Use Of Management Tools Research Articles

The Impact of Intellectual Capital on Firms’ Performance: Evidence from Saudi Arabia

The purpose of this study is to empirically investigate the relationship between intellectual capital (IC) measured by the value-added intellectual coefficient (VAIC) and firms’ performance (FP) in the Saudi context. Data are drawn from a sample of 25 Saudi firms listed on the Saudi Stock Exchange (Tadawul) for the period 2015-2018. Using the VAIC model, the multiple linear regression models were constructed to examine the relationship between intellectual capital (IC) and firms’ performance (measured in terms of financial and market performance). The findings indicate that there is a positive association between overall intellectual capital efficiency as well as each of its three components (human capital efficiency, structural capital efficiency, capital employed efficiency) and the firms’ financial performance. Additionally, there is a positive association between human capital efficiency(HCE), structural capital efficiency (SCE), and the firms’ market performance. Overall, the findings suggest that human capital efficiency (HCE) has a significant and positive impact on firms’ financial and market performance in Saudi Arabia. The VAIC method may be a useful tool for managers and investors in their decision process. This is the first study about the impact of intellectual capital on firms’ performance in four industry groups in Saudi Arabia using the VAIC model.

Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada.MethodsData from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression–based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data.ResultsOf 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model–based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables.ConclusionsWe used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.

Evaluation of Proteinuria Using Urine Protein : Creatine Ratio in Treatment with Molecular Targeted Agents for Advanced Renal Cell Carcinoma.

The usefulness of the urine protein : creatine ratio (UPCR) in management of molecular targeted therapy and immunotherapy has not been studied, although urine protein dipstick testing (uPr) is widely used in the clinical setting. The aim of this study was to investigate the usefulness of UPCR as compared to uPr in patients undergoing molecular targeted therapy for advanced renal cell carcinoma (RCC). A total of 25 patients (median age 68 years) with advanced RCC were included. Sunitinib, pazopanib, axitinib, sorefenib, everolimus, and nivolumab were administered to 15, 9, 16, 3, 7, and 13 patients, respectively, with duplication. Proteinuria was managed according to the grade determined by UPCR. Data at every treatment visit were retrospectively collected and uPr and UPCR were compared. The overall incidences of any grade of proteinuria associated with sunitinib, pazopanib, axitinib, sorafenib and everolimus were 86.7, 88.9, 93.8, 100, and 85.7%, respectively. There were discordances between the uPr-based grade and UPCR-based grade. UPCR did not meet the criteria of Grade 3 in 70.6, 100, 83.3, and 83.3% at visits in cases with uPr 3+ for sunitinib, pazopanib, sorafenib, and everolimus, respectively. In axitinib treatment, UPCR did not meet the criteria for withholding in 46.2% of the cases of uPr 2+ and more. Our study suggests that UPCR may be useful tool in management of adverse events associated with tyrosine kinase inhibitors, everolimus and can provide patients with optimal opportunities for receiving treatment.

Caveats in interpreting SARS‐CoV‐2 IgM+/IgG− antibody profile in asymptomatic health care workers

Serological testing is increasingly recognized as a useful tool for management of the COVID-19 pandemic.1 Cross-sectional serosurveys provide information on exposure levels in a target population, which is helpful for designing public health strategies to blunt community transmission.2 This article is protected by copyright. All rights reserved.

Managing intrapreneurial capabilities: An overview

In recent decades, there has been growing interest in intrapreneurial capabilities. The intrapreneurship and strategic management literatures have insights for entrepreneurs about how to apply entrepreneurial and strategic techniques and concepts in creating competitive advantage. More specifically, the dynamic capabilities framework has emerged as a useful tool for managers to better develop and manage intrapreneurial capabilities. Our essay and the papers in this special issue provide a timely opportunity to assess the rise of intrapreneurship and address organizational and policy implications.

Mid-upper arm circumference as an indicator of underweight in adults: a cross-sectional study from Nepal

BackgroundUndernourished people have an increased risk of premature mortality from both infectious and non-communicable diseases. Aside from screening purposes, assessment of nutritional status is a useful tool in management and evaluation of various chronic diseases. Body-Mass-Index (BMI) is today the most commonly used marker of nutritional status however, this method presents a challenge in many low resource settings and immobile patients. Mid-upper arm circumference (MUAC) is another anthropometric measure that requires minimal equipment and little training. So far, MUAC cutoffs for undernutrition are well established in children < 5 years but there is still no consensus for a specific cutoff in adults. The objective of this study was to compare MUAC with BMI and suggest a MUAC cut-off corresponding to a BMI of 18.5 kg/m2 to identify underweight in adults.MethodsA cross-sectional study was conducted at two urban public hospitals in Nepal. The following variables where collected: MUAC, weight, height, sex, age and self-reported medical history. Exclusion criteria: < 19 years of age, pregnancy and oedema. Sensitivity and specificity for a MUAC value corresponding to BMI < 18.5 was calculated. ROC analysis was performed for male and female as well as Pearson’s correlation of MUAC and BMI.ResultsA total of 302 people between 18 and 86 years of age, 197 women and 105 men, were included. Of these, 90 people suffered from rheumatic heart disease. MUAC was highly correlated with BMI in both women r = 0.889 and men r = 0.846. Best statistically derived MUAC cutoff corresponding to a BMI < 18.5 kg/m2 was 24.5 cm (Youdens Index = 0.75; sensitivity 92.86; specificity 82.48), with high predictive value (AUROCC> 0.9). The setting based optimal MUAC cutoff was also 24.5 cm. No considerable variation was found in sex- and disease specific subgroups.ConclusionMUAC is strongly correlated with BMI in adults in Nepal. For simplicity, a MUAC of 24.5 cm is the optimal statistically and setting based cutoff in both women and men to identify underweight (BMI < 18.5 kg/m2).

НЕОИНСТИТУЦИОНАЛИЗМ КАК МЕТОДОЛОГИЯ ИССЛЕДОВАНИЯ ЭФФЕКТИВНОСТИ УПРАВЛЕНИЯ ВЫСШИМ ОБРАЗОВАНИЕМ

Objective of the study is identification of the potential of the neoinstitutional approach for studying the tools of state management in improving the effectiveness of higher education. The methodological basis of the research is the neoinstitutional theory of Douglas North, which contains sufficient cognitive potential to study the effectiveness of the use of managerial tools in the management practices of higher education. Research results. The neoinstitutional paradigm has a high potential for studying the tools of state management in improving the effectiveness of higher education. This conclusion is supported by several reasons. First, general methodological issues. The theory of new institutions solves the conflict that has long been formed in sociology between the originally inherent emphasis on the search for criteria of social stability, social balance, and social stability, and the current shift in emphasis on individual requests and needs of individual actors, which potentially increase the conflict potential of society. Secondly, there is a common subject space between neoinstitutionalism and the topic of the stated problem, which is expressed in the commonality of the tasks to be solved, the similarity of methods, thematic spectra, and the commonality of the conceptual series. Prospects of the study. In the future, we plan to explore real managerial management practices aimed at improving the efficiency of higher education in Russia.

An assessment of functional outcome and management of metacarpal and phalangeal fractures of hand with universal mini external fixator

&lt;p class="abstract"&gt;&lt;strong&gt;Background:&lt;/strong&gt; Trauma to the hand is quite common and may result in fractures of the bones. Hand fractures can be complicated by deformity from no treatment, stiffness from over treatment and both deformity and stiffness from poor treatment. To evaluate the outcome of metacarpal and phalangeal fractures managed with UMEX (universal mini external fixator) in order to assess their usefulness in different fracture types and to make recommendations regarding potential applications.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Methods:&lt;/strong&gt; A one-year prospective study at Narayana medical college was conducted and 50 cases were included following the inclusion criteria. All the cases were evaluated and assessment of injury was done by Swnason’s criteria. UMEX application was done on all cases and follow up was done and functional assessment done following Duncan’s criteria.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Results:&lt;/strong&gt; Male predominance was observed, 21-40 years was common age group (30%) and RTA was common cause (72%). 57 total fractures with 66.7% in right hand, 45.62% were metacarpals, 36.84% proximal phalanx and 80% were shaft fractures. 61.4% of cases had radiological union within 8-12 weeks and partial stiffness was the commonest complication (52.17%). As per the Duncan’s criteria, 44% had good outcome, 40% had excellent outcome, 10% had fair and 6% had poor outcome.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; UMEX is an adequate treatment modality for multiple, intra articular and open fractures. Understanding the basic principles and correct application methodology is absolutely essential for optimal usage of the equipment. It’s a useful tool in management of small fractures of the hand.&lt;/p&gt;&lt;p class="abstract"&gt; &lt;/p&gt;

From Social Responsibility to Social Impact: A Framework and Research Agenda

While corporate social responsibility (CSR) has received increasing attention in the management literature, the focus of most prior work has been on the financial implications of CSR activities for firms, rather than on their consequences for social welfare. We contend that this relative neglect stems from the lack of a conceptual framework to evaluate social impact, and offer such a framework in this study. Specifically, we identify four criteria that an activity must meet in order to have an unambiguously positive effect on social welfare: it must be substantive, meaning that it delivers meaningful benefits to those it claims to serve; it must be unequivocal, meaning that these benefits must not be offset by harmful actions by the firm elsewhere; it must be inclusive, meaning that those that do not benefit from the firm’s actions are not left worse off; and it must be comparatively efficient, meaning that the same activity could not be carried out more effectively or efficiently under a different organizational form. By developing a coherent and rigorous framework to assess social impact systematically, our study offers both a research agenda for future scholars, and a useful tool for managers seeking to design more effective CSR initiatives.

Evaluation of the automated reference toolset as a method to select reference plots for oil and gas reclamation on Colorado Plateau rangelands

Rangelands are typically characterized by low precipitation and low biomass which makes them susceptible to disturbance and difficult to reclaim. These characteristics become a management issue when considering the widespread and significant impact of oil and gas development on rangelands. Reclamation from this land use involves the complexities of dealing with multiple state and federal agencies, private landowners, and their sometimes conflicting rules. Reference plots (e.g., nearby undisturbed sites) can help with these issues by providing an objective context for reclamation planning. They are selected to provide a comparison that is similar to a reclamation site in most aspects except for the disturbance activity. This allows for the relative condition of the reclamation site to be determined. Because selection of reference plots is normally expert-driven on a site-by-site basis, it can be time consuming and thus ineffective in helping to meet reclamation goals over large landscapes. The Automated Reference Tool (ART) was developed to improve the efficiency and efficacy of reference plot selection. The ART improves reference plot selection through remote sensing and indicators of land potential by selecting reference plots of similar land potential to the reclamation site based on soil texture, topography, and geology. We evaluated the ART in the context of well-pad reclamation to determine if ART-selected plots were appropriate to use as reference when compared to an existing reference plot network. We applied the ART to reclamation sites managed by the Bureau of Land Management's (BLM) White River Field Office, Colorado which had existing expert-selected reference plots. We found that the ART-selected reference plots and their matching expert-selected reference plot had similar large-scale vegetative cover characteristics (total foliar: R2 = 0.34, p-value = 0.0012) and dissimilar finer-scale cover characteristics (plant diversity: R2 = 0.079, p-value = 0.15). In addition, we detected similarities in their soil water content (R2 = 0.43, p-value<0.001), depth to restricting layer (RMSD = 21.90), and rock fragment (RMSD = 19.99). These results demonstrate that ART could be a useful tool for managers to help meet their reclamation goals over large landscapes, but it is not a complete automation of the reference selection process.

Modified dynamic fuzzy c-means clustering algorithm – Application in dynamic customer segmentation

The dynamic customer segmentation (DCS) is a useful tool for managers in implementing marketing strategies by observing dynamic changes that are happening in the customer segments over time. The Crespo’s dynamic fuzzy c-means (CDFCM) is one of the clustering algorithms introduced in the literature for DCS. We have suggested modifications to the CDFCM algorithm owing to certain shortcomings found in it, resulting in the modified dynamic fuzzy c-means (MDFCM) algorithm. To show the performance of the MDFCM algorithm, extensive experiments were carried out in comparison with the CDFCM algorithm using a retail supermarket dataset with eleven new data updates. To validate the results of the MDFCM algorithm, the fuzzy clustering evaluation measures such as Xie-Beni (XB) index, within sum of squared error (WSSE), root mean squared error (RMSE), Kwon index, and Tang index are utilized. The experimental results show that MDFCM is the most effective clustering algorithm for DCS, and the results are tested statistically to show its significance. The MDFCM algorithm is further compared with another successful algorithm available in the literature called Fathabadi’s dynamic fuzzy c-means (FDFCM). To show the usefulness of the MDFCM algorithm, a DCS framework is proposed and it has been demonstrated through a case study.

Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy

BACKGROUNDDegenerative cervical myelopathy (DCM) is the most common cause of spinal cord dysfunction worldwide. Current guidelines recommend management based on the severity of myelopathy, measured by the modified Japanese Orthopedic Association (mJOA) score. Patients with moderate to severe myelopathy, defined by an mJOA below 15, are recommended to undergo surgery. However, the management for mild myelopathy (mJOA between 15 and 17) is controversial since the response to surgery is more heterogeneous. PURPOSETo develop machine learning algorithms predicting phenotypes of mild myelopathy patients that would benefit most from surgery. STUDY DESIGNRetrospective subgroup analysis of prospectively collected data. PATIENT SAMPLESData were obtained from 193 mild DCM patients who underwent surgical decompression and were enrolled in the multicenter AOSpine CSM clinical trials. OUTCOME MEASURESThe mJOA score, an assessment of functional status, was used to isolate patients with mild DCM. The primary outcome measures were change from baseline for the Short Form-36 (SF-36) mental component summary (MCS) and physical component summary (PCS) at 1-year postsurgery. These changes were dichotomized according to whether they exceeded the minimal clinically important difference. METHODSThe data were split into training (75%) and testing (25%) sets. Model predictors included baseline demographic variables and clinical presentation. Seven machine learning algorithms and a logistic regression model were trained and optimized using the training set, and their performances were evaluated using the testing set. For each outcome (improvement in MCS or PCS), the machine learning algorithm with the greatest area under the curve (AUC) on the training set was selected for further analysis. RESULTSThe generalized boosted model (GBM) and earth models performed well in the prediction of significant improvement in MCS and PCS respectively, with AUCs of 0.72 to 0.78 on the training set. This performance was replicated on the testing set, in which the GBM and earth models showed AUCs of 0.77 and 0.78, respectively, as well as fair to good calibration across the predicted range of probabilities. Female patients with a low initial MCS were less likely to experience significant improvement in MCS than males. The presence of certain signs and symptoms (eg, lower limb spasticity, clumsy hands) were also predictive of worse outcome. CONCLUSIONSMachine learning models showed good predictive power and provided information about the phenotypes of mild DCM patients most likely to benefit from surgical intervention. Overall, machine learning may be a useful tool for management of mild DCM, though external validation and prospective analysis should be performed to better solidify its role.

Challenges Calibrating Hydrology for Groundwater-Fed Wetlands: a Headwater Wetland Case Study.

This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for ground-water dominated surface waterbodies by accounting for recharge from the aquifers. Using measured flow to a headwater slope wetland in Alabama's coastal plain region as a case study, we present challenges and relatively simple approaches in using the SWAT model to predict flows from the draining watershed and relatively simple approaches to model groundwater upwelling. SWAT-simulated flow at the study watershed was limited by precipitation, and consequently, simulated flows were several times smaller in magnitude than observed flows. Thus, our first approach involved a separate stormflow and baseflow calibration which included the use of a regression relationship between observed and simulated baseflow (E NASH = 0.67). Our next approach involved adapting SWAT to simulate upwelling groundwater discharge instead of deep aquifer losses by constraining the range of deep losses, β deep parameter, to negative values (E NASH = 0.75). Finally, we also investigated the use of artificial neural networks (ANN) in conjunction with SWAT to further improve calibration performance. This approach used SWAT-calibrated flow, evapotranspiration, and precipitation as inputs to ANN (E NASH = 0.88). The methods investigated in this study can be used to navigate similar flow calibration challenges in other groundwater dominant watersheds which can be very useful tool for managers and modelers alike.

Técnicas para promover la regeneración del arbolado en dehesas mediterráneas

Martinez-Munoz, M., Gomez-Aparicio, L., Perez-Ramos, I.M. 2019. Techniques to promote tree regeneration in Mediterranean savannah-like ecosystems. Ecosistemas 28(3):142-149. Doi.: 10.7818/ECOS.1798 Savannah-liked ecosystems have a serious problem of lack of natural regeneration of their dominant tree species. Although there are different techniques of assisted regeneration to alleviate this problem, further scientific studies will be necessary to analyze the efficiency level of these techniques under future climate change scenarios. In this study, we have reviewed the main factors limiting assisted regeneration of oak species in dehesa ecosystems, and we have identified the efficiency level of the main techniques commonly used to promote tree recruitment from an agro-ecological perspective. For this purpose, we have carried out a bibliographic review and conducted surveys to some researchers with relevant experience in this area. Results from this study indicate that water stress during summer, the unfavorable soil conditions over seedling establishment and the high seed predation rates and plant grazing are the most limiting factors for oak recruitment in the study ecosystems. In the second part of the study, we recommended a number of agro-ecological techniques as a function of the efficiency level reported in previous scientific studies, and we highlighted those techniques that require further investigation according to experts´ opinions (e.g. the use of nurse plants or the application of watering treatments). The information provided by this study will be used as a useful tool in management and conservation plans of dehesas with the aim of assuring their persistence and potentiating their adaptation abilities under future environmental scenarios.

Downregulation of imidacloprid resistant genes alters the biological parameters in Colorado potato beetle, Leptinotarsa decemlineata Say (chrysomelidae: Coleoptera)

Colorado potato beetle, Leptinotarsa decemlineata Say (coleoptera: chrysomelidae), is the important pest of potato all over the world. This insect pest is resistant to more than 50 active compounds belonging to various chemical groups. Potential of RNA interference (RNAi) was explored to knock down transcript levels of imidacloprid resistant genes in Colorado potato beetle (CPB) under laboratory conditions. Three important genes belonging to cuticular protein (CP), cytochrome P450 monoxygenases (P450) and glutathione synthetase (GSS) families encoding imidacloprid resistance were targeted. Feeding bio-assays were conducted on various stages of imidacloprid resistant CPB lab population by applying HT115 expressing dsRNA on potato leaflets. Survival rate of insects exposed to CP-dsRNA decreased to 4.23%, 15.32% and 47.35% in 2nd, 3rd and 4th instar larvae respectively. Larval weight and pre-adult duration were also affected due to dsRNAs feeding. Synergism of RNAi with imidacloprid conducted on the 2nd instar larvae, exhibited 100% mortality of larvae when subjected to reduced doses of GSS and CP dsRNAs along with imidacloprid. Utilization of three different dsRNAs against imidacloprid resistant CPB population reveal that dsRNAs targeting CP, P450 and GSS enzymes could be useful tool in management of imidacloprid resistant CPB populations.

The emergence of deviant behaviors in the physical work environment

PurposeThe purpose of this paper is twofold: first, the relationship between crowding perceptions (i.e. employees’ perceptions of insufficient personal space due to offices’ physical constraints) and deviant workplace behaviors (DWBs) directed at both the organization as a whole (DWB-O) and individuals (DWB-I); and second, privacy invasion from supervisors and peers as a mediator.Design/methodology/approachData were collected from 299 respondents working in open-plan offices at four medium-to-large sized IT-based companies. Drawing on conservation of resources theory, the paper suggests that under crowding conditions employees can perceive the physical workspace as a space-related resource that is threatened leading them to engage in DWBs out of a conservation strategy.FindingsStructural equation modeling results significantly supported main effects of employees’ crowding perceptions on the two types of DWBs, with privacy invasion from supervisors and peers as full mediator.Research limitations/implicationsThe study could suffer from mono-method/source bias, and specificities of the studied IT-based companies and their work can raise concerns about the generalizability of the results.Practical implicationsThe findings indicate that a proper physical office arrangement can be a useful tool for managers in combating employee DWB.Originality/valueTo date, the origin of workplace deviance has mainly been investigated in terms of the psychosocial work environment; however, the physical labor conditions (i.e. the layout of buildings, furniture, workspace, air conditioning, workplace density, etc.) have received little systematic attention.

Multilayer analysis of supply chain strategies’ impact on sustainability

Integrating lean, green and resilient strategies into Supply Chain (SC) decisions is a key success factor for transformation toward sustainability. These strategies are increasingly common in the SC, although their implementation and results are not the same across different SC levels (Tier 1, 2 and 3 suppliers and the focal company). In spite of this, earlier studies have not explored in detail their effects on environmental, economic and social sustainability dimensions nor differentiated between SC levels and therefore implementation strategies overlook the difference between these levels. Accordingly, a novel multilevel analysis of the aerospace manufacturing SC based on Interpretive Structural Modeling (ISM) is carried out. This approach allows exploring the effects of combining lean, green and resilient strategies on specific sustainability performance measures in the aerospace manufacturing SC. The findings reveal differences between SC levels on the effects of lean, green and resilient strategies on the different sustainability dimensions. Additionally, Tiers 2 and 3 show a higher resistance or lower implication in the development of lean, green and resilient strategies than the focal company and Tier 1 companies. Final ISM models become a useful tool for managers to specifically establish coordinated long-term SC sustainability programs for each SC level.

360º̉ SERVITIZATION AS STRATEGY FOR BUSINESS GROWTH

Servitization is type a business model innovation where companies extend their product offers through related services. A 360º̉ Servitization is when the manufacturing company uses a base, intermediary and advance service portfolio together with their product portfolio to co-create a unique value proposition to each customer. This research aims to understand how an organization ventures to grow the business through a 360º̉ Servitization. Underpinning the Resource Advantage Theory, a single-case study is conducted in order to achieve the aim of the paper. Data was collected from semi-structured interviews, company reports and the website. A participatory approach with the company CEO strengthened the esearch findings. A deductive content analysis was conducted and findings were reported. This study identifies the importance of a unique product service bundle for each existing and potential customer to co-create value to enhance business growth. The research further identifies the importance of all service types such as base services, intermediary services and advance services when creating a unique product service bundle. The Product- Service-Customer Matrix developed in this study is an import tool for business to business manufacturing organizations in the 21st century to co-create value with the customer. This matrix is a useful tool for managers in business to business manufacturing companies to amplify businessrowth opportunities. Due to limitations inherent in a qualitative case study approach it is not possible to generalize the findings beyond the case company. Moreover, researches in different manufacturing companies are required to improve the generalizability.KEYWORDSBusiness Growth, Business Model, Product-Service, Servitization

Containers in Ports can be Tracked Smartly

This article introduces a web platform to monitor cargo in containers status in real time using telematics and simulation practices. The platform was the main deliverable of a research project which aimed to strengthen security in the logistics handling procedures in port terminals. A systematic approach has been followed for its development and a pilot test was applied for tracking containers in a main port terminal. Lessons learned from this pilot study are also presented and discussed. According to the results of the pilot study, the proposed platform proved to be a useful tool for management, monitoring of the status and confrontation of security threats events, transforming the container to a “smart” container that uses sensors and systems to track and report data such as contents, unauthorized access, and physical location.

Efficient Computational Strategies for Dynamic Inventory Liquidation

We examine the dynamic inventory liquidation problem, in which a retailer liquidates a fixed number of identical items over a time period by strategically setting prices periodically according to knowledge about stochastic demand. We propose to solve the liquidation problem by deriving a deterministic representation of stochastic demand. Assuming that customer arrival and valuations follow known statistical distributions (e.g., estimated from past transaction data), the expected arrivals and expected order statistics of valuation distributions represent informative and advantageous approximations of demand. Under the deterministic demand representation, we develop a greedy heuristic for finding the optimal liquidation strategy that result in maximum total revenue. The heuristic approach is computationally highly efficient and provides optimal solutions under deterministic demand representation when customer valuation follows various typical statistical distributions. Compared with two simple and commonly used liquidation strategies (i.e., the fixed-price strategy and the fixed-quantity strategy), our heuristic yields higher liquidation revenue. Compared with sophisticated approaches that can find optimal liquidation strategies under stochastic demand (e.g., stochastic dynamic programming), our approach runs several magnitudes faster and still yields near optimal expected revenue. Therefore, the heuristic approach can serve as a useful tool for managers to make liquidation-related decisions in realistic, stochastic demand scenarios.