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Integrating Transportation Engineering and Business Administration:
Optimising Cost, Efficiency, and Service Delivery
Enigbokan, Richard Olufemi, Ph.D., FCA
Managing Partner, Femi Enigbokan & Co. (Financial and Management Consultants)
DOI: https://doi.org/10.51244/IJRSI.2025.1210000129
Received: 02 October 2025; Accepted: 14 October 2025; Published: 07 November 2025
ABSTRACT
This study bridges the critical divide between transportation engineering and business administration by
introducing the Strategic Transportation Optimisation Model (STOM)—a novel framework that dynamically
aligns road network operations with strategic business objectives. Moving beyond siloed optimisation of traffic
flows, signaling patterns, and journey times, STOM integrates customer satisfaction, logistics costs, and business
profitability directly into the mathematical core of route planning. Using a digital twin of 1.8 million deliveries
across North American and European freight networks, we demonstrate that STOM—powered by a regression-
based feedback loop—adapts multi-objective vehicle routing in real time based on corporate strategy (Cost
Leadership, Service Differentiation, Sustainability Commitment). The model simultaneously reduces total
business costs by 9.1%, improves on-time in-full delivery (OTIF) by 9.9 percentage points, enhances customer
lifetime value retention by 19.1%, and lowers average journey time by 16%, whilst increasing environmental
performance (Green Impact Index, GII) by 25.5%. Unlike static models that treat traffic signals, road network
constraints, or travel time as technical limits, STOM treats them as strategic levers influenced by customer
satisfaction and operational cost trade-offs. Results confirm that when logistics decisions are engineered to
reflect not just efficiency but economic and experiential outcomes, sub-optimisation is replaced by synergistic
performance. STOM transforms the transport network from a passive infrastructure into an adaptive, value-
generating system where traffic flows, journey times, and business costs are co-optimised for strategic alignment.
The study demonstrates the viability of strategic-logistics integration, with implications moderated by
organizational digital maturity.
Keywords: Transportation Engineering, Business Administration, Business Costs, Efficiency, Customers’
Satisfaction
INTRODUCTION
The global logistics and transportation ecosystem is entering an era characterised by unprecedented complexity,
volatility and customer expectations. Once seen as a passive facilitator of trade, transport is now a strategic
differentiation shaping brand loyalty, operational resilience and profitability in real time (Wieland & Durach,
2023). Consumers demand hyper-localised delivery windows, carbon-neutral fulfilment, end-to-end visibility
and zero tolerance for delays, a demand that is reinforced by the growth of e-commerce, the fragmentation of
supply chains and climate change (Gattorno et al., 2024; Ivanov et al., 2023). In this context, the traditional cost-
oriented view of freight transport is out of date. Success now depends on being able to optimise simultaneously
the cost-efficiency and service triad: minimising costs whilst maximising the use of assets and the reliability of
services, a triple objective that often runs in opposite directions.
Yet, despite increasing recognition of this interdependence, decision-making in transport systems remains
fragmented along disciplinary lines. Transportation Engineering (TE) models - including dynamic vehicle time
windows (VRPTW), multi-modal network optimisation and congestion-aware scheduling - are designed to
maximise technical efficiency: reducing journey times, minimising idle time or maximising load density (Crain
et al., 2022; Ribeiro et al., 2023). These models typically operate on fixed cost assumptions, do not take account
of customer-specific contractual penalties, and treat service levels as a hard constraint rather than an economic
variable. Meanwhile, Business Administration (BA) frameworks - such as Activity Based Cost (ABC), Total
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Cost of Ownership (TCO) and Customer Capital Management (CPM) - prioritise financial results, profit margins
and strategic positioning (Kaplan & Anderson, 2023; Wouters et al., 2024). They classify transportation as a cost
centre and not as a value-creating node, leading to decisions that reduce the cost of carriers at the expense of
their timeliness, causing SLAs to break and customers to leave (Chen & Wang, 2024).
This siloed approach leads to a system of sub-optimisation. A route optimised for minimum miles can bypass a
high-income client and breach a service contract that generates five times more energy savings in fines (Zhang
et al., 2023). A fleet consolidation strategy justified by the calculation of the return on investments may overload
urban terminals, increase dwell times beyond regulatory limits and trigger emission penalties (Fernández et al.,
2022). Even advanced technologies, such as artificial intelligence-driven demand prediction and IoT-enabled
tracking are still underused, because they are fed by isolated systems - engineering tools optimise routes, finance
teams adjust prices - without any feedback loops (Liu et al., 2024).
Whilst this divergence has long been recognised by scholars (for example, Christopher, 2016; Mentzer et al.,
2001), formal and testable frameworks for operationalising Transportation Engineering (TE) and Business
Administration (BA) integration for holistic and strategic optimisation are still lacking. Recent efforts have
explored hybrid models - such as including carbon taxes in routing algorithms (Huang et al., 2023) or matching
delivery windows to customer profitability tiers (Li & Zhao, 2024) - but these are still fragmented, context-
specific, and rarely verifiable on a large scale. Crucially, there is no framework to systematically parameterise
transport engineering models using strategic business goals - such as cost leadership versus service
differentiation - and to quantify the overall impact of this integration on all three performance pillars.
This study widens the gap. We propose that sustainable competitive advantage in modern logistics will not come
from the optimisation of individual components, but from the integration of business strategies directly into the
mathematical structure of transport models. To tackle this problem, we set two research goals:
RO1: Develop a conceptual framework - the strategic traffic optimization model (STOM) - integrating
quantitative TE models (for example, multi-objective VRPTW, network constrained flows) with qualitative BA
principles (for example, customer segmentation, value-based pricing, balanced KPIs);
RO2: To empirically verify the impact of STOM on the simultaneous improvement of cost reduction, operational
efficiency and performance in the provision of services using real-world logistics data.
Under these objectives, we are investigating:
RQ1: How can transport engineering models be dynamically parameterized by business strategies, for example,
by including customer tier-specific penalty costs, margin targets or sustainability thresholds in the weightings
of routing algorithms?
RQ2: What is the measurable performance gain - in cost, efficiency and service metrics - when ssssSTOM is
used in comparison to traditional siloed decision-making?
The urgency of this integration is exacerbated by modern supply chain volatilities—geopolitical disruptions,
demand hyper-uncertainty, and stringent ESG reporting mandates. These pressures render static, siloed decision-
making models not just suboptimal, but operationally hazardous. A framework that dynamically aligns
operational execution with strategic intent is, therefore, not merely an academic exercise but a critical
competitive necessity.
We are answering these questions with a mixed methodology combining discrete event simulation (AnyLogic),
longitudinal data on three medium-sized freight carriers in North America and Europe, and structural equation
modelling (SEM) to track the causal link between integrated decision-making rules and three-dimensional
results. STOM introduces a new mechanism: a type of business strategy (for example, cost leader versus service
premium) adjusts the multi-objective weightings in real time, transforming static optimisation into adaptive,
value-oriented orchestration.
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Convergence of transport engineering and business administration is not only an operational refinement, but a
fundamental recalibration of how value creation in modern logistics systems occurs. By embedding strategic
business goals - such as customer segmentation, margin retention, and service differentiation - directly into the
mathematical underpinnings of transport optimization models, this research goes beyond incremental
improvements and proposes a new paradigm: one in which technical decisions are intrinsically linked to
economic and experience outcomes. The proposed strategic transport optimisation model (STOM) does not treat
costs, efficiency and service delivery as competing goals to be exchanged, but as the interconnected dimensions
of a single performance architecture, dynamically guided by the organization’s strategy. This integration
addresses the entrenched separation between the how of movement and the why of value and offers a mechanistic
solution to the long-standing trade-offs that have plagued logistics decision making for decades. Empirical
validation of STOM, based on real-world operational data from a variety of freight networks, provides strong
evidence that when business logic informs engineering models, not only are metrics improved, but also decision-
making capacity transformed. This research paves the way for a new class of adaptive and strategic transport
systems capable of navigating modern supply chain volatility while maintaining financial and service
performance.
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The optimisation of transportation systems has long been fractured along disciplinary lines, resulting in chronic
misalignment between operational efficiency and strategic value creation. Transportation engineering (TE) has
developed powerful tools to model movement, while business administration (BA) has refined frameworks to
evaluate performance — yet rarely do these disciplines co-design decision systems. The result? Routes that
minimise distance but trigger SLA penalties; fleets that maximise utilization but alienate high-value customers;
sustainability initiatives that reduce emissions but erode margins. This review critically examines the evolution
of both fields, identifies the structural barriers to integration, and proposes the Strategic Transportation
Optimisation Model (STOM) — the first framework to treat transportation not as a technical subsystem, but as
a strategically configurable capability whose internal logic is dynamically shaped by corporate identity and
market positioning.
Transportation Engineering Foundations: From Static Routing to Dynamic Network Systems
While the foundational work of Dantzig and Ramser (1959) introduced the vehicle routing problem as a
combinatorial optimisation challenge, contemporary transportation engineering has evolved far beyond static
route planning. Today’s state-of-the-art models reflect a paradigm shift from algorithmic elegance to strategic
adaptability, grounded in dynamic, multi-modal, and context-aware architectures.
Modern TE literature emphasises:
a. Dynamic network models that respond in real time to traffic congestion, weather disruptions,
and infrastructure constraints (Crainic & Laporte, 2022);
b. Multi-modal freight assignment, integrating road, rail, inland waterways, and intermodal terminals to
optimize cost-speed-reliability trade-offs across heterogeneous systems (Barnhart et al., 2020; Zhang et al.,
2023);
c. Last-mile congestion pricing, where urban delivery zones are managed via dynamic tolling mechanisms that
internalise externalities such as noise, emissions, and dwell-time delays (González et al., 2024); and
d. Vehicle Routing with Time Windows (VRPTW), now extended with stochastic demand, heterogeneous
fleets, energy consumption constraints, and driver fatigue modeling (Ribeiro et al., 2023; Goeke & Schneider,
2023).
These advances represent a transition from optimising where goods move to orchestrating how mobility
ecosystems function. Yet, despite their sophistication, these models remain operationally isolated. They assume
fixed cost coefficients, ignore customer profitability tiers, and treat service-level agreements (SLAs) as binary
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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constraints rather than economic variables with differential penalties (Zhang et al., 2023). As Crainic and Laporte
(2022) observe, “most TE models operate under the assumption that the objective function is exogenously given
— a profound limitation when strategy dictates what should be optimised.”
In essence, TE provides the how, but not the why. It answers: What is the fastest route? But never: Which route
best aligns with our brand promise or margin targets?
Business Administration in Operations: From Cost Centers to Strategic Value Networks
Where TE focuses on movement, BA focuses on value — and its conceptual foundations have matured beyond
generic supply chain management into sophisticated, customer-centric paradigms.
Key theoretical pillars now include:
a. Service-Dominant Logic (S-D Logic): Vargo and Lusch (2004, 2016) revolutionised marketing theory by
arguing that value is co-created through service interactions, not exchanged through transactions. Applied to
logistics, this implies that delivery is not a transactional task — it is a service experience shaping customer
perception, loyalty, and word-of-mouth (Lusch & Vargo, 2023). A delayed delivery is not merely an
operational failure — it is a breach of a relational contract;
b. Cost-to-Serve Modeling: Kaplan and Anderson (2007) introduced Activity-Based Costing (ABC) applied to
logistics, shifting focus from unit freight rates to granular cost drivers: dwell time, handling complexity,
return rates, and last-mile premium delivery. This enables firms to identify which customers are truly
profitable — not just which lanes are cheapest. As they argue, “the cost of serving a customer is not
determined by the rate charged, but by the activities required to fulfill the order.” This insight is foundational
to STOM’s ability to embed true profitability into routing decisions (Kaplan & Anderson, 2023; Wouters et
al., 2024);
c. Customer Equity Management: Kotler and Keller (2022) define customer equity as the sum of lifetime values
across all customers, emphasizing retention, frequency, and margin. In freight, this demands linking delivery
reliability directly to churn probability — yet no existing model quantifies how a 15-minute delay impacts
CLV for Tier-1 e-commerce clients (Reinartz & Kumar, 2024; Chen & Wang, 2024); and
d. Performance Measurement Systems: The Balanced Scorecard (Kaplan & Norton, 1996) and SCOR model
(Supply Chain Council, 2000) were designed to align operations with strategic goals — yet they remain
descriptive tools. They report KPIs like “on-time delivery %” but offer no mechanism to change routing
logic to improve them.
Critically, BA scholarship treats transportation as a cost center — a black box to be measured, not engineered.
Even advanced analytics use BA metrics as outputs (e.g., “customer satisfaction dropped”) — not as inputs to
reconfigure operational models. As Hines et al. (2004) observed decades ago: “Managers measure what they
can’t control.” Today, that control remains absent.
The Intersection: Attempts at Bridging the Gap
Efforts to reconcile TE and BA have emerged, primarily in three domains — yet each suffers from fundamental
asymmetry.
Revenue Management in Freight
Belobaba (1987) pioneered revenue management in airlines; Bitran and Caldentey (2023) extended it to freight,
using yield optimization to allocate capacity among customers based on willingness-to-pay. However, this
requires standardised pricing structures — rare in truckload or regional LTL markets. Most carriers lack the data
infrastructure to implement such models meaningfully.
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Sustainable Supply Chain Management
Sarkis (2003) advocated for life-cycle assessment in logistics; later studies embedded carbon emissions into VRP
objectives (Fernández et al., 2022; Huang et al., 2023). Yet these models treat sustainability as a single-
dimensional constraint — ignoring broader ESG dimensions: investor sentiment, regulatory risk, or brand
dilution from greenwashing. Sustainability becomes a compliance checkbox, not a strategic lever.
Performance Integration via KPIs
The Balanced Scorecard (Kaplan & Norton, 1996) and SCOR model (Supply Chain Council, 2000) attempted
to link financial, operational, and customer metrics. Ramesh and Kumar (2025) reviewed 42 studies integrating
TCO and SLA metrics into routing models — and found that 92% treated BA variables as post-hoc filters or
penalties, never as dynamic inputs shaping optimization structure.
Li and Zhao (2024) introduced 'CLV-weighted routing'; however, by implementing it as a post-processing
heuristic, their approach fails to reconfigure the core combinatorial optimization of the VRPTW solver. This
limits its adaptability and leaves the fundamental cost-time trade-off unchanged. Similarly, digital twin platforms
(Wieland & Durach, 2023) simulate scenarios but cannot auto-adjust model parameters based on strategic shifts.
Limitations of Existing Work: The Persistence of Silos
The core flaw across all integrative attempts is unidirectional causality:
Business metrics inform evaluation — but not generation.
There is no mechanism by which a firm’s strategic posture — “We are the premium last-mile provider for luxury
goods” — alters the objective function of a routing engine to prioritize reliability over fuel economy, even at
higher cost. TE models are hard-coded; BA models are dashboards. Neither speaks the language of the other.
This reflects a deeper epistemological failure: transportation is viewed as an operational function, not a strategic
capability. As Barney (1991) argued, competitive advantage stems from resources that are valuable, rare,
inimitable, and non-substitutable. Yet, logistics networks — arguably one of a firm’s most critical assets — are
rarely configured strategically. They are optimised technically.
Furthermore, empirical validation is weak. Few studies test integrated models against longitudinal, real-world
datasets. Most rely on synthetic benchmarks or small-scale case studies, limiting generalizability (Crainic &
Laporte, 2022; Liu et al., 2024).
Conceptual Framework Proposition: The Strategic Transportation Optimisation Model (STOM)
We propose STOM — the first framework to reverse the hierarchy: business strategy does not evaluate
transportation outcomes — it defines them.
Core Principle:
Organisational Strategy → Dynamically Reconfigures TE Model Parameters → Generates Multi-Dimensional
Optimal Solutions → Feedback Loop Enables Adaptive Learning
STOM embeds strategic orientation — defined along three axes (Cost Leadership, Service Differentiation,
Sustainability Commitment) — as adaptive weights within a multi-objective VRPTW engine. Unlike prior
hybrid models, STOM does not add BA metrics as constraints — it makes them structural drivers of optimisation.
strategic orientation Objective function
weights
Constraint Modifications
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Cost Leadership Cost = 0.7, Time = 0.2,
Service = 0.1
Max dwell time ↑, route density ↑, SLA penalty
weights ↓
Service
Differentiation
Cost = 0.3, Time = 0.4,
Service = 0.3, CLV = 0.2
Time windows tightened, CLV-weighted priority rules
applied, minimum service score ≥ 85%
Sustainability
Commitment
Cost = 0.4, Time = 0.3,
Service = 0.2, GII = 0.1
Carbon footprint cap enforced, low-emission zones
prioritized, supplier ESG score ≥ 80%
Table 1: Core Principle
These weights are not static. A regression-based feedback loop — continuously fed by IoT telematics, ERP order
data, and NPS/customer complaints — detects drift between intended strategy and observed outcomes,
embodying the principle of dynamic capabilities. If Tier-1 clients consistently receive late deliveries despite
“Service Differentiation” settings, this self-correcting mechanism automatically recalibrates service weights
until SLA compliance is restored.
Figure 1 (The STOM model)
Figure 1: The Strategic Transportation Optimisation Model (STOM) framework, illustrating the four interlinked
modules (Business Strategy Input, TE Engine, Performance Output, Feedback Loop) and the flow of information
between them, creating a closed-loop adaptive system.
STOM operationalises Barney’s (1991) Resource-Based View by treating the transportation network as a
strategic, inimitable capability — dynamically configured to deliver unique value. It embeds Kaplan &
Anderson’s (2007) cost-to-serve by translating granular activity costs into routing weights, ensuring decisions
reflect true profitability, not just distance. It applies Vargo & Lusch’s (2004) Service-Dominant Logic by framing
delivery as a co-created experience, and Kotler & Keller’s (2022) Customer Equity by linking reliability directly
to retention and lifetime value. The feedback loop embodies Teece’s (1997) Dynamic Capabilities, enabling
continuous sensing, seising, and transforming of operational strategy.
Hypothesis Development
Grounded in RBV, S-D Logic, and multi-objective optimisation, we propose four hypotheses:
H1: Firms deploying STOM will achieve significantly greater simultaneous improvement in cost reduction,
operational efficiency, and service delivery compared to firms using siloed TE or BA models.
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Rationale: STOM resolves trade-offs coherently by embedding strategy into the optimization kernel, eliminating
functional myopia (Barney, 1991; Hines et al., 2004).
H2: The magnitude and direction of performance gains under STOM will vary systematically by strategic
orientation (Cost Leadership vs. Service Differentiation vs. Sustainability Commitment), producing distinct
Pareto-optimal frontiers.
Rationale: Strategic types define preference structures; STOM translates these into parametric adjustments,
yielding strategy-specific optima (Teece et al., 1997; Vargo & Lusch, 2016).
H3: The inclusion of real-time feedback loops in STOM will enhance long-term performance stability and reduce
strategy-execution drift by ≥30% compared to static implementations.
Rationale: Dynamic capabilities require continuous sensing and adaptation — STOM’s ML feedback loop
embodies this principle (Teece et al., 2022; Wieland & Durach, 2023).
H4: The performance gains achieved by STOM are positively moderated by the firm's level of digital maturity
(ERP/IoT integration)
Rationale: Digital infrastructure enables real-time data flows necessary for adaptive parameterization (Liu et al.,
2024; Kaplan & Anderson, 2023).
No prior study has successfully fused the technical rigor of modern transportation engineering with the strategic
depth of contemporary business administration. STOM is not an incremental hybrid — it is a paradigm shift. By
making strategy the architect of optimization, not its auditor, STOM transforms logistics from a passive cost
center into an active, adaptive, value-generating capability. It operationalizes Barney’s (1991) Resource-Based
View by treating the transportation network as a strategic asset; Kaplan & Anderson’s (2007) Cost-to-Serve by
embedding true profitability into routing logic; Vargo & Lusch’s (2004) Service-Dominant Logic by elevating
delivery to a relational experience; and Kotler & Keller’s (2022) Customer Equity by anchoring reliability to
lifetime value.
This research fills a critical void in both theory and practice — and establishes a new standard for
interdisciplinary excellence in transportation science.
MATERIALS AND METHODS
To answer the research question — How can transportation engineering models be dynamically parameterized
by business strategy to simultaneously optimize cost, efficiency, and service delivery? — we employed a Design
Science Research (DSR) methodology (Hevner et al., 2004; Peffers et al., 2007), which emphasises the
systematic design, implementation, and empirical evaluation of a novel artifact: the Strategic Transportation
Optimization Model (STOM). DSR was selected because it provides a structured framework for developing
prescriptive, theory-informed decision artifacts that bridge managerial intent with operational execution —
precisely our goal of integrating business strategy into transportation engineering.
Core Framework Components
STOM comprises four interlinked modules, as illustrated in Figure 2.
Business Strategy Input Module: Corporate strategic orientation — Cost Leadership, Service Differentiation,
or Sustainability Commitment — was formalized into quantifiable weight vectors derived from firm-level
strategy documents and managerial interviews. For example, “Service Differentiation” was encoded as
[Cost=0.3, Time=0.4, Service=0.3, CLV=0.2], where weights reflect relative priority and sum to 1.0. These were
calibrated using Kaplan & Anderson’s (2007) cost-to-serve principles and Vargo & Lusch’s (2004) service-
dominant logic to ensure alignment with strategic intent.
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Figure 2: Detailed architecture of the four STOM modules, showing the specific inputs, processes, and outputs
for each component.
TE Engine (Optimiser): The core optimisation engine was implemented as a multi-objective Adaptive VRPTW
model in AnyLogic 8.8.3, using NSGA-II (Deb et al., 2002) to generate Pareto-optimal routes. The objective
function was defined as:
Minimise Z = w₁·Cost + w₂·Time + w₃·Service + w₄·CLV + w₅·GII,
where wᵢ are dynamic weights provided by the Business Strategy Input Module. Constraints included
vehicle capacity, time windows, driver hours, and carbon caps (Goeke & Schneider, 2023).
Performance Output Module: For each optimised route plan, five key performance indicators were computed:
Total Operational Cost ($/shipment), On-Time In-Full (OTIF) rate (%), Customer Lifetime Value (CLV)
retention rate (%), Green Impact Index (GII), and average dwell time (min). These metrics were mapped directly
to real-world data from ERP and CRM systems.
Feedback Loop & Strategic Calibration Module: To enable adaptive decision-making, a feedback loop
collected simulated operational outcomes — including fuel consumption (IoT), delivery delays (ERP), and
customer satisfaction scores (CRM). These outputs were used to estimate, through multivariate linear regression,
the optimal adjustment to strategic weights that would have improved future performance. Specifically, for each
simulation cycle, we regressed observed deviations in OTIF, cost, and GII against contextual variables (demand
volatility, fuel price change, SLA breach frequency). The resulting regression coefficients were used to update
the next cycle’s weight vector according to:
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wᵢ(t+1) = wᵢ(t) + βᵢ·ΔKPIᵢ,
where βᵢ is the estimated sensitivity of strategy weight i to deviation in performance metric i. This
approach, rooted in classical econometric learning (Wooldridge, 2019), ensures transparency,
interpretability, and replicability without requiring complex machine learning infrastructure.
Feedback Loop Operationalisation
The regression-based calibration mechanism was operationalised with the following specifications to ensure
robustness and replicability. The multivariate linear regression was executed at the conclusion of each simulated
operational day (t), utilizing a rolling historical data window of the previous 14 days to estimate the
coefficients βᵢ. The deviation for each KPI, ΔKPIᵢ, was calculated as the absolute difference between the day's
observed value and its predefined strategic target value (e.g., for OTIF, the target was 98%; for cost, the target
was maximally 10% above the theoretical minimum). To prevent over-calibration to stochastic noise, weight
adjustments were only triggered if the absolute deviation for a given KPI exceeded a threshold of 5% from its
target. This process ensured that the model adapted to significant performance drifts while maintaining
operational stability.
Simulation Environment
We constructed a parameterised digital twin of three freight carriers using historical data (2021–2022) covering
1.8 million deliveries. Scenarios included:
a. demand shocks (+50% volume spikes);
b. fuel price fluctuations (±30% monthly swings);
c. regulatory changes (low-emission zone enforcement); and
d. customer churn triggers (SLA breaches.)
Each scenario ran 50 times with random seeds to ensure statistical stability. Data generation followed Monte
Carlo simulation principles (Law, 2015).
Digital Twin Calibration and Validation
The digital twin was architected in AnyLogic 8.8.3 and deployed on a cloud computing platform with 32 vCPUs
and 128 GB of RAM to handle the computational load of simulating 1.8 million deliveries. Historical data
underwent a rigorous cleansing process, removing outliers (e.g., delivery times exceeding ±3 standard deviations
from the mean, fuel consumption records with missing geolocation tags) and imputing missing values using
multivariate imputation by chained equations (MICE). Key simulation parameters were calibrated against real-
world data sources to ensure empirical validity, as detailed in Table 2. The twin's predictive accuracy was
validated by comparing its output against a held-out dataset of 180,000 deliveries from Q4 2022. The model
achieved a 94.2% accuracy in predicting journey times and a 96.5% accuracy in predicting total operational
costs, confirming a high degree of fidelity to real-world operations.
Table 2: Digital Twin Parameter Calibration
Parameter Dat Source Calibrated Value / Model
Congestion Delay Factor INRIX Traffic Data Time-Dependent Dijkstra's Algorithm
Fuel Consumption Fleet Telematics (IoT) Regression model based on load, speed, gradient
Dock Handling Time Warehouse WMS Logs Triangular Distribution (12, 15, 25 min)
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SLA Penalty Cost Customer Contracts Tiered function: $X for 1h delay, $Y for 2h, etc.
Evaluation Method
We compared three models over 12-month simulated cycles:
a. Baseline: Traditional VRPTW minimizing distance only (Laporte, 2009).
b. Benchmark: Static integrated model with fixed weights (no feedback).
c. Proposed Artifact: Dynamic STOM with adaptive weight updates via regression-based
calibration.
Primary metrics:
i. Strategic Alignment Index (SAI): Pearson correlation between intended strategy weights and actual
optimized weights (α = .91, Cronbach’s).
ii. Total Cost, OTIF, and GII as absolute performance indicators.
iii. Volatility: Standard deviation of KPIs over time to assess robustness (Wieland & Durach, 2023).
Significance testing used repeated-measures ANOVA with Bonferroni correction (p < .01). Effect sizes were
calculated using Cohen’s d (Cohen, 1988). All analyses were performed in R 4.3.2.
RESULTS AND ANALYSIS
The empirical validation of the Strategic Transportation Optimisation Model (STOM) reveals a transformative
capacity to resolve the enduring trade-offs between cost, efficiency, and service delivery in freight logistics.
Leveraging a digital twin calibrated with 1.8 million real-world deliveries across three medium-sized carriers in
North America and Europe, this analysis demonstrates that embedding strategic business logic directly into the
mathematical structure of transport optimization yields quantifiable, simultaneous gains across all three
performance pillars — gains unattainable by either siloed transportation engineering models or static hybrid
approaches.
Dynamic Parameterisation as Strategic Alignment (RQ1)
Business strategy is not merely reflected in STOM’s outputs; it actively reconfigures its optimization kernel. The
Feedback Loop & Strategic Calibration Module successfully translated observed KPI deviations into adaptive
adjustments of the multi-objective weight vector wᵢ, transforming static preferences into dynamic orchestration.
Table 3 documents the evolution of these weights under distinct strategic orientations over a simulated 12-month
cycle.
Table 3: Dynamic Evolution of Strategic Weight Vectors in STOM (Mean Values Across Simulation Cycles)
strategic orientation Initial weight vector
(Cost, time, service, CLV,
GII)
Final weight vector
(Cost, time, service,
CLV, GII)
Primary
Weight
Shift %
Strategic
Alignment
Index
(SAI)
Cost Leadership (0.70, 0.20, 0.10, 0.00,
0.00)
(0.68, 0.22, 0.08, 0.02,
0.00)
+10%
(Time)
0.89
Service
Differentiation
(0.30, 0.40, 0.30, 0.20,
0.00)
(0.25, 0.45, 0.25, 0.30,
0.00)
+50%
(CLV),
0.93
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+12.5%
(Time)
Sustainability
Commitment
(0.40, 0.30, 0.20, 0.00,
0.10)
(0.38, 0.28, 0.18, 0.02,
0.14)
+40% (GII) 0.91
Note: Weights sum to 1.0. SAI = Pearson correlation between intended strategy weights and optimized weights
per cycle (α = .91).
Under Service Differentiation, SLA breaches among Tier-1 clients triggered an automatic 50% increase in the
CLV weight within two cycles, driven by a regression coefficient (β_CLV = -0.32, p < .001) linking delayed
deliveries to customer attrition risk. This was not a reactive penalty but a recalibration of the optimization
objective itself — a direct operationalisation of Service-Dominant Logic, where delivery becomes a relational
contract. Conversely, Cost Leadership exhibited subtle yet significant adaptation: while cost remained dominant,
the Time weight increased by 10% as congestion-induced delays threatened overall network reliability,
demonstrating an emergent awareness of systemic risk beyond narrow cost metrics. The mean SAI of 0.91
confirms STOM’s capacity to maintain near-perfect alignment between declared corporate strategy and actual
routing behavior, validating RQ1: business strategy does not evaluate outcomes — it defines them.
Figure 3: Dynamic evolution of the strategic weight vectors (Cost, Time, Service, CLV, GII) over the simulated
12-month period for each strategic orientation, demonstrating adaptive recalibration in response to
performance feedback.
Simultaneous Performance Gains Against Siloed Paradigms (RQ2)
The comparative performance of STOM against the Baseline VRPTW and Static Benchmark models reveals a
decisive shift in the Pareto frontier of logistics optimization. As shown in Table 4, STOM delivers statistically
significant and practically meaningful improvements across all five core metrics — simultaneously.
Table 4: Comparative Performance of Baseline, Static Benchmark, and STOM Models (Mean ± SD over 50
Monte Carlo Runs)
Performance
Metrics
Baseline
VRPTW
Static
Benchmark
STOM
(dynamics)
ANOVA
F(2,147)
P-
VALUE
COHEN’S
D(STOM V
BASELINE)
COHEN’S
D(STOM
V STATIC)
Total
Operational
Cost
($/shipment)
14.23 ±
0.85
13.81 ± 0.72 12.95 ±
0.61
158.72 < .001 1.52 1.18
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On-Time In-
Full (OTIF)
Rate (%)
87.1 ±
3.2
89.4 ± 2.8 95.6 ± 1.5 284.31 < .001 2.35 2.01
CLV
Retention
Rate (%)
78.2 ±
4.1
81.5 ± 3.7 93.1 ± 2.0 327.85 < .001 3.10 2.89
Green Impact
Index (GII)
62.4 ±
5.1
65.8 ± 4.6 78.3 ± 3.4 245.17 < .001 2.87 2.42
Average
Dwell Time
(min)
28.7 ±
4.5
31.2 ± 3.9 24.1 ± 2.8 142.05 < .001 1.38 1.75
All comparisons Bonferroni-corrected (α = .01). Bold indicates superior performance.
STOM reduced total cost by 9.1% compared to the Baseline and 6.2% compared to the Static Benchmark, while
increasing OTIF by 9.9 percentage points and CLV retention by 19.1 points — gains achieved concurrently.
Crucially, these improvements were not the result of compensatory shifts (e.g., higher cost for better service);
they reflect a fundamental restructuring of the optimization landscape. The effect sizes, ranging from large
(d=1.18) to very large (d=3.10), confirm the magnitude of STOM’s advantage. Even under stressors — a +50%
demand spike or 30% fuel price surge — STOM maintained OTIF above 92%, while the Static Benchmark
faltered below 85%. This resilience stems from its ability to dynamically prioritize critical customers and routes
based on real-time feedback, rather than relying on fixed assumptions.
Figure 4: Comparative performance of the Baseline, Static Benchmark, and dynamic STOM models across the
five key metrics (Cost, OTIF, CLV Retention, GII, Dwell Time). Error bars represent standard deviation. STOM
demonstrates simultaneous improvement on all axes.
Hypothesis Validation: Mechanisms of Strategic Integration
The empirical results provide robust support for all four hypotheses, revealing the causal mechanisms through
which STOM operates.
H1 (Simultaneous Improvement): Supported. No other model achieved statistically significant gains
across all five KPIs. STOM dissolved the traditional cost-efficiency-service trilemma by treating them
as interdependent dimensions of a single value architecture, not competing objectives.
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H2 (Strategy-Specific Optima): Supported. Distinct Pareto-optimal frontiers emerged: Cost Leadership
prioritized density and cost minimization (mean OTIF: 91.2%, GII: 68.5%); Service Differentiation
maximized CLV retention (95.8%) and OTIF (97.5%) at moderate cost (13.10 $/shipment); Sustainability
Commitment achieved the highest GII (82.1%) without sacrificing OTIF (94.3%). Each orientation
yielded a unique, non-dominated solution set, proving that strategy dictates the shape of the optimal
frontier.
Figure 5:*Distinct Pareto-optimal frontiers generated by the STOM model for each strategic orientation (Cost
Leadership, Service Differentiation, Sustainability Commitment), visually confirming hypothesis H2 that
strategy dictates the shape of the optimal solution set.
H3 (Feedback Loop Impact on Stability): Strongly Supported. The inclusion of the regression-based
feedback loop reduced the standard deviation of key KPIs by 38% compared to the Static Benchmark.
For instance, OTIF volatility dropped from 2.8% to 1.5%. This reduction in strategy-execution drift
confirms that continuous sensing and adaptation — the essence of dynamic capabilities — are not
abstract concepts but measurable engineering outcomes enabled by STOM’s calibration mechanism.
H4 (Digital Maturity as Moderator): Supported. SEM analysis confirmed a significant moderating
effect (β = 0.34, p < .01). The performance gains of STOM were significantly amplified in firms with
high data integrity (ERP/IoT integration ≥85%); for instance, CLV retention improved by 22.4% versus
15.1% in low-maturity firms, and GII gains were 28.7% versus 20.1%. This finding reveals that digital
maturity is not a passive enabler but a critical moderator that determines the upper bound of STOM's
effectiveness. High-maturity firms provided the high-fidelity, real-time signals necessary for the
feedback loop to accurately capture causal relationships. Thus, digital infrastructure is a strategic
capability prerequisite for achieving the full benefits of strategic-transportation integration.
The convergence of theoretical foundations — Resource-Based View, Service-Dominant Logic, Cost-to-Serve,
and Dynamic Capabilities — is empirically realized in STOM’s design. The model does not add BA metrics as
constraints; it makes them structural drivers of the optimization engine. The feedback loop, grounded in
interpretable econometric learning, ensures transparency and replicability, distinguishing STOM from opaque
AI-driven black boxes. The results demonstrate that when business strategy becomes the architect of
optimization, not its auditor, logistics transforms from a cost center into a resilient, adaptive, value-generating
capability.
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DISCUSSION AND INTERPRETATION
The empirical validation of the Strategic Transportation Optimisation Model (STOM) does not merely confirm
improved performance metrics — it dismantles a foundational assumption in logistics science: that operational
efficiency and strategic value creation are inherently at odds. STOM demonstrates that when business strategy
is not appended as a post-hoc constraint or dashboard metric, but embedded as the generative engine of
transportation optimization, the long-standing cost-efficiency-service triad ceases to be a trade-off and becomes
a synergistic architecture. This is not an incremental improvement; it is a paradigmatic shift from optimising
movement to orchestrating value.
Interpretation of Hypothesis Tests: Mechanisms of Strategic Integration
The results provide compelling, statistically robust support for all four hypotheses, revealing the causal
mechanisms through which STOM achieves its transformative impact.
H1 (Simultaneous Improvement): Supported. The magnitude of gains across all five KPIs — a 9.1%
reduction in cost, a 9.9-point increase in OTIF, a 19.1-point rise in CLV retention, a 25.5% improvement
in GII, and a 16% reduction in dwell time — compared to the Baseline, and even significant
improvements over the Static Benchmark, conclusively refute the notion that these dimensions are
mutually exclusive. The effect sizes (Cohen’s d > 2.0 on CLV and OTIF) indicate not just statistical
significance, but practical dominance. STOM’s core innovation lies in its rejection of functional silos:
by allowing cost, time, service, and sustainability to co-evolve within a single, dynamically weighted
objective function, it eliminates the “compromise logic” that plagues traditional models. A route no
longer must sacrifice reliability to save fuel; instead, the model learns that delaying a Tier-1 delivery
costs five times more than the fuel saved (Zhang et al., 2023), and adjusts accordingly. The result is not
a compromise — it is alignment.
H2 (Strategy-Specific Optima): Strongly Supported. The emergence of three distinct, non-dominated
Pareto frontiers under Cost Leadership, Service Differentiation, and Sustainability Commitment is
perhaps the most theoretically profound finding. Under Cost Leadership, STOM did not simply minimize
distance — it maximized density while tolerating moderate delays, effectively treating time as a variable
cost rather than a fixed constraint. Under Service Differentiation, it redefined “efficiency” as predictable
reliability, tightening windows and prioritizing high-CLV nodes even at higher marginal cost. Under
Sustainability Commitment, it discovered routes that simultaneously reduced emissions and fuel
consumption by avoiding congested urban cores during peak hours — a synergy invisible to static carbon-
cap models. This confirms that strategy is not a target to be hit, but a lens through which the entire
solution space is reconfigured. STOM operationalizes Teece et al.’s (1997) concept of dynamic
capabilities not as an abstract managerial skill, but as a computable algorithmic behavior.
H3 (Feedback Loop Stability): Strongly Supported. The 38% reduction in KPI volatility observed in
STOM versus the Static Benchmark is a revelation. In supply chains characterized by demand shocks
and regulatory volatility, stability is as valuable as peak performance. The regression-based feedback
loop — simple yet powerful — transforms STOM from a reactive optimizer into a proactive strategist.
When SLA breaches increased among Tier-1 clients, the model didn’t just flag the problem; it recalibrated
its own priorities, increasing CLV weight until compliance was restored. This mirrors real-world
managerial intuition — “We need to prioritize our best customers” — but makes it executable,
measurable, and scalable. The feedback loop embodies the sensing-seizing-transforming cycle of
dynamic capabilities (Teece et al., 2022), proving that adaptability can be engineered, not just managed.
H4 (Digital Maturity as Moderator): Supported. The amplification of STOM's benefits in digitally
mature firms (ERP/IoT integration ≥85%) is not merely a technical observation --- it is a strategic
imperative. Digital maturity is not a passive enabler; it is an active mediator. Firms with fragmented data
systems could implement STOM, but they would experience diminished returns because their feedback
loop was blind. High-maturity firms provided the high-fidelity, real-time signals — dwell times from
telematics, NPS from CRM, cost deviations from ERP — necessary for the regression coefficients (βᵢ) to
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accurately capture causal relationships between strategy drift and operational outcomes. This finding
elevates digital infrastructure from a “nice-to-have” to a strategic capability prerequisite. Without rich
data flows, STOM cannot learn. Without learning, it cannot adapt. Thus, digital maturity is not a context
factor — it is a moderating mechanism determining the upper bound of STOM’s effectiveness.
Theoretical Contributions: Bridging the Epistemological Chasm
This study makes three seminal theoretical contributions that redefine the boundaries of logistics scholarship:
1. The Integration of Strategy as Parameterisation: We move beyond hybrid models that treat BA variables
as penalties or filters (Li & Zhao, 2024; Huang et al., 2023) and demonstrate that business strategy can
and should be encoded as the structural weights of the optimization kernel. This transforms STOM from
a decision-support tool into a strategic artifact — a physical instantiation of organizational intent. It
operationalises Barney’s (1991) Resource-Based View by treating the transportation network not as a
commodity asset, but as a unique, inimitable, and strategically configurable resource whose value is
derived from its adaptive configuration.
2. The Synthesis of Four Foundational Paradigms into a Unified Framework: STOM is the first model to
formally integrate four dominant theoretical streams:
a. Resource-Based View (Barney, 1991) → Transportation as a strategic asset;
b. Service-Dominant Logic (Vargo & Lusch, 2004) → Delivery as relational co-creation;
c. Cost-to-Serve (Kaplan & Anderson, 2007) → Profitability defined by activity, not rate; and
d. Dynamic Capabilities (Teece et al., 1997) → Continuous adaptation via feedback. By binding
these together within a single, testable, simulation-validated framework, we resolve the
epistemological fragmentation that has plagued logistics research for decades. We show that
“why” (strategy) and “how” (engineering) are not separate domains — they are two sides of the
same coin, rendered visible through mathematical parameterisation.
3. The Emergence of Adaptive Logistics as a New Class of Capability: Prior literature treated optimization
as a one-shot problem (“find the best route”). STOM introduces adaptive logistics: a continuous, closed-
loop system where optimisation is not a static output but an evolving process. The feedback loop,
grounded in interpretable econometrics (Wooldridge, 2019), ensures this evolution remains transparent
and auditable — a critical distinction from opaque AI-driven black boxes. This establishes a new research
agenda: not how to solve routing problems, but how to build organisations that continuously re-solve
them in response to market dynamics.
Practical Implications: From Theory to Transformation
For practitioners, STOM is not a theoretical construct — it is a blueprint for competitive reinvention.
a. For Logistics Managers: STOM provides a clear pathway to align operations with corporate strategy. A
carrier pursuing “Premium Service” can now articulate its promise not just in marketing materials, but
in the numerical weights of its routing engine. Every delay triggers a self-correcting adjustment, making
accountability systemic, not anecdotal.
b. For CIOs and Data Architects: The mediation effect of digital maturity underscores a critical investment
priority: data integrity is not IT’s concern — it is the foundation of strategic agility. Investing in seamless
ERP-IoT-CRM integration is investing directly in the organization’s ability to adapt.
c. For Executives and Board Members: STOM translates strategic goals — “Become the most reliable last-
mile provider” — into quantifiable, traceable operational actions. It turns strategy from a PowerPoint
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slide into a live, running algorithm. The ROI is not speculative; it is measured in retained CLV, avoided
SLA penalties, and reduced emission fines.
Limitations and Boundary Conditions
While robust, this study has limitations. The digital twin, though calibrated with real-world data from three
carriers, represents a specific segment (medium-sized, North American/European, multi-modal). STOM’s
performance may vary under extreme conditions (for example, hyper-localized micro-fulfillment, highly
regulated pharmaceutical transport) or with different data fidelity thresholds. The feedback loop assumes linear
relationships between KPI deviations and weight adjustments — future iterations may benefit from non-linear
machine learning techniques (for example, reinforcement learning) for complex, emergent behaviors. Finally,
the model focuses on internal strategic alignment; external factors like competitor pricing or macroeconomic
shocks were held constant in simulation and warrant future exploration.
Future Research Directions
Building upon this foundation, we propose four directions for future inquiry:
1. Cross-Industry Generalisation: Test STOM in other sectors where service delivery is strategic — e.g.,
cold-chain pharmaceutical logistics, emergency medical transport, or retail fulfillment for luxury goods.
Does the model adapt equally well to high-value, low-volume, time-critical environments?
2. Non-Linear Feedback and Reinforcement Learning: Replace the regression-based calibration with deep
reinforcement learning agents that learn optimal weighting policies through trial-and-error in simulated
environments, potentially capturing non-linearities and latent strategic interactions.
3. Multi-Stakeholder Value Co-Creation: Extend STOM to incorporate ESG stakeholders beyond the firm
— e.g., community noise pollution scores, driver satisfaction indices, or supplier sustainability ratings
— transforming STOM from a firm-centric to an ecosystem-centric optimizer.
4. Human-AI Collaboration Dynamics: Investigate how managers interpret, override, or trust STOM’s
recommendations. What cognitive biases emerge? How does transparency (via interpretable weights)
affect adoption? This bridges STOM’s engineering design with behavioral operations research.
CONCLUSION
This research has not merely improved a model — it has redefined a discipline. By demonstrating that business
strategy can be systematically, dynamically, and empirically embedded into the mathematical structure of
transportation engineering, we have shattered the artificial boundary between the “how” of movement and the
“why” of value.
The Strategic Transportation Optimissation Model (STOM) is not an incremental enhancement to existing
VRPTW solvers. It is the first formal, validated, and generalizable framework to treat the logistics network as a
strategic capability — one whose internal logic is continuously adapted to reflect the organization’s identity,
customer commitments, and market positioning. Through the integration of Resource-Based View, Service-
Dominant Logic, Cost-to-Serve, and Dynamic Capabilities, STOM resolves the chronic trade-offs that have
crippled logistics decision-making for decades. It proves that simultaneous improvement in cost, efficiency, and
service is not a myth — it is a design principle.
Empirically, STOM outperforms both siloed engineering models and static hybrid approaches on all five key
performance indicators, with effect sizes confirming practical dominance. Its feedback loop enhances stability,
reduces strategy-execution drift by over one-third, and reveals that digital maturity is not a background condition
— it is the essential medium through which strategic adaptation becomes possible.
The implications are profound. For theory, we offer a unified paradigm that transcends disciplinary silos. For
practice, we offer a replicable, transparent, and scalable artifact that enables any firm — regardless of size — to
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transform its logistics network from a cost center into a strategic lever of differentiation, resilience, and
profitability. In an era defined by volatility, customer expectation, and climate urgency, the competitive
advantage will not belong to those who move goods fastest or cheapest. It will belong to those who move them
in alignment with their purpose. STOM provides the means to make that alignment not aspirational, but
algorithmic. This research paves the way for a new paradigm of strategic logistics, where optimization is
continuously reconfigured by strategic intent.
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