Perancangan sistem balanced scorecard pada perusahaan properti (studi kasus elang group)
Bibliographic record
Abstract
Indonesia is one of the countries with the largest economy in Asia Pacific. Indonesia's economic growth remains strong despite the slowdown in the last few years. According to data from the Central Bureau of Statistics (BPS-Badan Pusat Statistik) in 2016 stated that Indonesia's economy grew 5.04 percent in the fourth quarter of 2015, increase 4.7 percent in the previous quarter. This economic growth, according to BPS is driven by increases in government spending by 7.3 percent on an annual basis and an increase in investment of 6.9 percent. The improvement in Indonesia's economy led to an increase in purchasing power has improved. With the fourth largest population in the world, Indonesia offers the growth of number of middle class as well as the higher of consumer purchasing power. \nThe population of Indonesia is increasing, it drives the need for a house needs to be very important. Currently, there is still the backlog among those who have a place to stay or not. Backlog is the quantity of houses that have not / not handled. Backlog is calculated based on the concept that one housing unit per one household. According to BPS backlog number has reached 13,012,107 units. In 2014 according to BPS gap of fulfillment house decreased 1.02 percent from 2013. Lack of fulfillment houses to public makes the share of property market is very profitable to invest, because the gaps number of houses still large. \nElang Group must prepare everything for expansion in order to face competition in this property. Therefore Elang Group should create a system where there are clear indicators of the success of the system that is therein, including performance measurement. Balanced Scorecard is required as the system performance measurement tools to determine how far the Elang Group capable for strategic planning in positive long-term contribution for the company. Designing a performance measurement system that can cover all the activities and existing activities. \nThis research was conducted at the office of the Elang Group located at Bhumi Elang, Batu Hulung 1 CIFOR, Bogor. This research was conducted from July to August 2016. The data used are primary data and secondary data. Secondary data from external organizations while the primary data obtained through questionnaires and structured interviews with expert respondents. The number of respondents in this study consists of 7 (seven) experts and practitioners with the consideration of the level of understanding, competence and capacity. \nThe data that have been collected should be processed first with the aim of summarizing the data collected from the interviews and questionnaires by expert respondents. This study is divided into three stages: the first stage is the strategy formulation and the second stage is the determination of the key success factor of Elang Group, and the third is designing BSC. In the first stage, before preparing a balanced scorecard, firstly performed the data analysis using the method of strength, weakness, opportunity, and threat (SWOT) analysis. In the second stage, carried out the analysis of key success factors (KSF). Ideally KSF determination obtained through focus group discussion (FGD) process but in this study the determination of KSF determined through in-depth interviews to the respondents who have been designated. Analysis of the data in the third stage is done by using the balanced scorecard method and the method of paired comparison. \nThe results of the identification of the business environment in Eagle Group include 8 (eight) internal factors in the company, and 5 (five) external factors that affect the company corporate strategy in order to survive and increase the competitiveness. The results of such identification is the basis to do a SWOT analysis process. The design of Key Performance Indicators (KPI) gained as much as 25 indicators divided into four BSC perspectives. On the financial perspective obtained a highest weighted average of KPI to return on investment (ROI) as much 41.4 percent. Key performance indicators are a percentage or the weighted average of the highest in the customer's perspective is the percentage of customers who are satisfied with the products and services of Elang group at 37.3 percent. In the internal business process perspective, for key performance indicators that received the highest average weight is the growth of a new project, with a weight of 20.7 percent. As for the weighting of key performance indicators on the learning and growth perspective obtained a highest weighted average KPI percentage of satisfied employees worked at 42.6 per cent. Based on the results of weighting, the indicator ROI gets the highest weight on the overall performance of which amounted to 19.7 percent. This means that management of Elang Group can focus on meet this KPI achievement. \nSome things that can be delivered as a suggestion to the management of Elang Group in improving the performance of the result is in order to improve return on investment, Elang Group can perform additional sales that will increase revenue, must be offset by the control of the cash inflows and outflows. One way to improve the marketing process is to establish a close relationship with customers. Expected future customers of Elang Group properties can sale a good word of mouth promotion. Measurement of the performance simulation needs to be done on the strategies undertaken using the staining technique and the average score, so we will get a clear picture about the performance based on each KPI has been achieved today. As a reference for further research, to do or decline KPI cascading process until the division level to the level of individual organizations to obtain indicators of assessment that may apply to the operational level of the organization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".