The impact of strategic alignment and responsiveness to market on manufacturing firm's performance

Drawing on dynamic capabilities theory and a sample based on the Indian manufacturing industry, we examine the influence of manufacturing operations' functioning, strategic alignment and responsiveness to market need for customization and firm performance. A multi-variate regression method is applied on the factors identified using confirmatory factor analysis. Our findings indicate that operations' strategic alignment to the firm's objectives is the single most key contributor to firm performance. The operations' capability to respond to market need for customization also significantly contributes to firm performance. Plant technology capability is also essential to respond effectively to market need for customization, and is positively and significantly related to firm performance. On the other hand, while delivery capability and cost control of the manufacturing operation are positively related to firm performance, they are not significant. Operations and marketing managers and firms' policy makers should emphasize operations' strategic alignment to firms' performance objectives, and build dynamic operational capability to be responsive to changing market needs

Analysis of waiting time for elective surgical procedures in neurosurgery department at a tertiary care teaching hospital in NCT, India

Background: Reported increases in waiting times for publicly-funded elective surgeries have intensified the need to decrease wait by healthcare providers and hence the study.Methods: Descriptive study done in neurosurgery department, to ascertain waiting times for its elective surgeries, included a retrospective analysis of admitted post-surgical patients and a prospective study using interviews with relevant stakeholders to do a process mapping.Results: Median time from decision of surgery to actual date of surgery was found to be 110.5 days. It was calculated that for optimum utilization of present available OTs, 19 extra beds are required and to address the existing load of patients waiting for their respective surgeries there is a need of 63 additional beds with 2 additional OTs functioning per day.Conclusions: The most common cause of waiting time was unavailability of vacant beds due to mismatch in demand-supply. The reason for postponement of surgery after admission was found to be lack of availability of theatre time followed by patient not being fit for surgery. Shortage of operating time was due to delayed start of operation theatre time. The study recommends improving admission process, restricting OPD time, standardized patient prioritization depending on relevant clinical criteria

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

A secretome profile indicative of oleate-induced proliferation of HepG2 hepatocellular carcinoma cells

Increased fatty acid (FA) is often observed in highly proliferative tumors. FAs have been shown to modulate the secretion of proteins from tumor cells, contributing to tumor survival. However, the secreted factors affected by FA have not been systematically explored. Here, we found that treatment of oleate, a monounsaturated omega-9 FA, promoted the proliferation of HepG2 cells. To examine the secreted factors associated with oleate-induced cell proliferation, we performed a comprehensive secretome profiling of oleate-treated and untreated HepG2 cells. A comparison of the secretomes identified 349 differentially secreted proteins (DSPs; 145 upregulated and 192 downregulated) in oleate-treated samples, compared to untreated samples. The functional enrichment and network analyses of the DSPs revealed that the 145 upregulated secreted proteins by oleate treatment were mainly associated with cell proliferation-related processes, such as lipid metabolism, inflammatory response, and ER stress. Based on the network models of the DSPs, we selected six DSPs (MIF, THBS1, PDIA3, APOA1, FASN, and EEF2) that can represent such processes related to cell proliferation. Thus, our results provided a secretome profile indicative of an oleate-induced proliferation of HepG2 cell