Performance Index Research Articles

Combination of Rank Reciprocal Method and Composite Performance Index for Promotion Decision Support System

Combination of Rank Reciprocal Method and Composite Performance Index for Promotion Decision Support System

Granular transfer learning

Transfer learning is aimed at supporting the design of machine learning models in the target domain Dt, given that the knowledge (model) has already been constructed in the source domain Ds. The domains Dt and Ds (as well as the corresponding tasks Ts and Tt) are similar, yet not identical. As a result, the model transferred from Ds to Dt in this new environment exhibits its relevance (credibility) only to some limited extent. In this study, we develop an original approach, where we advocate that the knowledge transfer (model transfer) gives rise to a granular model where the level of information granularity associated with the produced results quantifies the relevance (quality or credibility) of the transferred model. In other words, we stress that the quality of knowledge transferred to Dt becomes captured through a granular generalization of the original numeric model. The overall systematic design process is elaborated on by focusing on the development process of granular neural networks carried out on a basis of the numeric neural networks coming from Ds. The key aspect of the design is to elevate the existing numeric neural network to its granular counterpart by admitting that the connections of the developed model come in the form of information granules, in particular intervals and fuzzy sets. The optimization process is guided by adjusting (optimizing) the level of information granularity being regarded as an essential design asset. The optimized performance index builds upon the descriptors of information granules commonly encountered in Granular Computing. In particular, coverage and specificity measures are treated as sound performance indicators of the quality of knowledge transfer (viz. the performance of the granular neural network expressed in the target domain). Several illustrative examples are provided to visualize the performance of the established design environment.

Unveiling Urban Dynamics: A Bidirectional Analysis of Resilience and Logistics Performance in The Face of Global Challenges

Objective - This research investigates the intricate interplay between urban resilience and logistics performance, focusing on the Resilient Cities Index and the Logistics Performance Index (LPI) for the year 2023. Methodology/Technique - The study employs multiple regression analysis to explore how components of the Resilient Cities Index impact the LPI and vice versa, utilizing data from 25 cities globally. Findings – Noteworthy findings include that critical infrastructure and socio-institutional factors significantly influence logistics performance, underscoring the bidirectional relationship between urban resilience and logistics efficiency. The analysis reveals that cities with robust tracking and tracing capabilities exhibit higher resilience levels, while infrastructure and international shipment scores present complex, context-dependent relationships with urban resilience. Novelty - These insights provide a novel understanding of how logistics components contribute to urban resilience and suggest that enhancing critical infrastructure, improving socio-institutional frameworks, and addressing specific logistics components are pivotal for fostering resilient and efficient urban systems. Type of Paper: Empirical JEL Classification: R40, R11, O18. Keywords: Urban Resilience; Logistics Performance; Resilient Cities Index; Logistics Performance Index; Bidirectional Relationship; Critical Infrastructure; Socio-Institutional Frameworks; Regression Analysis; Sustainable Urban Development. Reference to this paper should be made as follows: Rahmat, A.K; Razak, A.H.A. (2024). Unveiling Urban Dynamics: A Bidirectional Analysis of Resilience and Logistics Performance in The Face of Global Challenges, J. Mgt. Mkt. Review, 9(2), 57 – 65. https://doi.org/10.35609/jmmr.2024.9.2(1)

ANALYSIS OF FACTORS INFLUENCING THE PROFITABILITY OF ISLAMIC BANKING

This study uses a quantitative approach to analyze the influence of operational costs and income, non-performing financing (NPF), and the Islamicity Performance Index on the profitability of Islamic banking, with intellectual capital as a moderating variable. The population includes Sharia Commercial Banks located in Indonesia and registered with the Financial Services Authority from 2015 to 2022. The sample was determined using purposive sampling, resulting in 56 data points. Hypotheses were tested using Moderated Regression Analysis (MRA) with Eviews 12 software.The results indicate that operational costs and income (BOPO) significantly influence the Return on Assets (ROA), while NPF does not. The profit-sharing ratio (PSR) and the Islamic Income vs Non-Islamic Income Ratio (IsIR) significantly influence ROA. Intellectual capital (IC) has a direct influence on ROA but does not moderate the relationship between BOPO and ROA, nor between NPF and ROA. However, IC can moderate the relationship between the profit-sharing ratio and ROA. It does not moderate the relationship between IsIR and ROA. These findings suggest that operational efficiency, profit-sharing mechanisms, and the proportion of Islamic income are crucial for enhancing profitability in Sharia Commercial Banks, with intellectual capital playing a significant but selective moderating role.

A novel RMPC strategy for three-phase inverters operating in grid-connected and standalone modes

One of the main features of microgrids is the capability of operating in both grid-connected (GC) and standalone (SA) modes. This paper presents a novel dual mode robust model predictive control (RMPC) strategy for a three-phase inverter with an LCL filter in both GC and SA operating modes under the filter’s parameter uncertainty. At first, a disturbance observer gain is obtained by solving a linear matrix inequality (LMI), which is determined to preserve the stability of the algorithm. The designed disturbance observer takes into account the polytopic uncertainty of system parameters. A performance index with two weighting matrices is then defined and solved in an infinite horizon by turning it into an optimization problem under LMI constraints. The performance of the control strategy highly depends on the weighting matrices. Hence, an optimization algorithm is formulated to ascertain the best matrices values for both GC and SA operation modes. Given the nonlinearity issue, particle swarm optimization (PSO) is employed to derive the optimal weighting matrices offline. To evaluate the proposed control strategy’s effectiveness, simulations and experiments are performed under several scenarios in both GC and SA operating modes. The results reveal the proposed control strategy’s powerfulness compared to other techniques in the presence of grid voltage and load current disturbances for both inverter operating modes.

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength.

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash-Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

EVALUASI WAKTU DAN BIAYA MENGGUNAKAN METODE EARNED VALUE ANALYSIS PADA PROYEK PEMBANGUNAN PUSKESMAS TALUN Kab. BLITAR

The Earned Value approach is employed to meticulously track project time and expenses, facilitating early identification of performance issues. Specifically, the construction project for the community health center in Talun, Blitar, encountered variations during the 11th to 13th weeks. Upon doing an analysis in the 13th week, it was discovered that the Schedule Variance (SV) amounted to IDR. -165,313,862.5, suggesting that the actual employment (BCWP) was lower than the anticipated employment (BCWS). The Cost Variance (CV) is Rp. 286,761,471.2, showing that the cost is less than the value of the work accomplished. The SPI (Schedule Performance Index) for week 13 was 0.7, signifying a significant delay in the execution of the project. The Consumer Price Index (CPI) recorded a value of 3.64 in week 13, suggesting that spending efficiency fell short of the budgeted target.

EVALUASI WAKTU DAN BIAYA MENGGUNAKAN METODE EARNED VALUE ANALYSIS PADA PROYEK PEMBANGUNAN PUSKESMAS TALUN Kab. BLITAR

The Earned Value approach is employed to meticulously track project time and expenses, facilitating early identification of performance issues. Specifically, the construction project for the community health center in Talun, Blitar, encountered variations during the 11th to 13th weeks. Upon doing an analysis in the 13th week, it was discovered that the Schedule Variance (SV) amounted to IDR. -165,313,862.5, suggesting that the actual employment (BCWP) was lower than the anticipated employment (BCWS). The Cost Variance (CV) is Rp. 286,761,471.2, showing that the cost is less than the value of the work accomplished. The SPI (Schedule Performance Index) for week 13 was 0.7, signifying a significant delay in the execution of the project. The Consumer Price Index (CPI) recorded a value of 3.64 in week 13, suggesting that spending efficiency fell short of the budgeted target.

Evaluation of Thermal and Mechanical Properties of Foamed Phosphogypsum-Based Cementitious Materials for Well Cementing in Hydrate Reservoirs

As detrimental byproduct waste generated during the production of fertilizers, phosphogypsum can be harmlessly treated by producing phosphogypsum-based cementitious materials (PGCs) for offshore well cementing in hydrate reservoirs. To be specific, the excellent mechanical properties of PGCs significantly promote wellbore stability. And the preeminent temperature control performance of PGCs helps to control undesirable gas channeling, increasing the formation stability of natural gas hydrate (NGH) reservoirs. Notably, to further enhance temperature control performance, foaming agents are added to PGCs to increase porosity, which however reduces the compressive strength and increases the risk of wellbore instability. Therefore, the synergetic effect between temperature control performance and mechanical properties should be quantitatively evaluated to enhance the overall performance of foamed PGCs for well cementing in NGH reservoirs. But so far, most existing studies of foamed PGCs are limited to experimental work and ignore the synergetic effect. Motivated by this, we combine experimental work with theoretical work to investigate the correlations between the porosity, temperature control performance, and mechanical properties of foamed PGCs. Specifically, the thermal conductivity and compressive strength of foamed PGCs are accurately determined through experimental measurements, then theoretical models are proposed to make up for the non-repeatability of experiments. The results show that, when the porosity increases from 6% to 70%, the 7 d and 28 d compressive strengths of foamed PGCs respectively decrease from 21.3 MPa to 0.9 MPa and from 23.5 MPa to 1.0 MPa, and the thermal conductivity decreases from 0.33 W·m−1·K−1 to 0.12 W·m−1·K−1. Additionally, an overall performance index evaluation system is established, advancing the application of foamed PGCs for well cementing in NGH reservoirs and promoting the recycling of phosphogypsum.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

Background: Chemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary.Methods: Firstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution.Significant findings: This paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

Experimental investigation of the performance attributes of a double pipe heat exchanger equipped with baffles of conventional or flower layouts

In industrial heat management, the design of baffles within heat exchangers is crucial. They guide fluid flow and enhance turbulence, optimizing heat transfer. Yet, their configuration must be carefully considered to balance thermal efficiency with energy expenditure and pressure management. This research conducts practical tests on the hydrothermal attributes of a counter-flow double-pipe heat exchanger. It evaluates two baffle layouts: segmental and flower, considering baffle cut-off ratio (16.7 %≤δ ≤ 50 %), pitch ratio (8.3 %≤λ ≤ 22.2 %), flower-design relative angle (30°≤γ ≤ 90°), and operating conditions of annulus-side (2660 ≤ Rean ≤ 13110, 5.21 ≤ Pran ≤ 7.48). The findings assure that installing the baffles results in notable increases in both Nu¯an and fan. Besides, lowering baffle cut-off and pitch ratios, and increasing the relative angle of flower baffles lead to additional increases. Moreover, raising the annulus-fluid temperature reduces Nu¯an with a negligible effect on fan. Increasing Rean leads to a reduced fan and greater Nu¯an. Compared with no baffles, the maximum recorded increases in the Nu¯an and fan are 147.4 % and 60.2 %, respectively, realized by incorporating flower layout. On average, the flower baffles boost the Nu¯an by 20.8 % more than those using conventional baffles, with an average increase of 4.3 % in the fan. To judge the benefit of utilizing baffles, the Hydrothermal Performance Index (HTPI) is calculated, and its highest value is recorded as 2.23, attained by running the annular pipe at the lowest flow rate and temperature, with inserting flower baffles (δ = 16.7 %, λ = 8.3 %, γ = 90°). Finally, correlations for Nu¯an, HTPI, and fan prediction are suggested.

A multimodal dataset for investigating working memory in presence of music: a pilot study.

Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli. Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session. A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session. Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

Short‐Term Soil Waterlogging Improves Cotton Tolerance to High Temperature by Triggering Antioxidant Defence System in Cotton Seedlings

ABSTRACTSoil waterlogging and high temperature (HT) are serious abiotic stresses that negatively affect cotton growth and yield. Yet effects of prewaterlogging to HT subsequently in cotton seedlings have not been obtained. To address this, two temperature conditions (30/20°C and 35/25°C) and two soil waterlogging levels (0 and 3 days) were established during the cotton seedling stage. Results showed that indexes of plant performance were decreased markedly under HT. Unexpectedly, plant performance for the treatment of HT combined with 3 days of soil waterlogging (HW) was better than HT treatment (specifically, increase of 7.9%, 9.0%, 10.2%, 5.4% and 4.6% in leaf area, plant height, belowground biomass, aboveground biomass and root‐to‐shoot ratio was detected). Decreases in MDA (malondialdehyde), H2O2 (hydrogen peroxide) contents and (superoxide radicals) generation rate under HW treatment were observed by 14.1%, 7.7% and 14.1%, respectively, compared with HT. Moreover, ASA (ascorbic acid) content and DHAR (dehydroascorbate reductase) activity were improved by 19.7% and 13.8% for HW treatment relative to HT, however, the opposite situation for activities of APX (ascorbate peroxidase) and GR (glutathione reductase). Besides, activities of SOD (superoxide dismutase), CAT (catalase) and POD (peroxidase) in HW treatment were increased by 16.7%, 8.3% and 18.4%, separately. Thus, we concluded that short‐term soil waterlogging improved cotton cross‐tolerance to the continued high‐HT stress by enhanced SOD, CAT, POD and DHAR activities, increased ASA content in cotton seedlings. These results were expected to provide a theoretical basis for understanding cotton's cross‐tolerance to abiotic stress.

Self-assembled coatings with durable flame retardancy for EPS foam

Layer by layer (LBL) self-assembly coatings upon expandable polystyrene (EPS) surface play an important role in improving its flame retardancy. However, a large number of layers is needed to achieve a satisfied fire performance, and the coatings are difficult to adhere on the inert EPS surface, resulting in the poor durability. In this work, the surface of EPS was activated by ultraviolet ozone (UVO) with the appearance of oxygen contained groups, then a LBL coating with only two bilayers (branched polyethyleneimine (BPEI)-kaolinite (kaol)-BPEI-diethylene triamine penta (methylene phosphonic acid)) was covalently connected on the EPS surface. Compared with neat EPS, the obtained E-K5D30 sample increased the value of limiting oxygen index (LOI) from 18.8 % to 37.7 %, improved the ignition time from 59 s to 95 s, reduced the peak heat release rate (pHRR) value from 282.0 kW/m2 to 220.9 kW/m2, and enhanced the char residues from 6.3 % to 32.3 % after the combustion. Moreover, an ultraviolet (UV) curing layer with P/N elements was prepared upon E-K5D30 sample to maximize the flame retardant durability. A novel amino-intercalated kaol was used to play a smoke suppression role. The results showed that E-K*5D30-P8N3 sample further increased the LOI to 39.2 %. Even after 12 h washing, the E-K*5D30-P8N3 sample exhibited a slight LOI reduction to 37.8 %, and fire performance index (FPI) value of 0.405 m2·s/kW, showing the successful construction of coatings with good fire safety and flame retardant durability.

Click train elicited local gamma synchrony: differing performance and pharmacological responsivity of primary auditory and prefrontal cortices

Auditory neural networks in the brain naturally entrain to rhythmic stimuli. Such synchronization is an accessible index of local network performance as captured by EEG. Across species, click trains delivered ∼ 40 Hz show strong entrainment with primary auditory cortex (Actx) being a principal source. Imaging studies have revealed additional cortical sources, but it is unclear if they are functionally distinct. Since auditory processing evolves hierarchically, we hypothesized that local synchrony would differ between between primary and association cortices. In female SD rats (N = 12), we recorded 40 Hz click train-elicited gamma oscillations using epidural electrodes situated at two distinct sites; one above the prefrontal cortex (PFC) and another above the Actx, after dosing with saline (1 ml/kg, sc) or the NMDA antagonist, MK801 (0.025, 0.05 or 0.1 mpk), in a blocked crossover design. Post-saline, both regions showed a strong 40 Hz auditory steady state response (ASSR). The latencies for the N1 response were ∼ 16 ms (Actx) and ∼ 34 ms (PFC). Narrow band (38–42 Hz) gamma oscillations appeared rapidly (<40 ms from stim onset at Actx but in a more delayed fashion (∼200 ms) at PFC. MK801 augmented gamma synchrony at Actx while dose-dependently disrupting at the PFC. Event-related gamma (but not beta) coherence, an index of long-distance connectivity, was disrupted by MK801. In conclusion, local network gamma synchrony in a higher order association cortex performs differently from that of the primary auditory cortex. We discuss these findings in the context of evolving sound processing across the cortical hierarchy.

Analyzing the Traffic Operational Performance of School Pick-Up and Drop-Off Dynamics in Saudi Arabia

In seeking sustainable, safe, and efficient school commuting tours as non-recurring sources of congestion, it is essential to investigate the dynamic interaction between school students’ pick-up and drop-off (P&amp;D) movements and the traffic operational performance of the surrounding area. This study investigates the traffic operational performance in the vicinity of schools at various P&amp;D time intervals. The Travel and Planning Time indices (TTI, PTI), along with the Level of Service (LOS) are utilized as traffic operational performance indices in this article. A Python script was developed to employ the Google API for estimating the travel times from a real traffic dataset comprising 40 schools distributed across six cities in the Kingdom of Saudi Arabia (KSA). The results indicate that LOS varies from C to D for all cities except Riyadh, which exhibits the poorest traffic performance during P&amp;D time intervals. This paper serves as a guideline for city planners and policymakers seeking to provide valuable insights to enhance traffic operational performance in Saudi Arabia.

A Strategic Approach to Sustainable Wastewater Management and Reuse in Saudi Arabia

A Strategic Approach to Sustainable Wastewater Management and Reuse in Saudi Arabia

Energy-to-peak filtering for uncertain discrete-time singular bilinear systems with multipath data packet dropouts

In this paper, the energy-to-peak filtering problem for the uncertain discrete-time singular bilinear systems is studied. This paper considers the uncertainties that consist of real systems and the possible data packet dropouts when performance output signals and measurement output signals are transmitted over digital channels. In the course of the study, the phenomenon is elucidated utilising the Bernoulli random binomial distribution. The goal is to design the desired filter, so that the filtering error system is admissible, and meets the specified energy-to-peak performance index. The design conditions of energy-to-peak filters for uncertain discrete-time singular bilinear systems are given by introducing the Lyapunov function and using linear matrix inequalities (LMIs). The impact of uncertainty on singular bilinear systems is addressed using a two-step approach. At last, a numerical illustration is provided to demonstrate the validity of the design.

Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology

Heat energy storage technology plays a significant role in energy systems, and the various technological solutions brought about by digitalization are especially valuable in the field of energy storage. This article proposes an innovative model based on digital twin technology to solve the supply–demand mismatch problem in geothermal heating systems. This model achieves multi-objective optimization of comprehensive cost, geothermal energy utilization rate, and carbon emission by constructing a heat storage geothermal heating system. Digital twin technology integrates data and information models of public buildings and facilitates their sharing and transmission throughout the entire lifecycle of the geothermal heating system. Initially, the proposed method employs a machine learning-based approach to accurately predict heating demand. Subsequently, the operation of the heat storage water tank and heat pump units is optimized to resolve difficulties in matching energy supply and demand. Finally, the method takes full advantage of time-of-use electricity pricing policies to reduce costs. The data utilized were collected from an office building in China over a period of six months. Experimental results demonstrate that: (1) In terms of predicting heating demand, the improved neural network proposed in this study achieved a prediction accuracy of 98%, which is a 10% improvement over comparative algorithms. Additionally, the experimental comparison of four types of errors showed that the machine learning method proposed had smaller errors across the board. (2) The method realized collaborative multi-objective optimization, and in five scenarios, the comprehensive performance index increased by up to 38.03% compared to the benchmark system. This indicates that intelligent technology is an effective means of enhancing the energy sustainability of geothermal heating systems, and the use of geothermal energy as a clean energy source effectively addresses issues related to the storage, utilization, management, and energy conservation of buildings.

Transports and Logistics

KEY MESSAGES The estimated potential of Pakistan’s logistics sector is USD 30.7 billion but it has not been realised as yet. Where is it today? akistan is nowhere in the global scenario according to the recent Logistics Performance Index prepared by the World Bank 2023. The modal shares are highly skewed towards roads accounting for more than 92 percent of passengers and 96 percent of freight. On average this should be 60 percent. he motorways and highways constitute less than 10 percent of the total road network but carry almost the entire freight traffic. he inadequacy and inefficiency of the public transport system in Islamabad bring more private vehicles on the roads. Heavy losses in the railway sector have necessitated an increase in grants from PKR 45 billion in 2022-23 to PKR 55 in 2023-24. Per-passenger grants amount to PKR 1,261 and PKR 5,556 per ton for freight in 2021-22. Pakistan Railways has so far failed to adopt the open access policy approved in 2011, which reflects bureaucratic hurdles and governance failures. PIDE survey reveals that freight forwarders do not use Pakistan Railways because of the non-availability of relevant information, but mainly because of the nonreliability of the services. Pakistan Post has 10,293 post offices in Pakistan, of which 87 percent are incurring losses, while TCS has a market share of 43 percent with less than 10 percent of branches than Pakistan Post. The labour unions in public sector organisations have played a major role in resisting reforms, ensuring inefficiencies and substantial losses to the exchequer.