RESEARCH DISCIPLINE
Statistical Analysis for Operations & Supply Chain Management Research
Consulting for Plant, Supplier & Demand Studies
Supply chain research statistical analysis for demand and lead-time series, plant and supplier performance data, inventory studies and simulation experiments, with serial dependence and intermittent demand handled in the models and results prepared for your journal.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Operations & Supply Chain Management Research
Supply chain research statistical analysis is the statistical design, analysis and reporting of studies of how goods are planned, produced, stored and delivered, using demand, lead-time and inventory series, plant and supplier performance records and simulation experiments. These data are autocorrelated and seasonal, many items sell in only some periods, lead times are skewed and incomplete for orders still open, and plants and suppliers recur across records, so a workflow built on independent, Gaussian-error models misstates both uncertainty and forecast accuracy. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides operations management data analysis through in-house PhD-level statisticians, who settle the unit, the time structure and the accuracy measure with you before any model is fitted.
WHEN TO ENGAGE
Statistical Support at Different Stages of Operations & Supply Chain Management Research
Operations researchers tend to bring us a study when time dependence, open orders or simulation output make the standard model doubtful.
Designing an Operations Study You are planning a study of plants, suppliers or inventory policies and need the unit, the observation period, the outcome measure and the comparison defined before data are collected.
Modeling Demand or Lead Times Demand, lead-time or inventory series are ready, and the model for trend, seasonality and zero periods, and the way forecasts will be evaluated, need to be chosen and checked.
Planning a Simulation Experiment A simulation of a supply network or queue is to be run, and its scenarios, replications, random-number seeds and output analysis must be specified so the conclusions can be reproduced.
Reviewing a Forecasting Analysis A forecast comparison or an operations model has drawn comments on its accuracy measure, validation or dependence, and the analysis needs an independent review.
RESEARCH CONTEXT
Research Designs and Data We Work With in Operations & Supply Chain Management
Operations data are mostly records over time at an item, plant or supplier, and each source brings its own dependence and its own gaps.
01
Demand and Sales Series
Weekly or daily demand by item and location, with trends, seasonal cycles, promotions and many periods with no sales at all.
02
Lead-Time and Delivery Data
Order-to-delivery times that are right-skewed and incomplete for orders still open when the data were pulled, repeated across suppliers and routes.
03
Inventory and Stockout Records
Stock levels, replenishment and stockout events by item and site, where one period's inventory depends on the previous period's.
04
Plant and Supplier Performance Panels
Defect, on-time and cost measures for plants or suppliers over months, with plants, production lines and suppliers repeated across records.
05
Operations Surveys
Questionnaires to plant or supply chain managers about practices and performance, with several informants per firm and multi-item scales.
06
Simulation Experiments
Discrete-event or Monte Carlo models of networks and policies, run over scenarios and replications, where the output is itself a designed experiment.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Operations & Supply Chain Management Research
These checks come before a forecast or an operational comparison is trusted, because dependence over time and the structure of the records can change a conclusion more than the model.
Serial Dependence and Seasonality Demand and lead-time series are autocorrelated and seasonal, so residual dependence is examined, and independent-error models are not used where they misstate the uncertainty.
Intermittent and Zero Demand Items that sell in few periods need models for counts or intermittent demand, not Gaussian forecasting, and the zero periods are modeled as part of the process, not removed.
Choosing a Forecast Accuracy Measure Percentage errors fail at zero demand. Scaled errors are used on rolling forecast origins, and every method is compared with a simple benchmark on the same periods.
Incomplete Lead Times Orders still open are right-censored. Averaging only delivered orders understates lead time, so censoring is handled with time-to-event methods or stated as a limit.
Plants, Lines and Suppliers as Units Records repeat within plants and suppliers, so a model that treats each record as independent is too precise. The grouping is modeled, and conclusions are limited to the number of plants or suppliers.
Reproducible Simulation Output Scenario assumptions, replications, warm-up periods and random seeds are documented, and output is analyzed with intervals across replications, so operational conclusions can be reproduced.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the time structure and the outcome in your data, and the line under each title explains why it is used in operations research.
Longitudinal & Multilevel Analysis
Demand, lead-time and inventory series are modeled with methods that represent trend and seasonality, and plants or suppliers with repeated records with panel or mixed models.
Regression & Predictive Modeling
Defect counts and intermittent demand follow count distributions, so Poisson and negative binomial models are used with overdispersion checked, and linear regression serves continuous cost outcomes.
Clinical, Survival & Epidemiological
Open orders and equipment lifetimes are censored, so survival methods use every record, and an interrupted time series estimates the change after a new ordering rule.
Bayesian & Computational Methods
Simulation experiments are analyzed with intervals across replications, and block bootstrap intervals describe accuracy measures computed on dependent forecast errors.
Comparative & Hypothesis Testing
Plants compared across practices use rank-based tests for skewed or ordinal outcomes, Kruskal-Wallis for separate groups and Friedman where the same plants are observed under several conditions.
Structural & Latent Variable Modeling
Survey constructs such as supplier integration are validated with confirmatory models before they are related to performance, with PLS-SEM considered for composite-based models.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of an operations study, from the review of item and site identifiers to the scripts that regenerate every figure.
Data & Study Review Item, site and date identifiers, units of measure, the treatment of returns and stockouts, censoring dates for open orders and the seeds of any simulation are reviewed before modeling.
Statistical Analysis Time-series, count, survival or panel models with checks for residual autocorrelation and overdispersion, forecast evaluation on rolling origins and the planned sensitivity analyses.
Effect Estimates & Uncertainty Forecast errors with intervals, rate or hazard ratios with confidence intervals, lead-time quantiles from survival estimates and simulation outputs with intervals across replications.
Tables & Figures Series and decomposition plots, forecast comparison tables against benchmarks, survival curves for delivery times, replication summaries and counts of items, plants and suppliers retained.
Statistical Interpretation Findings are explained for operational decisions, accuracy is stated for the evaluation periods used, and the limits of simulated scenarios and plant comparisons are noted.
Analysis Files R scripts, Python code, SAS programs or Stata do-files, with random seeds, scenario settings and forecast origins documented, so every figure can be regenerated.
REPORTING
Reporting Operations & Supply Chain Management Research
Operations journals name few reporting standards, so the guidance below is matched to simulation, forecasting or survey work as each applies.
Simulation studies of discrete-event models follow the STRESS-DES guidelines (Monks and colleagues, 2019), which cover the objectives, the model logic, the experimentation and the implementation, including random numbers and replications. Forecasting comparisons state the benchmarks, the accuracy measure, the rolling origins and the forecast horizon, so another team can repeat the evaluation. Survey components of operations studies follow APA JARS-Quant, with the reliability of each scale and the response rate by firm. Data and code requirements for your target journal are checked last, and each accuracy measure is reported with its interval.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a forecasting comparison on sparse demand records reaches accuracy estimates that can be rerun.
ILLUSTRATIVE EXAMPLE · SPARE PARTS DEMAND FORECASTING RESEARCH · UNITED STATES
A research group at an industrial parts distributor compared forecasting methods for spare parts, most of which sold in only a few weeks of the year.
Most items had no sales in most weeks, and a first comparison had used percentage errors, which are undefined in zero-sales weeks. Before any forecast was scored, the written plan named the methods to be compared, a scaled error measure, rolling forecast origins with identical periods for every method, a simple benchmark, and block bootstrap intervals for differences in accuracy between methods. Items were grouped by demand pattern in advance, so conclusions could be stated separately for intermittent and for regular items. Random seeds and the origins were recorded so the whole comparison could be rerun.
Outcome: The group received accuracy by method and demand pattern with 95% bootstrap confidence intervals, the paired differences against the benchmark, the series and error plots and the scripts with fixed seeds.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
SOFTWARE
Statistical Software for Operations & Supply Chain Management Research
Operations analyses are run in the software your team already uses, with scripts and seeds provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Operations & Supply Chain Management
Short answers to what operations researchers ask before sharing a study, from zero-demand items and open orders to reproducible simulation, confidentiality and the assessment.
What statistical methods are used in operations and supply chain research?
Operations and supply chain research most often uses time-series models for demand, count regression for intermittent items, survival methods for lead times and failures, panel or mixed models for plants and suppliers, and simulation. In operations management research statistics, the choice follows the time structure of the records and the scale of the outcome. Our Statistical Methods pages show how each family is applied.
How do you forecast items with many zero-demand periods?
Items with many zero periods need methods built for intermittent demand, and accuracy is measured with a scaled error, not a percentage error, which is undefined in zero-demand periods. Demand forecasting analysis for such items models counts or the timing and size of demand separately, evaluates methods on rolling origins against a simple benchmark, and reports the uncertainty of each accuracy comparison.
How should lead times be analyzed when some orders are still open?
Open orders have a lead time that is at least as long as their time so far, which is censored information. Averaging only delivered orders understates lead time and flatters suppliers whose late orders are still open. Supply chain data analysis therefore uses survival methods, so every order contributes, and it reports quantiles with intervals rather than a mean alone.
Can a simulation study be documented so it can be reproduced?
Yes. The scenarios, the model assumptions, the warm-up and run length, the number of replications and the random-number seeds are recorded, and results are reported with intervals across replications. With the code and the seeds, another researcher can regenerate every table and figure, which is what reviewers of simulation studies increasingly expect.
How is confidential operations data handled?
Plant records, supplier data and analysis files are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before commercial or supplier data are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Operations & Supply Chain Management Research With Us
Share your research question, design and available data, even if only a demand extract or a simulation outline exists. We review the time structure of your records and confirm the approach before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014