Predictive Analytics & Demand Forecasting in Logistics Training Course

Demand rarely holds steady in logistics. Promotions, seasonality, economic shifts, and supplier disruptions all pull actual orders away from the plan, and every gap between forecast and reality shows up as either idle stock or missed service. This course gives logistics and supply chain professionals a working understanding of how demand is modeled, how forecasts are measured, and how those numbers flow into day-to-day planning decisions about inventory, transport, and capacity.

Across five units, participants move from the fundamentals of time-series behavior through established statistical methods and into modern predictive analytics. The emphasis is on judgment as much as technique: knowing which model suits a demand pattern, reading forecast error honestly, and translating a number on a screen into a stocking or replenishment decision. By the end, attendees can evaluate a forecasting approach critically instead of trusting whatever a system outputs by default.

Why this matters

Forecasting sits upstream of almost every logistics cost, so small improvements in accuracy compound quickly across the network.

Most demand signals contain a mix of level, trend, and seasonality, and the method chosen has to match that structure. Simple moving averages smooth short-term noise but lag on trending series; exponential smoothing weights recent observations more heavily, and Holt-Winters extends that idea to capture trend and seasonal cycles together. Where relationships between demand and external drivers matter, regression brings in variables such as price, weather, or promotional calendars, while ARIMA models the internal autocorrelation of a series directly. None of these are useful without a disciplined view of error, which is why practitioners track metrics like MAPE, forecast bias, and the tracking signal to tell whether a model is drifting or systematically over- or under-forecasting. For teams building this foundation, our Demand Planning and Forecasting in Supply Chain Management course covers the planning side in more depth.

Forecasts also do not live in isolation. The output feeds safety stock calculations that buffer against demand and lead-time variability, and it must reconcile with the numbers held in ERP and advanced planning systems (APS) so that procurement, production, and distribution all work from one demand picture. When forecasting is treated as a standalone spreadsheet exercise rather than an integrated input, planners end up overriding the system by instinct, and the value of any statistical method is lost. Understanding both the models and the systems they connect to is what separates a reliable planning process from a reactive one.

Course objectives

By the end of the course, participants will be able to:

Decompose a demand series into level, trend, and seasonal components.
Select a method that suits the forecast horizon in view.
Fit smoothing techniques to logistics demand data.
Diagnose demand series structure to choose regression or ARIMA.
Judge when machine-learning outperforms classical forecasting.
Detect overfitting in predictive demand models.
Quantify how far forecasts deviate from actual demand.
Convert forecast error into safety stock decisions.
Estimate where manual overrides improve automated forecasts.

Course outline

Unit 1: Foundations of demand forecasting in logistics

The role of forecasting in inventory and transport planning.
Seasonality, promotions, and bullwhip in demand variability.
The place of qualitative judgment beside statistical models.
Forecast horizons and granularity across usable techniques.

Unit 2: Forecasting models and techniques

Damping short-term demand noise with moving averages.
Single, double, and Holt-Winters exponential smoothing.
Regression models linking demand to promotional activity.
ARIMA selection guided by autocorrelation and stationarity.

Unit 3: Predictive analytics applications

Identifying demand drivers and feature variables from data.
Machine-learning versus classical statistical models.
Responsible use of larger datasets and cleansing steps.
Worked examples of predictive accuracy and overfitting.

Unit 4: Technology and forecasting tools

Forecasting engines inside ERP and advanced planning systems.
Visual dashboards for tracking demand and forecast error.
Reconciling forecasts with planner overrides and consensus.
Data flow from forecast to replenishment and distribution.

Unit 5: Performance measurement and best practices

Calculating MAPE, bias, and tracking signal for drift.
Linking forecast variability to service-level targets.
Establishing a forecast review cadence and ownership.
Capstone case study: critiquing a forecasting scenario.

How the course is delivered

The course is expert-led and built around discussion, worked examples, and documented case studies rather than passive lecturing. Participants work through sample demand datasets, review how different models behave on the same series, and take part in guided walkthroughs of forecasting dashboards and planning-system outputs.

Group analysis of real scenarios runs throughout, with attendees comparing method choices, debating error results, and reasoning about what a planner should do next. The aim is to build practical judgment about forecasting, so the sessions stay close to the kinds of decisions logistics teams face instead of abstract theory.

Who should attend

This course suits professionals who own or influence demand and logistics planning decisions.

Logistics and supply chain managers responsible for planning accuracy.
Demand planners and forecasting analysts.
Inventory and replenishment managers.
Operations managers coordinating capacity and distribution.
Analysts moving into data-driven logistics roles.

About EuroQuest International Training

EuroQuest International Training, founded in 2015 and headquartered in Bratislava, Slovakia, delivers more than 1000 courses to over 15,000 participants, with training hubs including Dubai, London, Barcelona, Istanbul, Vienna, Paris, and Geneva.

Frequently asked questions

Do I need a statistics or coding background to follow this course?

No. The methods are explained from first principles, starting with how demand series behave and building up to models such as exponential smoothing, regression, and ARIMA. A comfort with basic arithmetic and reading charts is enough; the focus is on understanding and applying the techniques, not on writing code.

Which forecasting methods does the course actually cover?

It covers moving averages, single and Holt-Winters exponential smoothing, regression, and ARIMA on the statistical side, plus an introduction to machine-learning approaches for more complex demand. It also covers forecast error metrics including MAPE, bias, and the tracking signal, and how forecasts feed safety stock and planning decisions.

How does the course connect forecasting to the systems my team already uses?

One unit is dedicated to how forecasting fits within ERP and advanced planning systems (APS), including dashboards, planner overrides, and the flow from forecast to replenishment. The goal is to help you evaluate and improve the process around whatever platform your organization runs.

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Register for this course

To reserve a place or ask about scheduling this course for your team, contact EuroQuest International Training and our team will help you plan your enrollment.