Perspectives on Various Issues of Interest

Features, Business Process Models and Architectural Design of Smart Warehouses in Industry 4.0

A traditional warehouse typically includes several core functions such as receiving, storing, tracking and tracing, picking, and shipping. It also involves interactions with both upstream and downstream stakeholders, as well as a centralized management system. While a planning function is recommended, it is often considered optional in traditional warehousing setups. When goods arrive at a warehouse, they need to be tagged. Tagging can occur at multiple levels—truck, pallet, tray, or individual product—but pallet and tray tagging are the most commonly used due to their efficiency and ability to distinguish between different product types. Once tagged, goods are stored either manually or through automated systems like conveyor belts. If the incoming products are hazardous, compatibility checks are performed before storage to ensure safe handling.

In traditional warehouses, tracking is usually done manually using handheld scanners. The planning department plays a key role in determining the order fulfillment schedule and allocating the necessary resources. After planning, the order-picking process begins, which is also typically manual. Orders can be picked by entire trays or pallets, but often require manual assembly of different items to fulfill custom orders. Once picked, orders are packed and shipped to the customers. The warehouse supply chain involves both upstream and downstream stakeholders. Upstream stakeholders, typically suppliers, are responsible for delivering goods to the warehouse. Downstream stakeholders, usually customers, place orders that the warehouse fulfills. In some cases, the same entity may serve as both an upstream and downstream stakeholder—for example, manufacturers storing surplus products in warehouses. Clear communication with stakeholders is crucial, particularly when notifying them about order readiness, inventory levels, or any disruptions in the delivery process.

Effective warehouse operations also require a robust management system that covers finance and accounting, data processing, and sales management. Sales management focuses on handling orders and inventory, ensuring supply meets demand. Finance and accounting oversee the warehouse's financial health and ensure operational continuity. Data processing manages information from scanners and other sources, working closely with sales management to maintain accurate stock levels and support efficient supply intake.

While smart warehouses share many of the same high-level components as traditional warehouses, smart warehouses make planning a mandatory component and incorporate a warehouse communication network for enhanced coordination and efficiency. Each feature in the top layer has various optional and obligatory elements.

A smart warehouse functions through the seamless communication and integration of multiple systems. Within its business process management (BPM) structure, various technologies support the overarching warehouse management system (WMS). Advanced Planning and Scheduling (APS) software is used to oversee the planning and scheduling operations, ensuring that resources are optimally allocated. Simultaneously, an inventory management system is employed to maintain optimal stock levels and streamline inventory control. Financial and sales management modules handle all monetary interactions—ranging from processing incoming orders to managing restocking events—ensuring smooth financial operations throughout the warehouse. Order picking is directed by an Automated Storage and Retrieval System (AS/RS), which interfaces with Automated Guided Vehicles (AGVs) and other material-handling equipment to carry out tasks efficiently. Furthermore, a Transport Management System (TMS) is integrated into the process, coordinating with AGVs to prepare and load shipments onto trucks.

The figure outlines three key roles in the system—represented as swimlanes: the Supplier/Client, the Warehouse Management System (WMS), and the Warehouse. The client initiates a request, which is processed by the supplier. The WMS manages the planning and coordination of that request, while the Warehouse role executes the operational tasks. Each role consists of specific actions that may trigger related tasks across the system.

Architecture design of smart warehouses need to consider various viewpoints. In the following context diagram for a smart warehouse, the Warehouse Management System (WMS) serves as the central hub, orchestrating the core operations of receiving, storing, picking, and shipping goods. The WMS interfaces with a range of human operators, including corporate supervisors, warehouse managers, and floor-level employees. These users interact with the system to log actions, monitor activities, make operational decisions, and extract performance reports from the collected data. Depending on warehouse policy, truck drivers may also interact with the WMS as external operators.

To function effectively, the WMS depends on real-time data inputs gathered through scanners and sensors. These devices are often mounted on Automated Guided Vehicles (AGVs) or embedded within Augmented Reality (AR) systems. The Transport Management System (TMS) uses this data to guide AGVs and employees—via AR devices—to specific locations within the warehouse. This operational flow is further supported by the Advanced Planning and Scheduling (APS) system, which determines which goods should be retrieved. In some configurations, an Order Picking Operation System (OPOS) is also integrated to enhance the efficiency of picking tasks, and shelves may be outfitted with RFID tags to enable adaptive, self-adjusting storage.

To further enhance warehouse performance, Multi-Agent System (MAS) methodologies can be implemented. In such setups, robotic agents operate collaboratively within a distributed architecture. Intelligent agent-based communication allows for decentralized task allocation, optimizing efficiency through distributed algorithms. This approach helps to reduce battery usage and latency while maximizing utilization through task decomposition techniques.

The finance/accounting and sales management components manage all monetary and transactional functions within the warehouse. These include procurement, order processing, employee payroll, and broader financial oversight. In an optimized smart warehouse, seamless information sharing occurs between internal systems and external partners, enabled through automated data exchanges and system integration. This real-time visibility enhances responsiveness, reduces uncertainty, and helps maintain operational stability.

Reliable communication is critical for the success of smart warehouse operations. To ensure low-latency and high-reliability connections, the WMS communicates with other systems via robust 4G LTE and 5G networks. These technologies provide the infrastructure needed for agile, responsive operations within the Industry 4.0 framework. Additionally, cloud-based smart warehouses often leverage hardware virtualization to enable resource pooling, improving scalability and resource efficiency.

The decomposition view shown below outlines all essential modules required for smart warehousing. It includes not only the top-level functional components but also the sub-modules associated with each enabling technology. Key technologies featured in this view include barcoding, Augmented Reality (AR), Automated Guided Vehicles (AGVs), the Internet of Things (IoT), Warehouse Management Systems (WMS), scanning, RFID, and communication infrastructure.

For example, within the IoT module, several sub-components are identified: Artificial Intelligence (AI), Ambient Intelligence (AmI), a Security Module, and Real-Time Information processing. These elements highlight the critical considerations a smart warehouse designer must account for when implementing IoT-based solutions.

The AI sub-module encompasses techniques such as machine learning and deep learning algorithms, which enable predictive analytics, anomaly detection, and adaptive process control. Closely related, Ambient Intelligence (AmI) builds on AI, sensor networks, and pervasive computing to create an environment that dynamically adapts to the needs and behaviors of users and stakeholders. In this context, AmI contributes to making the warehouse responsive, intelligent, and context-aware.

Real-time information gathered from distributed sensors and devices supports the execution of both AI and AmI functions, enabling timely decision-making and responsive automation. A critical aspect of any Industrial IoT system is security—hence, the inclusion of a dedicated Security Module. This module ensures that proper security controls are in place to protect IoT devices, data transmissions, and system integrity from potential threats.

Together, these sub-packages form the technological backbone of a smart warehouse, ensuring that operations are not only efficient and intelligent but also secure and adaptable to evolving operational demands.

The uses view is presented below. The two core modules in the smart warehouse architecture are the Warehouse Management System (WMS) and the warehouse communication network. The WMS serves as the central control unit, interacting with various back-end systems and external modules to coordinate operations. The communication network functions as a critical link between the WMS and front-end technologies such as Augmented Reality (AR) hardware and Automated Guided Vehicles (AGVs). Additionally, other systems—including the Transport Management System (TMS), Internet of Things (IoT) devices, RFID infrastructure, and various external platforms—connect directly to the communication network to enable seamless data exchange and real-time coordination across the warehouse ecosystem.

Finally, in the deployment view, the mapping of software modules to their corresponding hardware components is illustrated. The data processing module is deployed on both the Warehouse Management Server and Warehouse Manager nodes, which serve as the central hubs for managing operations and analytics. Additional nodes are dedicated to cameras, sensors, scanners, and augmented reality (AR) hardware.

Automated Guided Vehicles (AGVs) are equipped with their own onboard cameras, sensors, and scanners, enabling autonomous navigation and interaction with the warehouse environment. Similarly, smart shelves are outfitted with embedded sensors to monitor inventory and environmental conditions. Each of these hardware components is represented as a separate node within the deployment architecture, emphasizing the distributed and interconnected nature of the smart warehouse system.

Source: https://doi.org/10.1016/j.compind.2020.103343

 

Augmented Reality for Training Applications in Small Enterprises

The fragmented and technically demanding nature of augmented reality (AR) content development continues to limit its broader adoption in industrial settings. Current AR authoring methods often require specialized knowledge in areas such as 3D modeling, programming, computer vision, tracking, and rendering. While automated tools attempt to streamline content creation, they are typically rigid and unsuitable for dynamic or undefined processes.

Many AR development approaches depend on existing resources like CAD models or PDF manuals. However, in cases where these materials are outdated, incomplete, or unavailable—such as with legacy equipment or obsolete electronics—content creation becomes a significant bottleneck.

Although model and symbol libraries are emerging to address this, they require constant updates and rely on fiducial markers for registration. This introduces further challenges, including the need for infrastructure changes (e.g., marker placement and visibility), time-consuming setup, and ongoing maintenance. Natural feature recognition can eliminate the need for markers but instead demands complete, high-quality 3D models and clear visibility of all object features, which is often impractical in industrial environments. Furthermore, while depth sensors and image capture can help generate 3D models without CAD data, these methods may interrupt workflows, require invasive equipment, and struggle to capture small or occluded components.

To address these challenges, AR content creation methods must evolve to accommodate non-experts, operate without dependencies on pre-existing resources or infrastructure modifications, and adapt to changes in processes or tasks. The proposed method introduces a low-disruption, template-based approach that bridges traditional and AR interfaces by capturing expert knowledge—both explicit and tacit—through eye-tracking and structured information mapping.

A Multi-Modal Framework for AR Content Creation and Delivery

The framework supports two user roles:

• Content creators (e.g., trainers or experts), and

• Content consumers (e.g., trainees or task novices).

It comprises five main steps:

1. Business Need Identification – Pinpoint a requirement for efficient knowledge transfer between experienced and inexperienced staff, particularly for hands-busy tasks.

2. Task Recording via Eye Tracking – The expert performs the task while wearing eye-tracking glasses, generating visual and audio data that are then broken down into clear training steps. These are populated into a Unity 3D-based AR content template.

3. Iterative Refinement – Content is refined through usability testing and feedback loops.

4. User Feedback Collection – Feedback during real-world use is captured to guide further optimization.

5. Knowledge Preservation and Empowerment – Organizations can retain critical knowledge through archiving and moderation, while users gain autonomy through self-paced learning.

(a) Task Recording & Information Mapping: The initial stage involves capturing the training task using eye-tracking technology. The recorded content is then structured through information mapping to define clear instructional steps. (b) Unity Development Environment: Unity is used as the core development platform, providing a visual interface for organizing and integrating multimedia elements into the AR training experience. (c) Augmented Repair Training Application Template with Mixed Reality Toolkit: The template, built on Unity and enhanced with the Mixed Reality Toolkit, offers a pre-configured structure for rapidly building AR training applications with intuitive interaction components. (d) Content Import: Trainers upload task-related media—such as video clips and images—into the template to visually support each instructional step. (e) Step Customization: Trainers define and organize the number of training steps, and for each one, they add supporting images, descriptive text, and other guidance to enhance user comprehension.

The training content (video, audio, gaze data) is captured directly during task execution. Eye-tracking glasses combine a front-facing video feed with a gaze fixation marker and integrated audio. This method enables the transfer of explicit procedural steps and tacit expertise, such as visual attention and workflow patterns. The resulting media is edited using standard software (e.g., Apple iMovie) and mapped into structured instructional steps.

Design of the AR Interface: Augmented Repair Training Application 

The template organizes content using user-centric design principles. A numbered step menu allows users to track progress and navigate between instructions. Controls such as “home,” “back,” and “next” replicate traditional interface metaphors, easing the transition for new AR users. To enhance clarity, content is displayed with high-contrast text (white font with blue accents).

Instructional content is structured using information mapping, a method that includes:

• Chunking related steps and concepts

• Highlighting essential information

• Ensuring consistency in formatting and structure

• Integrating visuals effectively

• Presenting content hierarchically for intuitive learning

Verification of Learning and Task Completion

Verification of knowledge transfer can be achieved through interactive assessments or expert review. While quizzes may suit well-defined tasks with quantifiable outcomes, they lack flexibility for variable, real-world processes. For this application, expert review was chosen for its simplicity and alignment with the goal of maintaining learner autonomy while ensuring training effectiveness.

USE CASE: 

The AR training assistance method was implemented to create shop floor training content for a small enterprise. While participants expressed enthusiasm about the innovative approach, many were unfamiliar with the tools involved—specifically Unity3D, eye-tracking software, and the AR device itself. This unfamiliarity led to several usability challenges during the content editing and deployment stages. Technicians required support to navigate unexpected issues such as accidentally deleted scripts or misplaced files.

Despite these challenges, subjective feedback from stakeholders was highly positive. Senior management and the sales team viewed the AR training tool as a valuable and forward-thinking investment. When showcased at trade events and to existing clients, the tool generated strong interest and favorable responses. Shop floor staff were also enthusiastic about the interface, interpreting it as a sign that their company was embracing modern technologies and investing in employee development. However, a small number of staff expressed discomfort using certain features of the AR device, such as voice commands and air tap gestures.

In response to the challenges identified during deployment, it became evident that a more accessible content creation interface would improve usability and reduce technical barriers. To this end, the development of an "intermediary interface" is proposed for future work. This interface would offer a form-based editing environment, simplifying the interaction between non-expert users and the underlying AR content formatting system.

Key features of the proposed intermediary interface include:

• Form-style input fields for guided data entry (e.g., text, video, audio annotations)

• Built-in tools to edit and align eye-tracking outputs into AR-friendly formats

• Tooltips and help documentation embedded within each section to guide users step-by-step

• Automated conversion of form inputs into AR code via a secure back-end service

• Responsive layout using a bootstrap-style grid system to accommodate various AR form factors

The Integrated Development Environment (IDE) would be decoupled from the front end and managed in the back end, ensuring secure storage, script execution, and formatting integrity. Additional layers of security and content moderation can be introduced to prevent accidental corruption, unauthorized access, or unapproved deployments.

This redesigned workflow aims to empower non-technical users to contribute to AR content creation while preserving the integrity, flexibility, and scalability of the training system.

Source: https://doi.org/10.1016/j.compind.2020.103208

Managing Project Cost Overruns Using Bayesian Network

High levels of project management maturity are often associated with improved operational outcomes—such as enhanced quality, timely delivery, and cost efficiency. However, this correlation does not always hold true. Many existing project management maturity models lack clarity and consistency; they are typically constructed from extensive lists of project domains, vague categories, ambiguous criteria, and loosely defined practices. These models often suffer from the absence of standardized terminology, fragmented integration of key project areas, and poor usability—frequently overwhelming practitioners with an excessive number of elements to assess during maturity audits.

In light of these limitations, Bayesian networks offer a promising alternative for modeling and analyzing the complexities of project management maturity. They provide a structured, visual, and data-informed approach to understanding causal relationships among variables. By leveraging causal reasoning, Bayesian networks help explain how multiple factors contribute to a particular outcome. Their adaptability makes them well-suited to the current landscape shaped by widespread IT adoption and the growing influence of machine learning.

A Bayesian network is a probabilistic graphical model that uses a Directed Acyclic Graph (DAG) to represent a set of variables and the causal dependencies between them. In this framework, nodes represent random variables, while directed edges (arcs) denote the influence of one variable (the cause) on another (the effect). Each connection reflects a conditional dependency, and the strength of these relationships is quantified using probability distributions.

Bayesian networks support two fundamental types of inference:

1. Backward (diagnostic) inference – used to identify the most likely cause of an observed effect.

2. Forward (predictive) inference – used to estimate the likelihood of an outcome based on known or assumed causes.

A critical strength of Bayesian networks lies in their ability to incorporate both expert judgment and empirical data. A robust network is built on a foundation of accessible and well-structured data, relevant in volume and quality to the issue at hand, and presented in a clear, visual, and interpretable format. This enables organizations to model project management maturity using a cohesive and data-driven causal structure, ultimately enhancing both understanding and decision-making.

To assess cost overrun risks linked to low project management (PM) maturity, the proposed method leverages two complementary data sources within a Bayesian Network (BN) framework: expert knowledge and historical project data. Experts contribute to structuring the BN by identifying input, intermediate, and outcome nodes. In contrast, a relevant dataset is used to train the algorithm, enabling the calculation of each node’s Conditional Probability Table (CPT).

It’s essential to differentiate between two modeling layers: semantic modeling and Bayesian (causal) modeling. Semantic modeling refers to the conceptual representation of project management maturity, while Bayesian modeling encompasses the systematic steps required to construct a causal BN structure. The methodology follows a seven-step process, starting with the definition of a streamlined framework for evaluating PM maturity.

Step 1: Define a Simplified PM Maturity Framework
Traditional Project Management Maturity Models (PMMMs) often involve exhaustive audits with over 150 criteria, which, while comprehensive, are impractical for correlational analysis due to the volume and lack of sufficient audit data. Instead, a more efficient two-tiered model is proposed:

• A macro-level model with a reduced number of key maturity indicators to help consultants quickly identify major weaknesses.

• A detailed level, informed by expert input, to be used when more granular analysis is needed.

This simplification allows for easier integration into causal models while maintaining diagnostic value.

To structure PM maturity, project features are organized into a three-layer classification system:

• Layer 1: Project Domains — Aligned with PMBOK® areas:

  • Social (S): Covers communication, HR, and integration management; emphasizes team dynamics and the project manager’s leadership.

  • Contract (C): Encompasses scope and risk management.

  • Results (R): Relates to cost, schedule, and quality management.

  • Interface (I): Includes procurement and stakeholder management.

• Layer 2: Project Phase Timing — Determines when a PM practice occurs:

  • Prepare (P): Before execution.

  • Monitor (M): During the project.

  • Valorize (V): At project closure or evaluation.

• Layer 3: Execution Characteristics — Describes how PM activities are conducted:

  • Activity Granularity (A): Level of task detail or time frame.

  • Resource Involvement (R): Who is involved and what tools are required.

  • Frequency (F): Timing and repetition of activities (e.g., daily, cyclical, or milestone-based).

These categories are used to build a tagged matrix of PM maturity criteria, creating a standardized nomenclature for assessing each PM task.

Step 2: Define Measurement Scales for BN Input Nodes
Maturity is typically assessed using a five-level performance scale, where advancing from level n to n+1 means all best practices at level n are fully implemented, and implementation of the next level is underway. The levels include:

• Level 1: Absent/Discovery — PM processes are undefined or applied inconsistently and informally.

• Level 2: Definition and Implementation – At this stage, the project manager and team define and establish key project management (PM) practices to be used throughout the project.

• Level 3: Measurement and Analysis – Performance data is collected from various sources (e.g., monitoring and forecasting systems), enabling the team to analyze results and make data-driven adjustments.

• Level 4: Managing Gaps and Interdependencies – The team becomes capable of handling complexity and uncertainty, especially when managing interrelated tasks across concurrent projects.

• Level 5: Capitalization and Continuous Improvement – The final stage focuses on institutionalizing learning. The team adapts quickly to changes, manages complexity, and shares improvements across the organization.

Step 3: Identify Drift Factors
This step links project maturity levels to performance deviations (drift). Drift factors represent recurring, high-impact causes of performance shortfalls such as cost overruns or delays. The identification process includes:

1. Drift Cause Identification – Experts compile a set of potential causes of deviation, based on project data.

2. Cause Selection – Non-recurring or low-impact causes are filtered out to ensure predictive reliability.

3. Cause Matching – Relationships between selected drift factors and PM maturity model (PMMM) components are established as tuples, forming the basis for Bayesian modeling.

Step 4: Define Aggregation Rules
Experts define how each maturity level correlates with the selected drift factors. Their insights are used to construct the conditional probability tables (CPTs) for the Bayesian Network (BN), effectively translating domain knowledge into mathematical rules.

Step 5: Prepare and Structure the Dataset
Since Bayesian Networks are data-driven, data preparation is critical. This involves cleaning the dataset by removing invalid values, standardizing formats, and handling outliers. Each row should represent a unique project instance. The quality and structure of the data must support the number of variables (columns) and ensure meaningful learning.

Step 6: Define the Target Node
The target node in the BN represents a measurable project performance issue (e.g., budget overrun or schedule delay). This variable should be specific enough to inform PM decision-making but generalized enough to avoid data fragmentation. The number of its possible states must balance interpretability with statistical reliability, typically optimized using metrics such as the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC).

Step 7: Apply Learning and Inference Algorithms
Once the network structure is defined, it is trained using historical project data, with additional validation against a separate test set. The expectation–maximization (EM) algorithm is employed for parameter learning due to its robustness in handling incomplete data. BN models serve two core purposes:

• Diagnostic analysis – Identifying the most likely causes (drift factors) of current or future project failures and mapping them to key maturity criteria.

• Predictive analysis – Estimating the likelihood of future performance issues based on assumed changes in PM maturity levels.

Bayesian Networks rely on two types of algorithms:

1. Structure and parameter learning algorithms – Define the network’s architecture and CPTs.

2. Inference algorithms – Propagate evidence throughout the network for analysis and prediction.

Hyperparameter tuning in BNs is inherently iterative, requiring trial-and-error testing to find an optimal balance between model complexity and predictive accuracy.

BN Validation and Quality Metrics

To ensure a valid and effective BN, several evaluation criteria should be considered:

• Semantic coherence and interpretability – Results must be understandable and actionable for PM experts.

• Completeness and coverage – Nodes and states should accurately reflect key PM dimensions, supported by sufficient data.

• Relevance and granularity – The target variable should have meaningful, precise outcomes.

• Model conciseness – The structure must avoid combinatorial explosion, ensuring computational efficiency.

• Learning quality – The CPTs must be well-defined and complete. Metrics like AIC and BIC can assess whether the model will generalize well to new data.

A well-constructed BN supports more informed project decisions by linking PM practices to measurable performance outcomes through data-driven, causal modeling.

USE CASE:

This model is especially relevant for consulting firms working with large-scale engineering projects in sectors such as oil and gas. These firms are often brought in when project performance has deviated significantly from expectations. Their clients expect:

• A clear diagnosis of the issues causing underperformance,

• Immediate recommendations for corrective actions, and

• A forecast of the likely outcomes if those improvements are implemented.

With access to detailed long-term data—such as deviation dates, financial impacts, root causes, and implemented corrective actions—consultants can use Bayesian networks to create predictive models. These models not only explain why issues occurred but also help anticipate future risks and guide decision-making to prevent recurrence.

The application of the BN model predicted that the project had a 15% probability of overcost corresponding to less than 1% of the expenses. An overcost between 1% and 10% has a probability of 49%. An overcost between 10% and 100% had a probability of 21% and an overcost corresponding to more than the 100% of the budget had a probability of 15%. The training and testing accuracy were within the 90% to 96% range with the relative error between the training data and the test data being less than 6%.

The model helps to predict the probability of entering into an overcost situation, based on the level of maturity evaluated in the audited project. 

Source: https://doi.org/10.1016/j.compind.2020.103227

Platform for Intelligent Automation in a Pharmaceutical Laboratory

The acquisition, processing, and utilization of data have become fundamental drivers behind the adoption of emerging technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), predictive maintenance, dynamic scheduling, big data analytics, cloud-based platforms, and digital twins. As a result, efficient data exchange and management are critical—if mishandled, they can become significant obstacles.

In the development of decision support tools, data preparation stands out as the most vital and time-intensive phase. Real-world data often arrives in inconsistent formats, may be incomplete or error-prone, lacks proper structure or traceability, and is frequently contaminated with noise. The core challenge typically lies not with the analytical tools themselves, but with the underlying data communication and integration infrastructure that governs data acquisition and handling.

To enable the effective deployment of dynamic decision support systems (DSSs), next-generation industrial architectures aligned with Industry 4.0 must ensure real-time data consistency, especially in time-sensitive applications. Addressing this need calls for a robust platform capable of automating data acquisition, preprocessing, and management—achieved through the development of an intelligent middleware layer.

This middleware functions as a bridge between field-level devices and higher-level DSSs. It facilitates seamless integration with technologies such as industrial communication protocols, fog computing architectures, and big data storage solutions. A compelling example of this platform's application can be found in pharmaceutical industry quality control labs, where a prototype system integrates an autonomous robotic module for material preparation with a dynamic scheduling decision support tool.

The integration of production operations with business planning and logistics relies heavily on the development of data-driven industrial architectures. A key framework for achieving this integration is the automation pyramid, as defined in the ISA-95 standard.

This model consists of five hierarchical layers that collectively enable seamless information flow across industrial environments:

1. Field Layer: This base layer includes sensors and actuators, providing real-time data directly from the physical environment. Technologies such as barcodes and Radio-Frequency Identification (RFID) enable resource tracking and monitoring, supplying critical input to intelligent systems. In sectors like pharmaceuticals, smart sensors also support Quality by Design (QbD) approaches through Process Analytical Technologies (PAT).

2. Control Layer: At this level, Programmable Logic Controllers (PLCs)—robust industrial computers with IP65/67 protection—manage machine-level control with exceptional precision. Their deterministic cycle times (typically <1ms) make them ideal for time-sensitive applications such as motion control.

3. Supervisory Layer: The Supervisory Control and Data Acquisition (SCADA) system acts as a bridge between field devices, controllers, and supervisory systems. It is the de facto standard for industrial data acquisition and ensures reliable communication across various automation layers.

4. Manufacturing Execution Layer: Manufacturing Execution Systems (MES) monitor, analyze, and report real-time production activities, tracing the full transformation process from raw materials to finished products. With the decentralization of decision-making systems, MES must increasingly integrate with distributed and intelligent systems.

5. Enterprise Layer: Enterprise Resource Planning (ERP) systems manage core business functions through modular and integrated applications. With advancements in data science, ERP systems are increasingly leveraging machine learning and data mining to extract actionable insights from vast datasets.

Each layer in the automation pyramid plays a distinct role in acquiring, processing, and utilizing data. Traditionally, information flowed hierarchically from bottom to top. However, the rise of mass customization and the need for real-time responsiveness require high-volume, bidirectional data exchange across all layers—continuously, in parallel, and often over internet-based infrastructure.

While the structure of the automation pyramid remains relevant, its static, hierarchical nature is evolving. Emerging technologies are driving a shift toward highly interconnected systems with greater horizontal integration. Direct communication between lower-level devices (e.g., field buses) and higher-level applications now demands enhanced interoperability and standardized communication protocols.

In this transformed landscape, cloud computing has become a central enabler. It offers a globally accessible platform for data storage and management, along with scalable services that enhance flexibility, security, and cost-efficiency. Cloud connectivity eliminates the need for local data storage, allowing data to be accessed and analyzed remotely, anytime and anywhere. Cloud-based Industrial IoT (IIoT) platforms like MindSphere (Siemens) and PI System (OSIsoft) are already revolutionizing process industries with plug-and-play analytics capabilities.

To complement the cloud, fog computing has emerged as a critical intermediary. It enables local data processing, filters sensor data, and ensures low-latency responses necessary for time-sensitive tasks involving actuators and robotics. Fog nodes act as intelligent gateways between industrial assets and cloud services, managing unstructured data while balancing speed and bandwidth requirements.

As industrial systems become more interconnected, the need for machines and applications to communicate seamlessly becomes paramount. This requires shared standards, common languages, and unified infrastructure that supports both centralized and decentralized intelligence—ushering in a new era of cyber-physical convergence in the smart factory environment.

USE CASE:

In the pharmaceutical sector, many labor-intensive operations offer significant potential for automation. One particularly complex area is the dynamic scheduling of tasks within Quality Control (QC) laboratories. These labs are essential for ensuring that every drug batch meets strict safety and quality criteria through comprehensive sampling and testing procedures.

Each laboratory test typically comprises six main stages: system preparation, system suitability, sample preparation, analytical run, data processing, and final review. These stages break down into numerous detailed and often customized subtasks, each requiring specific analysts, equipment, or both, depending on the nature of the test.

While production workflows are generally organized through pre-defined schedules, QC operations are often interrupted by the arrival of urgent, high-priority samples, which must be addressed immediately. This requires the system to dynamically reschedule tasks in real-time. The challenge lies in coordinating both equipment capabilities and operator skill sets, taking into account resource availability and operational constraints. This creates a dual-resource constraint problem, where both personnel and machines must be optimized simultaneously.

A critical and precision-sensitive component of this workflow is material preparation, which falls under the sample preparation stage. To improve efficiency and reduce manual handling, a prototype automation module has been developed to autonomously manage material preparation tasks. This module is designed to share resources and operate across multiple test sequences concurrently, enhancing throughput, flexibility, and overall laboratory efficiency.

Despite these advancements, several challenges remain. Among the most notable are the complexity of test recipes and the short shelf life of prepared materials, both of which impose additional constraints on automation and scheduling systems.

The prototype system is composed of several integrated components, including two industrial identification platforms—PC-based and PLC-based, an automation module, a SCADA system, the Middleware for Intelligent Automation (MIA), a dynamic Decision Support System (DSS) with scheduling capabilities, and a Data Warehouse (DW).

The PC-based platform utilizes a personal computer configured to manage data acquisition from two identification technologies: Barcode scanners and the RFID RFH620 system.

The PLC-based platform consists of a PLC, an Extension Terminal, distributed I/O system, an HMI Basic Panel, and a SCADA PC, all working together to handle data from Siemens' Barcode and RFID systems. This setup is responsible for real-time monitoring and control within the automated environment.

The MIA platform is hosted on a dedicated PC running SQL Server Enterprise 2017 and a Python environment, serving as the core of data integration and communication between systems. It acts as a bridge linking lower-level control devices with higher-level decision tools.

The Data Warehouse (DW) plays a crucial role by aggregating and managing data extracted from both the Enterprise Resource Planning (ERP) system and the Laboratory Information Management System (LIMS). In this setup, LIMS functions similarly to a Manufacturing Execution System (MES) but is tailored specifically to meet the needs of laboratory operations, including test tracking, compliance, and quality management.

Together, these components create a cohesive infrastructure that supports automated identification, real-time data acquisition, intelligent scheduling, and data-driven decision-making in the quality control processes of pharmaceutical laboratories.

Source: https://doi.org/10.1016/j.compind.2020.103329

Detecting Hidden Failures in Cryogenic Pumps by Combining Artificial Neural Networks and Simulation

LNG tank cryogenic pumps are installed vertically inside a storage tank, with the suction (foot) valve positioned at the bottom. This design eliminates the risk of major tank leaks caused by issues with pipes or connections.

Both the pump and motor units are submerged, with the column serving as a guide during installation and also functioning as the discharge pipe that channels the pumped liquid to the top of the tank. Each pump is equipped with an inducer, an axial flow impeller positioned at the lowest possible level in the tank to enhance the Net Positive Suction Head Required (NPSHR). A suction valve is used to isolate the tank contents from the pump column; it is flanged to the lower end of the pump column and is closed by coil springs as well as the hydrostatic pressure of the liquid inside the tank.

Plant operators rely on information from various plant information systems to monitor the pump’s operating conditions and identify any potential issues. However, despite the wealth of information provided, it can be challenging for operators to make clear judgments about possible pump malfunctions due to the numerous operating variables, such as varying tank levels, flow rates, pressure, LNG density, and pump operating hours. Additionally, given the pump's functional setup, condition monitoring is restricted to operational variables, meaning data such as vibration levels, bearing temperatures, and motor temperatures are not available.

In these conditions, an Artificial Neural Network (ANN) can offer valuable support by predicting the motor's energy consumption over time based on any operating condition, and comparing it to actual energy usage to detect potential malfunctions.

The ANN dynamic simulation model considers seven inputs: flow, pressure, inlet temperature, output temperature, tank level, LNG density, and operating time since the last overhaul. The model has a single output, which is the energy consumption per hour. To achieve the desired accuracy in predictions, the hidden layer was set to contain a minimum of 20 neurons.

14,988 data points (simulation steps) were used for analysis.

• 10,082 data points were collected during the pump’s operation leading up to its overhaul. The first data point corresponds to the pump running for 2,828 hours, and the last before the overhaul was logged at 16,126 operating hours.

• 4,857 data points were recorded after the overhaul, starting from zero operating hours (i.e., as-new condition) until a sudden and catastrophic failure occurred at 5,171 hours.

The unexpected nature of the failure, with no prior warning signs, prompted strong interest from plant managers in applying machine learning techniques to explore the potential for predictive analytics.

For training the Artificial Neural Network (ANN), at least 70% of the pre-overhaul data was selected. During this period, the pump was operating under a wide range of conditions, yet performed well, with only normal wear detected during the overhaul—no significant damage was found.

Once the training phase achieved a satisfactory level of accuracy, the ANN model was applied to test pump performance both before and after the overhaul.

The outcomes revealed several noteworthy insights. During the testing phase prior to the overhaul, the accumulated error showed only a slight upward trend. It's important to note that during this period, the pump had already accumulated a significant number of operating hours—much higher than those seen during the training period. This discrepancy could account for the mild increase in the slope of the error curve.

In contrast, after the overhaul, there is a noticeable and consistent rise in the slope of the error curve. This steep increase persists over time, even though the pump's operating hours are comparable to those within the training dataset. Such behavior strongly indicates an abnormal condition or malfunction occurring immediately after the overhaul.

To evaluate the robustness of the ANN algorithm, its results were benchmarked against those produced by other established machine learning techniques used in predictive analytics. This comparative analysis helps support informed decision-making for asset management (Olivencia et al., 2015). For this purpose, two additional algorithms were implemented and tested on the same dataset: Support Vector Machines (SVM) and Random Forest.

The findings revealed that all three models—ANN, SVM (non-linear), and Random Forest—demonstrated comparable predictive accuracy, with Random Forest achieving the best performance in terms of error reduction. Specifically, the Root Mean Square Error (RMSE) values for the testing phase before the overhaul were:

• ANN: 0.00043

• SVM (non-linear): 0.00045

• Random Forest: 0.00038

For the testing phase after the overhaul, the RMSE values were:

• ANN: 0.0022

• SVM (non-linear): 0.0023

• Random Forest: 0.0020

These results confirm that while all models are effective, the Random Forest algorithm slightly outperforms the others in both scenarios.

Source: https://doi.org/10.1016/j.compind.2019.103164

Service Network Design By Combining Expert knowledge and Machine learning Techniques

Service network design and delivery have a wide range of applications, spanning industries such as manufacturing-oriented product service networks, emergency medical services, agricultural maintenance networks, supply chain logistics, and maintenance service networks. In many cases, it becomes crucial to select a group of service providers within the entire network. This problem can be categorized as a multi-objective optimization challenge, for which various AI-based decision tools, particularly artificial neural networks (ANNs), have been employed for classifying schemes. ANN-based approaches have been developed to assess and select potential service providers, determine industrial site locations, and map mineral resources. Furthermore, ANNs can be integrated with fuzzy logic, and a two-phase decision model can be used to select suppliers in third-party reverse logistics networks. Additionally, an adaptive neural network-based decision support system, combined with fuzzy expert-designed rules, can be utilized to select the optimal industrial site.

A flexible fuzzy-neuro approach can be developed to select service providers for building a maintenance service network and for making the final decision. To assist managers in making informed choices, it's crucial to categorize the set of potential schemes and narrow them down to a manageable number of carefully selected alternatives through decision-making methods.

PROCESS:

The process begins with identifying the evaluation criteria. Relevant data must be gathered to meet the basic requirements based on these criteria.

The second step, which is the most critical, involves classifying the set of service providers and presenting decision-makers with an optimized set of alternatives.

The final step is the human decision-making phase, where the optimal group is chosen.

For data collection, in certain contexts such as agricultural maintenance service network, geographic information systems (GIS) and enterprise information systems can be utilized to gather the necessary data for evaluation.

During the classification phase, an innovative adaptive fuzzy-neuro approach, informed by expert knowledge, can be employed to aid decision-making. This (combined) methodology can address the challenges of service provider selection and service network construction.

The fuzzy-neuro network integrates the reasoning capabilities of fuzzy logic with the interconnected structure of neural networks, offering an effective approach for a range of applications, including medical services, production scheduling, robotics control, product evaluation, and fault diagnosis. A fuzzy-neuro network is typically structured as a five-layer neural network, as described below:

• Layer 1 (Input Layer): This layer is responsible for receiving and importing the input samples.

• Layer 2 (Fuzzification Layer): Here, the inputs are fuzzified using a set of predefined membership functions, transforming crisp values into fuzzy values.

• Layer 3 (Rule Layer): This layer generates initial rules from the input samples, with each rule corresponding to a neuron in this layer. The connections between the fuzzification layer and the rule layer are determined by these rules.

• Layer 4 (Defuzzification Layer): In this layer, the fuzzy input is defuzzified, converting fuzzy values back into crisp values.

• Layer 5 (Output Layer): The final processed results are presented in this layer, providing the output of the fuzzy-neuro network.

Recently, the adaptive fuzzy-neuro network, which combines adaptive techniques with fuzzy logic, has gained widespread application. The Takagi–Sugeno model is often used to represent fuzzy rules in an IF-THEN format. Unlike the standard fuzzy-neuro network, the adaptive fuzzy-neuro network is structured with six layers.

• Layer 1 (Input Layer): This layer handles the import of sample data into the fuzzy neural network.

• Layer 2 (Fuzzification Layer): In this layer, input characteristic variables are transformed into fuzzy variables using a set of membership functions.

• Layer 3 (Rule Layer): This layer contains a set of fuzzy rules, which are expressed according to the Takagi–Sugeno model.

• Layer 4 (Normalized Firing Strengths Layer): Each rule is evaluated, and normalization is applied to the firing strengths of the rules.

• Layer 5 (Defuzzification Layer): The fuzzy values generated by the fuzzy inference system are processed. Various methods, such as the mean of maximum method and weighted average defuzzification method, are used to eliminate fuzziness.

• Layer 6 (Output Layer): This layer has a single node that generates the output data.

 

A fuzzy reference system is developed to capture and represent the expertise and knowledge of specialists. Meanwhile, an adaptive neural network is used to fine-tune the parameters. The combination of these components enhances both learning accuracy and generalization capabilities.

USE CASE:

The service provider plays a vital role in the maintenance service network, working alongside the manufacturer and the customer to form a comprehensive maintenance service system. Currently, such networks are in place across various industries, including agricultural machinery, the automobile sector, military equipment, and other complex machinery.

Agricultural machinery operates in a dynamic and continuous manner, which makes it prone to failures, particularly during peak farming seasons. Designing the layout for agricultural equipment maintenance services is therefore more complex. To address this, multiple service providers are selected to create a dynamic service network that can deliver cross-regional maintenance for agricultural machinery. This approach aims to enhance equipment reliability and extend the operational lifespan of the machinery.

The challenge of service network design and the joint selection of service providers can be framed as follows:

1. The service area is divided into multiple service districts.

2. Each service district has a set of potential service providers, and only one provider can be chosen per district.

3. The selected service providers together form the maintenance service network.

Given the large number of possible permutations and combinations in selecting service providers for different districts, the potential number of configurations for the maintenance service network can be enormous. As a result, it becomes essential to identify and select the best possible solutions from a vast array of options.

The goal of intelligent decision-making system is to determine the optimal group of service providers to deliver the best maintenance service for customers at the right time in maintenance service network. A set of evaluation criteria have to be defined prior to evaluate prospective service providers in the maintenance service network. These evaluation criteria could include reputation service provider, size and quality of fixed assets, geographic advantage, operational performance, experience in similar products, cost of services, quality of service, long-term relationship, relationship with neighboring service providers, total delivery cost of spare parts, service network construction cost, and geographical spread and range of service provided. Some features, such as cost of services, total delivery cost of spare parts and service network construction cost, are quantitative, while others are qualitative.

These evaluation criteria can be categorized into various clusters. The first cluster focuses on service provider's performance: reputation, size and quality of fixed assets, and geographical advantage. The second measures the business capability of service providers in four aspects: operational performance, experience in similar products, cost of services, and quality of service. The third is related to the manufacturer and whole service network: long-term relationship, relationship with neighboring service providers, total delivery cost of spare parts in the whole network, service network construction cost, geographical spread and range of service provided. Some features are developed to evaluate the service providers in whole service network by evaluating the service provider in each area, and others are defined to assess the performance of all service providers in the service network.

The decision-making methodology can be divided into four stages: generating alternatives and data collection, expert analysis, classification and final decision. 

The first step is to generate a set of potential alternatives and assess the value of evaluation criteria for each service provider group. The data collected includes information on the service providers, such as their location, the frequency of failures in the maintenance service network, geographic details, and service-related costs—such as labor, logistics, spare parts, and service station construction costs. Given the complexity and time-consuming nature of this task, data is gathered through various methods, including maps, GPS, computer records, and reports generated by the service providers. Additionally, the list of available service providers in each service area can be sourced from their enterprise information system. Similarly, the evaluation criteria values must be established for expert analysis. For quantitative features, values can be calculated using specific formulas based on the collected data. However, for qualitative features, direct calculation is not possible. In this case, a group of leading experts and relevant stakeholders is consulted to determine the value of these criteria and evaluate each service provider group. The experts are selected from the company’s expert database, and their roles vary from professors and maintenance service center managers to field service managers. The stakeholders involved come from various departments, such as finance, marketing, planning, and human resources.

As a second step, leading experts and stakeholders collaborate to review the information on each service provider and assess the performance of each group. For every service provider group, the decision group analyzes and discusses the available data, ultimately reaching a consensus among all members.

An adaptive fuzzy-neuro-based approach can be used in the third stage to assess the suitability of each service provider group by determining their ranking scores. This classification method combines the adaptive fuzzy-neuro approach with a two-class classification method, where varying ranking scores reflect the differences in the quality of service provider groups within the maintenance service network. Several parameters need to be adjusted in this approach, and the adaptive fuzzy-neuro method is found to be more appropriate than other machine learning techniques, such as the fuzzy inference system (FIS), particularly when sufficient training samples are available. Unlike the fuzzy rules used in FIS, which are typically defined by experts, the adaptive fuzzy-neuro method allows for both offline training and online learning, enabling the system to self-adjust its fuzzy inference rules.

Therefore, a fuzzy-neuro network-based selection system is better suited for evaluating service provider groups with a fuzzy knowledge base and large datasets. Additionally, the two-class classification technique is employed during the classification process, with the advantage of presenting the results in an ordered sequence.

n two-class classifiers can be used, and the method generates return values in a cascading mode, starting with i = 1. The computational process ranks the alternatives into n classes.

The adaptive fuzzy-neuro classifiers can be utilized to compute ranking scores representing the class of each service provider group. These classifiers can be refined using a set of training samples. In this way, the adaptive classification approach integrates FIS with neural networks. The adaptive fuzzy-neuro network consists of six layers, and unlike previous fuzzy-neuro implementations, detailed information for each layer is provided as follows:

• Layer 1 (Input Layer): The adaptive fuzzy-neuro approach receives a set of evaluation criteria for the service provider group.

• Layer 2 (Fuzzification Layer): Gaussian membership functions are used to fuzzify the inputs.

• Layer 3 (Rule Layer): Rules arre defined by the decision group, and fuzzy rules from the knowledge base are applied for approximate reasoning.

• Layer 4 (Normalized Firing Strengths Layer): The firing strengths are normalized.

• Layer 5 (Defuzzification Layer): The weighted average defuzzification method is applied in this layer.

• Layer 6 (Output layer): The floating-point data is the output and the range of output is between the values 1 to n. The outputs of the method are rounded to the nearest integers to represent the ranking score of these group.

Adaptive fuzzy-neuro method can be trained in a supervised manner. Least squares estimation method and back propagation approach can be integrated and applied to train the proposed n two-class adaptive fuzzy-neuro classifier.

In the final stage, based on the classification results obtained by the proposed adaptive fuzzy-neuro approach, the groups with the highest ranking score can be analyzed and assessed by the invited decision group and the appropriate group of service providers can be selected.

 

Source: https://doi.org/10.1016/j.knosys.2019.105263

Demand Forecasting with Deep Reinforcement Learning

Case: Implementing deep reinforcement learning based demand forecasting for a leading global semiconductor components distributor who has to deal with a huge number of short life cycle products with very different demand patterns and potential perishability concerns. The organization serves as a franchise partner for hundreds of suppliers worldwide. The distributor equipped with a strong service team provides a range of value-added services such as product information, demand generation, turnkey solution, technical support, after-sales services, warehousing, logistics, e-commerce, and credit to different customers including OEM (Original Equipment Manufacturer), ODM (Original Design Manufacturer), EMS (Electronic Manufacturing Services), and SME (Small and Medium Enterprises). With orders from customers all over the world, the distributor must satisfy thousands of product demands with local flexibility.

Demand forecasting plays an important role for smart production in semiconductor supply chains due to long lead-time for capacity expansion, shortening product life cycles and pervasive uncertainty.

Some of the traditional forecasting techniques have limitations such as lack of data for estimating parameters for employing multivariate models (it is also very time-consuming when there are many products involved) and aggregating demand at monthly or quarterly levels reduces supply chain agility needed for smart production. Reinforcement learning (RL) enables agents to learn to select an action that maximizes the reward from the interaction with environment. High dimensional state of most real-world problems make it challenging for RL to derive efficient representation of the environment. Additionally the use of completely raw inputs for learning optimal control policies create additional limitations in terms of memory complexity and computational complexity. With increasing computing power being widely available, neural network and deep learning are providing effective solutions to overcome these obstacles. Deep Reinforcement Learning (DRL) is a combinational agent of deep learning and the convolutional reinforcement learning that can learn to optimize a sequential decision making under demand uncertainty through the environment state. 

It is important to design a forecasting selection mechanism that may integrate a number of forecasting approaches. The selection of forecasting model has limitations. In particular, rule-based approach may have the problem of overfitting or insufficient definition. Feature-based selection is highly dependent on the feature formulation and selection step that is time-consuming. Since the product quantities and types for a semiconductor distributor are quite large, it is not suitable to apply the rule-based method due to the difficulty to define all the required rules. In addition, as demand fluctuation is high in the semiconductor industry, the feature formulation will be complex or insufficient due to diverse features and thus affect the capability for further forecasting. For the ensemble model, since the weight assigned to each predictor will not be updated in each step, it may have limitations for forecasting the products with rapid changing time-series patterns such as high-tech products with shortening product life cycles. The DRL model provides the advantage that it can select the forecasting model automatically and update the chosen model dynamically through the interaction with the environment.

The implementation involves six phases - (1) defining the problem scope; (2) identifying gaps in decision quality; (3) establishing objectives, considering alternatives, and identifying uncertainty Influences; (4) assessing and outlining likely outcomes; (5) conducting holistic evaluations and value assessments; and (6) assessing trade-offs between attributes and making a choice.

Defining the Problem Scope

The framework begins with defining and structuring the problem at hand. Both the decision maker and the context are central to this process. For this case, the decision content revolves around estimating future demand, while the decision maker is the demand-planning manager. This individual responds to demand forecasting for a semiconductor distributor, relying on both past experience and customer forecasts.

To meet the global demand of customers, a semiconductor distributor must place timely and accurate orders with suppliers based on these demand forecasts. However, challenges arise from the semiconductor industry's relatively short product life cycle, long lead times, and the high volatility in supply and demand. These factors make precise forecasting particularly difficult. Overestimating demand often leads to excess inventory and high storage costs, while underestimating demand increases the risk of stockouts. Therefore, the decision maker requires a reliable, efficient, and accurate method to forecast future demand and mitigate unnecessary inventory costs.

Identifying Gaps in Decision Quality

Semiconductor components are known for having diverse demand patterns, some of which exhibit significant fluctuations due to the short product life cycles inherent in the industry. Many companies lack advanced data acquisition systems that can collect sufficient data for comprehensive, multifactorial analyses. As a result, the focus is on improving decision quality using univariate data.

Since future demand for various semiconductor products must be forecasted, and given that each forecasting method has its own strengths and limitations, it is essential to automatically select the appropriate forecasting model for each product. By combining analytical insights into demand with expert knowledge from domain specialists, companies can make more informed and intelligent decisions within a smart supply chain. Furthermore, due to the rapid changes in time series data linked to the short product life cycle, choosing the right forecasting model at different time intervals becomes a critical challenge.

Establishing Objectives, Considering Alternatives, and Identifying Uncertainty Influences

After defining the problem and identifying key areas for improvement, relevant demand data is prepared. Using the theoretical demand pattern classification rule, demand patterns are classified into four groups to enhance the forecasting model’s prediction accuracy. These categories are based on two key thresholds: inter-demand intervals (IDI) and the coefficient of demand variation. The four groups are smooth (low IDI and low CV), erratic (low IDI and high CV), intermittent (high IDI and low CV), and lumpy (high IDI and high CV).

To address demand uncertainty, this step explores the relationships between various sources of demand uncertainty, considering the unique characteristics of each forecasting model. A comparison of different models, each with its own properties, is performed and integrated into a comprehensive strategy. Specifically, traditional forecasting methods such as the Naïve forecast, simple moving average, single exponential smoothing, and the Syntetos-Boylan approximation (SBA) are evaluated. Additionally, three machine learning approaches—artificial neural network (ANN), recurrent neural network (RNN), and support vector regression (SVR)—are selected for comparison.

Assessing and Outlining Likely Outcomes

Deep Reinforcement Learning (DRL) is utilized to evaluate the potential outcomes of various actions, enabling effective learning by combining deep neural networks with reinforcement learning (RL). Given that demand forecasting is performed at a weekly granularity, Deep Q-Network (DQN), a DRL model well-suited for discrete states, is selected to establish a model selection mechanism.

Traditional Q-learning stores the discrete states in a table, but it faces limitations when it comes to extracting meaningful features from high-dimensional data, especially as the number of states grows. To overcome this issue, DQN employs stochastic gradient descent within a deep neural network to update the model, instead of storing large volumes of Q-values in a table. This approach makes DQN ideal for learning the complex and stochastic demand patterns of semiconductor products and selecting the most appropriate forecasting method. The agent’s objective is to maximize the cumulative future reward by optimizing the action-value function.

Conducting Holistic Evaluations and Value Assessments

To evaluate the overall performance of the selected methods, Mean Absolute Scaled Error (MASE) is used as the primary metric. Given the high fluctuations in the demand patterns, some periods in the time series may contain zero values. The advantage of using MASE is that it accommodates the presence of zeros in the historical demand data.

Assessing Trade-offs Between Attributes and Making a Choice

The decision maker will make the ultimate decision based on the performance evaluations from the previous stage. The proposed approach, with its self-learning function, will update periodically and select the method that minimizes forecast error. The quantified forecasting results, along with the measurement metrics, will serve as a reference for the demand-planning manager, who can then adjust the final demand forecast using their domain expertise and insights.

OVERALL FINDING

The DRL model is compared with the seven forecasting models, the ensemble model that combines the forecasting results from the seven forecasting model with the simple average, and with Support Vector Machine whose input is the same as the observed state in the proposed DRL model. The output is the selected forecasting model. The DRL model had the lowest MASE for the all demand scenarios - smooth, erratic, intermittent, and lumpy.

Source: https://doi.org/10.1080/00207543.2020.1733125

US federal government contracting for disaster management

 

 

In a recently published article, my co-author and I focus on the issue of contracting by US federal agencies in response to disasters. Specifically, we investigate the choice of procurement contract type (cost reimbursement vs. fixed price) by US federal agencies during disaster management operations. The research relies on 47,560 contracts issued by the US federal government in response to 14 major disasters between 2005 and 2016. While earthquakes represent the majority of disasters in our dataset (10 out of 14 disasters), we also consider other disasters (such as earthquakes, tornado, and oil spill) for which federal agencies procure products and services for relief operations. The dataset includes events that mainly affected the US; however, two events outside this geographical scope were included (Haiti earthquake in 2010 and Pacific earthquake in 2011) because they represented a significant expenditure by US Federal Government agencies. We build on agency theory to investigate the choice of the contract type made by federal agencies at the different stages of a relief operation. This research provides empirical evidence of the key factors underpinning the choice of contract in the context of disaster management, namely the amount of spend per contract and the type of acquisition (product or service), and reveals the moderating role of the stage of the relief operation.

Our finding shows that in disaster-relief operations, as spend increases, the likelihood of choosing cost reimbursement also increases. Our results indicate that government agencies tend to be more guarded in this respect by preferring cost reimbursement deals as the contract value increases. Our findings show that service contracts are more likely to be cost reimbursement. We found no significance in the direct relationship between stage of disaster (preparation, response, and recovery) and contract choice, when the response stage was operationalized as 100 days after a disaster strikes. However, the relationship was positive and significant for 200-day model. Interestingly, we expected more cost-reimbursement contracts in the Response stage. Similar logic would lead us to expect that more cost-reimbursement contracts relative to fixed-price contracts during all disaster stages relative to “normal times.” However, a report by the Government Accountability Office (GAO, 2017) reveals that the split of contract types for all government contracts across all agencies between 2011 and 2015 was 63% fixed-price; 37% cost-reimbursement. This contrasts with our dataset in that fixed-price is much more prevalent during disaster contracting, accounting for 92% of all contracts, notably driven by Hurricane Katrina contracts. It appears that the preference for fixed-price arrangements is emphasized during disasters, particularly during the response stage. One explanation may be that fixed-price contracts might be quicker to construct (and much easier to monitor) than cost-reimbursement contracts; give the hectic nature of disasters, this may dominate. This is in line with prior arguments about the ease of execution of fixed-price contracts. This also helps explain that as we expand the response-stage window to 200 days, that is, we include more contracts that are not subject to time pressure, the relationship between stage and contract choice becomes significant.

The split-sample analysis of the Katrina/non-Katrina contracts revealed that in the non-Katrina sample there is a higher likelihood of using cost-reimbursement contracts during the response stage than in the preparation and recovery stages. However, the finding was opposite and significant for the Katrina only sample. Katrina is the first and most severe event in our dataset, and the response effort was heavily criticized for handing out large contracts without competition. These criticisms resulted in an investigation by the White House and a legislation reform with extensive revisions to emergency response provisions (Post-Katrina Emergency Management Reform Act of 2006, 2006). While these recommendations and legislation do not specifically refer to fixed-price contracts, they stipulate changes to staffing, organization structure, and contract competition. In 2015, 10 years after Katrina, the GAO reported (GAO-15-783, 2015) on the changes to emergency response provisions triggered by Hurricane Katrina reported a tripling in the number of contract officers and the establishment of the Disaster Acquisition Response Team (DART) and a new rule that prevents contracting officers from issuing non-compete contracts exceeding 150 days without proper authorization. These changes to staffing, legislation, and procedures are likely to have affected contract officers' behavior post-Katrina, particularly regarding the award of service contracts, which tend to extend over time.

Interestingly, while we hypothesized that larger contracts during the response stage would lead to more cost-reimbursement contracts, the opposite was true in the whole sample. The result is driven by extremely large contracts. To explore this further, we inspected the largest contracts during the response stage, specifically those over $75 million, which is the threshold at which the senior procurement executive of the agency is required to approve the contract according to FAR 6.304. We found 39 contracts, of which 31 (79.5%) are fixed-price and eight (20.5%) are cost-reimbursement. However, this split is not homogenous across government departments. Of the eight cost-reimbursement contracts in this sub-set, seven were issued by one Agency, FEMA; the agency tasked with “helping people before, during and after disasters.” Of these seven contracts, five relate to architectural/ construction services and appear to be associated with providing accommodation for people in the aftermath of a disaster, in line with FEMA's mission. The remaining two are technical assistance contracts.

In our sample, as expected, FEMA has the largest number of contracts 13,742 (29% of all contracts), because of its mission. Even though FEMA has a larger proportion of the extremely large cost-reimbursement contracts, they predominantly award fixed-price contracts during the response stage due to the speed of awarding and monitoring them. It seems that FEMA follows a dual strategy, relying on fixed-price contracts for speed, and also using extremely large cost-reimbursement contracts for major projects. While practitioners have long recognized the benefits of fixed-price contracts in terms of speed, this is not broadly recognized in the extant literature.

We ran a sub-sample analysis for the two major agencies: FEMA and the Department of the Army, which represent over 43% of all contracts in the dataset. This analysis indicates that the findings regarding FEMA are significant and align with the results in our main model results. Even the outcome pertaining to the disaster response stage exhibits a higher likelihood of using cost-reimbursement contracts during the response stage than in the preparation and recovery stages. Conversely, in the case of the Army, we find that in disaster-relief operations, the amount of spend associated with a contract increases the likelihood of using cost-reimbursement contracts over fixed-price contracts. We also find that in disaster-relief operations, the Department of Army was more likely to consider cost reimbursement contracts for procuring services than for procuring products. In the case of the Department of Army  we couldn't find evidence of higher likelihood of using cost-reimbursement contracts during the response stage than in the preparation and recovery stages. Neither did we find evidence of higher use of cost reimbursement contracts during the response stage (as compared to other stages) to be related to (a) the amount of spend and (b) the procurement of services as compared to products. 

This indicates that while both government departments issue many contracts during a disaster, only FEMA appears to be under pressure during the response stage. This outcome corresponds to FEMA's primary mission of addressing urgent needs in the immediate aftermath of a disaster. An alternative explanation is that because agencies might have already negotiated per-unit costs with selected suppliers for services commonly needed during emergencies, such as medical care, provision of meals, and so forth, pre-positioned contracts could thus help mitigate opportunistic behavior. Policy guidelines under FAR make pre-established arrangements available to government officials, and researchers have reported on their effectiveness. For example, in 2019, the Army Corp of Engineers established a $999 million advance joint venture agreement with six contractors to provide time sensitive disaster support to repair critical infrastructure such as roads, bridges, and electric distribution systems.

Our robustness checks reveal that results regarding the amount of spend during the response stage for the two largest sub-samples regarding the type of competition, “Not Competed” and “Full and Open Competition,” (which together represent about 47% of all the contracts in the dataset) are significant and negative, in line with our main model results. These two competition categories, which are at opposite ends of the spectrum (from no competition to full and open competition), indicate that the negative interactions between the stage of the disaster and the amount remains under vastly different types of competitive environment. Finally, our results regarding the interaction between the service variable and disaster stage, show that services contracted during the response stage are more likely to use cost-reimbursement contracts than those awarded during other disaster stages, in line with agency theory predictions and our logic presented there.

Source: Mena, C., & Nair, A. (2024). US federal government contracting for disaster management. Journal of Operations Management, 70(4), 523-547.

Cybersecurity and Supply Chain Risk Management

Cybersecurity is an essential element of supply chain risk management because of the increasing interconnectedness and digitalization of supply chains. As supply chains become more reliant on digital technologies, they also become more vulnerable to cyber threats.

The following factors makes cybersecurity crucial in pursuit of supply chain risk management:

1. Data Protection: Supply chains involve the exchange of sensitive information, such as product designs, customer data, and financial transactions, between multiple parties. Effective cybersecurity measures are necessary to protect this data from unauthorized access, theft, or manipulation.

2. Operational Continuity: Cyberattacks can disrupt critical supply chain operations, such as production, transportation, and inventory management, leading to delays, disruptions, and financial losses. Robust cybersecurity measures help ensure the continuity of operations and minimize the impact of cyber incidents on the supply chain.

3. Regulatory Compliance: Many industries are subject to regulatory requirements regarding data privacy and security, such as the General Data Protection Regulation (GDPR) in the European Union or the Health Insurance Portability and Accountability Act (HIPAA) in the healthcare industry. Compliance with these regulations requires implementing cybersecurity measures to protect sensitive data and mitigate supply chain risks.

4. Reputation Management: A cybersecurity breach in the supply chain can damage a company's reputation and erode customer trust. Customers expect companies to safeguard their data and ensure the security of the products they purchase. Effective cybersecurity practices help maintain brand reputation and customer confidence.

5. Financial Impact: Cybersecurity incidents can result in significant financial losses for companies, including remediation costs, legal fees, regulatory fines, and loss of revenue due to operational downtime or reputational damage. Investing in cybersecurity can mitigate these financial risks and protect the bottom line.

The above listed issues present the following strategic imperatives for addressing cybersecurity in supply chain risk management:

1. Supplier Cyber Risk Assessments: Conduct risk assessments to evaluate the cybersecurity posture of their suppliers and third-party vendors. This includes assessing their security policies, practices, and controls to ensure compliance with cybersecurity standards and requirements. Cisco conducts regular cyber risk assessments of its suppliers to evaluate their cybersecurity posture and ensure compliance with security standards. The company uses third-party tools and audits to assess supplier security practices, identify vulnerabilities, and address any gaps in security controls. Boeing conducts thorough cyber risk assessments of its suppliers to evaluate their cybersecurity readiness and resilience. The company assesses suppliers' security policies, practices, and controls to identify vulnerabilities and address any weaknesses in their security posture.

2. Contractual Obligations: Include cybersecurity requirements and clauses in supplier contracts to enforce compliance with security standards, data protection measures, and incident response protocols. This holds suppliers accountable for maintaining adequate cybersecurity measures throughout the supply chain. Ford includes cybersecurity requirements and clauses in supplier contracts to ensure compliance with security standards and data protection measures. The company requires suppliers to implement specific security controls, conduct regular security assessments, and adhere to industry best practices for cybersecurity. Cisco includes cybersecurity requirements and clauses in supplier contracts to enforce compliance with security standards and data protection measures. The company requires suppliers to adhere to specific cybersecurity guidelines, undergo security audits, and report any security incidents or breaches promptly.

3. Continuous Monitoring: Implement continuous monitoring and threat intelligence programs to detect and respond to cyber threats in real-time. This includes monitoring network traffic, analyzing security logs, and employing intrusion detection systems to identify and mitigate cybersecurity incidents promptly. Cisco implements continuous monitoring and threat intelligence programs to detect and respond to cyber threats in real-time. The company monitors network traffic, analyzes security logs, and uses advanced security analytics tools to identify and mitigate cybersecurity incidents across its supply chain ecosystem.

4. Cybersecurity Training and Awareness: Provide cybersecurity training and awareness programs for employees, suppliers, and partners to educate them about cyber risks, phishing scams, and best practices for data protection. This helps foster a culture of security and accountability throughout the supply chain. P&G provides cybersecurity training and awareness programs for employees, suppliers, and partners to educate them about cyber risks and best practices for data protection. The company offers online training modules, workshops, and resources to help stakeholders recognize and respond to cyber threats effectively.

5. Incident Response Planning: Develop incident response plans and playbooks to outline procedures for responding to cybersecurity incidents effectively. This includes establishing communication protocols, escalation procedures, and coordination mechanisms to contain and mitigate the impact of cyber threats on the supply chain. P&G develops incident response plans and playbooks to outline procedures for responding to cybersecurity incidents in its supply chain. The company establishes communication protocols, escalation procedures, and coordination mechanisms to contain and mitigate the impact of cyber threats on its operations and partners. Ford develops incident response plans and protocols to respond to cybersecurity incidents effectively. The company conducts tabletop exercises and simulations to test its incident response capabilities and ensure readiness to address cyber threats and disruptions in its supply chain.

6. Technology Investments: Invest in cybersecurity technologies and solutions, such as firewalls, antivirus software, encryption tools, and security analytics platforms, to protect their digital assets and infrastructure from cyber threats. This includes deploying advanced security measures to detect, prevent, and respond to cyber attacks in real-time. P&G invests in cybersecurity technologies and solutions to protect its digital assets and infrastructure from cyber threats. The company deploys firewalls, intrusion detection systems, and encryption tools to safeguard sensitive data and prevent unauthorized access to its systems and networks.

7. Collaboration and Information Sharing: Collaborate with industry peers, government agencies, and cybersecurity organizations to share threat intelligence, best practices, and lessons learned from cyber incidents. This collective approach helps strengthen cybersecurity defenses and resilience across the supply chain ecosystem. Ford collaborates with its suppliers and partners to strengthen cybersecurity defenses and resilience across its supply chain ecosystem. The company shares threat intelligence, best practices, and cybersecurity guidelines with suppliers to help them enhance their security posture and mitigate cyber risks. Boeing collaborates with industry peers, government agencies, and cybersecurity organizations to share threat intelligence and best practices for cybersecurity. The company participates in information-sharing forums, cybersecurity conferences, and working groups to stay informed about emerging cyber threats and trends affecting its supply chain.

Reinforcement Learning in Operations & Supply Chain Management

Reinforcement learning (RL), a branch of machine learning, is increasingly being applied in operations and supply chain management to optimize decision-making, enhance efficiency, and improve performance. 

In Inventory Management applications, RL algorithms enable dynamic pricing, optimizing pricing strategies based on real-time demand and inventory levels, maximizing revenue while minimizing stockouts or overstock situations. Amazon employs RL algorithms to optimize pricing strategies dynamically based on factors such as demand, competitor prices, and inventory levels. This ensures that prices are adjusted in real-time to maximize revenue while maintaining competitiveness. RL models learn optimal inventory replenishment policies by considering demand forecasts, lead times, and cost constraints, ensuring optimal stock levels. Alibaba leverages RL algorithms for optimizing inventory management across its e-commerce platforms. By considering factors such as demand forecasts, lead times, and supply chain constraints, RL models help Alibaba maintain optimal inventory levels, reduce stockouts, and improve customer satisfaction.

For Warehouse Management, RL algorithms help with order fulfillment by optimizing picking routes and allocation of orders to minimize travel time and labor costs within warehouses. RL is used to optimize warehouse layout and storage configurations, reducing congestion, improving throughput, and enhancing operational efficiency. Amazon utilizes RL for optimizing warehouse operations, including inventory allocation, picking routes, and replenishment decisions. RL algorithms help minimize travel time, improve order fulfillment efficiency, and optimize resource utilization within warehouses. DHL utilizes RL for automating warehouse operations, including order picking, packing, and sorting. RL algorithms help DHL optimize warehouse layouts, design efficient workflows, and deploy robotic systems for tasks such as inventory management and order fulfillment.

In Transportation and Logistics, RL algorithms optimize delivery routes, considering factors such as traffic conditions, vehicle capacities, and delivery time windows, to minimize transportation costs and delivery times. UPS uses RL for optimizing delivery routes and schedules to minimize fuel consumption, reduce transportation costs, and improve delivery times. RL algorithms consider factors such as traffic conditions, package volumes, and delivery time windows to optimize route planning. RL models optimize fleet operations by dynamically allocating vehicles and resources based on demand fluctuations and operational constraints. UPS employs RL to optimize fleet operations, including vehicle routing, scheduling, and maintenance decisions. RL models help allocate vehicles dynamically based on demand fluctuations and operational constraints, ensuring efficient use of resources.

Production Planning and Scheduling is facilitated as RL algorithms optimize production sequences and schedules to minimize changeover times, idle capacity, and production costs while maximizing throughput and resource utilization. P&G utilizes RL for optimizing production planning and scheduling in its manufacturing facilities. RL algorithms help P&G minimize changeover times, optimize production sequences, and improve resource utilization, leading to increased throughput and operational efficiency. RL is used to optimize maintenance schedules for production equipment, considering equipment health data, production priorities, and resource availability. DHL employs RL for predictive maintenance of its logistics infrastructure, including vehicles, equipment, and facilities. RL models analyze sensor data, equipment usage patterns, and environmental conditions to predict maintenance needs, reduce downtime, and optimize asset performance.

For Supply Chain Optimization, RL models help in supplier selection since it can learn to select optimal suppliers based on criteria such as quality, reliability, and cost, optimizing supply chain performance and reducing supply chain risks. Alibaba uses RL to optimize various aspects of its supply chain, including supplier selection, order fulfillment, and logistics operations. RL algorithms help Alibaba identify optimal sourcing strategies, improve order processing efficiency, and enhance supply chain visibility and responsiveness. RL algorithms improve demand forecasting accuracy by learning from historical data, market trends, and external factors, enabling better inventory management and resource planning. P&G employs RL for improving demand forecasting accuracy by learning from historical sales data, market trends, and promotional activities. RL models help P&G better anticipate demand fluctuations, optimize inventory levels, and enhance supply chain planning. 

RL help with Resource Allocation decisions as it can be used to optimize labor allocation and scheduling in manufacturing facilities and distribution centers, ensuring adequate staffing levels to meet demand while minimizing labor costs. RL algorithms optimize energy usage in facilities by dynamically adjusting equipment settings, production schedules, and energy consumption patterns to minimize costs and environmental impact.

Risk Management applications are getting enhanced with RL since these models can learn adaptive strategies for responding to supply chain disruptions, such as supplier failures, natural disasters, or geopolitical events, minimizing the impact on operations and mitigating risks. RL algorithms assist in developing contingency plans and risk mitigation strategies by simulating various scenarios and recommending optimal responses in real-time. Ford uses RL for modeling and simulating supply chain risks and disruptions. RL algorithms help Ford identify vulnerabilities, evaluate mitigation strategies, and develop contingency plans to minimize the impact of supply chain disruptions on its operations.

Multi-Agent Systems can be modeled and simulated with RL. For instance, RL is used to model and simulate market dynamics in supply chains, where multiple agents (e.g., suppliers, manufacturers, distributors) interact and make decisions autonomously, optimizing overall system performance. Ford employs RL for optimizing supplier relationships and collaboration. RL models help Ford identify strategic suppliers, negotiate contracts, and manage supplier performance, ensuring a resilient and efficient supply chain.