The Financial Value of a System: how to determine how much to pay for your ICT. Reply

Much work has been done in the field of Applied Information Economics (Hubbard, John Wiley & Sons 2007).  Most of the analysis goes towards how much a business should pay for a large commercial decision but glosses over the individual value of systems, human activity and infrastructure.

SYSTEM VALUE EQUALS THE VALUE THE DIFFERENCE THE SYSTEM MAKES.

THE FINANCIAL VALUE OF A SYSTEM

It’s easy to say that a company should spend no more than $375k on an advertising campaign, for instance, but that’s easy.  How much should the company spend on it’s technology?  How much should the firm spend on its ERP system?  How much should it spend on its CRM system? Is it even possible to measure the value derived from good customer relations management and if so how much of that value can be attributed, accurately, to the technology?  So, how much is it worth the company spending to come up with that figure?

RISK v OPPORTUNITY

If we take this analysis further, then what is the value of a back-office system?  What decisions do back office systems assist businesses in making? ERP systems assist in monthly financial reports and variance analysis.  Project modules of ERPs assist companies to determine whether project costs have overrun.   The core value of back-office systems, as opposed to operational systems, is that they reduce risk (cost) rather than create opportunity (profit).  Operational systems which can directly increase discounted cash flows, therefore, are better suited to NPV analysis.  Back-office systems, which are largely seen as sunk costs (the cost of doing business) are better appraised through NPC analysis.  Before the business gets to that stage it must come up with the detailed cost model.

THE VALUE OF INFORMATION 

Although the types of systems is the topic of another blog, the investment goal for a back-office system should be one which reduces uncertainty in decision making.  Therefore, what is the expected opportunity loss (EOL) from poor decision making?  How does one calculate the amount of revenue lost from poor decision support?  More importantly, how does a business calculate the value of the decision support which a back-office system delivers?

  •   Estimate Financial Value.  In operational systems it is far easier.  For instance, in an investment appraisal of new systems to automate certain plant and equipment experts may attest that system controls improve efficiency by 20%.  The decision is likely to be clear cut.  What about a new ERP system?

In such cases it is important to take a holistic view of the whole ‘capability’ (i.e. the technology, people and processes together).  Imagine that the new ‘system’ will enable an engineering services firm to quote and estimate proposals (in this example it is important to imagine that the new system will enable them to do so ‘perfectly’).

In this case, what is the current value of bidding and tendering information?  With the current information, for instance, a firm may have $10 million EBIT from tenders won, based on a 60% win rate as well as a 40% cost blowout. The firm wishes to improve their profitability by increasing their bid capability (which includes cost and schedule estimation).  If each project were tendered perfectly (let us forget for the moment about failures of project delivery) they could achieve almost $17 million in EBIT.  To this end they want to know how much to invest in ICT and how much to invest in people and process in order to achieve an additional $7 million profit?

THE VALUE OF PERFECT INFORMATION

For the system to work perfectly it must not only contribute perfect information but the information must also be perfectly usable, i.e. lost revenue should be a factor of human error not human input.  Note that the system will only increase the accuracy of bids/proposals (costs and schedules etc).  It may or may not increase the probability of winning.

Using the example above, how much should should the company invest in technology?  Firstly, in this example our ‘experts’ estimated that they had 90% confidence that the business would achieve $15,500,000 with a new capability.  This is largely because the problems seem relatively known to them.  They had a 10% confidence that the business would only achieve $100,000 largely because they don’t think their analysis is wrong.  They also estimate that the break-even threshold is about $375,000 which is roughly the equivalent of one new role (to account for capacity) and some new technology to improve workflow.

Without going through Hubbard’s detailed calculations that gives us an estimated $616,878,163.00 value of information.  This means that the company should invest no more than this sum in their bid capability to achieve the desired $7 million profit.

This means that the overall capital implementation and operating expenses (let us say out to 3 year projections) should be no more than $616,000.

HOW TO INVEST THE CAPITAL?

How much of the approximately $600k should be spent on technology and how much on people (new roles/new hires and training), and process?

CAPABILITY CONFIGURATIONS – parametric modelling

The simplest and easiest way to assess how much to spend on technology is to develop a calibrated estimate of the configuration of the capability.  In summ, there is no system or tool that can authoritatively tell a company how it should spend its cash.  The business is the expert and the best way is still to calibrate its experts to give the best options to management.

With Capability Configurations one must note that any given capability may have multiple configurations.  There is no right configuration just one that is optimised for the given parameters.  It is up to the business to offer a variety of configurations with a range of costs.

After each configuration is developed it is run through Monte Carlo simulations to determine the probability of achieving the cost target within the desired range.  The success of this method is twofold.  Firstly, it simulates a range of costs knowing that static costs cannot be predicted.  Secondly, the cost ranges are determined by experts in the first place.

It is worth noting:

  1. estimates should be calibrated and given by experts.  These are not wild guesses but represent the true high and low ends of the likely (not way-out possibilities) spectrum.
  2. the cost models for the capability configurations must be decomposed to the lowest level.  To be effective, the Monte Carlo simulations run best on more detailed models.

CAPABILITY SIMULATIONS

The simulation shown above was performed on a cost model with high application, training and infrastructure costs.  The analysis showed that there was a 99% chance of achieving costs within the $617,000 range and a 46% chance of achieving ideal costs (here estimated at approx $450,000).  The second capability configuration included more people at a greater expense and estimated a lower probability of achieving the desired $450k goal.  Intuitively, one knows that when simulating technology scenarios in the low ten thousands as opposed to people in the hundreds of thousands (salary), the probability of success will be greater.  Intuitively we know that it is less riskyto invest in technology and systems rather than people.  People always cost the most so, ideally, a business will wish to spend more on technology than people.  Some money should ideally go towards training of high-performing staff who are difficult to replace.

This was a highly simplistic model.  A more decomposed parametric showing greater detail may have yielded a slightly different result.

QUALITATIVE FACTORS

The major criticism is that it only takes into account hard costs and does not account for the integration of the capability into the business.  How will the capability take to the business?  Will the firm be able to develop it? implement it? run it?  These use of qualitative factors and more will be examined in another blog soon.

 

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