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Phorecaster365: A Human-Supervised Reference Architecture for Hybrid Pharmaceutical Sales Forecasting and Planning Decision Support

Houman Kazemzadeh, Kamyar Naderi

arXiv:2609.13907v1cs.CLcs.AI

TL;DR

Pharmaceutical sales patterns can reflect demand, availability, fulfillment, distribution, or reporting conditions, so a prediction alone may not preserve the information needed for planning review. Phorecaster365 proposes a human-supervised architecture that links forecast inputs and evidence to review and publication. Its development metrics remain descriptive and do not establish real-world forecasting performance or operational benefit.

  • Problem

    Sales forecasts are difficult to interpret when transaction semantics, availability, and information known at issuance are not preserved alongside predictions.

    Source: §1 · p. 2

  • Method

    Phorecaster365 connects enterprise data to reviewable forecasts through a context package that records the target, history, permitted covariates, data states, horizon, and temporal and hierarchy metadata.

    Source: §1 · p. 2, §4.3 · p. 5

  • Results

    420 window-level diagnostics had a macro mean RMSE of 12.55 units, but these historical quantities do not estimate corrected-pipeline or future-deployment performance.

    Source: §9.2 · p. 14

  • Takeaways & Limitations

    The architecture and proposed evaluation pathway specify requirements for testing real-world accuracy and operational outcomes before planning reliance.

    Source: §14 · p. 18

    Evidence is limited to a clean one-year synthetic panel and historical outputs, with no independent calibration study, user study, live ERP assessment, or prospective evaluation.

    Source: §13 · p. 18

Abstract

from arXiv · show

Pharmaceutical sales forecasts inform planning across products, regions, and distribution channels, yet their interpretation depends on inventory availability, transaction semantics, product lifecycle, and the information available when each forecast is issued. A model prediction alone does not preserve these conditions or establish whether a forecast is suitable for operational use. We present Phorecaster365, a human-supervised reference architecture that connects enterprise resource planning data to reviewable pharmaceutical sales forecasts. The architecture separates source ingestion, product-region series construction, temporally valid feature generation, statistical and machine-learning modeling, ensemble formation, uncertainty assessment, planner review, and lifecycle governance. Its central intermediate representation is a forecast context package that preserves the source snapshot, forecast target, temporal cutoff, available covariates, data-quality state, and hierarchy version. A corresponding forecast evidence package links predictions to model and calibration versions, exceptions, human adjustments, and publication history. Development experience with a synthetic panel of 10,950 daily records across 30 product-region series informs the design. Historical experiment summaries are retained only as descriptive evidence of development because their evaluation does not establish independent predictive validity. We specify a rolling-origin evaluation protocol, baseline and ablation requirements, uncertainty and robustness assessments, and a staged pathway from synthetic testing to use in governed planning. The contribution is an implementation-neutral system design and validation framework; the report does not establish real-world forecasting accuracy, comparative superiority, or operational benefit.

1 Izlek Technology, Ankara, Türkiye

Pharmaceutical sales patterns can reflect demand, stock availability, fulfillment, coverage, or reporting changes, which carry different planning implications. Phorecaster365 proposes a modular, human-supervised architecture that preserves forecast context and evidence for evaluation and review.

1 Introduction

  • Sales declines can reflect lower demand, unavailable stock, delayed fulfillment, changed distribution coverage, or reporting revisions, despite similar time-series patterns.These explanations have different planning implications.Source: §1 · p. 2
  • Phorecaster365 links ERP systems and planning workflows through modular daily product–region forecasting over a 30-day horizon.Statistical seasonal signals and tree-based predictors can be evaluated within a common temporal framework.Source: §1 · p. 2
  • Each forecast retains issuance-time data, target, feature, model, calibration, and review information to distinguish model predictions from planner-approved forecasts.This provenance supports reproducible evaluation, accountable oversight, and controlled use in planning workflows.Source: §1 · p. 2
  • The report specifies a temporal data contract, context and evidence packages, evaluation design, and deployment governance, without claiming a new forecasting algorithm.The temporal contract distinguishes observed sales, availability constraints, and information known at the forecast cutoff.Source: §1 · p. 2

2 Related work and design rationale

  • Phorecaster365 places model comparison within stable data, evidence, and publication contracts, regardless of prediction-engine family.The design draws on forecasting, error-measurement, hierarchy-reconciliation, and uncertainty methods while retaining the need to test the complete workflow.Source: §2 · p. 3

3 Intended use and system requirements

The system forecasts declared units-sold targets for planning review, not unconstrained demand or downstream purchasing and production decisions. Its proposed requirements preserve temporal provenance, data states, and the information boundary shared by forecasting components.

3 Intended use and system requirements

  • Forecasts support review by planners and analysts, but do not determine purchase quantities, production schedules, or distribution allocations.Those decisions require additional objectives and constraints such as inventory, lead times, expiry dates, service policies, and capacity.Source: §3 · p. 3
  • The initial target is units sold under a declared transaction convention; revenue and unconstrained demand require separate conventions or demand information.The report distinguishes lost sales, backorders, and other sources of demand information from recorded sales.Source: §3 · p. 3

4.1 Canonical entities and temporal provenance

  • Historical replays must reconstruct what was available at the forecast cutoff, including versioned master data and plans, while validation failures can hold or quarantine affected scopes.Late actuals may score a past forecast under a declared policy without changing its original input snapshot.Source: §4.1 · p. 4

4.2 Sales availability and demand censoring

  • Availability and censoring states distinguish realized sales from demand; zeros, missing feeds, stockouts, and returns cannot be treated as interchangeable observations.A demand-reconstruction model would require separately evaluated assumptions, outputs, and uncertainty.Source: §4.2 · p. 5

4.3 Context representation

  • The forecast context package holds the target, history, observation states, permitted covariates, horizon, temporal and hierarchy metadata, quality decision, and exclusions.Scenario inputs such as proposed promotions remain separate from the operational information set and do not identify causal effects.Source: §4.3 · p. 5

5 Reference architecture

The reference architecture separates forecast production from governance, connecting validated enterprise records to reviewable forecasts while reserving publication authority for configured controls. It defines services for series construction, modeling, uncertainty checks, review, and publication.

5 Reference architecture

  • The data path transforms validated records into forecast evidence, while governance controls access, versioning, exceptions, model changes, and publication.The governance layer applies to every stage; outcome monitoring can initiate candidate evaluation but does not authorize release.Source: §5 · p. 6, Figure 1 · p. 6

5.1 Ingestion and series construction

  • Ingestion freezes a source snapshot, validates data, and builds daily product–region observations with separate gross sales, returns, and net sales.The service preserves lifecycle and remapping information; a regular date index alone does not establish observed sales for every date.Source: §5.1 · p. 6

5.2 Feature and modeling services

  • Feature and modeling services use only context-permitted inputs and return predictions, fitting metadata, and failures through a common interface.The architecture allows local or pooled models, but pooled learning is an extension requiring separate comparison; organization boundaries must remain preserved in multi-organization deployments.Source: §5.2 · p. 7

5.3 Uncertainty and consistency services

  • Uncertainty and consistency services version calibration evidence, record interval status, and check horizon completeness, dates, units, values, and hierarchy relationships.Any transformation that changes a forecast must be included in the evaluated pipeline.Source: §5.3 · p. 7

5.4 Review and publication services

  • Review exposes forecasts with context and exceptions, while publication accepts only versions meeting configured approval rules and records export acknowledgments.The design permits integration with existing planning systems or separate review applications, but requires site-specific integration testing.Source: §5.4 · p. 7

6 Hybrid forecasting methodology

The hybrid design combines statistical signals and tree predictors within a temporal feature policy, explicit ensemble and multi-step forecasting choices, and hierarchy-aware aggregation and fallback behavior.

6.1 Statistical signals and tree predictors

  • Prophet supplies a statistical signal to tree predictors in the later hybrid, while the final ensemble weights tree outputs; Prophet-only forecasting remains a comparator.A Prophet-derived input feature is distinct from assigning Prophet a separate ensemble weight.Source: §6.1 · p. 7

6.2 Temporal feature policy

  • Features and transformations must respect the issuance-time information boundary, excluding target-day outcomes and using only covariates known by the forecast cutoff.Prophet training features require temporal out-of-fold predictions; approved plans need documented knowledge times, and preprocessing is fitted within the temporal training window.Source: §6.2 · p. 8

6.3 Ensemble formation and multi-step prediction

  • Ensemble weights are nonnegative and sum to one; performance-based weights use earlier validation predictions, with shrinkage or pooling worth evaluating when validation data are sparse.Weighting is not selected using outer-test outcomes.Source: §6.3 · p. 8
  • The prototype’s recursive 30-day forecasts update future lags and rolling summaries with predictions, not observed test-period sales, throughout the horizon.A direct strategy with horizon-specific targets is a planned comparator, but requires enough training examples at each horizon.Source: §6.3 · p. 8

6.4 Hierarchy consistency and fallback behavior

  • Product–region predictions can be aggregated to product, region, and portfolio totals, but point coherence does not ensure calibrated aggregate or cumulative intervals.Aggregate uncertainty requires cross-series dependence or separate aggregate evaluation; cumulative intervals require joint path information or a separately evaluated procedure.Source: §6.4 · p. 9
  • Fallbacks are explicit: seasonal naïve can serve series with adequate weekly history, sparse or new products may use approved pooled or analog estimates, and unsupported cases are held for review.Reusing the last approved plan is age-limited business continuity, not a new model forecast.Source: §6.4 · p. 9

7 Forecast evidence and planner interaction

The section specifies a traceable forecast evidence package, a review workflow that versions human decisions, and a matched-comparison approach to measuring adjustments.

7.1 Evidence package and lineage

  • The evidence package links the context package to predictions, ensemble and calibration state, model versions, quality checks, and review decisions, making published values reconstructible.It preserves distinctions among measured inputs, modeled outputs, scenario assumptions, and human adjustments.Source: §7.1 · p. 9, Figure 2 · p. 9

7.2 Review workflow

  • Reviewers can inspect forecast and data evidence, then accept, adjust, defer, or escalate; adjustments record reasons and evidence in a new version while retaining the original model forecast.Approval and publication are separate events, and exception prioritization should display its rationale.Source: §7.2 · p. 10

7.3 Measuring human adjustments

  • Forecast value added compares earlier and later losses on matched series, origins, horizons, and actuals, while reviewer comparisons must account for selective assignment of difficult cases.An association between overrides and errors alone does not establish that a reviewer caused the change.Source: §7.3 · p. 10

8 Evaluation protocol

The protocol separates temporal model selection, calibration, and testing, then specifies matched baselines, metrics, dependence-aware inference, and a final confirmation stage.

8.1 Study questions and temporal design

  • Each outer origin replays a forecast at a historical cutoff, with earlier inner folds for selection and calibration separated from the frozen outer test.Calibration residuals must come from issued predictions whose full outcomes matured before issuance; the protocol and aggregation rules are fixed before confirmation.Source: §8.1 · p. 11, Figure 3 · p. 11
  • Calibration design trades off data use and separation: strict split calibration freezes the point model, while operational replay uses earlier origins under the declared refitting policy.Refitting on calibration labels while retaining earlier split-calibration residuals changes the predictor, and nominal intervals are not distribution-free coverage guarantees.Source: §8.1 · p. 11

8.2 Baselines and ablations

  • Methods are compared at identical origins and horizons against naïve, moving-average, Prophet, individual-tree, ensemble, and available incumbent forecasts; ablations must preserve other conditions.The retained development summaries are not a controlled ablation study.Source: §8.2 · pp. 11–12, Table 3 · p. 12

8.3 Metrics and aggregation

  • The protocol reports target-unit, percentage, and scaled errors with explicit denominator conventions, alongside bias by series and horizon and both macro and exposure-oriented summaries.Uncertainty uses coverage, width, and interval score; system reliability measures include publication success, fallback rate, freshness, runtime, and acknowledgment latency.Source: §8.3 · p. 12

8.4 Dependence and confirmation

  • Overlapping windows and shared shocks make window losses dependent, so comparative uncertainty needs justified dependence-aware resampling or inference and a final untouched confirmation period.Benchmark superiority requires matched comparisons and practically meaningful effects; operational gains require separate downstream evaluation.Source: §8.4 · p. 13

9 Synthetic development experience

The reported development uses a synthetic product–region panel and retained historical diagnostics, while explicitly limiting what those materials establish.

9.1 Dataset characterization

  • The synthetic panel has 10,950 daily records across 30 complete product–region series, but its completeness and single annual cycle limit the conditions exercised.It does not directly test missing feeds, intermittent demand, zero-sales periods, or late corrections; its patterns are not estimates of actual pharmaceutical demand.Source: §9.1 · p. 13

9.2 Development progression and diagnostic status

  • The historical rolling summary reports 420 window-level diagnostics, with macro mean RMSE of 12.55 units, macro MAPE of 10.09%, and sMAPE of 9.71%.These historical diagnostics do not estimate corrected-pipeline or deployment performance; the underlying experiments had leakage and evaluation-separation limitations.Source: §9.2 · p. 14

9.3 What the present evidence supports

  • The evidence supports a descriptive synthetic panel and an exercised forecasting prototype, but not controlled superiority, calibrated coverage, planner benefit, or real ERP deployment.The proposed reference protocol is prospective; corrected experiments need frozen data and their own result records.Source: §9.3 · p. 14

10 Uncertainty and robustness

Phorecaster365 treats uncertainty, robustness, monitoring, release, and governance as evidence-governed parts of forecasting. The report specifies checks for these functions but does not establish their completion in a real ERP setting.

10.1 Calibration at the decision horizon

  • Calibration must match the target horizon and issuance procedure; generic residual quantiles or train/calibration splits do not inherit validity without method-specific assumptions and testing.Coverage must also be named precisely: marginal event coverage, product-and-origin conditional coverage, and whole-path coverage are different quantities.Source: §10.1 · p. 14

10.2 Stress tests and expected system behavior

  • Synthetic stress tests can verify intended system responses, but cannot establish how often those events occur in a real ERP environment.The proposed generator documentation includes distributions, dependencies, availability mechanisms, event timing, seeds, and induced failure modes; censoring tests should separate latent demand from realized sales.Source: §10.2 · p. 15

11.1 Monitoring with delayed outcomes

  • Performance monitoring begins only after actuals mature under the finalization policy, while data and prediction checks can run earlier.Drift indicators are investigation signals rather than proof of performance deterioration; thresholds and review cadence depend on intended use and false-alert burden.Source: §11.1 · p. 15

11.2 Retraining and release

  • Retraining produces a candidate for review, not an automatic replacement; rollback must preserve compatible features, calibration, context schema, and hierarchy mapping.Previously consumed forecasts remain in downstream systems, so corrections or superseding publications identify affected versions and follow-up.Source: §11.2 · p. 16

11.3 Governance and access

  • Governance assigns ownership across data meaning, model behavior, planning review, operations, and release approval, with auditable actions and role-restricted access.Referencing the NIST AI Risk Management Framework and documentation practices does not establish certification or compliance; the task requires no patient-level data.Source: §11.3 · p. 16

12 Deployment pathway and prospective validation

The proposed pathway moves from synthetic testing toward governed planning only as evidence supports each next use. The report leaves that pathway and its design specifications requiring implementation and validation.

12 Deployment pathway and prospective validation

  • The five-stage pathway advances according to evidence for the next use, and a failed assessment or later incident can return or suspend a component.The synthetic development record does not establish completion of later stages.Source: §12 · pp. 16–17, Figure 4 · p. 17
  • The sequence runs from synthetic contract and failure testing through retrospective ERP replay, shadow operation, a bounded planner-assisted pilot, and governed operation.The stages successively assess historical operational semantics, live data and computation outside active decisions, planner usefulness, and ongoing monitoring and change control.Source: §12 · p. 17
  • Retrospective ERP studies should cover multiple seasonal cycles where feasible, representative products and regions, stockouts and returns, and historical commercial-plan versions.They should report exclusions and supported-series coverage; clean-subset performance cannot be generalized to excluded products without evidence.Source: §12 · p. 17
  • A planner-assisted pilot should predefine users, products, horizon, review rules, and comparison process, then assess workflow and forecast-value endpoints.Service-level, stockout, waste, or financial outcomes additionally require control for ordering-policy, supply-availability, and other changes; forecast-error gains alone do not establish those benefits.Source: §12 · p. 17

13 Discussion and limitations

  • The architecture entails storage and operational costs, while strict temporal splits can constrain fitting or uncertainty estimation and pooled models add evaluation and isolation requirements.Local models are easy to associate with series but may be unstable with short histories; the trade-offs should be measured in the intended setting.Source: §13 · p. 18
  • Evidence is limited to a clean, one-year synthetic panel and historical outputs, with no independent calibration study, controlled ablation, user study, live ERP assessment, or prospective outcome evaluation.The generator is undocumented, and the proposed packages, controls, and release pathway remain design specifications requiring implementation and validation.Source: §13 · p. 18
  • The report omits source code, pseudocode, hyperparameters, and proprietary implementation details, so it does not provide an independently executable prototype reproduction.The authors state that public data and reproducible result records would strengthen subsequent empirical work.Source: §13 · p. 18
  • Future work prioritizes a frozen retrospective ERP study, calibrated multi-horizon forecasts, and a monitored planner-assisted pilot before comparing further model extensions.More complex model families are justified only if they improve relevant accuracy, reliability, or workflow outcomes under that evaluation.Source: §13 · p. 18

14 Conclusion

Phorecaster365 is a human-supervised reference architecture linking enterprise records to reviewable, versioned pharmaceutical sales forecasts. Its synthetic development record leaves real-world accuracy and operational benefit unresolved.

14 Conclusion

  • Phorecaster365 specifies how enterprise records become temporally valid inputs and how predictions retain evidence and uncertainty status through planner review and versioned publication.The proposed evaluation and deployment pathway sets requirements for testing outcomes before planning reliance.Source: §14 · p. 18

Data availability

  • The development data are synthetic, and this version provides neither a public data repository nor a generation procedure.Reported descriptive statistics and historical diagnostics are not a reproducible data-generation package.Source: Data availability · p. 19

Code availability

  • Source code and implementation artifacts are not publicly released with the report.The report discloses the conceptual methodology, evidence boundaries, and proposed validation process.Source: Code availability · p. 19

Competing interests

  • The authors’ affiliations with Izlek Technology and Xylemed are relevant to further development and potential commercialization of Phorecaster365.The report asks readers to consider these commercial affiliations when interpreting it.Source: Competing interests · p. 19

Funding

  • The work received no external grant funding and was undertaken as part of the authors’ professional activities at Izlek Technology and Xylemed.Source: Funding · p. 19

Ethics and scope

  • The report uses synthetic aggregate sales records and reports no human participants, identifiable patient records, or clinical intervention.No formal ethics committee determination is claimed; later institutional-data or participant studies require applicable review and governance processes.Source: Ethics and scope · p. 19

Appendix A: Synthetic Data Dictionary

The appendices describe the synthetic panel, preserve historical diagnostics without validated rankings, and specify minimum release evidence. They do not claim that the proposed release criteria have been met.

Appendix A: Synthetic Data Dictionary

  • The synthetic ERP data dictionary covers all 21 development-panel fields, while production use requires definitions, units, temporal provenance, and an approved purpose for each field.Table A1 is titled as a listing of fields in the synthetic ERP panel.Source: Appendix A · p. 22, Table A1 · p. 22

Appendix B: Historical diagnostic record

  • Table B1 retains selected historical diagnostics, but its values were not recomputed under the proposed confirmatory protocol and do not rank models through controlled ablations.MAE and RMSE are expressed in units sold.Source: Appendix B · p. 23, Table B1 · p. 23
  • 12.55 units is the macro-average RMSE across 420 rolling-origin windows on 30 series, while mean series-level rolling RMSE ranges from 10.09 to 14.96 units.These historical outputs remain affected by feature-generation and model-selection limitations; the approximate 90% residual-derived visualization interval lacks independent coverage validation.Source: Appendix B · p. 23

Appendix C: Minimum release evidence

  • A candidate release should document its target, users, data contract, temporal reconstruction, benchmark and ablation evidence, calibration, robustness, supported scope, workflow, and operational controls.The proposed framework records approval status and conditions; completion is not claimed for the current development system.Source: Appendix C · p. 23
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