Behind The Wheels -Management of Logistics Road Transport.

Define the scope of a France-based steel company case study, detailing inbound and outbound flows, facilities, and transport modes including road, barge, train, and sea.

Explore how key performance indicators steer scheduling, capacity planning, route and fleet optimization, and dispatching in road transport logistics, covering service levels, costs, and safety compliance.

Explore vendor managed inventory, automatic ordering, optimization software, and real-time tracking that streamline road transport, improve client visibility, and optimize dispatch, while addressing adoption and privacy considerations.

explore how to define and use KPIs across transport planning, including scheduling, capacity management, route optimization, and load and cost management, to improve dispatching, service levels, and fleet utilization.

Leverage electronic ordering through the Steel Co portal to automate orders, reduce errors, and track delivery, shortening order-to-delivery time in logistics road transport.

Optimization software automates truck scheduling by linking orders, tracks, and locations with constraints; enables cost reduction, automated courier orders, and what-if analysis and validation.

Real time tracking uses GPS to monitor trucks, enabling clients to know exact location and arrival times for unloading, with benefits in accuracy, load management, and safety checks.

Explore the steel supply chain from raw materials through casting and rolling to final hot and cold rolled products, highlighting flows, multi-facility logistics, and transport effects on cost and service.

Explore the hierarchy of transport decisions: strategic, tactical, and operational, and learn how make-or-buy choices, fleet investments, capacity planning, and backhauling optimize outbound and inbound deliveries.

Identify strategic, tactical, and operational transport decisions, from fleet right sizing and lane segmentation to backhaul opportunities, carrier selection, route optimization, and real-time load matching.

Analyze historical demand to plan capacity, determining track needs and contract mix using seasonality and trend. Assess confidence intervals and outliers to guide forecasting with orange.

Explore how to size a fleet by analyzing historical demand using range, median, and standard deviation, and by accounting for seasonality and trend in forecasting capacity.

Analyze a 3-year daily steel tonnage demand time series to identify seasonality and an upward trend, and use a linear forecast with a trend line to project 2026 demand.

Calculate seasonality index from three years of monthly volumes using a pivot table, then identify which months have high or low seasonality to prepare more spot or dedicated trucks.

Utilize a box plot to determine daily tonnage ranges, identify outliers, and compute the 25th percentile, median, 75th percentile, and interquartile range for 2025.

Discover how to identify outliers with interquartile range fences, compute descriptive statistics in Excel, and balance fleet capacity using mean and standard deviation in a normal distribution.

Create a histogram of volume data using adjustable buckets, interpret the mean and standard deviation with sigmas, and identify outliers beyond mean plus or minus three standard deviations.

Sum up how to identify outliers using countif beyond three sigma, visualize with median and quartiles, and plan truck allocation with dedicated, semi-dedicated, and on-spot options.

Develop capacity planning for track utilization by analyzing median, 75th percentile, and upper fence to determine daily truck requirements, exploring dedicated, semi-dedicated, and on spot fleets to manage variability.

Explore traditional logistics KPIs for fleet scheduling, including on-time delivery, schedule adherence, and turnaround time, then examine capacity management, lead time error, and peak capacity.

Learn dispatching fundamentals in fleet scheduling, including dispatch accuracy, loading time, equipment reliability, and order backlog; track delivery with shipment visibility, delivery confirmations, return and complaint rates, and customer satisfaction.

Focus on on-time deliveries as a key KPI by analyzing schedules and arrival dates, and use segmentation to target the critical few big customers to improve logistics KPIs.

Analyze demand to allocate capacity across buckets from median to 75th percentile. Plan one year ahead by balancing dedicated, semi-dedicated, and on-spot tracks to optimize costs and avoid idle trucks.

Explore geocoding and reverse geocoding using the orange data mining tool with a geo mapping add-on to map data points, compute the distance matrix, and optimize transportation costs.

Geocode supply chain locations in Google Sheets to obtain latitude and longitude, enabling distance calculations and map creation using the Awesome Tables add-on.

Discover Instamaps for Google Sheets, an extension that extends geocoding by providing latitude and longitude, distance calculations, visit order, live map, and route links to optimize stops.

students learn the haversine formula to estimate straight-line distance between two points using longitude and latitude, and build an Excel distance matrix to estimate transportation costs.

Calculate the distance matrix for supply chain points in excel using a transposed table with latitude and longitude to estimate transportation cost, carbon emission, and value per ton.

Install Orange data mining on Windows or Mac, then open Orange to explore its features as part of your logistics road transport management course.

Analyze steel transport data for one day in orange, map the movements, and classify backhauling by distance, using steel row and steel segmentation files for workflow.

Apply Orange to import and transform data, perform geo analysis with k means clustering and outlier detection, and explore add-ons for forecasting and time series in supply chain data.

Visualize your supply chain on a geographic map by importing latitude and longitude in Google Sheets, coloring nodes by role, and using geocoding to locate customers and stations.

Explore visualizing steel transport data by inspecting latitude, longitude, and delivery features, then apply distribution plots, histograms, bar plots, and K-means clustering.

Apply k-means clustering to latitude and longitude in orange, using cosine distance to group deliveries and guide backhaul areas.

Visualize logistics patterns with choropleth maps and geo maps, adjust k means clusters to reveal regional transportation activity, and compare inbound versus outbound freight using the metric ton.

Merge the clusters with the original steel segmentation data using append columns, join, transpose, and pivot tables to create a metadata-rich table, and save the result as segmentation clusters added.

Explore time series analysis with arima modeling, visualize daily volume data from 2023 to 2025, and forecast 100 days ahead using weekly and monthly seasonality.

Explore backhauling to save travel distance by combining trips with nearby shipments, perform segmentation to identify regular backhaul opportunities using orange, and reduce time, cost, and carbon footprint.

Apply backhauling by loading at the plant or supplier to serve a new client, reducing empty trips in inbound and outbound cycles, with rules on distance, time, and same-date lanes.

Link the merged data to save it, not the data table. Add or remove annotations as needed, then save with segmentation cluster to replace and confirm the saved data.

Apply ABC customer segmentation to prioritize key clients in road transport using pivot tables to analyze outbound shipments by frequency or volume.

Use abc analysis to segment clients, apply vlookup with approximate match to map key customers, and prioritize the 80% of shipments to improve on-time delivery and service level.

Explore an introduction to optimization using Excel solver and Open Solver, with hands-on exercises in facility allocation and transshipment, tailored for transport planners.

Explore the basics of optimization using Excel or OpenSolver to maximize revenue and minimize costs in logistics, covering decision variables, objective functions, and capacity constraints.

Explore how to set up Excel Solver and OpenSolver, define objective, variables, and constraints, and apply linear or non-linear engines to facility allocation and transshipment problems.

Identify optimal facility allocation by selecting three warehouse locations using a distance matrix to minimize total transportation distance from city demands of 10,000 units each to opened warehouses.

Define the facility allocation model in the Excel solver, enable the add-in, minimize the shipping variables, and enforce binary warehouse decisions with one warehouse and equality constraints.

Use Excel solver to model facility allocation with binary variables deciding which warehouses to open and how to route shipments, highlighting how fewer warehouses increase distance and costs.

Explore the transshipment problem with two factories, four warehouses, and customer demand to minimize total transportation cost using flows from factories through warehouses to customers.

Formulate a transshipment problem with flow conservation across warehouses, define inbound, direct delivery, and outbound variables, and minimize transportation cost under demand, capacity, and integer constraints.

Revise and validate a transshipment model by adjusting warehouses and factories to satisfy capacity constraints and enable direct delivery to customers, yielding a total cost of 144.

Install Open Solver for Excel and Google Sheets, unzip and enable macros, then run the transshipment and facility allocation models to compare results with the Excel Solver and handle variables.

Evaluate freight forwarders on cost and service, considering safety, certifications, client references, and financial health, while negotiating bundles and volume discounts using Excel with OpenSolver.

Identify freight forwarders using criteria such as cost, service, timeliness, response, safety, truck age, policy compliance and GPS adoption to select optimal routes and bundles.

Issue tenders and reverse auctions to compare bids from logistics providers, evaluate references and certificates, and select the lowest bid; use solver tools for complex carrier allocation.

Organizes supplier data for logistics procurement by mapping shipment origins and destinations, quantity, and cost per ton, then builds pricing and flow matrices for modeling.

Minimize total cost by allocating road flows to meet a 3900 demand, using an objective function that multiplies flows by costs, with non-negativity constraints, solved by a solver.

Explore how limited supplier capacity imposes a supply constraint, enforcing demand and flow bounds to minimize costs; the model uses per-supplier 10,000 ton capacity and multiple suppliers per route.

Explore integrating measured service levels into pricing using a standard, rewarding or penalizing deviations with $0.20, and computing an evaluation price to guide supplier selection and optimization.

Explore how fixed costs influence road transport procurement, modeling bid decisions, supplier capacity, and forcing constraints in Excel to include fixed costs.

Learn to model conditional tonnage in a Liverpool Port to Ashington Steel logistics problem, adjusting capacity and using binary decisions to minimize fixed and variable costs.

Explore how to model volume discounts in logistics pricing using Excel solver, applying a 7000-unit threshold to unlock discounted rates while respecting capacity constraints.

Apply volume discount strategies in a logistics context by analyzing a solver-driven model with multiple iterations to determine when to initiate offers for suppliers.

Explore marginal volume discounts: set a base price for the first 7000 units and offer a preferential price for units beyond 7000, without binary variables, comparing efficiency to volume discounts.

learn how combinatorial bids create bundled transport offers, model with decision variables and an integer package indicator, and solve the problem to minimize total cost under demand constraints.

Optimize road transport by assigning trucks under time and distance constraints using Excel solver and OpenSolver. Build stepwise models with constraints on trips per truck and work areas.

Develop a transport optimization model to maximize fleet utilization with single full-truck-load steel shipments on dedicated tracks, respecting delivery windows. Balance workload and quantify cost and CO2 savings.

Model orders data, plant capacities, and the distance matrix to plan multi-truck deliveries, incorporating transit time and client demand for a single consignor.

Minimize total transport compensation using a solver model that decides the number of trips to meet demand under travel-time constraints, with integer trips and a 2.35 per ton rate.

Explore the solver model by setting a time limit, observe track usage and demand satisfaction, and minimize fuel cost to achieve a minimum cost of 6815.

Examine a bigger open solver model for road transport with 40-ton capacity and multiple vehicles, defining capacity, distance, time, and cost to minimize total cost, illustrating open solver necessity.

Explore the open solver by reflecting model definitions, saving the model, selecting a linear solver, enforcing integer constraints, and minimizing the objective within a 1000 time limit.

Open solver demonstrates solving a logistics routing problem by assigning trips to tracks, tracking time, distance, capacity, and demand, yielding total cost and flexible constraints.

Set minimum and maximum trips per truck with a solver, ensuring each truck has at least one and at most seven trips, and mirror the constraints in the model.

Analyze how a simple routing model respects track usage, avoids zero trips, and minimizes cost within time constraints, while binding variables influence the cost and driver trips.

Explore aggregate planning for multi-origin logistics, deciding which plants to open or close, set utilization targets, and allocate production and fixed costs and shipments based on customer locations.

Explore aggregate planning that combines transportation and production decisions, including which plant serves which customer, factory openings, utilization targets, and track speeds for time calculations.

Solve the assignment of customers to plants and schedule routes to minimize total cost while meeting delivery windows. Account for truck trips, plant capacity, and 60% utilization targets.

Formulate logistics costs by calculating tonnage transported from trips at eight tons each, then compute production and fixed costs, plus transport costs using trips, distance, and time.

Solve a facility opening and production problem in Excel using a binary open variable, a linking capacity constraint, and solver to minimize total cost while meeting demand and analyzing utilization.

Explore optimizing the number of factories and truck resources using a solver, balancing utilization rates and minimal costs to determine which factories to open in a logistics network.

Learn how to optimize plant counts and routes to reach a target service level, calculating trips under 80 km and adjusting solver constraints to 5 factories and 80% service.

Set a target service level from 84% to 90% and analyze costs using a solver; explore adjusting trips and handling nonlinear problems, including division by zero issues.

Balance cost and service level in road logistics by enforcing a 90% target and analyzing how near customer plants raise utilization while distant plants reduce efficiency using linear solvers.

Explore multidrop shipments and how vehicle capacity, order sequencing, and fleet size affect feasibility, time, and cost while satisfying demand and leveraging distance matrices and geocoding.

Demonstrates how to model a mult-drop logistics problem with a distribution center and six retailers using a distance matrix, binary voyage variables, and 180-unit capacity; optimize cost with open solver.

Formulate the objective as a product of the distance matrix and transport cost per kilometer with vehicle costs, using vertical and horizontal flows to ensure each retailer receives one shipment.

Compare capacity before delivery and after delivery to determine satisfied demand, using a capacity matrix and binary matrix to bound delivery by truck capacity.

Solve a linear vehicle routing model with binary flows and capacity constraints, using a solver to show two tracks are optimal and how time limits permit a possible third track.

Build an hourly matrix with constraints to test three tracks using open solver and minimize transportation costs. Analyze why two vehicles idle in DC routing.

Apply the course tools to your job by completing quizzes, assignments, and the practice exam, reinforcing logistics road transport concepts learned throughout this concise farewell session.

Learn agent-based modeling and simulation with AnyLogic to model cement company dispatch, comparing bag trucks (8 tons) and bulk trucks (40 tons) to analyze time, resources, and costs.

Model a simple one service line where trucks enter a single entrance, wait in a queue, and are serviced at validation desks before exiting.

Explore a realistic dispatch operations case from Simenko Limited, modeling bag and bulk truck arrivals with exponential distributions to assess KPIs like turnaround time, utilization, and cost.

Register trucks at the gate via RFID linked to ERP, verify drivers, weigh loads, run parallel bag and bulk loading, perform sampling QC, and issue shipping documents on exit.

Explore case parameters for a logistics road transport model, detailing stochastic variability and distributions, daily operating hours, and benchmarks like 18–25 minutes turnaround and 80% bay utilization.

Explore a pdf case and mock-up for modeling road transport logistics with heterogeneous agents and resource contention. Review arrival rates, phase two verification, kpis, and loading distributions in the model.

Learn how to install AnyLogic, download the free version, and set up the simulation platform to explore logistics management, transport optimization, and simulation modeling.

Learn to set up an AnyLogic model, create a SimMenko project, and configure time in minutes with a stop at 86,400 minutes for 40 days of a simulation.

Define sources, bag and bulk, using process modeling library; connect to a fifo waiting area with capacity 16, and run simulation with arrivals 8.5 per hour and 1.4 per hour.

Model exponential truck arrivals at 10 per hour and 3 per hour for bulk, with a 1-minute RFID gate scan, 5% failure, and a 0.95 probability that trucks proceed.

Combine driver identity check and inbound wait bridge times to yield an average of seven minutes with a standard deviation of 1.8 minutes, using variance addition to merge distributions.

Add resources for the dispatch operation by configuring an inspector and an inspection area, plus two wait bays, and adjust queue capacity to study delays.

Create a new truck agent in AnyLogic to separate bag and tanker paths through a combined inbound weight and order processing system using select output.

develop a bag and bulk separation path in a logistics model, defining agents and tracks, assigning bag and ton attributes, and troubleshooting capacity and exit issues in driver checks.

Define a day function to replace the fixed run time, set each day to 960 minutes (6 hours), and adjust the simulation stop time from 86400 minutes.

Define the day in AnyLogic with a getDay function that computes day numbers by dividing model time by 960 minutes, then track tonnage, trucks, and tankers using conditionals at exit.

Define a recurring 960-minute event to reset daily logs for tonnage, tankers, and trucks; record daily totals in dstonnage, dstankers, and dstrucks for later analysis of utilization.

Learn to measure time from gate scan to separation using TMS1 to TME, analyze throughput, and adjust arrival rates to hit 2,000 tons per day with a histogram of minutes.

Combine bag loading and bulk loading into a single service block with four bag-related resources and one bulk resource, then consolidate outputs through QC and post-loading documentation.

Learn to model bag lane services by combining bag and bulk operations, recognize the disadvantage of shared resources, and set distributions and resource pools for forklift, palletizer, and packing machine.

Analyzes bulk services by configuring bay resources and capacity, evaluating overload on the bay, palletizer, and packing, and adjusting waiting capacity to assess model performance.

Run a simulated queue unifying bulk and back service in one lane to compute average waiting times, with two bays and three of each resource—forklifts, palletizers, and packing machines.

Analyze the total time of the system in a logistics road transport simulation, tracking back and bulk resources, waiting times, and QC reweighting to improve performance and utilization.

Learn how sampling selects 10% for quality control, how weighting bridge calculates net tons, and how 3-5% rework, visual inspection, and exit check complete the process.

Explore a quality control workflow in road transport logistics, selecting 10% for QC sampling and routing 90% to outbound, with normal and triangular delays, a 5% rework rate, and inspection.

Develop and validate the exit procedure and document generation for road transport logistics, assigning a gate officer, analyzing time distributions, and reviewing queue capacity and service statistics.

Generate charts directly from blocks to visualize daily tonnage with a time plot and compute mean utilization over time, comparing bag and bulk service times and total duration.

Measure the total time for bag versus bulk services with a stopwatch, then explore practice questions on reducing these times and optimizing resources.