How to plan honestly?
The limits of planning through the evaluator’s eyes: biases, the limits of forecasting, information asymmetry and the critique of models.
Rolandas Rakštys · Lecturer · Vytautas Magnus University, Agriculture Academy · Faculty of Bioeconomy Development, Department of Business and Rural Development · [email protected] · ORCID 0000-0003-0299-9930
Chapter 2 “The business planning and evaluation process”, section 2.1.3 · English translation of v1.0.11 (2026-10-15) · set v1.0.3 · 15 slides, ≈ 30 min
Rakštys, R. (2026). Chapter 2. The business planning and evaluation process (English translation of v1.0.11, 2026-10-15). Verslo planavimas ir vertinimas [Business planning and evaluation]. Vytautas Magnus University, Agriculture Academy. https://rakstys.eu/studijos/verslo-planavimas-ir-vertinimas/en/skyriai/ch02/ · CC BY-NC-ND 4.0
Business plan
Can this figure be trusted?
One scenario, an exact figure…The answer comes in a few slides. First – the five problems that the section examines:
Not a choice but a spectrum
Brews and Hunt (1999)
Organisations achieving better results combine a formal process with a readiness to adjust strategies in the light of feedback; the association with performance emerges where the experience of formal planning extends to four years or more.Does the planning method match the level of uncertainty in the business environment?Whoever appraises the plan – an investor, a bank, an expert at the National Paying Agency (NMA) – should assess not only the content of the plan but also the adequacy of the planning process.
Strategic ambidexterity – the ability to exploit existing advantages and to explore new opportunities at the same time:
Structured
for stable activity – rational planningExperimental
for innovation and adaptation – emergent learningOptimism bias
Optimism bias
Planners systematically overestimate favourable outcomes and underestimate unfavourable ones – costs, time, risks. This is not deliberate manipulation but a deeply rooted cognitive tendency. The cause is the dominance of the inside view: attention to the specifics of the particular project rather than to the statistics of similar projects (Kahneman & Lovallo, 1993). Data on Lithuanian projects are given in section 2.1.5.Correction
Reference class forecasting (RCF): test the forecast against the statistical distribution of similar projects, not only against the particular circumstances.Inside view: we see only our own project.
The iron law of megaprojects
The systematic underestimation of time and costs and overestimation of benefits. Bent Flyvbjerg’s research: cost overrun is the rule, not the exception.
9 out of 10 megaprojects exceed their budget.
The planning fallacy is not a random inaccuracy: it is systematic and directed one way – the optimistic one.
Which bias?
Even the most perfect planning model cannot eliminate human errors of thinking. Behavioural safeguards are needed:
Where forecasting ends
Models try to identify breaks in real time (Bayes’ rule), but it is in principle impossible to forecast reliably something that has never happened before.
Makridakis’s forecasting competitions M1–M5 (1982–2020):
- Simpler methods often outperform more complex ones.
- Combinations of statistical and judgemental methods outperform individual methods.
- The complexity of a model is not directly related to its accuracy.
- Machine learning first showed a significant advantage only in M4 (hybrids with neural networks; Makridakis et al., 2020) and M5 (purely machine learning methods; Makridakis et al., 2022).
Accuracy should be expected not from a more complex model but from a better process: combining several methods, regular review and interval forecasts.
Point or range?
Philip Tetlock
The average expert forecasts about as accurately as a dart-throwing chimpanzee – particularly over a 3–5 year horizon. But the average conceals differences: some experts had modest but real foresight (Tetlock & Gardner, 2015).Rare “superforecasters”: quality depends more on the style of thinking than on the model.
Extrapolation without structural analysis – the commonest and potentially the most dangerous error (Makridakis et al., 1998).
“Revenue after 5 years will be EUR 2 347 521” – epistemologically incorrect: not because the figure is necessarily wrong, but because it conceals uncertainty.
Who knows more?
Biases and the limits of forecasting are unintentional errors. Asymmetry also leads to deliberate distortion. Akerlof (1970): asymmetry creates adverse selection; he mentions the related notion of moral hazard with reference to Arrow (1963).
The plan as a “sales document”
A plan for an investor, a bank or an application is naturally optimised for its addressee. The investor knows this and applies a discount – but the size of the discount is a guess, not an analysis.Strategic misrepresentation
Flyvbjerg distinguished unconscious optimism from the deliberate manipulation of forecasts – the “Machiavelli factor” (Flyvbjerg, 2003, p. 121; 2014): costs are understated so that the project is approved, in the expectation that a project once started will be hard to stop.Internal information asymmetry: managers at lower levels may withhold information to obtain easier targets or larger budgets. Click a manager.
Better models do not help here – institutional safeguards are needed: independent appraisal, audit, testing assumptions against reference classes, clear accountability for the quality of assumptions (more in Chapters 3 and 10).
When a model fails
Sales and operations planning (S&OP) and integrated business planning (IBP) are implemented with unequal maturity:
The executive meeting turns into a forum where everything is discussed (Dreyer et al., 2018; Ivert & Jonsson, 2014, p. 11). It is a problem of process, not of content.
Planning nervousness: rolling horizon procedures may increase the variability of plans (Lin & Uzsoy, 2016). Remedies: frozen zones, tolerance bands, decision priorities.
Iteration is a virtue only where the frequency and the consequences of change are under control.
One loop or two?
Single-loop learning
Correcting errors without changing the assumptions. The forecast was too optimistic → next time we forecast more conservatively. Operational but limited.Double-loop learning
The forecast was too optimistic →- is our forecasting process adequate?
- is our model of the market correct?
- does our planning culture tolerate bad news?
The value of planning arises not from the document but from the process in which the organisation learns continuously about the logic of its business and its environment.
What does a “good” business plan mean?
Seven properties – not a portrait of the ideal plan but the minimum threshold of quality below which planning becomes dangerous: it provides misleading information with the appearance of certainty.
Determining the level of uncertainty
First – what kind of uncertainty predominates?
Parameter
The relationship is known, the values are not (price, yield). Managed with scenarios, ranges, sensitivity analysis.Model
The form of the relationship is in doubt (linear or with saturation). Comparing several models and measuring accuracy help.Structural
Even the possible outcomes are unknown. Scenarios and sensitivity tables do not help – what is calculated is what can already be imagined.Not “whether to plan?” but “how to plan honestly?”
Instead of a single figure – a range with probabilities
Planning is necessary even when it is imperfect. Next: the extended exercise in section 2.6 (justifying a planning conception), Chapter 3 (business risk), Chapter 10 (the appraisal of investment decisions).
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Rakštys, R. (2026). Chapter 2. The business planning and evaluation process (English translation of v1.0.11, 2026-10-15). Verslo planavimas ir vertinimas [Business planning and evaluation]. Vytautas Magnus University, Agriculture Academy. https://rakstys.eu/studijos/verslo-planavimas-ir-vertinimas/en/skyriai/ch02/ · CC BY-NC-ND 4.0
These slides are based on the textbook Verslo planavimas ir vertinimas [Business Planning and Evaluation]. The text was translated, structured and visually laid out with the help of artificial intelligence tools (Claude), following the protocol for working with AI and the verification procedures set by the author.