DYNAMICS LAB · OPERATIONS TWIN · SCIENTIFIC REPORT
OTE-003 · Enterprise What-If Simulation

Study context

Problem addressed: How do hypothetical staffing, demand and automation assumptions change modeled queues?
Contribution relative to existing methods: Paired scenario runs separate the effects of stated assumptions from variation across runs.
Potential commercial use: Frame a capacity pilot with explicit hypotheses to test against real operations.
Market context and value assumptions on the engine page
OTE-003
Experiment conclusions

Across 50 replicates per scenario, hypothetical automation shortened the mean cycle from 49.14 to 48.81 simulated days. Higher demand or lower capacity lengthened the queue. The model shows the direction of the effects under the chosen assumptions.
Study limits. Capacity, service times and interventions are hypothetical. The differences do not demonstrate savings or improvements achievable in the company.
HYPOTHETICAL SCENARIO. Observed elapsed time, simulated service assumptions and business outcomes are distinct. This report does not assess individual employees or calculate monetary savings.
Objective and dataset

BPI Challenge2019 · public Purchase-to-Pay log · Boudewijn van Dongen · 4TU.ResearchData · version1 · CC BY4.0. Original input:728,558,522bytes, checked against official MD5.
Official dataset and attribution
Method and parameters

Category semi-Markov routes calibrated on historical elapsed gaps are combined with a separate hypothetical SimPy FIFO resource layer. Target activity is selected using calibration mean incoming elapsed delay among activities with100transitions. Default5percent of each target incoming gap is assumed to be service;95percent remains exogenous delay. Abstract capacity is designed from specified sample workload and target utilization0.7, not identified real staffing.750SHA-selected heldoutarrivals,50replicas per scenario,seed20261106. Seven scenarios: base,demand+30percent,capacity-25percent,assumed service-20percent,Change/Cancel transition weight-20percent,nonpreemptive temporary capacity reduction days30–45,and combined. Per-case/replicate/component random streams preserve original-agent draws; route interventions alter later correspondence. Nine service-share/utilization sensitivity cells use150cases and5replicas each. All outcomes are HYPOTHETICAL SCENARIOS / NOT VALIDATED. Paired MC95percent confidence intervals represent conditional model differences, not causal organizational improvements. No economic savings are calculated.
Hypothetical scenarios · paired conditional Monte Carlo

scenario	mean_cycle_day	median_cycle_day	p90_cycle_day	throughput_per_day	mean_queue_day	max_queue	change_cancel_rate	capacity
base	49.1438	45.4982	82.2327	6.60457	0.0404943	9	0.096	18
demand_growth	49.9848	46.3077	83.915	8.33391	0.848231	38.42	0.0961846	18
capacity_reduction	49.9497	46.3405	83.7386	6.42804	0.833284	29.56	0.096	14
automation	48.8078	45.171	81.7351	6.63217	0.00349738	3.94	0.096	18
rework_reduction	49.0947	45.4511	82.0259	6.61326	0.044026	9.58	0.0786933	18
disruption	49.1848	45.5357	82.2372	6.60457	0.0808639	13.2	0.096	18
combined	48.7972	45.1493	81.7255	8.62543	0.00514462	5.34	0.0786256	22
Sensitivity to unidentifiable service/capacity assumptions

cases	replicates	assumed_service_fraction	designed_target_utilization	cycle_mean_day	abstract_capacity
150	5	0.02	0.5	{"mean": 49.25787783333334, "median": 49.515520277777775, "std": 1.6959356638800962, "mean_CI95": [47.15209557622362, 51.363660090443055], "outcome_interval95": [47.211244712962966, 51.21798291666667], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	2
150	5	0.02	0.7	{"mean": 49.25787783333334, "median": 49.515520277777775, "std": 1.6959356638800962, "mean_CI95": [47.15209557622362, 51.363660090443055], "outcome_interval95": [47.211244712962966, 51.21798291666667], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	2
150	5	0.02	0.9	{"mean": 49.25787783333334, "median": 49.515520277777775, "std": 1.6959356638800962, "mean_CI95": [47.15209557622362, 51.363660090443055], "outcome_interval95": [47.211244712962966, 51.21798291666667], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	2
150	5	0.05	0.5	{"mean": 49.19659032407408, "median": 49.425282407407416, "std": 1.7005429474593319, "mean_CI95": [47.08508736881451, 51.30809327933365], "outcome_interval95": [47.16816069444445, 51.19091918981482], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	5
150	5	0.05	0.7	{"mean": 49.44045175925926, "median": 49.58405972222222, "std": 1.8207437477470416, "mean_CI95": [47.17969979772717, 51.701203720791355], "outcome_interval95": [47.373254027777776, 51.69036199074075], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	4
150	5	0.05	0.9	{"mean": 50.9871, "median": 50.562632175925934, "std": 2.2530503830561703, "mean_CI95": [48.18956845321995, 53.78463154678005], "outcome_interval95": [48.88720733796296, 54.15403518518519], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	3
150	5	0.1	0.5	{"mean": 49.1608199074074, "median": 49.370186574074076, "std": 1.7043607411363315, "mean_CI95": [47.044576535186536, 51.277063279628265], "outcome_interval95": [47.12742527777778, 51.17223638888889], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	10
150	5	0.1	0.7	{"mean": 49.84504555555556, "median": 49.82135138888889, "std": 2.028403781171778, "mean_CI95": [47.32644960665417, 52.36364150445694], "outcome_interval95": [47.792164166666666, 52.53144087962964], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	7
150	5	0.1	0.9	{"mean": 51.04702185185185, "median": 50.637485185185184, "std": 2.2849701404417346, "mean_CI95": [48.20985669149475, 53.884187012208955], "outcome_interval95": [48.786268148148146, 54.169822407407416], "replicates": 5, "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"}	6
Calculated figures

OTE-003 computed research figure — Generated from the result JSON. No manually inserted scientific values.
Uncertainty and limitations

All resource/service parameters are explicitly hypothetical; historical elapsed intervals cannot identify them.
These are model responses, not demonstrated organizational improvements or causal effects.
Change/Cancel intervention changes transition weights20percent then renormalizes; it does not guarantee20percent fewer repeats.
Arrival count grows30percent within the selected cohort; throughput levels cannot be extrapolated to the whole company.
No money, staff productivity or guaranteed savings are calculated.
Sensitivity design changes both assumed share and designed slots, rather than identifying a unique real capacity.
Evidence and reproducibility

Acquisition: 2026-11-06T11:24:17.701083+00:00 · Execution: 2026-11-06T12:21:11.116276+00:00.
Scientific fingerprint 6855a0170325dffa811a51831f47be6741f66f879d8619900076fddba8db8742
Results JSON · Manifest
Complete computed result
{
  "experiment": "OTE-003",
  "title": "Enterprise What-If Simulation",
  "kind": "HYPOTHETICAL SCENARIO",
  "status": "NOT VALIDATED",
  "seed": 20261106,
  "cases_base": 750,
  "replicates_per_scenario": 50,
  "scenarios": {
    "base": {
      "kind": "HYPOTHETICAL SCENARIO",
      "status": "NOT VALIDATED",
      "parameters": {
        "model": "semi_markov",
        "seed": 20261106,
        "replicate": 2049,
        "scenario": "base",
        "resource_layer": true,
        "horizon_day": 92.0,
        "service_fraction": 0.05,
        "designed_basecapacity": 18,
        "target_utilization": 0.7,
        "changes": {
          "demand_growth": 0.0,
          "capacity_factor": 1.0,
          "service_factor": 1.0,
          "rework_factor": 1.0
        }
      },
      "metrics": {
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          "median": 49.049618518518514,
          "std": 0.7753109483040757,
          "mean_CI95": [
            48.9235027784976,
            49.364184645576465
          ],
          "outcome_interval95": [
            47.82207080208333,
            50.80024456365741
          ],
          "replicates": 50,
          "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
        },
        "median_cycle_day": {
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          "std": 1.1979852958739496,
          "mean_CI95": [
            45.15768912376028,
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          "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
        },
        "p90_cycle_day": {
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          "std": 1.5594020017467882,
          "mean_CI95": [
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            85.36441145833335
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          "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
        },
        "throughput_per_day": {
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          "mean_CI95": [
            6.576816708468054,
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        },
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        "max_queue": {
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        },
        "unfinished_cases": {
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        },
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        }
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      "assumed_service_fraction": 0.02,
      "designed_target_utilization": 0.9,
      "cycle_mean_day": {
        "mean": 49.25787783333334,
        "median": 49.515520277777775,
        "std": 1.6959356638800962,
        "mean_CI95": [
          47.15209557622362,
          51.363660090443055
        ],
        "outcome_interval95": [
          47.211244712962966,
          51.21798291666667
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 2
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.05,
      "designed_target_utilization": 0.5,
      "cycle_mean_day": {
        "mean": 49.19659032407408,
        "median": 49.425282407407416,
        "std": 1.7005429474593319,
        "mean_CI95": [
          47.08508736881451,
          51.30809327933365
        ],
        "outcome_interval95": [
          47.16816069444445,
          51.19091918981482
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 5
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.05,
      "designed_target_utilization": 0.7,
      "cycle_mean_day": {
        "mean": 49.44045175925926,
        "median": 49.58405972222222,
        "std": 1.8207437477470416,
        "mean_CI95": [
          47.17969979772717,
          51.701203720791355
        ],
        "outcome_interval95": [
          47.373254027777776,
          51.69036199074075
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 4
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.05,
      "designed_target_utilization": 0.9,
      "cycle_mean_day": {
        "mean": 50.9871,
        "median": 50.562632175925934,
        "std": 2.2530503830561703,
        "mean_CI95": [
          48.18956845321995,
          53.78463154678005
        ],
        "outcome_interval95": [
          48.88720733796296,
          54.15403518518519
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 3
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.1,
      "designed_target_utilization": 0.5,
      "cycle_mean_day": {
        "mean": 49.1608199074074,
        "median": 49.370186574074076,
        "std": 1.7043607411363315,
        "mean_CI95": [
          47.044576535186536,
          51.277063279628265
        ],
        "outcome_interval95": [
          47.12742527777778,
          51.17223638888889
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 10
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.1,
      "designed_target_utilization": 0.7,
      "cycle_mean_day": {
        "mean": 49.84504555555556,
        "median": 49.82135138888889,
        "std": 2.028403781171778,
        "mean_CI95": [
          47.32644960665417,
          52.36364150445694
        ],
        "outcome_interval95": [
          47.792164166666666,
          52.53144087962964
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 7
    },
    {
      "cases": 150,
      "replicates": 5,
      "assumed_service_fraction": 0.1,
      "designed_target_utilization": 0.9,
      "cycle_mean_day": {
        "mean": 51.04702185185185,
        "median": 50.637485185185184,
        "std": 2.2849701404417346,
        "mean_CI95": [
          48.20985669149475,
          53.884187012208955
        ],
        "outcome_interval95": [
          48.786268148148146,
          54.169822407407416
        ],
        "replicates": 5,
        "assumption": "Independent seeded simulation replicates conditional on fixed data/model; not total epistemic uncertainty"
      },
      "abstract_capacity": 6
    }
  ],
  "target_activity": "Clear Invoice",
  "target_selection": "Largest mean incoming elapsed gap among activities with at least100calibration transitions; potential observed delay, not proven bottleneck",
  "assumptions": {
    "service_fraction_of_observed_elapsed_gap": 0.05,
    "designed_utilization": 0.7,
    "horizon_day": 92.0,
    "queue": "FIFO shared abstract slots for selected activity; not inferred actual personnel",
    "disruption": "Nonpreemptive capacity hold requested day30,released no earlier than45; active work is never interrupted. Late acquisition has shorter/no hold; actual replay records it.",
    "arrivals": "Observed selected test-cohort starts; demand growth draws extra empirical starts. Not a population-wide demand forecast.",
    "common_random_numbers": "Per-case,replicate,component seeds preserve unchanged original agents across scenarios; route modifications can alter later draw correspondence."
  },
  "limits": [
    "All resource/service parameters are explicitly hypothetical; historical elapsed intervals cannot identify them.",
    "These are model responses, not demonstrated organizational improvements or causal effects.",
    "Change/Cancel intervention changes transition weights20percent then renormalizes; it does not guarantee20percent fewer repeats.",
    "Arrival count grows30percent within the selected cohort; throughput levels cannot be extrapolated to the whole company.",
    "No money, staff productivity or guaranteed savings are calculated.",
    "Sensitivity design changes both assumed share and designed slots, rather than identifying a unique real capacity."
  ],
  "results_sha256": "6855a0170325dffa811a51831f47be6741f66f879d8619900076fddba8db8742"
}