Strategi Coverage, Distribution, Merchandising, Promotion Sebagai Upaya Peningkatan Sales Force Dan Revenue (Studi Kasus Pada PT. Telekomunikasi Seluler Cabang Malang)

The purpose of this research is to clarify the application of the marketing strategies used by management to implement Coverage, Distribution, Merchandising and Promotion (CDMP) strategies as the basis for management decision in an effort to increase sales force performance and revenue. The influence of Coverage, Distribution, Merchandising and Promotion (CDMP) strategies on increasing sales force performance and revenue could look at a very significant sales sector. The locus of this research is PT Telekomunikasi Seluler Branch Malang. This research uses qualitative descriptive study with the case study approach. Data collection techniques used in this research is the observation, interviews, and documentation as well as the method of data analysis data reduction, data display, drawing conclusion validity checking and eventually research using the Kredibility Test. The results of this research indicate that PT Telekomunikasi Cellular Branch Malang implements Coverage strategies, Distribution, Merchandising and Promotion (CDMP). This strategy becames one of the main stratrgies done by PT Telkomsel to attract consumer interest. The company determines the first products offered, selection of the appropriate outlet, until finally on the evaluation of the activities of all four strategies Coverage, Distribution, Merchandising and Promotion (CDMP)

DRACULA Microscopic Traffic Simulator

The DRACULA traffic simulator is a microscopic model in that the vehicles are individually represented. The movement of vehicles in the network are represented continuously and updated every one second. The network is modelled as a set of nodes and links which represent junctions and streets respectively. Vehicles are generated at their origins with a random headway distribution and are assigned a set of driver/vehicle characteristics (according to user-specified probabilities) and a fixed route. The movement of the vehicles on a network is governed by a car-following law, the gap acceptance rules and the traffic regulations at intersections. They can join a queue, change lane, discharge to another link or exit from the system. The traffic regulation at an intersection is actuated by traffic lights or right-of-way rules. The inputs to the simulation are network data, trip matrix, fixed-time signal plans, gap-acceptance and car-following parameters. Outputs are in forms of animated graphics and statistical measures of network performance. The program is written in C-language. All types of vehicle attributes are represented as one entity using the structure data type which provides a flexibility in storing and modifying various types of data. Attributes of nodes, links and lanes are also represented as structures. The large number of variables associated with vehicles and the network imply that the performance of the simulation depends on the size of the network and the total number of vehicles within the network at one time. The simulator can be applied in many areas of urban traffic control and management, such as detailed evaluation of traffic signal control strategies, environmental issues such as air pollution due to emission from vehicles in idling, accelerating, decelerating or cruising, and analyses of the effects of variable demand and supply upon the performance of a network

Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean

Aim Information on the spatio-temporal distribution of marine species is essential for developing proactive management strategies. However, sufficient information is seldom available at large spatial scales, particularly in polar areas. The Southern Ocean (SO) represents a critical habitat for various species, particularly migratory baleen whales. Still, the SO’s remoteness and sea ice coverage disallow obtaining sufficient information on baleen whale distribution and niche preference. Here, we used presence-only species distribution models to predict the circumantarctic habitat suitability of baleen whales and identify important predictors affecting their distribution. Location The Southern Ocean (SO). Methods We used Maxent to model habitat suitability for Antarctic minke, Antarctic blue, fin and humpback whales. Our models employ extensive circumantarctic data and carefully prepared predictors describing the SO’s environment and two spatial sampling bias correction options. Species-specific spatial-block cross-validation was used to optimize model complexity and for spatially independent model evaluation. Results Model performance was high on cross-validation, with generally little predicted uncertainty. The most important predictors were derived from sea ice, particularly seasonal mean and variability of sea ice concentration and distance to the sea ice edge. Main conclusions Our models support the usefulness of presence-only models as a cost-effective tool in the marine realm, particularly for studying the migratory whales’ distribution. However, we found discrepancies between our results and (within) results of similar studies, mainly due to using different species data quality and quantity, different study area extent and methodological reasons. We further highlight the limitations of implementing static distribution models in the highly dynamic marine realm. Dynamic models, which relate species information to environmental conditions contemporaneous to species occurrences, can predict near-real-time habitat suitability, necessary for dynamic management. Nevertheless, obtaining sufficient species and environmental predictors at high spatio-temporal resolution, necessary for dynamic models, can be challenging from polar regions

From eco-efficiency to eco-effectiveness: The policy-performance paradox

The internalisation level of sustainability issues varies among topics and among countries. Companies give up less internalised issues for more internalised ones. Discrepancies between legal, market and cultural internalisation lead to different escape strategies: firms develop a high level environmental management system and they have nice sustainability policy and reports. These achievements cover the fact that their total emission keeps increasing and they do not proceed in solving the most crucial global community or corporate governance problems. ‘Escaper’ firms are often qualified as ‘leading’ ones, as a current stream of research is also ‘escapist’: it puts too much emphasis on sustainability efforts as compared to sustainability performance. Genuine strategies focus on hardcore sustainability issues and absolute effects rather than on issues easily solved and having high PR effects. They allow for growth in innovative firms, if they crowd out less efficient or more polluting ones. They produce positive environmental value added when sector average eco-efficiency is used as benchmark and do not accelerate market expansion and consumerism

Race to the Top: Colorado May Be Used to High Altitudes But Can It Compete in Race to the Top?

Outlines expected requirements for the American Recovery and Reinvestment Act's funding to the states for education reform. Offers strategies for improving teacher quality in Colorado, as well as data infrastructure, low-performing schools, and standards

Risk assessment and relationship management: practical approach to supply chain risk management

The literature suggests the need for incorporating the risk construct into the measurement of organisational performance, although few examples are available as to how this might be undertaken in relation to supply chains. A conceptual framework for the development of performance and risk management within the supply chain is evolved from the literature and empirical evidence. The twin levels of dyadic performance/risk management and the management of a portfolio of performance/risks is addressed, employing Agency Theory to guide the analysis. The empirical evidence relates to the downstream management of dealerships by a large multinational organisation. Propositions are derived from the analysis relating to the issues and mechanisms that may be employed to effectively manage a portfolio of supply chain performance and risks

Supporting mediated peer-evaluation to grade answers to open-ended questions

We show an approach to semi-automatic grading of answers given by students to open ended questions (open answers). We use both peer-evaluation and teacher evaluation. A learner is modeled by her Knowledge and her assessments quality (Judgment). The data generated by the peer- and teacher- evaluations, and by the learner models is represented by a Bayesian Network, in which the grades of the answers, and the elements of the learner models, are variables, with values in a probability distribution. The initial state of the network is determined by the peer-assessment data. Then, each teacher’s grading of an answer triggers evidence propagation in the network. The framework is implemented in a web-based system. We present also an experimental activity, set to verify the effectiveness of the approach, in terms of correctness of system grading, amount of required teacher's work, and correlation of system outputs with teacher’s grades and student’s final exam grade

Characterizing the performance of low impact development under changes in climate and urbanization

Over the past decades, climate change and urbanization have altered the regional hydro-environments, causing a series of stormwater management problems including urban flood and non-point pollution. Low impact development (LID) has been identified as a sustainable strategy for stormwater management. However, given the complex impacts of climate change and urbanization on hydro-environments, the performance of LID strategy under future changes remains largely unexplored. Accordingly, this research characterized the LID performance under changes in climate and urbanization. To provide an additional reference to sustainable stormwater management, the following specific topics were addressed: (1) Through hydraulic and water quality modeling, the LID performances of flood mitigation and pollution removal were systematically evaluated at the city scale. (2) Through uncertainty analysis, the impact of model parameter uncertainty on the LID performance was taken into account. (3) Through sensitivity analysis, the impact of LID technical parameters (e.g., surface features, soil textures) on the LID performance was quantified. (4) Through scenario analysis, the LID performances under different hydrological patterns were compared. (5) Through spatial analysis, the distribution of LID performance on different land-cover types was determined. (6) Through adopting general circulation model (GCM) projections, the LID performance under future climate scenarios with different representative concentration pathways (RCPs) was investigated. (7) Through adopting integrated assessment model (IAM) projections, the LID performance under future urbanization scenarios with different shared socioeconomic pathways (SSPs) was explored. (8) By coupling climate and urbanization projections with land-cover distribution, the spatiotemporal trends of LID performance in the future were characterized.:Table of Contents List of Abbreviations VII List of Peer-Reviewed Publications on the Ph.D. Topic IX List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic X 1 General Introduction 1 1.1 Background 1 1.2 Objectives 3 1.3 Innovation and Contribution to the Knowledge 3 1.4 Outline of the Dissertation 4 1.5 References 5 2 Literature Review 9 2.1 Hydraulic and Water Quality Modeling 9 2.1.1 Hydraulic Model 9 2.1.2 Water Quality Model 10 2.2 Low Impact Development (LID) 10 2.2.1 LID Practice 10 2.2.2 LID Performance 11 2.3 Performance Evaluation 13 2.3.1 Scenario Analysis 13 2.3.2 Spatial Analysis 13 2.3.3 Uncertainty Analysis 14 2.3.4 Sensitivity Analysis 14 2.4 Future Changes in Climate and Urbanization 15 2.4.1 Climate Change 15 2.4.2 Future Urbanization 16 2.5 References 17 3 Impact of Technical Factors on LID Performance 27 3.1 Introduction 28 3.2 Methods 30 3.2.1 Study Area 30 3.2.2 Model Description 31 3.2.2.1 Model Theory 31 3.2.2.2 Model Construction 31 3.2.2.3 Model Calibration and Validation 32 3.2.2.4 Model Uncertainty Analysis by GLUE Method 34 3.2.3 Hydrological Pattern Design 35 3.2.4 LID Strategy Design 35 3.2.4.1 Implementation of LID Practices 35 3.2.4.2 Sensitivity Analysis by Sobol’s Method 36 3.2.5 Correlation Analysis Using a Self-Organizing Map 37 3.2.6 Description of the RDS Load Components 37 3.3 Results 38 3.3.1 RDS Migration and Distribution in Baseline Strategy 38 3.3.1.1 RDS Migration under Hydrological Scenarios 38 3.3.1.2 RDS Distribution on Land-Cover Types 39 3.3.2 RDS Removal in LID Strategies 40 3.3.2.1 RDS Removal by LID Strategies 40 3.3.2.2 Spatial Distribution of the RDS Removal 42 3.3.2.3 LID Parameter Sensitivity Analysis Result 43 3.4 Discussion 45 3.4.1 Factors Influencing RDS Migration in the Baseline Strategy 45 3.4.2 RDS Removal Performance by LID Strategy 46 3.5 Conclusions 47 3.6 References 47 4 Impact of Hydro-Environmental Factors on LID Performance 53 4.1 Introduction 54 4.2 Methods 56 4.2.1 Study Area 56 4.2.2 Modeling Approach 56 4.2.2.1 Model Theory 56 4.2.2.2 Model Construction 56 4.2.2.3 Model Calibration and Validation 57 4.2.2.4 Model Uncertainty Analysis 57 4.2.3 LID Performance Analysis 58 4.2.3.1 LID Practice Implementation 58 4.2.3.2 LID Performance Evaluation 58 4.2.4 Hydrological Pattern Analysis 59 4.2.4.1 Scenario of ADP Length 59 4.2.4.2 Scenario of Rainfall Magnitude 59 4.2.4.3 Scenario of Long-Term pre-Simulation 60 4.2.5 Sensitivity Analysis of Hydrological Scenarios 60 4.3 Results 61 4.3.1 LID Performance under Different ADP Lengths 61 4.3.2 LID Performance under Different Rainfall Magnitudes 62 4.3.3 Spatial Distribution of LID Performance 63 4.3.4 Sensitivities of LID Performance to ADP Length and Rainfall Magnitude 66 4.4 Discussion 68 4.4.1 Impact of ADP Length and Rainfall Magnitude on LID Performance 68 4.4.2 Spatial Heterogeneity of LID Performance 68 4.4.3 Research Implications 69 4.5 Conclusions 70 4.6 References 71 5 Impact of Future Climate Patterns on LID Performance 77 5.1 Introduction 78 5.2 Methods 80 5.2.1 Study Area 80 5.2.2 Hydraulic and Water Quality Model 80 5.2.2.1 Model Development 80 5.2.2.2 Model Calibration and Validation 81 5.2.3 Climate Change Scenario Analysis 81 5.2.3.1 GCM Evaluation 81 5.2.3.2 Greenhouse Gas (GHG) Concentration Scenario 82 5.2.3.3 GCM Downscaling 83 5.2.4 LID Performance Analysis 83 5.2.4.1 Implementation of LID Practices 83 5.2.4.2 Evaluation of LID Performance 84 5.2.4.3 Sensitivity Analysis on LID Performance 86 5.3 Results 86 5.3.1 Hydrological Characteristics under Future Climate Scenarios 86 5.3.2 LID Performance under Future Climate Scenarios 87 5.3.2.1 LID Short-Term Performance 87 5.3.2.2 LID Long-Term Performance 90 5.3.3 Impact of ADP Length and Rainfall Magnitude on LID Performance 92 5.3.3.1 LID Performance Uncertainty 92 5.3.3.2 Spatial Distribution of LID Performance 93 5.3.3.3 Sensitivity of LID Performance to Climate Change 95 5.4 Discussion 97 5.4.1 LID Performance in Short-Term Extremes and Long-Term Events 97 5.4.2 Impact of Climate Change on LID Performance 97 5.4.3 Research Implications 99 5.5 Conclusions 100 5.6 References 100 6 Impact of Climate and Urbanization Changes on LID Perfor-mance 109 6.1 Introduction 110 6.2 Methods 112 6.2.1 Study Area 112 6.2.2 Modeling Approach 112 6.2.2.1 Model Development 112 6.2.2.2 Model Calibration and Validation 113 6.2.3 Future Scenario Derivation 113 6.2.3.1 Climate Change Scenario 113 6.2.3.2 Urbanization Scenario 115 6.2.4 Flood Exposure Assessment 115 6.2.5 Implementation and Evaluation of LID Strategy 117 6.2.5.1 Implementation Scheme of LID Strategy 117 6.2.5.2 Performance Evaluation of LID Strategy 117 6.3 Results 118 6.3.1 Flood Exposure in Baseline and Future Scenarios 118 6.3.1.1 Hydrological Change in Future Climate Scenarios 118 6.3.1.2 Catchment Change in Future Urbanization Scenarios 118 6.3.1.3 Population and GDP Exposures to Flood in Future 121 6.3.2 Flood Exposure with Consideration of LID Strategy 123 6.3.2.1 Runoff Mitigation Performance of LID Strategy 123 6.3.2.2 Peak Mitigation Performance of LID Strategy 124 6.3.2.3 Population and GDP Exposures to Flood under LID Strategy 125 6.4 Discussion 126 6.4.1 Climate Change and Urbanization Exacerbated Flood Exposure Risk 126 6.4.2 LID Strategy Mitigated Flood Exposure Risk 126 6.5 Conclusions 127 6.6 References 127 7 Discussion and General Conclusions 133 7.1 Stormwater Management Performance of LID Strategy 133 7.2 Impact of Influencing Factors on LID Performance 134 7.3 LID Performance under Future Changes 135 7.4 Research Implications 136 7.5 References 137 8 Outlook of Future Research 139 8.1 Optimization of LID Performance 139 8.2 Cross-regional Study on Future Changes 139 8.3 Macro-scale Flood Risk Management 140 8.4 References 141 9 Appendices 143 9.1 Appendix for Chapter 3 143 9.1.1 The Determination of the GLUE Criteria 143 9.1.2 Model Uncertainty Analysis 143 9.1.3 The LID Installation Location 144 9.1.4 Figures 145 9.1.5 Tables 147 9.2 Appendix for Chapter 4 153 9.2.1 Scenario of Long-term pre-Simulation 153 9.2.2 Figures 153 9.2.3 Tables 158 9.3 Appendix for Chapter 5 164 9.3.1 Tables 164 9.4 Appendix for Chapter 6 169 9.4.1 Figures 169 9.4.2 Tables 170 9.5 Data Source 177 9.6 References 17