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Academic Editor: Baofeng Huo

Received: 22 August 2025

Revised: 7 October 2025

Accepted: 15 October 2025

Published: 30 October 2025

Citation: Nguyen, L.; Mayet, O.;

Desai, S. Operational and Supply

Chain Growth Trends in Basic

Apparel Distribution Centers: A

Comprehensive Review. Logistics 2025,

9, 154. https://doi.org/10.3390/

logistics9040154

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license

(https://creativecommons.org/

licenses/by/4.0/).

Review

Operational and Supply Chain Growth Trends in Basic Apparel

Distribution Centers: A Comprehensive Review

Luong Nguyen 1,2, Oscar Mayet 2and Salil Desai 1,*

1Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State

University, Greensboro, NC 27411, USA; ltnguyen@aggies.ncat.edu

2Continuous Improvement and Industrial & System Engineering Department, Hanesbrands Corporation,

Winston-Salem, NC 27101, USA; oscar.mayet@hanes.com

*Correspondence: sdesai@ncat.edu; Tel.: +1-336-285-4646

Abstract

Background: In a fast-changing sector, apparel distribution centers (DCs) are under increas-

ing pressure to meet labor intensive operational requirements, short delivery windows, and

variable demand in the rapidly changing apparel industry. Traditional labor forecasting

methods often fail in dynamic environments, leading to inefﬁciencies, inadequate stafﬁng,

and reduced responsiveness. Methods: This comprehensive review discusses AI-enhanced

labor forecasting tools that support ﬂexible workforce planning in apparel DCs using a

PRISMA methodology. To provide proactive, data-driven scheduling recommendations,

the model combines machine learning algorithms with workforce metrics and real-time

operational data. Results: Key performance indicators such as throughput per work hour,

skill alignment among employees, and schedule adherence were used to assess perfor-

mance. Apparel distribution centers can signiﬁcantly beneﬁt from real-time, adaptive

decision-making made possible by AI technologies in contrast to traditional models that

depend on static forecasts and human scheduling. These include LSTM for time-series

prediction, XGBoost for performance-based stafﬁng, and reinforcement learning for ﬂexible

task assignments. Conclusions: The paper demonstrates the potential of AI in workforce

planning and provides useful guidance for the digitization of labor management in the

clothing distribution industry for dynamic and responsive supply chains.

Keywords: Artiﬁcial Intelligent (AI); basic apparel; distribution centers; labor planning;

supply chain; warehouse

1. Introduction

Due to unpredictable customer demand, shortened delivery times, and mounting

demands for operational agility, the apparel distribution industry is rapidly changing. As

regional microtrends break apart traditional planning models and global fashion cycles

quicken, distribution hubs need to adopt data-driven, ﬂexible frameworks instead of static

labor methods. Forecasting, scheduling, and supply chain optimization are the main topics

of this paper, which examines the relationship between operational growth trends and

AI-enhanced labor planning in basic apparel distribution centers. Efﬁcient labor planning is

essential in the fast-paced apparel business to maintain operational efﬁciency and adjust to

changing consumer demand. In evolving environments, such as basic apparel distribution

centers (DCs), where labor intensity, stock-keeping-unit (SKU) variability, and seasonal de-

mand spikes pose signiﬁcant challenges, traditional labor forecasting tools which frequently

Logistics 2025,9, 154 https://doi.org/10.3390/logistics9040154

Logistics 2025,9, 154 2 of 26

rely on static historical data lack the agility needed [

]. To solve these issues, artiﬁcial

intelligence (AI) and machine learning (ML) technologies are increasingly being utilized

to enhance labor forecasting and ﬂexible workforce planning. Utilizing real-time data,

including order volumes, product assortments, inventory levels, and workforce availabil-

ity, these systems produce adaptive forecasts that maximize responsiveness and resource

allocation [

–

]. DCs may respond to the increasing need for speedier fulﬁllment, individu-

alized customer experiences, and cheaper operational costs by implementing AI-enabled

technologies that improve service levels, minimize labor costs, and increase supply chain

agility [

]. Mass data-driven labor planning is becoming an essential component of the

apparel industry’s competitiveness and operational resilience as omnichannel retail and fast

fashion models gain traction [

]. Although artiﬁcial intelligence (AI) and machine learn-

ing (ML) techniques like LSTM, XGBoost, and reinforcement learning are widely used, their

use in labor planning for the garment industry is still in its infancy. Contextual sensitivity

to SKU complexity, seasonal variations, and staff diversity is sometimes lacking in existing

models. Traditional forecasting techniques also do not react to real-time demand signals,

and hybrid planning frameworks have trouble scaling over dispersed networks. In order to

progress the area, we have identiﬁed a number of important issues and research gaps that

need to be ﬁlled. Current models offer minimal contextualization of AI models. Thus, most

of the research presents general algorithms without adjusting them to variables unique

to apparel, such as color, size, and fashion turnover. There exist inadequate empirical

validation and the usefulness of AI-driven workforce planning in distribution centers has

not been adequately illustrated by real-world case studies. Methodology reproducibility is

hampered by the fact that many reviews leave out information about database selection,

inclusion/exclusion criteria, and review duration. Research on labor forecasting does not

address algorithmic bias, data privacy, and human-AI interaction. There exist concerns

about generalizability and scalability as AI models frequently work well in isolated pilots

but have trouble adapting to a variety of facilities and worker demographics. The goal of

this work is to close the gap between theoretical modeling and practical execution by com-

bining structured review processes with a synthesis of recent work. To meet the changing

needs of the clothing supply chain, we suggest a framework for ethical, context-aware, and

scalable labor planning systems.

This review sets itself apart by concentrating on AI-driven labor forecasting in basic

apparel distribution centers, a ﬁeld that is frequently disregarded in the literature on supply

chains. It presents a scenario-based modeling methodology designed to account for factors

unique to the apparel industry, such as labor ﬂuctuations at the shift level, seasonal demand,

and SKU complexity. In contrast to previous assessments, the current one incorporates the

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a body

of research-based recommendations aimed at enhancing the quality and transparency of

reporting in these types of studies. Researchers can more easily document their technique,

from data analysis to literature search and study selection, by using its organized checklist

and ﬂow diagram. This makes the researchers’ work more dependable, repeatable, and

simple to assess. Utilized extensively in the health sciences and other ﬁelds, PRISMA

improves the validity of research results and facilitates well-informed decision-making.

PRISMA-style transparency standards to improve repeatability and provides a dashboard-

ready taxonomy of AI/ML models matched to operational contexts. The study offers a

more practical and context-sensitive roadmap for future research and real-world application

by addressing empirical gaps, ethical issues, and difﬁculties with human-AI interaction.

In the PRISMA review we compiled research from a broad range of academic, business,

and technology works that discuss workforce and operational planning using AI in basic

clothing distribution centers. Scopus, Web of Science, JSTOR, IEEE Xplore, and Google

Logistics 2025,9, 154 3 of 26

Scholar were the ﬁve main databases that were queried using a certain set of phrases.

They comprised “supply chain optimization,” “scenario modeling,” “labor forecasting,”

“apparel distribution centers,” along with “AI workforce planning.” The review focused on

works published between 2016 and 2025 and was restricted to English-language sources. To

ensure that theoretical insights and real-world applications were covered, pertinent industry

reports were also included to support peer-reviewed ﬁndings. A total of 262 articles were

surveyed, of which 136 references were narrowed based on relevance to the topic as the

inclusion criteria. The entire literature search was conducted over 6 months followed by

compilation into judicious sub-topics for this comprehensive review paper.

Figure 1presents the PRISMA ﬂow chart detailing the systematic review process for AI-

based workforce and operational planning in clothing distribution centers. It outlines each

phase—from initial database searches to ﬁnal article selection and thematic categorization

(Supplementary Materials File S1).

Figure 1. PRISMA methodology for comprehensive literature review.

The role of AI in labor forecasting for agile workforce planning in apparel distribution

centers is examined in this literature review, which looks at important topics like forecasting

models, real-time data use, AI integration, labor efﬁciency metrics, labor planning strategies,

and the effect of employee skill levels on overall performance. The operational schematic

of a distribution center is shown in Figure 2, which gives a visual summary of the main

functional areas such as receiving, storage, order picking, packing, and shipping as well as

the associated processes between them.

Logistics 2025,9, 154 4 of 26

Figure 2. Apparel distribution center process ﬂow diagram.

2. Labor Planning Strategies in Apparel Supply Chains

Traditionally, the cornerstones of labor planning in apparel supply chains have

been static scheduling, historical data, and experience-based adjustments. Lean and

just-in-time (JIT) approaches sometimes fail in highly unpredictable environments, albeit

offering some benefit [

]. To reduce waste and improve responsiveness, the apparel

sector needs integrated planning models that link labor, production, and inventory

choices. In addition, the logistics of apparel are increasingly adopting agile supply

chain solutions, which place an emphasis on responsiveness and adaptability [

]. Short

product life cycles, shifting demand, and worldwide sourcing have made supply chains

more complicated, necessitating more flexible and data-driven labor scheduling. To

adapt to sudden shifts in demand, these tactics promote proactive workforce planning,

decentralized decision-making, and real-time modifications. Because traditional labor

planning frequently depends on past trends and static headcount predictions, it cannot

adapt to abrupt changes in workforce availability or demand. On the other hand, AI-

driven methods offer predictive analytics, real-time data integration, and transformative

scenario modeling, which help businesses better identify interruptions, maximize cost-

performance, and assign workers. With this change, reactive scheduling will give way

to proactive, skills-based labor strategies that are constantly adjusted to meet changing

operational requirements. A paradigm shifts in the way apparel distribution centers

handle manpower allocation, demand volatility, and operational resilience may be seen

in AI-driven labor planning. Real-time, adaptive decision-making is made possible

by AI technologies like LSTM for time-series prediction, XGBoost for performance-

based staffing, and reinforcement learning for flexible task assignments, in contrast

to traditional models that depend on static forecasts and human scheduling. These

models use large datasets, such as historical throughput, demand at the SKU level,

and productivity at the shift level, to more accurately forecast labor needs [

]. AI

also makes it easier to respond to changes in regional demand, fashion cycles, and

promotional spikes, all of which are very erratic in the clothing industry. By matching

human resources with operational demand, AI integration enhances prediction accuracy,

Logistics 2025,9, 154 5 of 26

lowers overtime expenses, and promotes the deployment of ethical labor, according

to recent literature [

]. These features can be integrated into labor planning tools to

help distribution centers develop data-driven, scalable workforce strategies that strike a

balance between supply chain agility, employee well-being, and cost effectiveness.

While pointing out obstacles such legacy system integration and high implementation

costs, the Raymond 2025 article emphasizes how AI, particularly machine learning and

computer vision, improves supply chain resilience, forecasting, and quality control [

Furthermore, Kothapalli demonstrates how AI makes it possible to simulate labor scenarios,

optimize cost-performance, and visualize the workforce in real-time for more intelligent

planning [

]. A key aspect often overlooked is human adaptation to AI technologies

especially in low-cost labor industries like apparel, where technological adoption tends to

lag. Noor’s analysis of AI applications in the clothing sector looks at how the technology

is being used to improve operational effectiveness in the face of rising competition and

shifting consumer demands. The study emphasizes the application of multi-agent systems,

neural networks, and genetic algorithms for tasks including supply chain coordination,

production layout optimization, and defect detection. These strategies have limits when

it comes to actual implementation, notwithstanding their potential. Noor highlights the

necessity of greater research tailored to a particular business and a closer connection

between theoretical models and practical implementation [

]. Siddhu and Mohibi’s recent

paper look at how AI is transforming the apparel industry into underdeveloped countries.

It highlights AI’s contribution to improved design, production, and supply chain efﬁciency

while addressing challenges like data ethics and employment displacement. They support

inclusive innovation and more in-depth research to align AI with local industrial needs [

In addition, An AI-based framework for fashion forecasting is presented in the paper by

Banerjee, Mohapatra, and Saha, and it is veriﬁed by a case study. It demonstrates how

machine learning enhances proﬁt margins, inventory management, and product prediction

and advocates for wider usage in the fashion sector [

]. Moreover, in order to enhance

fashion forecasting, Banerjee, Mohapatra, and Saha’s develop a framework of AI for fashion

forecasting. It incorporates machine learning methods to improve inventory management,

proﬁtability, and product prediction. Through a real-world case study, the methodology

is tested and shows quantiﬁable gains in operational efﬁciency and forecasting accuracy.

The authors urge more widespread use of AI in fashion forecasting and demand that it be

further improved to meet industry demands [17].

Implementing ﬂexible labor methods into practice presents operational difﬁculties,

particularly in labor intensive settings like distribution centers (DCs), where efﬁciency and

ﬂexibility must be balanced. To reduce lead times and enable faster workforce modiﬁca-

tions, businesses are increasingly looking for localized or regionalized supply chain nodes,

according to H. Moradlou [

]. This action step could result in higher labor expenses. This

change has pushed businesses to embrace workforce models that incorporate ﬂexible shift

arrangements, cross-training initiatives, and multiskilled labor pools that can react quickly

to interruptions or spikes in demand. Furthermore, workforce planning in agile supply

chains needs to consider real-time decision-making and predictive skills in addition to

standard productivity measures. Distribution centers may instantly reassign personnel

based on real-time workload data and order priority by integrating cloud-based labor man-

agement systems, mobile tasking, and intelligent scheduling algorithms [19]. By enabling

managers to rapidly make data-driven labor decisions, these technologies improve agility

by lowering bottlenecks and boosting throughput during peak hours. Recent studies by

Uzozie investigate the use of AI in design, manufacturing, distribution, and retail [

Using empirical research and a synthesis of the literature, it places a strong emphasis on

sustainability, operational effectiveness, and competitive advantage. Robotics, computer

Logistics 2025,9, 154 6 of 26

vision, and predictive analytics are examples of technologies [

]. AI-enabled planning for

labor disruptions, including supply shocks, demand spikes, and absenteeism, is the main

topic of Raymond’s research study. For workforce agility, it suggests resilience modeling

and decision-tree frameworks [

]. Businesses can beneﬁt from using AI in many ways,

but there are hazards as well, like data privacy, algorithmic bias, and the challenge of

integrating AI with old HR systems and staff skills.

Figure 3shows how real-time data is used by AI-driven labor planning in clothing

supply chains to predict demand, match skills to tasks, and react to interruptions. It allows

for quick, data-driven decisions that maximize labor, boost productivity, and preserve

service levels in dynamic contexts by utilizing edge computing and predictive analytics.

Figure 3. Labor planning aligns workforce with strategic goals [22–24].

Agile labor planning further highlights the value of cooperation throughout the supply

chain. Ketokivi and Choi (2014) [

] assert that improving responsiveness and lowering the

risk of over- or under-stafﬁng can be achieved by coordinating labor tactics with upstream

production and downstream demand signals. Despite the scalable and responsive solutions

provided by AI-driven workforce planning, many apparel distribution centers continue to

rely on manual coordination and disjointed systems. Siloed platforms frequently house

client orders, supplier schedules, and transportation logistics, which restricts visibility and

delays decision-making. Excel-based models are not ﬂexible enough to meet changing

demands in these settings, while sophisticated AI frameworks are still underutilized

because of a lack of expertise, limited infrastructure, and high costs.

Using scalable labor planning tools, such as Excel or AI frameworks like TensorFlow,

apparel distribution centers enhance coordination and responsiveness by adjusting staff

strategies to changing demand. Effective labor allocation is made possible by utilizing au-

tomation, real-time data, and predictive analytics. This enhances customer satisfaction and

cost effectiveness while cultivating a workforce that is knowledgeable and ﬂexible

[26,27]

Logistics 2025,9, 154 7 of 26

In their 2025 study, Yanamala effectively describes how generative AI makes it possible

to analyze skill gaps, model scenarios, and allocate labor compelling. It highlights the

transition to ﬂexible, skill-based workforce strategies from static headcount planning [28].

Table 1lists common labor planning techniques used by distribution centers in a vari-

ety of industries, emphasizing how they can be utilized practically to manage operational

agility and worker efﬁciency.

Table 1. Labor planning tools streamline scheduling, resource use, and productivity in distribution centers.

Tool Type Key Function Beneﬁts

Workforce Management System [29] Schedule & track labor Staff, time tracking

AI Forecasting Tool [24] Forecast needs Seasonal, demand-driven stafﬁng

Labor Management System (LMS) [30] Monitor productivity Performance insights, incentive

APS System [31] Aligning with Operations Supply chain coordination

Simulation/Digital Twin [32] Simulate plans

Scenario testing, bottleneck reduction

Resource Optimization Tool [33] Skill-Based assignment Flexible, multi-skilled teams

Collaborative Scheduling Tool [34] Mobile management Agile, hourly workforce

KPI Dashboards & Analytics [35] Performance evaluation Continuous improvement

Robotic Process Automation [36] Automate Admin Efﬁciency planning & reporting

3. Demand Forecasting and Workforce Scheduling Models

Linear programming, queuing theory, and statistical forecasting are some of the

techniques used in traditional labor scheduling [

]. However, these models are not flex-

ible enough for changing industries like apparel. Genetic algorithms, simulation-based

models, and constraint programming are examples of stochastic and robust optimization

techniques that have demonstrated promise for workforce scheduling in the face of

uncertainty [

]. Lin et al. claim that hybrid models improve forecast accuracy and

flexibility that may be applied across industries by combining machine learning and

operational research methodologies [

]. Nevertheless, such models are not flexible

enough for industries as unstable as clothing. They frequently make assumptions about

constant demand or resource availability, which are inconsistent with the regular fluc-

tuations observed in supply chain interruptions, promotional activities, and seasonal

product cycles. Consequently, companies are moving toward scheduling systems that

are more flexible and impactful.

According to Cordeau et al. [

], workforce scheduling under uncertainty has

demonstrated the potential of effectiveness and stochastic optimization techniques

including constraint programming, evolutionary algorithms, and simulation-based

models. By taking into consideration variations in job durations, shift preferences,

and demand patterns, these methods enable more reliable scheduling choices. Agent-

based simulations can be used, for example, to mimic the relationships and behaviors

of individual workers to evaluate the effects of scheduling decisions on morale and

productivity. The primary elements of personnel scheduling and demand forecasting are

described, together with their salient characteristics and contributions to distribution

center operations, in Table 2below.

Logistics 2025,9, 154 8 of 26

Table 2. Demand forecasting & scheduling predicts labor needs and aligns workforce skills and

availability [40].

Component Description Key Features Operational Contribution

Historical Data Analysis Forecast demand using

historical data

Time series, trend &

seasonality

Baseline for labor planning

Real-Time Data

Integration

Use real-time inventory &

sales IoT, ERP, APIs Improve forecast accuracy

Forecasting Algorithms Predict labor needs ML model (ARIMA,

LSTM, etc.)

Increasing decision

accuracy

Scheduling Engine Optimize work schedules

Shift planning, simulations

Efﬁcient labor allocation

Capacity Planning Assess capacity vs. demand Bottleneck & simulation

tools

Prevents stafﬁng

mismatches

Labor Skill Mapping Match skills to tasks Skill matrices, training

records Enhances ﬂexibility

Scenario Analysis &

Simulation Simulate scenarios What-if, Monte Carlo, AI

sims Build resilient plans

Real-Time Feedback Loop

Monitor & adjust plans Dashboards, KPI alerts Enables continuous

improvement

Compliance & Labor

Rules Ensure compliance Automated labor law

checks Reduces legal risk

Furthermore, forecast accuracy and ﬂexibility are enhanced by hybrid models that

combine machine learning and operational research techniques (Lin et al., 2018) [

Hybrid systems employ machine learning to produce operational insights and demand

estimates, which are then fed into scheduling optimization solvers. Reinforcement learning

is being investigated to self-tune rules through constant feedback, and these systems use

historical data to adjust to trends [

]. Advanced analytics knowledge and AI modeling

skills are required for this hybrid strategy, which can be difﬁcult for businesses with limited

technological resources. AI-driven scheduling systems dynamically modify labor plans

in response to real-time operational parameters by integrating real-time data streams

from sources such as WMS, RFID, and IoT enabled equipment. Especially in apparel

distribution facilities, where adaptive workforce planning facilitates quicker fulﬁllment and

better alignment with changing demand. This strategy increases responsiveness, decreases

downtime, and boosts service efﬁciency [42,43].

The Demand Forecasting and Workforce Scheduling Models ﬂowchart provides an

organized summary of the essential elements that support efﬁcient labor planning in

clothing distribution facilities. The core model, which is at the heart of the framework,

is impacted by eight interrelated components that work together to improve operational

agility and forecasting accuracy. Historical Data Analysis is the ﬁrst step in the process,

which uses historical performance trends to forecast future labor and demand. Real-

Time Data Integration complements this, enabling the system to adapt dynamically to

the state of operations. The analytical engine that forecasts workforce demands based on

both historical and real-time inputs is a forecasting algorithm, which is frequently driven

by AI and machine learning. Labor Requirement Estimation determines the required

stafﬁng levels after demand projections, and Shift and Scheduling Optimization matches

labor availability with workload distribution throughout time periods. Flexibility and

Contingency Planning add buffers and alternate methods for managing interruptions like

demand spikes or absenteeism to guarantee ﬂexibility. Additionally, the model includes

Logistics 2025,9, 154 9 of 26

Performance Metrics and Feedback Loops, which analyze throughput, efﬁciency, and

schedule adherence to continuously improve forecasting accuracy. Finally, integration

capabilities guarantee that the system may easily connect with larger garment business

systems, allowing for end-to-end supply chain function coordination and visibility.

These elements in Figure 4work together to create a thorough and adaptable workforce

scheduling system that strikes a balance between operational ﬂexibility and predictive

analytics to satisfy the changing needs of apparel distribution settings. Demand forecasting

methods enable adaptive, closed-loop operations by generating short- and long-term

predictions based on market trends, sales data, and promotions, as seen in Figure 4. Data

driven methods are used in demand forecasting and workforce scheduling models to

forecast labor requirements and optimize stafﬁng levels, guaranteeing effective operations

and ﬂexibility in response to ﬂuctuations in demand.

Figure 4. Demand forecasting and workforce scheduling models [44–48].

Although workforce scheduling models and demand predictions provide fundamental

insights into labor planning, the limits of hybrid supply chain systems frequently restrict

their efﬁcacy. The complexity and volatility of contemporary apparel distribution are difﬁ-

cult for hybrid models that combine lean and agile principles to handle, especially when

dealing with short-term trends, variable SKUs, and dispersed supplier networks

[49,50]

Reactivity and scalability are hampered by these models’ frequent reliance on static as-

sumptions and delayed feedback loops. Therefore, there is a rising need to move toward

optimization frameworks driven by AI and machine learning that can simulate various

operating scenarios, ingest real-time data, and adapt to dynamic market signals. The next

section examines how cutting-edge algorithms like LSTM, XGBoost, and reinforcement

learning are being modiﬁed to get around these restrictions and enable apparel supply

chain planning to reach new heights of accuracy, adaptability, and resilience.

4. AI and Machine Learning in Supply Chain Optimization Models

AI and machine learning tools have been implemented across several ﬁelds including

manufacturing [

–

], biomedical research [

], oil and gas exploration [

]. Supply chain

optimization has enhanced because of AI and ML’s increased capacity for prediction and

Logistics 2025,9, 154 10 of 26

decision-making [

]. In highly variable contexts, labor forecasting methods such as

gradient-boosted trees (e.g., XGBoost), neural networks, and support vector machines per-

form better than conventional models. To provide more contextual forecasts and improve

the accuracy of labor planning, AI systems can also incorporate outside variables such as

promotions and weather [27,44–48].

Deep learning models and reinforcement learning are becoming popular techniques

for ongoing schedule improvement [

]. By learning from intricate patterns in multi-

source data, including sales projections, SKU velocity, order history, and shift logs, these

AI-driven models allow for more precise labor forecasting. For instance, long short-term

memory (LSTM) architectures and recurrent neural networks (RNNs) are especially well-

suited for time-series forecasting, capturing cross-temporal relationships that impact labor

requirements [

]. These models can adjust to fast fashion fads, seasonal variations, and

even geographical variations in customer behavior when used in clothing distribution

hubs. By gathering data, predicting demand, and improving labor and inventory planning.

The effects of AI and machine learning on apparel supply chain operations are depicted

in Figure 5. It draws attention to a powerful feedback loop in which AI models improve

workﬂow over time, increasing output and facilitating more intelligent, instantaneous

decision-making. Increased responsiveness and lower operating expenses result from

this. Through the integration of sophisticated algorithms in demand forecasting, inventory

control, and logistics, AI and ML enable ﬂexible, data-driven supply chain strategies and

result in notable efﬁciency gains.

Figure 5. AI & ML in apparel supply chain optimization process ﬂow [62–64].

AI also makes scenario planning and risk-aware forecasting easier. By using methods

like Monte Carlo simulations in conjunction with machine learning forecasts, planners

may assess workforce requirements under various demand volatility scenarios, improving

responsiveness and agility. This is particularly pertinent to the clothing industry, where

Logistics 2025,9, 154 11 of 26

if not well predicted, demand surges brought on by fashion drops or promotional events

may lead to either understafﬁng or overstafﬁng. Further democratizing access to advanced

analytics has been the incorporation of AI into workforce management systems and cloud-

based platforms. AI enhanced dashboards are increasingly widely used by businesses,

enabling supply chain managers to identify bottlenecks, simulate labor schedules, and

test the effects of changes in real time. The cognitive load on planners is lessened, and

decision-making quality is enhanced by these platforms’ frequent predicted alerts and

recommendations. The combination of artiﬁcial intelligence (AI) with digital twin virtual

models of actual distribution settings represents yet another signiﬁcant breakthrough.

Digital twins allow for predictive simulations of labor performance under different demand

or scheduling situations by combining AI models with real-time operational data [

]. This

enables distribution centers to ﬁnd the best conﬁgurations prior to implementation and

stress-test their workforce plans. Explainability and interpretability are becoming important

as AI models develop, particularly in regulated settings [66–68].

Explainable artiﬁcial intelligence (XAI) Techniques that enable human users to under-

stand and comprehend AI model decisions are known as explainable artiﬁcial intelligence

(XAI). Unlike conventional “black-box” models, XAI highlights key characteristics, decision

routes, and conﬁdence levels to reveal how predictions are made. Since human planners

must verify and implement AI-generated recommendations in practical situations like

labor forecasting, this improves trust, accountability, and ethical oversight. In order to

assist stakeholders comprehend, audit, and improve model behavior without sacriﬁcing

speed, post hoc explanations are made possible by tools like SHAP and LIME [

]. XAI

(Explainable AI) involves producing tools that assist labor planners in comprehending the

rationale behind a certain prediction or timetable, promoting trust and enhancing oper-

ational decision acceptance. In this context, apparel distribution centers serve as critical

components in effective labor planning [

]. Demand forecasting, task categorization,

talent mapping, shift scheduling, performance tracking, and technology integration are

some of these. When combined, they create a strategic framework that guarantees efﬁcient

production ﬂow and best use of available resources. To maintain operational efﬁciency and

reach production targets in the apparel industry, key pillars such labor forecasting, task

assignment, skill alignment, and scheduling are necessary. Together, these components

enable data-driven planning and real-time decision-making to increase ﬂexibility, boost

efﬁciency, and match worker capacity with operational demands.

Future modeling initiatives should consider industry-speciﬁc factors including SKU

complexity, color and size assortments, seasonal fashion cycles, and promotional demand

volatility to improve applicability. For instance, time-series data reﬂecting weekly changes

in style preferences can be used to train LSTM models, and real-time inventory turnover

across a variety of product categories can be used to improve labor allocation via reinforce-

ment learning. Enhancing forecasting accuracy and operational relevance by including

these domain-speciﬁc elements will guarantee that AI tools are not only theoretically sound

but also customized to the complex dynamics of apparel distribution centers.

5. Use of Real Time Data in Distribution Center Operations

Distribution centers can improve visibility and operational management by utilizing

real-time data from RFID tracking, Warehouse Management Systems (WMS), and Internet

of Things (IoT) devices [

]. By enabling the responsive reallocation of labor resources, these

systems improve responsiveness to process delays or spikes in demand. By incorporating

real-time feeds into forecasting and scheduling systems, advanced analytics platforms

facilitate AI-driven decision-making [73].

Logistics 2025,9, 154 12 of 26

Predictive analytics combined with real-time data turns static labor planning into an

ongoing, ﬂexible procedure. IoT-enabled wearables and mobile devices, for example, can

track the whereabouts of workers, their task completion rates, and their physical states.

This information can be used to track performance, detect weariness, and balance labor [

These data streams give shift supervisors the ability to effectively reallocate employees

to packing stations or high priority picking zones in an apparel DC setting when order

volume increases occur.

Barcode and RFID scanning also provide insight into SKU-level activity and inventory

movement. These technologies cause labor schedule adjustments to be automatically made

when combined with AI-based rules engines. For instance, reallocating workers to return

processing during busy post-holiday times or sending more staff to inbound docks in the

event of a large shipment. A framework for real-time data gathering that facilitates ﬂexible

and effective labor planning is shown in Figure 6. It demonstrates how AI systems gather,

analyze, and use labor data continually to facilitate adaptable decision-making. In order to

improve system responsiveness and operational management, this design enables smooth

data ﬂow capturing, processing, and visualizing information in real time.

Figure 6. Real-time data capture schematic [

]. Solid arrows represent direct and continuous

data ﬂows, illustrating how data or processes move sequentially between systems in real time or

as part of core operations. Dashed arrows, by contrast, denote indirect, supportive, or feedback

relationships; they often highlight non-linear integration, auxiliary data exchange, or inﬂuence. This

visual distinction clariﬁes which elements contribute contextual or analytical value and which are

tightly embedded in operational workﬂows.

Logistics 2025,9, 154 13 of 26

Additionally, by spotting operational disturbances and recommending real-time labor

modiﬁcations, AI enhanced Warehouse Management Systems (WMS) provide proactive

exception management, enhancing efﬁciency and service levels [

]. By streamlining ex-

ception management, predictive analytics promotes managerial ﬂexibility and expedites

fulﬁllment [

]. While real-time data-driven solutions, including wearable sensors and au-

tomated inventory tracking, improve productivity and visibility, they also present dangers,

such as cybersecurity and legacy system integration [79].

A summary of the main digital tools utilized in apparel distribution centers are stated

in Table 3. Improved visibility, faster fulﬁllment, and better planning are some beneﬁts of

technologies ranging from IoT to AI forecasting and mobile devices. However, challenges

still persist which include high costs, complex integration, and data demands.

Table 3. Tools and systems utilizing real-time data in distribution center operations [80–83].

Tools/Systems Key Features Beneﬁts Challenges

IoT Devices & Sensors [80]Real-time tracking (goods,

temp, location)

Improves visibility, reduces

errors

High cost, complex

integration

Warehouse Management

System (WMS) [80–83]

Order/task management

systems

Faster fulﬁllment, better

control

Requires system

integration, training

Labor Management System

(LMS) [80–83]Live productivity tracking Boosts utilization,

accountability

Customization &

Scalability

Automated Material

Handling Systems [80,81]

Automation (robots,

AGVs) Increases speed, 24/7 ops Expensive, coordination

needs

Predictive Analytics & AI

Models [80]Real-time forecasting Enhancing planning,

efﬁciency

Needs quality data,

training

Order Management System

(OMS) [80–83]

Sync or-

ders/inventory/shipping

Reduces back orders,

streamlines ﬂow

Complex platform

integration

Real-Time Inventory

Visibility [80]

RFID/barcode stock

updates

Prevents stockouts, aids

planning

Hardware investment

required

Digital Twin & Simulation

Tools [80–82]Digital twins & simulation Better decisions, ﬁnd

inefﬁciencies

Complex and

data-intensive

Cloud Infrastructure &

Edge Computing [80,81]Edge/cloud computing Fast analytics, mobile

access Security & latency issues

Mobile & Wearable

Devices [80,81]

Wearables (smart glasses,

devices)

Improves tracking,

communication

Battery, adoption,

connectivity

Furthermore, by processing data locally, edge computing reduces latency and band-

width consumption, enabling prompt labor planning decisions even in the event of limited

cloud access. Workﬂow optimization and real-time workforce analysis are made possi-

ble by decentralizing computation close to data sources, such as automated systems and

wearable technology [

]. Edge-based models that are integrated with AI improve oper-

ational resilience and service levels in an unstable situation by automatically adjusting

labor allocation, identifying inefﬁciencies, and enhancing scheduling using predictive

analytics [85].

Supply chain visibility is improved by cloud technologies such as SAP Leonardo,

Microsoft Azure IoT, and Oracle SCM Cloud, which offer real-time dashboards and AI-

driven analytics. These platforms give labor planners vital performance insights for well-

Logistics 2025,9, 154 14 of 26

informed decision-making by combining data from WMS, TMS, and IoT devices [

Through intelligent recommendations and predictive alerts, cloud-based control towers

enable quick workforce adjustments, increasing agility in the face of interruptions [

Furthermore, multi-site activities are supported by centralized collaboration platforms;

nonetheless, issues like data integration and privacy concerns continue to be important

factors [88].

The development of self-optimizing labor systems that continually learn from op-

erational data, anticipate workload shifts, and independently modify labor allocation to

maintain optimal ﬂow is made possible by these technologies’ maturing convergence with

AI and ML models.

6. Metrics for Evaluating Labor Efﬁciency and

Operational Responsiveness

The labor utilization rate, throughput per work hour, order lead time, and schedule

accuracy are key performance indicators (KPIs) that are pertinent to labor forecasting.

These parameters are optimized by AI-based systems, which also use them as input to

improve algorithms [

]. AI models with continuous learning capabilities and real-time

dashboards improve the capacity to forecast labor requirements and assess the efﬁcacy of

labor plans in practical settings.

One important indicator that measures the percentage of scheduled time spent on

productive work is the labor utilization rate. In AI enabled environments, labor bottle-

necks and high-intensity processes are identiﬁed by breaking down utilization at the task

level such as picking, packing, replenishment, etc. [90]. Increased labor tracking granular-

ity dramatically boosts warehouse visibility and resource allocation, according to recent

studies [26].

Throughput per labor hour, or TPLH, is becoming increasingly important, especially

in fast-moving apparel DCs where order complexity and SKU diversity shift signiﬁcantly.

TPLH connects task output with labor input and system throughput, going beyond con-

ventional productivity metrics. TPLH can dynamically suggest the best stafﬁng for various

order mixes when paired with machine learning algorithms [

]. Order cycle time, or

the period of time between receiving an order and shipping, is crucial for quick fashion

and e-commerce fulﬁllment.

AI enabled workforce planning solutions provide insights into how labor deployment

affects customer satisfaction and on-time delivery by correlating labor allocation with cycle

time parameters [

]. In dynamic, AI managed situations, schedule adherence and real-

time labor variance are especially crucial. These measurements highlight delays and allow

for predictive rescheduling by evaluating how closely real labor execution matches the

anticipated timelines. Machine learning models trained in variance data can predict future

deviations and proactively recommend modiﬁcations, according to research by Ivanov &

Dolgui [92,93].

Metrics measuring labor efﬁciency evaluate how well workers’ time and effort are

transformed into useful output. Key performance metrics considered in this assessment,

such as productivity, utilization rate, extra hours, order fulﬁllment time, lead time, and

on-time delivery rate, are highlighted in Figure 7. Every statistic is essential for evaluating

and improving operational responsiveness and labor efﬁciency, offering a thorough perspec-

tive that aids businesses in pinpointing opportunities for development and streamlining

their procedures.

Logistics 2025,9, 154 15 of 26

Figure 7. Metrics for evaluating labor efﬁciency and operational responsiveness [94–96].

Multi-skilling metrics and the Employee Efficiency Index show an increasing

demand for cross-functional labor pools. Worker flexibility and task-switching agility,

two essential skills in agile operations are assessed by these indicators. Both workforce

readiness and redeployment effectiveness are enhanced by tracking the number of

job functions per worker, cross training completion rates, and average task transition

timeframes [

]. By combining workforce flexibility, order fulfillment speed, inven-

tory movement, and task turnaround time, a composite KPI known as the Operational

Responsiveness Index (ORI) has surfaced in contemporary DCs analytics. ORI is a

strategic guidance for workforce scaling and automation investments when compared

across shifts or facilities [

]. AI Forecast Accuracy offers feedback loops for enhanc-

ing predictive labor planning models using statistical metrics such as Mean Absolute

Error (MAE) and Root Mean Squared Error (RMSE). Frequent comparison of projected

and actual labor demands guarantees ongoing model improvement and flexibility in

response to shifting circumstances [100,101]. Distribution Center managers and labor

planners may react swiftly to operational changes, identify inefficiencies, and promote

continuous improvement by integrating these analytics into real-time dashboards and

interactive visualization tools. These analytical capabilities are increasingly supported

by platforms that use AI and IoT technologies, giving clothing supply chains agility

and resilience [102].

Table 4outlines the core metrics used to evaluate labor efﬁciency and operational

responsiveness. These indicators such as labor utilization, order fulﬁllment time, produc-

tivity rates, and schedule adherence demonstrate how effectively the workforce adapts to

operational demands and maintains performance in real-time environments. The table also

illustrates their practical applications in enhancing workforce planning and optimizing

overall operational outcomes.

Logistics 2025,9, 154 16 of 26

Table 4. Labor efﬁciency and responsiveness metrics [103].

Metric How It Works Beneﬁts

Labor Utilization Rate

Tracks productive time vs. scheduled

time, using task-level data

Identiﬁes inefﬁciencies, boosts labor

productivity

Throughput per Labor Hour (TPLH) Measures responsiveness by

factoring order complexity and SKUs

Optimizes stafﬁng in real-time

Order Cycle Time

Links labor input to order cycle times

via AI

Enhances fulﬁllment speed &

customer satisfaction

Schedule Adherence Compares actual vs. planned labor,

predicts deviations

Minimizes disruptions, improves

schedule accuracy

Real-Time Labor Variance Monitors forecast vs. actual labor

needs

Enables adaptive & efﬁcient

rescheduling

Employee Efﬁciency Index Assesses skill depth & ﬂexibility Supports agility & effective

redeployment

Operational Responsiveness Index

(ORI)

Combines labor, inventory, and task

data into one KPI

Guides training and tech investments

AI Performance Accuracy Uses metrics like MAE/RMSE to

assess forecast accuracy

Improves model reliability and

planning precision

7. Employee Skill Levels and Their Impact on Performance

Task assignment effectiveness, learning curves, and overall productivity in DCs are all

impacted by the skill level variety of employees. Flexible job designs and multiskilled labor

models are supported by research as ways to maximize labor allocation [

104

]. According to

Heilala et al. [

105

], AI driven task matching and skill-based scheduling systems improve

labor utilization by matching tasks with worker competencies. Additionally, the integration

of skill matrices into AI systems allows planners to identify capability gaps and enhance

training techniques. Multiskilled workforce increases agility by facilitating seamless work

reassignment without the need for retraining or supervision. Olhager and Rudberg [

106

]

assert that skill ﬂexibility makes it possible to use resources more effectively, especially

when demand varies, or equipment breaks down. More consistent throughput and more

resilience during busy times or disturbances are facilitated by workers who can do a variety

of tasks.

AI-powered skill-based scheduling systems that are coupled with labor management

systems (LMS) can automatically assign employees to assignments according to their

competency proﬁles, past performance, and degree of fatigue. By assigning the most

qualiﬁed employees to urgent or high-priority jobs, these systems maximize labor efﬁciency

and lower error rates [107].

A schematic diagram in Figure 8below showing the connection between workers’

skill levels and how they affect performance can be found below. The graphic illustrates

how various skill levels basic, intermediate, and advanced affect important performance

indicators like output, output quality, efﬁciency, and creativity. While intermediate skills

improve efﬁciency and creativity, basic skills largely affect productivity and job quality.

Advanced skills have a wide-ranging effect, enhancing efﬁciency, production, and work

quality while spurring creativity. This graphic illustration emphasizes how crucial it is to

train staff members to maximize organizational effectiveness.

Logistics 2025,9, 154 17 of 26

By examining employee performance data, skill levels, and contextual elements like

demand spikes and absenteeism trends, AI-driven labor forecasting enhances workforce

allocation. While reinforcement learning gradually improves labor deployment, machine

learning models such as Random Forests and Neural Networks allow for exact schedul-

ing [

108

–

110

]. By matching activities with individual talents, this strategy supports reten-

tion and operational resilience while increasing productivity, decreasing inefﬁciencies, and

improving employee satisfaction [111].

Employees' Skill Levels and

Their Impact on Performance

Skill Levels Performance Metrics

Advanced

Skills Productivity

Intermediate

Skills

Basic Skills Quality of

Work Efficiency Innovation

Figure 8. Employee skill levels and their impact on performance [112–114].

By lowering task switch ramp-up times and eliminating downtime brought on by skill

mismatches, ongoing training and cross-training initiatives enhance operational responsive-

ness. AI-powered learning management systems may tailor training programs according

to performance data-identiﬁed ability gaps, guaranteeing focused skill development for all

employees [115,116].

The advent of wearable technology and collaborative robots (cobots) has changed

the skill needs in high-tech settings, moving the focus from manual work to tech-enabled

job management. Workers with digital literacy and the ability to use AI-based interfaces

play a crucial role in enabling intelligent distribution systems. Accordingly, in contem-

porary supply chains, digital upskilling is now regarded as a crucial component of labor

planning [117].

Employee skill levels have a big impact on daily task performance as well as more

general corporate outcomes like competitive advantage, cost effectiveness, and operational

agility. Table 5shows how worker retention and digital transformation are two essential el-

ements for long-term success in clothing distribution centers are facilitated by multiskilling,

focused training, job-role alignment, and technological proﬁciency. Along with increasing

operational ﬂexibility and productivity, these capabilities also raise staff engagement. In-

creased skill proﬁciency results in improved accuracy, reactivity, and overall efﬁciency in

dynamic work contexts.

Logistics 2025,9, 154 18 of 26

Table 5. Employee skill development and its impact on performance.

Success Factor Explanation Pros Cons Business Impact

Skill Versatility and

Multi-skilling [118]

Multi-role capability

boosts ﬂexibility

Increases agility,

reduces downtime

Needs training, risk

of burnout

Cuts overtime,

improves labor

utilization

Training and

Development

Programs [112]

Ongoing training

keeps skills updated

Enhance adaptability,

morale

Costly and

time-intensive

Speeds up tech

adoption

Skill-to-Task

Alignment [119]

Role-skill alignment

improves accuracy

Reduces errors,

boosts output

Requires strong HR

systems

Raise productivity

and fulﬁllment

quality

Performance

Feedback Loops

[120]

Real-time feedback

drives growth

Supports

improvement,

learning

Can cause stress if

mismanaged

Increases

accountability,

reduces waste

Technology

Proﬁciency [121]

Tech proﬁciency

supports digitization

Fewer disruptions,

faster adoption

Steep learning curve

for some

Smoothens tech

integration

Cross-Functional

Collaboration [122]

Cross-functional

teamwork enhances

ﬂow

Better coordination,

ﬂexibility

Possible role

confusion

Improves

responsiveness

Retention & Morale

[123]

Skilled, engaged staff

increase retention

Cuts turnover and

training costs

Risk of attrition

without growth

Stabilizes labor

supply, boosts

efﬁciency

In labor-constrained economies, investing in employee training and upskilling in-

creases retention, lowers absenteeism, and boosts productivity. Programs for contin-

uous learning promote engagement and assist employees in adjusting to technology

changes [

124

]. By efﬁciently using analytics and predictive models, skilled workers maxi-

mize AI-enhanced systems, increasing organizational responsiveness [

125

]. Additionally,

training encourages people to view AI as a tool for enhancement rather than a replace-

ment. Cross-functional abilities and digital literacy foster creativity, ﬂexibility, and moral

technology integration in automated settings [126].

8. Research Gap and Future Directions

Although labor forecasting with AI has improved, there are still a number of crucial

areas that require further study. These include how AI planning is affected by varied and

multigenerational workforces, how AI tools and humans interact, how using real-time data

affects employment over the long run, and how scalable AI solutions are across complex

networks. Closing these gaps will be essential to developing scalable, moral, and inclusive

labor planning systems. Several emerging trends inﬂuence the future of labor forecasting

and agile workforce planning:

•

Integration with Internet of Things (IoT): IoT devices in DCs can provide real-time

operational data to AI models [47].

•

Explainable AI: Efforts to make AI decision-making processes transparent and under-

standable to human managers [127].

•

Human-in-the-loop AI: Systems designed to combine AI recommendations with hu-

man oversight for better decision quality [128].

The following table (Table 6) lists the main research gaps in AI-enhanced labor forecast-

ing in apparel distribution centers. Workforce diversity, human-AI interaction, long-term

employment impact, ethical issues with real-time data use, and the scalability of AI so-

Logistics 2025,9, 154 19 of 26

lutions are among the areas that have not received enough attention. Closing these gaps

is crucial to creating scalable, ethical, and inclusive forecasting systems that promote

responsible technology integration, employee well-being, and operational efﬁciency.

Table 6. Research gaps.

Research Gap Recommendation Potential Impact

Apparel-Speciﬁc Forecasting Models

Forecast SKU-level demand by size and

color using AI/ML, considering sales and

trend cycles.

Increases forecast precision, reduces

overstocks and stockouts, and enhances

inventory movement.

Lack of Integrated Labor Scheduling

Utilize hybrid models to connect

forecasts with labor shifts, overtime, and

cross-training.

Increases demand responsiveness,

reduces overtime, and increases worker

efﬁciency.

Omnichannel Labor Complexity

Examine how labor is distributed among

B2B, B2C, and returns, paying particular

attention to peak times.

Reduces bottlenecks, enhances service

across channels, and maximizes staff

utilization.

Inﬂexible Stafﬁng Models Examine ﬂexible stafﬁng options such as

temp pools and gig labor.

Increases resilience, reduces personnel

costs, and improves adaptability.

Unquantiﬁed Forecasting Impact

Examine the effects of forecast accuracy

on labor KPIs such as cycle time, cost,

and utilization.

Improved cost management, data-driven

labor planning, and more strategic

decision-making.

Workforce Diversity & Multigenerational

Labor

Create inclusive labor models that

consider factors like age, gender, ability,

and culture; use AI to customize

onboarding and ergonomics.

Increases team productivity, decreases

bias, and improves retention.

Human-AI Interaction

Examine user acceptability, trust, and

transparency in AI-driven planning;

create cooperative user interfaces

Strengthens human-AI cooperation,

makes better decisions, and increases

conﬁdence in AI

Long-Term Employment Impact

Analyze how AI will affect career

trajectories, skill changes, and job

stability over the long run; investigate

reskilling techniques.

Promoting moral planning and getting

ready for long-term job changes.

Scalability of AI Solutions

Create modular AI that adapts to

different DC sizes, clothing types, and

geographical areas; evaluate the

cost–beneﬁt of doing so.

Enables global scalability, reduces risk,

and supports adoption.

In addition, to help address these identiﬁed gaps, the following questions need to

be answered:

How can labor scheduling be optimized using reinforcement learning in the face of

seasonal demand spikes and SKU volatility?

Which data governance approaches guarantee that employee performance data is

used ethically in algorithms for predictive scheduling?

What is the performance of AI-driven labor projections at different facilities with

different workforce diversity and automation levels?

In multi-site clothing networks, how do centralized and decentralized labor forecast-

ing models perform differently in real time?

These inquiries serve as a guide for upcoming empirical and modeling studies and

represent operational priorities including cost effectiveness, equity, and scalability.

Although the use of AI models for labor forecasting in clothing distribution centers is

expanding, it is important to recognize a few signiﬁcant drawbacks to guarantee ethical

implementation. First, the availability of high quality, detailed data such as demand

signals at the SKU level, productivity measures at the shift level, and real-time inventory

Logistics 2025,9, 154 20 of 26

turnover is crucial for these models. This dependence can result in erroneous forecasts

and mismatched stafﬁng decisions in fast fashion settings, where trends change every

week and prior data may not be useful [

129

]. Second, data privacy is still a major issue,

especially when predictive systems incorporate biometric, behavioral, or performance-

based inputs. AI-driven labor planning runs the danger of breaching employee privacy and

undermining conﬁdence in the absence of strong governance frameworks [

130

]. Third, the

scalability and fairness of many models are limited by their inability to generalize across

facilities, labor demographics, and geographical areas. These restrictions highlight the

necessity of ethical data handling, transparent model validation, and ongoing performance

monitoring to guarantee that AI improves rather than compromises labor equity and

operational resilience.

9. Conclusions

Seasonal variations in demand, the proliferation of SKUs, and the emergence of om-

nichannel fulﬁllment have made the apparel distribution industry increasingly complex.

Because of these factors, traditional labor scheduling techniques are insufﬁcient for handling

real-time ﬂuctuations, the deployment of a multiskilled workforce, and ergonomic consid-

erations. This study emphasizes the urgent need for AI-enhanced labor planning tools that

boost operational resilience through predictive scheduling and skill-based cross-training.

Further enabling inclusive, data-driven workforce strategies in line with performance

metrics; and synching workforce capacity with volatile demand cycles. AI-driven labor

forecasting is revolutionizing the distribution of apparel by substituting dynamic, data-

driven models that use real-time inputs from RFID, WMS, and IoT technologies for static,

experience-based scheduling. These solutions increase response to changes in demand,

facilitate ﬂexible workforce deployment, and boost ﬂexibility through skill-based planning.

Integrated systems speciﬁcally designed for apparel logistics are still lacking, despite ad-

vancements in discrete ﬁelds like analytics and forecasting. More resilient and intelligent

supply chain operations are made possible by AI-powered workforce planning, which

enhances cost control, service quality, and operational ﬂexibility. Scalable, human-centered

AI frameworks that take workforce diversity, ergonomics, and forecasts into account should

be the main emphasis of future research.

By connecting theoretical AI frameworks with real-world applications speciﬁc to the

apparel and fashion industries, especially in emerging economies, this paper makes a

unique contribution. This work synthesizes AI approaches across the full value chain, from

forecasting and production to retail and sustainability, in contrast to previous studies that

concentrate on design or supply chain optimization. The combination of experimental

and modeling developments, which are rarely discussed together in literature currently in

publication, is a crucial area of originality. The paper lays the groundwork for the adoption

of scalable, context-sensitive AI by identifying both methodological innovation and practi-

cal problems. A layer of practical relevance that is frequently absent from more general

presentations about AI is also added by including case studies and labor forecasting scenar-

ios that are particular to a given region. In addition, the study highlights gaps in existing

literature and suggests structured research guideline questions, providing a roadmap for

further investigation. The study is positioned as a key resource for academic and industry

stakeholders due to its forward-looking viewpoint, emphasis on reproducibility, ethical

considerations, and interdisciplinary collaboration.

Supplementary Materials: The following supporting information can be downloaded at: https:

//www.mdpi.com/article/10.3390/logistics9040154/s1, File S1: PRISMA 2020 Checklist.

Logistics 2025,9, 154 21 of 26

Author Contributions: L.N., O.M. and S.D. contributed to the conceptualization, methodology,

background literature, and formatting of this review article. S.D. contributed to reviewing and

editing, supervision, and funding acquisition. All authors have read and agreed to the published

version of the manuscript.

Funding: The authors would like to thank the US Department of Defense (North Carolina Defense

Manufacturing Community Support Program).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: No new data were created or analyzed in this study.

Conﬂicts of Interest: The authors Luong Nguyen and Oscar Mayet were employed by Hanesbrands

Inc. The remaining authors declare that the research was conducted in the absence of any commercial

or ﬁnancial relationships that could be construed as a potential conﬂict of interest. The funders had

no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing

of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:

AGV Automated Guided Vehicle

AI Artiﬁcial Intelligence

APIs Application Programming Interfaces,

APS Advanced Planning and Scheduling

ARIMA Autoregressive Integrated Moving Average

B2B Business to Business

B2C Business to Consumer

DC Distribution Center

ERP Enterprise Resource Planning

HR Human Resource

IoT Internet of Thing

KPIs Key Performance Indicators

LMS Labor Management System

LSTM Long short-term Memory

MAE Mean Absolute Error

ML Machine Learning

OMS Order Management System

ORI Operational Responsiveness Index

RFID Radio Frequency Identiﬁcation

RMSE Root Mean Squared Error

RNNs Recurrent Neural Networks

SCM Supply Chain Management

SKU Stock Order unit

TMS Transportation Management System

TPLH Throughput per Labor Hour

WMS Warehouse Management System

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