Perspectives on Various Issues of Interest

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

Virtuous spillover effects of quality penalties on the continuity of health care

In a recently published article my co-authors and I examine two effects of the quality penalties on an important determinant of health-care quality, namely, continuity of care for ailments targeted (intra-ailment effects) and ailments not targeted (spillover effects) by the regulation. Continuity of care, broadly defined as enabling appropriate care across patient conditions, settings, and time, is an important part of the health-care delivery continuum that helps patients discharged after intensive hospital-based care to smoothly transition into their communities. Continuity of care can refer to relational continuity, where a patient’s care is managed by the same provider over the entire care cycle; informational continuity, where the patient’s information is transferred over care episodes; continuity of physical location, where the entirety of a patient’s care occurs in the same location; management continuity, which refers to how care is integrated across sites and episodes; or transitional continuity, which occurs when patients are discharged from intensive care to another type of care. We focus on transitional continuity, which encompasses “a broad range of time-limited services designed to ensure health-care continuity, avoid preventable poor out-comes among at-risk populations, and promote the safe and timely transfer of patients from one level of care to another or from one type of setting to another”. Transitional continuity of care can occur in hospital-based step-down or recovery units or in other facilities such as skilled nursing facilities (SNFs), inpatient rehabilitation facilities, long-term care hospitals, and home health agencies.

The findings of this study offer directions for considering routines when developing regulatory policies in the health-care context. Health-care organizations are structured around medical specialties, and the services within these specialties share strong complementarities owing to common knowledge-base, processes, and physical assets. Since it is not practically possible to devise all-encompassing regulations, information about complementarities can help in developing policies that consider potential spillovers due to the underlying complementary processes and assets. Additionally, our findings also shed light on how policymakers can consider spillovers due to these complementarities and the underlying routines to positively influence performance measures that are often difficult to regulate. Post-acute care facilities are scattered, and it is not easy to create policy instrument/regulations to directly improve the quality of transitional continuity of care. We show that current policies that focus on readmissions, bundled care, and accountable care organization can address continuity of care-related quality measures.

For health-care organizations, our study has implications for considering processual and structural routines in quality improvement efforts. Resource allocation decisions can take these routines and complementarities into account. One key criticism of regulatory policies is that they motivate organizations to redirect resources from other activities to respond to the requirement. We show that these regulations can trigger a different sequence of actions whereby organizations develop routines that can aid in not only improving the outcome required by the regulation but also of other related tasks. This finding motivates careful considerations of routines at individual, processual, and structural levels to not only fulfill the requirements of a regulation but to gain operational and strategic benefits. Based on the results of this study, large health-care organizations should invest in acute care facilities to develop necessary routines for improving transitional continuity of care.

Our study has implications beyond healthcare. Manufacturing, merchandise, and service industries can experience quality spillovers from regulatory changes owing to complementarities in production functions. For example, the department of transportation requires the disclosure of on-time performance for domestic flights. Process reengineering to improve ground turnaround times for domestic flights motivated by such focused disclosures can also benefit international flights. Our study speaks to outcomes from other types of regulations, such as environmental regulations. 

Source: Deore, A., Krishnan, R., & Nair, A. (2023). Virtuous spillover effects of quality penalties on the continuity of health care. Production and Operations Management, 32(9), 2846-2863.

Innovation and supply chain management for uplifting underprivileged communities

Last month (July 2022) I spent time in an Indian village working with an organization that connects rural communities to solar, water, education, professions and advocacy to help communities and individuals take control of their lives and the well being of their communities. As part of its mission, Barefoot College International (BCI) aspires to increase economic mobility by making vocational and educational opportunities accessible to women and girls from the most marginalized communities around the world. The organization seeks to ensure that every woman and girl has the skills and knowledge she needs to be a catalyst for change to the benefit of herself, her family and her entire community. The organization is involved in four programs - Solar, Enriche, Water, Livelihoods.

The Solar program seeks to ensure that every woman and girl has the skills and knowledge she needs to be a catalyst for change to the benefit of herself, her family and her entire community. BCI achieves this by making vocational and educational opportunities accessible to women and girls from the most marginalized communities around the world. By training women to become Solar Engineers, even if they are illiterate or have no formal education, rural resilience is forged by each village at a time.

The Enriche program supports women’s aspiration to transform themselves and their communities. It provides them with the opportunity to embark on an empowerment journey; a journey to gain the confidence, skills and knowledge they need to reach their full potential as agents of sustainable change and to support their entrepreneurial aspirations.

As part of the Water program, the organization aims to harness every drop of fresh water that falls from the sky rather than relying on hand pumps, wells, and unpredictable groundwater levels. By combining traditional harvesting practices with new technologies, BCI make water accessible, clean and safe to drink for millions of people in need.

The Livelihoods program plans to address the challenges of climate change by envisioning soil-based livelihoods that generate resilience and dignified wages for small landholding farmers using Regenerative Agriculture, Permaculture Principles and Agroecology built systems to sustain life and care for land.

I primarily worked with the team that manages the supply chain for the Solar program. 

Notwithstanding the lack of formal education (in the case of many team members), I observed a strong desire to learn and improve operations and supply chain. I shared some thoughts on improving supply management, inventory management, and quality management practices with the team and also learnt from them.

My work with the organization gave me an opportunity to understand how best practices that are widely used in developed (and for-profit) contexts need to be tailored in light of the underlying contingencies.

I also learnt about the innovative ways in which educational programs are being developed for women.

Overall, an enlightening experience and I hope to continue my efforts to contribute to strengthening grassroots innovation, operations and supply chain management practices that are aimed at uplifting underprivileged communities.

Quality penalties and continuity of health care

In a study my co-authors and I examine whether quality penalties aimed at reducing readmissions improve continuity of health care. We investigate if the quality penalty policy impacts continuity of care for services that were targeted by the policy through what we refer to as primary spillover effects. We also investigate the secondary spillover effects of the quality penalty policy on continuity of care for related ailments that were not targeted by the policy. The findings of this study show that externalities from quality penalties for readmission not only benefit continuity of care for the regulated ailments, but also allow health care organizations to adapt their existing routines and unlock continuity of care improvements for related, but non-targeted ailments. The finding that continuity of care does not diminish for unrelated services assuages concerns that hospitals respond strategically by neglecting or redirecting resources away from unrelated services. We provide novel insights as to how organizations respond to regulation by actively re-configuring their care patterns and coordinating service provision with attendant spillover effects on a variety of patients. Processual mechanisms such as the source of patient admissions and length of stay, and structural mechanisms such as system size and investments in continuity of care facilitate the spillover effects. 

Source: Ananthakrishnan, R., Deore, A., Nair, A. Virtuous Spillover Effects of Quality Penalties on the Continuity of Health Care. Working paper.

 

External auditors and certification bodies of ISO 9001

 

In a research study my co-authors and I seek to advance the knowledge on ISO 9001 implementation by examining two research issues. First, we investigate the relationship between external auditors, certification bodies, and the operational and market-oriented outcomes from the ISO 9001 certification. Second, we study the different roles played by external auditors and certification bodies in affecting the outcomes achieved from the quality of implementation of the standard. Using a dataset comprising of 537 firms from Australia and New Zealand that are ISO 9001 certified, our findings show that the quality of external auditors enhances operational outcomes as well as strengthens the relationship between ISO 9001 implementation and operational outcomes. This affirms the role of external auditors in the implementation of the standard. On the other hand, the reputation of certification bodies has a positive direct effect on market-oriented outcome, and its effect is strengthened by the quality of ISO 9001 implementation. The findings, therefore, show the contrasting roles between external auditors and certification bodies in enhancing different outcomes of ISO 9001 adoption in terms of operational and market-oriented ones.

There are a number of practical implications drawn from the findings. First, our study confirms once again that quality implementation is the key determinant of the operational outcomes of ISO 9001 adoption. At the same time, however, the findings also confirm the important role of external auditors in enhancing the effect of implementation on the operational outcome from ISO 9001 standard. This result is instructive in that it is the quality of auditors rather than the reputation of certification bodies that help firms in gaining the operational outcomes. For many firms, however, this finding could raise a practical problem: how to find good auditors? The answer to this question is not straightforward because, unlike certification bodies who have certain brand equity, auditors are employed by certification bodies and their quality is somewhat hidden from the customers and is only revealed after they are engaged after the ISO 9001 certification is over. Indeed, this problem is exacerbated by the fact that firms cannot simply resort to reputable certification bodies in order to find good auditors. The positive correlations between quality of auditors and reputable certification bodies only show a medium strength and, hence, this does not provide a strong assurance that reputable certification bodies will always send quality auditors. Therefore, while engaging reputable certification bodies is recommended, firms should request that they are served by experienced and quality external auditors whose characteristics are partly captured in this study. At the same time, however, the findings also infer that high quality auditors are not necessarily always employed by reputable certification bodies; they could also be part of less reputable certification bodies. In this regard, our findings also provide a ray of hope for firms who cannot afford to engage reputable certification bodies that normally charge higher costs than their counterparts. In particular, smaller firms with limited resources still have a chance to engage high quality auditors and to benefit from their services.

Our findings show that the reputation of certification bodies (rather than the quality of external auditors) helps firms improve their market-oriented outcome, including broadening their market scope and winning more customers. This finding suggests that customers still rely on “brand image” of reputable certification bodies. Customers perceive ISO 9001 certifications issued by reputable certification bodies to be of higher value than others. While this may be true to a certain extent, it is important to balance this notion with our finding, which shows that certification bodies do not have a direct positive effect on operational outcomes. Instead, firms ought to seek high quality ISO 9001 implementations that are then vetted by high quality auditors so as to obtain operational outcomes. The operational outcomes in turn exert a positive effect on market performance. This result therefore sends a strong message to firms that are trying to attain market-oriented outcomes solely from compliance to the standard’s requirements without internalizing the implementation process. More importantly, since it is operational outcomes that are mainly desired by customers (rather than the certification), simply signaling high quality ISO 9001 implementation by tapping into the reputation of certification bodies could be a misleading strategy to gain market-oriented outcome.

Source: Prajogo, D., Nair, A., Castka, P. 2022. The effects of external auditors and certification bodies on the operational and market oriented outcomes of ISO 9001 implementation. IEEE Transactions on Engineering Management. (Forthcoming).

The Effects of External Auditors and Certification Bodies on the Operational and Market-Oriented Outcomes of ISO 9001 Implementation

There are a number of practical implications drawn from the findings of our study. First, our study confirms once again that quality implementation is the key determinant of the operational outcomes of ISO 9001 adoption. At the same time, however, the findings also confirm the important role of external auditors in enhancing the effect of implementation on the operational outcome from ISO 9001 standard. This result is instructive in that it is the quality of auditors rather than the reputation of certification bodies that help firms in gaining the operational outcomes. For many firms, however, this finding could raise a practical problem: how to find good auditors? The answer to this question is not straightforward because, unlike certification bodies who have certain brand equity, auditors are employed by certification bodies and their quality is somewhat hidden from the customers and is only revealed after they are engaged after the ISO 9001 certification is over. Indeed, this problem is exacerbated by the fact that firms cannot simply resort to reputable certification bodies in order to find good auditors. The positive correlations between quality of auditors and reputable certification bodies only show a medium strength and, hence, this does not provide a strong assurance that reputable certification bodies will always send quality auditors. Therefore, while engaging reputable certification bodies is recommended, firms should request that they are served by experienced and quality external auditors whose characteristics are partly captured in this study. At the same time, however, the findings also infer that high quality auditors are not necessarily always employed by reputable certification bodies; they could also be part of less reputable certification bodies. In this regard, our findings also provide a ray of hope for firms who cannot afford to engage reputable certification bodies that normally charge higher costs than their counterparts. In particular, smaller firms with limited resources still have a chance to engage high quality auditors and to benefit from their services.

Our findings show that the reputation of certification bodies (rather than the quality of external auditors) helps firms improve their market-oriented outcome, including broadening their market scope and winning more customers. This finding suggests that customers still rely on “brand image” of reputable certification bodies. Customers perceive ISO 9001 certifications issued by reputable certification bodies to be of higher value than others. While this may be true to a certain extent, it is important to balance this notion with our finding, which shows that certification bodies do not have a direct positive effect on operational outcomes. Instead, firms ought to seek high quality ISO 9001 implementations that are then vetted by high quality auditors so as to obtain operational outcomes. The operational outcomes in turn exert a positive effect on market performance. This result therefore sends a strong message to firms that are trying to attain market-oriented outcomes solely from compliance to the standard’s requirements without internalizing the implementation process. More importantly, since it is operational outcomes that are mainly desired by customers (rather than the certification), simply signaling high quality ISO 9001 implementation by tapping into the reputation of certification bodies could be a misleading strategy to gain market-oriented outcome.

Withstanding the economic recession: Examining the efficacy of manufacturing strategy alignment and process integration

In this paper, we examine how manufacturing strategic alignment and process integration affect manufacturing plant performance under two distinct environmental contexts presented by long-term macroeconomic instability. A large-scale dataset was specifically assembled for the purpose of this research investigation by combining plant-level survey data on practices and performance with secondary macroeconomic data for 14 countries from multiple sources. The results from multi-level regression analyses indicate that while manufacturing strategy alignment and process integration are beneficial for plant performance, they have distinct effects when the contingency factor, macroeconomic instability, is taken in consideration. Notably, when plants face high macroeconomic instability, process integration has a stronger effect on performance whereas in low instability environment manufacturing strategy alignment provides higher performance benefits. 

The findings have implications for managers in pointing out ways in which firms can successfully compete during times of recession. In particular, the study found that overly emphasizing the internal manufacturing alignment during recession could lead to opposite result than expected. Although, it could be a common policy to strengthen the internal alignment during recession, too much internal focus could lead firms to go far away from the requirements of the market. The study also offers practical insight regarding the lack of cost implication of manufacturing alignment during recession. The study finds no costs, delivery and flexibility benefit of alignment but negative impact of the same on quality performance. Hence, it would be important for the managers to re-consider the justification of investigating in alignment, especially during recessions. Recessions do not have a set start and conclusion time; hence, decisions about any questionable investment during recession can be detrimental to the future survivability of the firms.

On the other hand, the findings validate the need for process integration during economic instability. Supply and operation managers might hesitate to support processes integration due to initial negative impact on costs and popular negative news such as the cost overruns at Boeing and Airbus due to poor suppliers’ integration. However, the findings of this study highlight that process integration exerts a positive effect on all dimensions of performance when there is no recession, and provides higher quality, delivery and flexibility performance in the presence of economic recession (although cost performance does suffer). Hence, during recession, process integration efforts by firms present a trade-off between costs and other three operational performance factors. Moving the focus from internal alignment to external integration through improving real time information sharing with suppliers and customers will help firms identify the problems quickly and respond accordingly, especially during recession. It is also suggested by several studies that during recession, firms’ sole focus on cost cutting could be detrimental for brand image, future recovery and overall performance. Nonetheless, considering the range of practices required for supplier integration, such as, product/process integration, supplier involvement with new product development, joint decision making, and information sharing, the focal firms need to identify the priority areas for integration rather than integrating all aspects between the parties. To summarize, managers should not expect universal impact of recession on the manufacturing performance but should carefully consider the wide-ranging impact of the recession on the firm’s performance. This is in line with other contingency theory studies that found that contingency factors could have varied influence on manufacturing performance.

Internationalization of manufacturing is a growing trend necessitating a joint examination of international business and operations management issues. Our study aimed at finding the role played by organization-level and supply chain-level factors that impact perfor- mance under different environmental conditions, particularly as pre- sented by the severity of the recent Great Recession. The conceptual framework and the results of this study allow us to integrate these issues and show that careful attention to these factors is needed for manufacturing competitiveness. The organizational and supply chain factors have distinct performance implications. 

The Impact of Chain Organization Size on Efficiency and Quality of Affiliated Dialysis Facilities

In a recently published article we find that the size of chain organizations has a nonlinear effect on performance of dialysis facilities. Medium-sized chain organizations have lower efficiency and higher quality than smaller and larger chain organizations. 

The results call attention to being cognizant of the advantages and disadvantages of their growing size. Our study indicates that as chain organizations grow from a small size to an intermediate size, their efficiency drops and quality improves. We find that the efficiency bottoms out when chain organizations grow to have about 882 dialysis facilities whereas quality reaches its peak when the number of dialysis facilities in a chain organization reaches to about 796. After reaching these inflection points associated with efficiency and quality, facilities within the chain organizations become more efficient, but their quality levels suffer. This presents implications for decision makers to carefully think about the underlying mechanisms. The initial drop in efficiency as chain organization size increases may reflect the investments facilities affiliated with medium-sized chain organizations make when adding dialysis stations and personnel to address the demand growth. We conjecture that facilities affiliated with medium-sized chain organizations do not fully utilize their stations, thereby bringing down the efficiency. Their quality however increases, since staff in these medium-sized chain organizations has more time to give to each patient.

As chain organizations grow in size, it is likely that they see an increase in demand due to cross referrals among facilities within the chain. This increase in demand eventually provides the needed efficiency gains through economies of scale, but the associated deterioration in the quality of care raises concerns. It is possible that the approaches large chain organizations take to increase staff capacity, which differ from those taken by medium-sized chain organizations, could contribute towards deterioration in quality. Larger chain organizations tend to employ fewer registered nurses and licensed practical nurses as a percentage of their staff than medium-sized organizations. To offset the cost associated with staffing, large chain affiliated facilities employ higher levels of patient care technicians as compared to medium-sized chain organizations, which can lower their quality levels. As chain organizations grow, decision makers should appropriately manage the staffing patterns so as to avoid erosion of quality with a corresponding increase in efficiency.

To address both efficiency and quality performance, chain organizations should be strategic and develop appropriate coordination capabilities so that affiliated facilities can improve their quality while retaining efficiency gains through economies of scale. Technological innovations may make it easier to spread tacit knowledge pertaining to quality-oriented best practices. Opportunities for enabling interactions among people involved with clinical care, as well as nonclinical support services, can enhance learning and improve the quality of care. To facilitate quality improvement efforts, chain organizations can oversee the establishment of cross-disciplinary teams comprised of members from the affiliated dialysis facilities as well as the area manager from the chain organization. Organization-wide policies and procedures, including standards for clinical processes and quality improvement activities, should be widely communicated with affiliated dialysis facilities. Performance goals and objectives for dialysis facilities should be established and appropriate reporting structure of quality improvement initiatives and performance should be put in place. Chain organizations should provide feedback to affiliated facilities and, when needed, offer training and cross learning opportunities to the affiliated dialysis facilities.

For facilities within the chain organization, the results present implications for awareness of the influence of the contextual environment that chain organizations create. An independent facility has a better understanding and awareness of its resources and practices than facilities within chain organizations. Our results show that as these independent facilities join others to form a small- to medium-sized chain organization, they are able to improve quality but their efficiency suffers. The efficiency suffers, as these facilities have to coordinate efforts toward pooling their resources. As the size of chain organizations crosses a certain threshold level, the affiliated facilities gain economies of scale and increased demand, such that their efficiencies improve. However, they also witness growing heterogeneity and have difficulty managing the associated ambiguities, which have detrimental effects on their quality performance. An awareness of the changes in the environmental context and adoption of proactive adaptation measures, such as information exchange site visits to other affiliated facilities and training of staff members, can help manage efficiency and quality. Our results may be useful in other service settings beyond healthcare that have multiunit organizational forms, in that becoming part of a network, such as joining a chain organization, can have a positive or negative effect on efficiency and quality, depending on the size of the network.

Source: Dreyfus, D., Nair, A. and Talluri, S., 2020. The Impact of Chain Organization Size on Efficiency and Quality of Affiliated Facilities—Implications for Multi‐Unit Organizational Forms. Production and Operations Management, 29(7), 1605-1623

 

Gavi to make Parsyl’s quality assurance platform available in Uganda and Senegal to address last mile distribution challenge

Vaccine supply chain in Uganda 🇺🇬 and Senegal 🇸🇳 to be boosted by a partnership between @gavi and @ParsylHQhttps://t.co/zpsJbargCH#GotLife pic.twitter.com/X8vPHoKyDg

— Gavi (@gavi) January 25, 2019