How we calculated, and what deliberately not
1. Every parameter has a source or is marked as an assumption. 2. No module has independent evidence for its degree of effect; the lever ratios are assumptions of the sample company, oriented at documented bands where they exist. 3. No double counting: write-offs, shelf gaps, rush costs, cocoa costs, scrap, promotion budget and direct sales are each counted exactly once. 4. The total is a gross effect before implementation costs; module 06 stands outside the total.
The scenarios are multipliers on the module values of the base calculation, not a second calculation with different assumptions. They show the band in which the model calculation would move under different lever ratios.
Input data - what is documented, what is estimated
The sample company is fictitious. Its orders of magnitude are aligned with the industry frame so that the calculation stays sound.
| Parameter | Value | Source | Certainty |
|---|---|---|---|
| Net revenue per year | €95,000,000 | Freely set; €7.92 per kg against €7.59 per kg in the industry mean (source 1) | Estimate |
| Employees | 380 | Freely set; €250,000 revenue per head, industry predominantly mid-sized (source 1) | Estimate |
| Chocolate products per year | 12,000 t | From revenue and value per kg; industry mean source 1 | Documented |
| Active items | 140 | Assumption | Estimate |
| Production lines | 4 | Assumption | Estimate |
| Material input | 50 % = €47,500,000 | Assumption without anchor; cost structure survey not verified | To verify |
| Cocoa products (liquor, butter, powder) | 3,600 t × €6,000 = €21,600,000 | Volume about 30 % of weight (milk- and filling-heavy range); blended price assumption with semi-finished mark-up, bean price source 2 is no anchor | To verify |
| Contribution margin on net revenue | 30 % | Assumption | Estimate |
| Budget for trade promotions | 8 % = €7,600,000 | Assumption; industry estimate for consumer goods worldwide around 20 % (source 9, tier 3), deliberately below | To verify |
| Revenue in direct sales | 3 % = €2,850,000 | Assumption | Estimate |
| Write-offs after the best-before date | 0.6 % = €570,000 | Assumption; processing carries 19 % of the food waste of the EU chain (source 3), no chocolate rate published | To verify |
| Revenue lost through shelf gaps | 1.2 % = €1,140,000 | Assumption; survey finding on shelf gaps (source 4) as a frame, not as a rate | To verify |
The twelve parameters of this table are the input of the whole series. In a data workshop they are jointly replaced by your actual figures; the calculation paths stay the same. The result is a model calculation on your figures instead of the sample company.
Nine modules, nine open calculations
Click a module for formula, calculation and anchor. All values come from the same configuration as the module pages.
| Parameter | Calculation |
|---|---|
| Fewer write-offs | €570,000 × 30 % = €171,000 |
| Recovered contribution margin from shelf gaps | €1,140,000 × 25 % × 30 % = €85,500 |
| Fewer rush costs | €250,000 × 30 % = €75,000 |
| Module result | €331,500 |
Accuracy: the M5 competition (source 5) documents on retail data that the best learning method forecasts 22.4 % more accurately than the best statistical reference - accuracy, not a euro effect. A consulting study (source 6) cites 20 to 50 % fewer forecast errors; it stands here only next to the documented anchor. The 30 % reduction of write-offs and the 25 % recovery are assumptions. The survey finding on shelf gaps (source 4) provides shopper reactions, not a loss rate.
| Parameter | Calculation |
|---|---|
| Endangered revenue | 4 × €300,000 = €1,200,000 |
| Retained revenue | 30 % → €360,000 |
| Retained contribution margin | 30 % → €108,000 |
| Module result | €108,000 |
An association survey (source 7) reports that a third of brand manufacturers were affected or threatened by delisting - an industry finding without independent review. Number of marginal listings, revenue per listing and retained share are assumptions. Promotions to secure a listing are not part of module 03.
| Parameter | Calculation |
|---|---|
| Shifted budget | €7,600,000 × 25 % = €1,900,000 |
| Contribution margin per budget euro, shifted against target | 0,9 → 1,02 = +12 percentage points |
| Return difference on the shifted budget | €1,900,000 × 12 % = €228,000 |
| Module result | €228,000 |
That a substantial share of trade promotions is unprofitable is peer-reviewed (source 8); a market research study (source 9) puts the share at 59 % and stands here as an industry estimate next to it. Share, base return and target return are assumptions of the sample company; the module is the second-largest item with the weakest derivation.
| Parameter | Calculation |
|---|---|
| Changeover time and gained line hours | 900 × 40 min = 600 h; × 20 % = 120 h × €500 = €60,000 |
| Less transition scrap | 90 t × €4,000 × 20 % = €72,000 |
| Less seasonal overtime | €150,000 × 20 % = €30,000 |
| Module result | €162,000 |
A peer-reviewed case study from food processing (source 10) reports 34 % less changeover time and 11 % more capacity; a methods paper (source 11) describes the procedure. The 20 % lever lies below. Value per line hour (about a third of the theoretical contribution margin), scrap and overtime are assumptions; part of the transition scrap is reusable.
| Parameter | Calculation |
|---|---|
| Remaining write-offs after module 01 | €570,000 − €171,000 = €399,000 |
| Fewer residual write-offs | €399,000 × 20 % = €79,800 |
| Fewer returns | €250,000 × 30 % = €75,000 |
| Module result | €154,800 |
The retail one-third rule and the finding that over three quarters of unsold goods in retail end as waste are institutionally documented (sources 12, 13). Storage temperature as the main driver of fat bloom is peer-reviewed (source 14). The remainder is derived from module 01, never set as a literal; reductions and returns are assumptions.
| Parameter | Calculation |
|---|---|
| Cocoa purchasing | 3,600 t × €6,000 = €21,600,000 |
| Avoided dispersion | €21,600,000 × 1.0 % = €216,000 |
| Series total | without this module |
| Module result (avoided dispersion, not added) | €216,000 |
That an optimal coverage ratio can lie below full hedging is peer-reviewed (source 15); a meta-analysis over 1,699 hedge ratios (source 16) shows the band. In-house research (sources 17, 18) is a negative result against price forecasting with an open calculation. The 1.0 % dispersion effect is an assumption without anchor and describes a risk measure, not a cost advantage.
| Parameter | Calculation |
|---|---|
| Fewer sensory complaints | €80,000 × 25 % = €20,000 |
| Avoided rework | €120,000 × 0,5 = €60,000 |
| Module result | €80,000 |
The standard for selection, training and monitoring of sensory assessors (source 19) documents the procedure, not the effect. All amounts are assumptions of the sample company. Sensory analysis finds drift, not foreign bodies.
| Parameter | Calculation |
|---|---|
| Review costs today | 1,5 × €70,000 = €105,000 |
| Automated review | €105,000 × 50 % = €52,500 |
| Module result | €52,500 |
Obligation and dates are officially documented (source 20): application from 30.12.2026 for large and medium-sized, from 30.06.2027 for small companies. In-house research (sources 21, 22) describes procedure and limits. Review effort and automation share are assumptions; supply risks are deliberately not quantified.
| Parameter | Calculation |
|---|---|
| Additional revenue | €2,850,000 × 5 % = €142,500 |
| Additional contribution margin | €142,500 × 40 % = €57,000 |
| Module result | €57,000 |
That targeting by effect beats targeting by risk is peer-reviewed (source 23). The 5 % effect and the contribution margin in direct sales are assumptions of the sample company.
* Module 06 (€216,000) is a risk measure and is not added to the series total.
Three scenarios, eight modules added up
That is 0.74 % of net revenue - as a gross effect before the costs of implementation, which are quantified in the conversation, not on this page.
Capital tied up in raw material and seasonal stock, energy for conching, tempering and cooling, sugar, milk powder and packaging are not calculated. The total is a gross effect before implementation costs. No module has independent evidence for its degree of effect; every lever ratio is an assumption that is replaced with your data.
Source list with evidence class
Evidence classes: peer-reviewed (tier 1), institutional (tier 2), industry and consulting studies (tier 3, never stand alone), in-house research with open calculation (tier 4). Retrieved on 27 August 2026.
- Bundesverband der Deutschen Süßwarenindustrie (BDSI): Confectionery industry in figures 2025 - 275 companies, 78.5 % with up to 249 employees; chocolate products 1.103 million t worth €8.373 billion. Tier 2
- International Cocoa Organization (ICCO): daily cocoa price of 26 August 2026, 5,896.83 US dollars per tonne; monthly reports 2025. Tier 2
- Eurostat: Food waste and food waste prevention, reference year 2022 - processing carries 19 % of food waste, 25 kg per head. Tier 2
- Gruen, Corsten, Bharadwaj (2002): Retail Out-of-Stocks, study for the Grocery Manufacturers of America - shelf gaps worldwide 8.3 %; reactions: 26 % brand switch, 9 % no purchase, 31 % other store, 15 % postponement, 19 % same brand. Tier 3
- Makridakis, Spiliotis, Assimakopoulos (2022): M5 accuracy competition, International Journal of Forecasting - best learning method 22.4 % more accurate than the best statistical reference on retail data. Tier 1
- McKinsey & Company: AI-driven operations forecasting in data-light environments - 20 to 50 % fewer forecast errors; stands only next to source 5. Tier 3
- Markenverband / European brand manufacturer survey (2022): a third of surveyed manufacturers affected or threatened by delisting (cited from business press). Tier 3
- Ailawadi, Harlam, César, Trounce (2006): Promotion Profitability for a Retailer, Journal of Marketing Research 43 - quantification of the net profit of promotions. Tier 1
- Nielsen (2015): Trade Promotion Performance - 59 % of promotions without break-even; consumer goods manufacturers invest around 20 % of revenue in trade promotions. Industry estimate next to source 8. Tier 3
- Maalouf, Zaduminska (2019): A case study of VSM and SMED in the food processing industry, Management and Production Engineering Review - changeover time −34 %, capacity +11 %. Tier 1
- Lozano, Saenz-Díez, Martínez et al. (2017): Methodology to improve machine changeover performance on food industry based on SMED, International Journal of Advanced Manufacturing Technology 90. Tier 1
- Verbraucherzentrale: Grocery retail and best-before date - one-third rule of delivery. Tier 2
- Thünen-Institut (2024): How food waste can arise - over three quarters of unsold goods in retail end as waste. Tier 2
- Zhao, James (2018): Fat bloom formation on model chocolate stored under steady and cycling temperatures, Journal of Food Engineering. Tier 1
- Rolfo (1980): Optimal Hedging under Price and Quantity Uncertainty: The Case of a Cocoa Producer, Journal of Political Economy 88. Tier 1
- Białkowski, Bohl, Perera (2023): Commodity futures hedge ratios: A meta-analysis, Journal of Commodity Markets 30, article 100276 - 1,699 hedge ratios. Tier 1
- myBytes Research: The Single-GARCH Limit on Soft Commodities - volatility model on cocoa, coffee, sugar, cotton; no early warning; open calculation, fixed seed. Tier 4
- myBytes Research: The Second Layer: regime model and lead time - cocoa detection on 16 May 2023 as a whisper, not an alarm; open calculation, fixed seed. Tier 4
- ISO 8586:2023: Sensory analysis - Selection and training of sensory assessors. Tier 2
- European Commission, Access2Markets (28 January 2026): Regulation (EU) 2025/2650 amending Regulation (EU) 2023/1115 - application from 30.12.2026 and 30.06.2027 respectively. Tier 2
- myBytes Research: Six Months before the New EUDR Deadline - placement of the amended regulation, 8 to 14 first placers per mid-sized company. Tier 4
- myBytes Research: Reliability of individual pixels in EUDR evidence - threshold sensitivity of the cocoa probability layer. Tier 4
- Ascarza (2018): Retention Futility: Targeting High-Risk Customers Might Be Ineffective, Journal of Marketing Research 55. Tier 1
Industry and consulting studies (tier 3) never stand alone in this series and never as potential; they complement a peer-reviewed or institutional anchor or are marked as an industry estimate.
All euro amounts are model calculations. They are derived from clearly named assumptions (assumption base → lever → result) and illustrate the order of magnitude, not a commitment. The total is a gross effect before implementation costs; no module has independent evidence for its degree of effect, the lever ratios are assumptions.
This calculation rests on publicly accessible sources and marked assumptions. It may be too high or too low; the reality in your factory decides. A data workshop puts your figures into the same formulas - then the question "where do these numbers come from" is answered, because you have seen how they arose.