Big4 Consulting: Value at Minimum Cost?

What software can calculate today – and why management remains management nonetheless

Mathematical optimisation can automate parts of complex CAPEX analyses, shorten recalculation cycles and make professional decision intelligence accessible to a wider range of people within an organisation. Not as a substitute for strategy, consultancy or management – but as a mathematical infrastructure for decision-making.

What happens when the strategy changes within a matter of hours?

A profit warning. A slump in demand. New regulatory requirements. Higher financing costs. An acquisition. A geopolitical shift. Or simply a new directive from the board:

CAPEX must be reduced.

Let’s take a company with an investment budget of EUR 500 million.

Management decides on a 15 per cent reduction.

New CAPEX budget: EUR 425 million.

At first glance, the task seems simple:

Where do we cut EUR 75 million?

But that is not the crucial management question.

The far more interesting question is:

Which combination of the remaining investments generates the highest defined value contribution under the new budget, resource, risk, dependency and strategic conditions?

This fundamentally changes the task

at hand

.

A budget cut becomes a mathematical allocation problem.

From a change in strategy to a recalculation loop

Strategic management consultancy is far more than just mathematics.

It encompasses market understanding, strategy, organisation, transformation, regulatory issues, industry knowledge, communication and human judgement.

Software cannot replace that.

Within complex decision-making processes, however, there is one area that can certainly be quantified:

namely

the quantitative assessment of alternatives.

A possible decision-making process might look like this, for example:

BOARD → FINANCE → CONTROLLING → RISK → OPERATIONS → STRATEGY → CONSOLIDATION → BOARD

Then management poses a new question.

What happens if CAPEX is 400 million EUR instead of 425 million EUR?

What changes if a particular business unit is prioritised?

What happens if a project absolutely must be implemented?

What are the implications of an additional resource constraint?

Every change in an assumption can trigger a new cycle of calculation and coordination.

Not because Finance, Controlling or external consultants work inefficiently.

But because complex decisions are often organised sequentially.

What does a decision actually cost?

Companies measure the cost of their capital.

They measure ROI, NPV, IRR, cash flow, investment volume and budget variances.

But how often is the cost of the process used to make decisions about this capital actually measured?

A conceptual management and controlling framework can be formulated for this purpose:

Decision Cost = Internal Labour Cost + Management Capacity + External Advisory Cost + Rework Cost + Cost of Delay + Opportunity Cost +, where applicable, Cost of Continuing Misallocated CAPEX.

In this context, Decision Cost is not a standardised accounting metric, but a conceptual framework for the economic analysis of decision-making processes.

Internal Labour Cost

Finance, Controlling, Risk, Operations, Strategy, Engineering, Procurement and IT may all be involved in major investment decisions.

A purely illustrative calculation example:

20 employees × €150 full cost per hour × 30 hours × 5 iterations = €450,000.

This example expressly does not represent a benchmark for actual decision-making costs. It merely illustrates how repeated cycles of analysis and recalculation can generate a significant internal cost component.

Management Capacity

The management capacity tied up in this process may be even more significant.

CFOs, board members, business unit heads and investment committees have limited time.

The economic question is therefore not just:

How much does an hour of management time cost?

But rather:

What decisions, changes or implementations could be carried out in the same amount of time?

External Advisory Cost

Strategy consultants, corporate finance specialists, technical consultants and other specialists can contribute important additional expertise to complex decision-making processes.

However, external analytical capacity is also an economic resource.

The more variants, recalculations and reconciliations are required, the greater the associated effort can become.

Rework Cost

A change in management guidance can render some previously prepared calculations, scenarios and presentations obsolete.

Models are adjusted. Data is re-aggregated. Specialist departments re-examine the figures. Presentations are amended.

The organisation performs the calculations again.

Recalculation is work in itself.

The potentially larger cost component: Cost of Delay

However, the immediate process costs may not be the greatest economic lever.

Another question may be far more relevant:

What is the cost of each additional day on which a new capital allocation cannot yet be implemented?

Whilst a decision is being prepared and recalculated, existing activities can continue.

Projects are carried on.

Engineering capacity remains tied up.

Orders may be placed.

External service providers continue to work.

Planned cost savings may be delayed.

More attractive investment opportunities may be delayed.

Capital remains in the existing allocation for longer.

This ‘cost of delay’ is specific to each company and project and therefore cannot be reliably quantified using a flat percentage.

However, it must be included in the economic assessment of a decision.

Opportunity cost: A good project can still be the wrong choice

A project may have a positive NPV or ROI and yet still not form part of the economically optimal overall portfolio.

Why?

Because capital, resources and time are limited.

The relevant question is therefore not solely:

Is this project economically viable?

But rather:

Is this project part of the economically optimal combination of projects available under the given constraints?

Prioritisation is not optimisation

This creates an important distinction.

Prioritisation:
Which project has the higher priority based on defined criteria?

Optimisation:
Which permissible combination of projects maximises the defined objective of the overall portfolio?

With N binary project decisions, there are theoretically 2N possible subsets.

With 100 projects, there are:

2^100 ≈ 1.27 × 10^30 theoretically possible subsets.

The practically feasible solution space is significantly restricted by budgets, resources, dependencies and other constraints. Furthermore, modern optimisation methods do not need to try out every theoretical combination individually.

Nevertheless

,

the order of magnitude illustrates the problem:

Portfolio

decision-making is not the same as project ranking.

Decision technology has long been enterprise technology

Mathematical optimisation, advanced analytics and data-driven decision support are not a vision of the future.

Major technology providers such as IBM have been offering technologies for mathematical optimisation and complex decision-making problems for years. Platform providers such as Palantir also address data-driven operational decision-making processes.

However, this does not mean that specialised decision intelligence solutions are functionally equivalent to these platforms.

The crucial point is something else:

Decision technology has long been an integral part of modern enterprise technology.

At the same time

,

not every mathematically definable decision problem requires a full-scale enterprise platform or a comprehensive transformation project.

This is giving rise to a new category of specialised decision intelligence.

Rather than mapping an organisation’s entire data and decision-making architecture, a specialised solution can address a clearly defined mathematical problem.

For example:

Which combination of our investment projects maximises the expected value contribution given our actual constraints?

Decision Intelligence 4 All

This is where the real change lies.

Professional mathematical decision support need not be exclusively part of large-scale consultancy, transformation or enterprise IT projects.

A decision-making problem can initially be reduced to a few key variables:

Project ID. Investment. Expected value contribution. Constraints.

Building on this, a mathematical model can calculate different portfolio compositions.

Not every organisation needs a new enterprise architecture for this.

Not every mathematical problem requires a transformation project lasting several months.

And not every new management question should necessarily trigger a completely new round of manual recalculations.

That is the idea behind Decision Intelligence 4 All.

Not Big Four consultancy at software prices.

Not IBM at an entry-level Software-as-a-Service price.

Not Palantir for SMEs.

But rather:

low-threshold access to a clearly defined mathematical decision-making function.

The Algorithm Calculates. Management Decides.

Decision Intelligence must not be confused with automated corporate management.

A mathematical model does not automatically define corporate strategy.

It does not decide which factory is strategically important.

It does not determine what level of risk a board of directors is willing to accept.

It does not define corporate culture.

And it bears no management responsibility.

Management defines:

Objectives. Priorities. Budgets. Constraints. Dependencies. Risks.

Within this defined decision space, the mathematics calculates the relevant portfolio alternatives or – depending on the model – the optimal solution.

The algorithm calculates. Management decides.

From Consulting Cycles to Live Decision Intelligence

This means

that the decision loop can also change.

A sequential process might look like this

,

for example:

QUESTION → DEPARTMENTS → CALCULATION → CONSOLIDATION → PRESENTATION → NEW QUESTION → RECALCULATION

If data, target values and constraints are already structured within a mathematical model, the process can increasingly proceed as follows::

QUESTION → CALCULATE → COMPARE → DECIDE.

This expressly does not mean that a comprehensive executive board decision is reached within a matter of seconds.

Computing time and the time taken to reach an organisational decision are two distinct factors.

However, if a mathematical model can recalculate alternative portfolios within a short space of time, a previously time-consuming recalculation stage can be significantly shortened.

This enables management to obtain a quicker answer to one of the most important questions in strategic decision-making processes:

What if?

Three economic levers

1

. Better Capital Allocation

The question shifts from:

Which projects are good?

to:

Which combination of these projects generates the highest defined value contribution under the given conditions?

The aim is to achieve better capital allocation within existing budgets and constraints – without inferring a blanket guarantee of results from this.

2

. Lower Decision Cost

If recalculations can be automated, there is potential to reduce manual calculation effort, rework and the need to repeatedly create scenarios.

The actual extent of this effect depends on the existing decision-making process and the organisation in question.

3

. Higher Decision Velocity

If a new management assumption can be translated more quickly into a new portfolio alternative, the entire decision-making process can also be accelerated.

This gives time an additional economic dimension.

It is not only capital that has a price. Decision-making time also has economic value.

Mathematics can enhance transparency

Not because mathematics automatically produces the correct management decision.

A model can only perform calculations within the limits of its data, assumptions, objective functions and constraints.

However, mathematical optimisation can support four characteristics of a decision-making process:

Transparency:
What data and assumptions underpin the calculation?

Traceability:
How was the result arrived at?

Reproducibility:
What are the results when parameters are identical or altered?

Trade-off Transparency:
What are the consequences if the budget, priorities or constraints are changed?

This also changes the quality of the discussion.

From:

“We believe that this is the right combination of projects.”

becomes a more quantitatively precise question:

“What combination of projects results from precisely these documented assumptions?”

What does this mean for listed companies?

Here, too, a clear distinction is important.

Faster mathematical calculations do not automatically mean a higher share price.

Following a strategy shock, however, management may be forced to reassess strategic assumptions and, consequently, its capital allocation.

The sooner robust portfolio alternatives are available, the sooner management can, in principle, make concrete decisions regarding investments, cutbacks and priorities.

This, in turn, can support a more precise internal basis for decision-making and – whilst complying with the relevant capital markets regulations – more well-founded external communication.

Technology does not determine the capital market’s reaction.

However, it can help to shorten the time between two points:

STRATEGY CHANGE → QUANTIFIED CAPITAL ALLOCATION.

This is not the end of consultancy

On the contrary.

The more quantitative work can be automated, the more human expertise can be deployed where it is of greatest value:

Strategy. Interpretation. Organisation. Transformation. Negotiation. Leadership. Judgement.

This is therefore not a story about:

Software versus consultancy.

It is a story about:

Software + management + expertise.

The next stage in the development of data-driven business decision-making does not involve replacing management with algorithms.

It lies in automating the mathematically calculable parts of the decision-making process to such an extent that management gains more time for the actual decision.

Better Capital Allocation.
Lower Decision Cost.
Faster Decisions.

Decision Intelligence 4 All.

DON’T TRUST US.
CALCULATE IT.