has several graphical interfaces available to work on the plan, the main one being a Gantt chart of the proposed activities, coupled with histograms of all the resources available to the organisation, and plots of cashflow and risk. Behind the Gantt chart, and controlling the placement of the activities, is a knowledge network.
It represents the complete logical structure of the plan, incorporating all the options, costs, risks, resources, constraints and alternative ways of doing things that are in the current version of the plan. The network is carrying tentative information through its connections, and the connections themselves can change as the tentative information changes.
Using the tools provided with the chart, the planner can push proposed activities around, or add or remove resources anywhere in the plan, control timing of regulatory limits, or switch alternatives on and off. After each user action, information flows through the new network structure added as a result of the action. The chart, resource plot and display of costs are updated to represent the new state of the plan. If a logical error, or inconsistency, is encountered while updating the knowledge network, the user can examine the site of the inconsistency, work out ways of avoiding the problem, and the plan then reverts to the previous state. The user can see a list of the changes they have made, undo any of the changes, all of the details of the plan reverting to the state before the change. The Dozer tools can be used to make the system search for a maximum or minimum on any aspect of the plan - cost, duration, risk, NPV, or any amalgam of the many dimensions of the plan.
The constraints added to the charts are relatively simple, but constraints of any complexity can be added in a Model Editor and have immediate effect, or constraints can be added graphically by assembling operators in a network of connections using the Drawing Editor.
The Graphical User Interface, or GUI, to the plan ensures that the effects of any change are immediately shown to the user. The effects propagate through the plan on both the activities and cashflow side, and the network represents the complete logical structure of the plan, so no effect is overlooked, or delayed until a program runs. Instead of struggling with several versions of output from a program, and trying to reconcile the good features of several run outputs, here the structure of the plan itself can be smoothly changed in the desired direction, while any missteps are simply undone. The planner can be sure that nothing has been done which will later need to be undone because it violates some constraint.
The main focus of the planner's interaction is to add constraints that represent new limitations, and to fix gross resource hot spots that would prevent a useful plan from being obtained (the system will show a red light on conflicts it couldn't fix). When the planner is satisfied that the plan represents all of the potential options to be planned, and all of the constraints on the organisation, the Automatic Planner can be started. This has multiple goals, maximising the return, minimising the risk while maintaining feasibility. It will normally begin by looking for the point of maximum conflict, and attempt to reduce that conflict by hard starting an option. Hints embedded in the network provide guidance as to whether the option should be started as soon as possible, as late as possible, or some dynamic variation based on what else is happening. The Automatic Planner can choose among several alternatives by trying each in turn, noting the cost and the risk, and then choosing the best alternative. The Automatic Planner then searches for the new point of maximum conflict, and repeats the process. If hard starting an option causes an inconsistency, the Automatic Planner undoes the option and tries some other alternative.
Automatic planning of the options is not being done through a clever algorithm operating on the data the planner has supplied, but instead is using the structure of the knowledge network, and allowing the interaction of the constraints and any other logic the planner has implanted in the network. Its method of undoing its actions is the same as the planner achieves by pressing the Undo button. The planner can be interrupted, and changes made, even new constraints added to drive it in a particular direction. This ability, to interact with the user while in the middle of its planning, comes from the knowledge about the plan being expressed in a network of operators, instead of in a stack of procedure calls in an algorithm.
Using The ORION Planning System
The strategic plan that you build using Constraint Reasoning has many more uses than a spreadsheet. The network plan behaves much more like the real environment, and can be used as a simulation model to determine the response of the plan to various alternatives, decisions, failures. Some of the uses:
You or others can use it as you would a financial model, just to add up the costs in particular time periods.
You can switch alternatives on and off manually, and observe the effect on the return from the plan, or run the Automatic Planner to find a viable plan for any combination of options you have set up. Switching alternatives can change when things occur, so you have a plan with dimensions of time, coat and risk interacting together.
You can automate the switching on and off of alternatives, allowing the system to find a maximum return/minimum risk solution, where you have set up risk profiles, cost versus time relationships.
You can put in control of resource bookings, so the Automatic Planner can "buy" extra resources at a cost, and find the best plan within these more realistic limitations.
You can use a Monte Carlo analysis, where options have a probability of success (20%, 90%, etc.), the system repeatedly throwing the dice to generate random numbers to determine success or failure of particular activities, and then solve the resulting plan for the particular set of random numbers. With many runs, this becomes an excellent way of doing sensitivity analysis on a complex plan that has many alternative paths and activities with attached risks. Where this differs from the simplistic Monte Carlo approach is that the model structure can change as probabilities change, making its behaviour realistic rather than a statistical fiction.
If something goes wrong in the execution of the plan, the plan can support "planning under pressure", where the snapping of alligators' jaws makes it hard to think clearly. Critically assessing the alternatives to dig yourself out of a hole is a good way to ensure the hole doesn't get any deeper.
An advantage of the knowledge network approach is that you can wrap more analysis around the strategic plan at any time, and add more detailed logic inside the plan as well. The model is extensible in any direction at any time.
A good plan requires adequate planning detail, and appropriate behaviour built in so it responds realistically to change. As you work with the plan, a model of it is also forming in your head. The more closely the plan simulates the real environment, the more subtle and sophisticated your mental model will be, so you can continually steer the organisation well.
How Subtle Can The Plan Be
Complex financial analysis is easily embedded in the plan description. You can constrain the cost of the plan over time, force some options to be largely self-financing, the system seeing through logic about incentive payments so that cost and time of action are directly linked. The linkage of time of action, cost and resources is partly what makes strategic planning so hard.
The transmission of tentative information through the financial analysis and the choosing between alternative funding schemes is another example of "knowledge is structure" adaptability.
The network activity resulting from the interaction of many alternatives, jostling with each other for resources and continually reversing the information flows at their connections, and the ability to control the existence of activities and turn resource usage on and off from other parts of the network, provides a large part of the flexibility of the knowledge network approach for Strategic Planning applications.
The swirl of activity in the network model as changes occur mirrors the swirl of activity and change in the plan. The ability to embed logic within the model, and have it participating in the swirl of activity, is unique to ORION.
A knowledge network planner uses techniques quite different to a financial modeller or spreadsheet. "Knowledge is structure" means that knowledge about the organisation provides the structure of the plan, and changing the structure changes the knowledge being used in the plan.
Set up constraints - Start_Date_A < Start_Date_B - and make sure that no constraint is ever violated. Constraints can be numeric, or involve logic - IF A < B THEN C > D, with the test or assertion as yet undecided. ORION's constraints use reasoning at the operators that make up the constraints, rather than treating the constraints as black boxes, so many more inferences are possible.
Ranges Of Values
A variable, like a date for exercising an option, can have a singular value, say 5, or a range of values, say 5..25, meaning the variable can have any integer value between 5 and 25. Ranges can also vary negative to positive, to model reversible conditions of supply and use. This ranging of alternatives can also be applied to objects with attributes - brands, factories, people.
When optimising, there is usually a hierarchy of choices to make. One can either make each choice in turn, or propagate the effects of one choice to reduce the number of downstream choices. As a result of propagation, either the ranges of downstream variables are reduced, or inconsistencies are encountered, forcing an immediate backtrack without wasted effort. This technique can reduce the number of choices to be tried by a factor of thousands or millions, making the optimisation of complex planning models possible.
Sometimes you need to make a decision, "build a castle in the air", observe the effects, then work out if you want to get there from here. A planning system has the same need, of creating, testing, then destroying transient structure. A planning system which uses the computer's stack for backtracking can't build transient structure - everything must be known beforehand. ORION handles knowledge, so it can build new structure, and backtrack while maintaining user interaction.
A Strategic Planning system needs to be accurate in terms of its assessments, and responsive to change. Sometimes the change is slight, an option slips a few weeks, sometimes the change is massive, with the structure of the plan being hacked around. If a rapid response is required with a mixture of paper and spreadsheet plans, the executives implementing the plan need to decide what to do with virtually no useful input from the plan, then have the planners scramble to bring the plan up to date.
A "knowledge is structure" strategic plan, which is continuously "live" and interactive, and will immediately display any inconsistencies in it, allows the planner to make rapid changes to the plan, knowing that the changes are not introducing errors. The knowledge based plan is much more of a simulation of the real environment than a means of calculating the cost.
The planning environment is necessarily volatile. Delays in recasting the plan increase the instability of the overall control of the process and decrease the willingness of the planners to explore alternatives.
The ORION Planning System can provide accurate and timely information, while allowing its planning model to be drastically changed to reflect new goals, priority shifts and other new constraints on the organisation. Models can be assembled together to form increasingly higher level views of the options confronting an organisation, while still maintaining some variability in the options.
Introduction to ORION