Analyzing The Relationship Between Key HR Metrics Using Predictive Analytics Methods

1. Analyse relationship between key HR metrics using predictive analytics methods

2. Provide recommendations on staffing policy to enhance organizational performance

Human resources metrics and Predictive Analytics

Human resources metrics and Predictive Analytics

In firms, human resources metrics frequently track down the performance of employees and the organization in the department. This supports the section analysis, the risks, and the threats while overlooking their assets and hide their weakness from competitors. Human resource (HR metrics) gives the company a clear vision of the specific growth in terms of data collection and analyzing likely outcomes through the daily working activities. The usage of such techniques can really boost the organization to work resourcefully and effectively. Many administrations are not visionaries in the current competitive economic era since they emphasize predictive analysis (Edwards, 2018).

To critically select, evaluate an employee there must have certain strategic measures and plans that organizations need to follow. Some HR metrics allows managers to follow up on the data that flows in and out to be interpreted and analyzed for possible future and current outcomes. The techniques that can be used to show the relationship of the metrics to remove non-applicable ones. Proposals for using a variety of predictive analytics like linear polynomial regression to site probabilities, variables relationship and forecasting is on the best interest of the company. Management is advised to understand the models in order to reduce misinterpretation of biased data.

The Statement Problem

The problems that are recognized is that organizations cannot come up with a viable resolution and effective HR metric to solve administrative and casual associated issues like poor networking, hiring issues, risk management crisis, and fraud. One of the HR metrics is turnover in the hire, where you find numerous workers leave the company early due to some reasons comparing to the low numbers of employees being retained or hired. Applying a system in logistical regression to find out why employees leave the rate and the probability of retrenchment and hiring. Logistical regression will come up with two possible outcomes after analyzing the data. This will display the relationship between employees and employers that may affect the turnover.

Another case showing the relations between HR metric, ratios of HR to employees with linear regression as a predictive analytics tool to assist companies identifies professionals and maximizing their usage during employment to ensure cost efficiency. Linear regression approximates proportions; interpret variables outcomes that are definite to occur. If the number of departmental professionals reduces then the performance will gradually be low.

Relatively, there need to have policy recommendations that every manager should be keen to review so as to realize prolific and competitive organization. Having the best source of information and a realistic game plan to improve your company’s operations, revenues and minimize threats. Being an executive will help you gauge the firm in a capacity to view the pros and cons of the applied system like predictive models. Recommendations promote sufficient gateways to review a certain section of the business in different aspects and departments (Iwu, 2016).

The Statement Problem

Firstly, social networks and infrastructure to mobilize larger crowdsourcing. This will endorse the business image and maintain stability. Secondly, recruitment policy is fundamental in making sure executives revise their hiring techniques and have quality manpower and qualified professionals. This will definitely reduce immorality, poor presentation, and industrial strikes. Thirdly, risk assessment is a vital tool to eradicate possible hostile takeovers and industrial strikes. When management has ensured that their financial budget and strategic plans for the financial year is on track, then it will increase more opportunities for the company. Lastly, fraud and embezzlement of funds policy are neglected since managers are the extravagant top earners with ridiculous allowances than what do you expect from your employees?

HR Metrics

The following are specific HR metrics that guide the department in monitoring and evaluating employees. HR metrics provide the platform of having the data and analyzing it and forecast outcomes. (Krishnaswamy, 2018) In the marketing of your products, production chain of operations, the steady supply and demand flow of products all are verified by the analytics that provides metrics with adequate data. This will promote the HR operations that are essential in running the day to day operations.

Organizational performance as a metric guarantee that employees know what they are doing and what is needed.  The costs of HR per employee show the cost those employees in the company being utilized. Money is required to run an organizational; operations and increase production. Adequate performance and potentials are key HR metrics that monitor the acts and evaluates which employees should be retained in the company. The work capacity and the incentive that employees get to ensure the task is done.  HR revenue and outflow is s grave metric that shows the flow of profit and loss in the financial year. The company should not spend on what they cannot afford. These metrics will support and give data to the Human Resource department the chance to evaluate the progress by using efficient analytical methods to interpret data to predict an outcome.

In reference to ‘Predictive Analytic in Action,’ problems can be solved by making the HR metrics workable. (Sen, 2016). In connection to the above recommendations, to make sure that networking which includes benchmarking and out souring is reliable. Information in each department has to manifest a systematic flow. Networking requires analysis to follow market trends and evolving social patterns in order to provide deep understanding. Data network is the next big thing in the business world where it can apply directly to the marketing sector. Using regression as a method to make all this possible. Justifying the enrollment policy whereby there is corruption, poor section, and evaluation of pre-employees. By multiple regressions in a discrete model of analysis, the response is not continuous hence this model is the best to predict two or more choices.

HR Metrics

 Choosing the best person for the job according to merits is easily solved by this model. Evaluation of resource selection is usually assisted by discreet- choice models. Logistical regression can be used in solving the policy of fraud by grouping and creating odds of an outcome. The way administration misuse funds, it does not mean that all employers are corrupt but, there is a more like hood of it happening. The policy of risk assessment is usually done using its own analysis that calculates the probability of something to occur by the loss to occur. This clearly shows that predictive analytic techniques can be used to reduce cost and increase profits. For example, venturing in a major construction under bankrupt status is financials suicide and relatively risky to put the company in receivership.

Staffing of employees in strategic parameters to increase productivity, maximize profit by evaluating employees’ performance. Retaining virtuous employees is a process to ensure that an organization maintains its higher standards. Staffing as a recommendation is better than hiring an inadequate number of employees to do a task that can be divided into teams and groups. Organizations are stimulated to use staffing as an HR tool to increase efficiency in operations, marketing strategies, and labor division.

Corporate staffing does not undermine the powers of the administration but increases the respect that the department will have on them. Ways to improve or use staffing can be using matrix formation of human resources. This technique will arrange employees in a beneficial system to improve their effectiveness. Staffing in firms ensures that division of labor is dealt with and work is done at the right time. Staffing levels the playing field in controlling certain policies and HR activities. Managers are advised to use staffing to increase productivity so that they have a competitive advantage.

Regression method here is to solve production issues in Company X. The objective is to establish the effects of the increase in productivity due to team formations in Company X.

Y=a+bx   Y=increase in productivity   X=number of team members   A, b=constants. (Cohen, 2014)

The below diagram is a short illustration of simple regression on how productivity is measured against the number of employees being hired. This will have proved that by applying such methods there are greater chances of remedying such issues.

Where if you increase the number of team members in a given group the productivity in Company X would also increase due to the increase in efficiency.

Predictive Analytic in Action

Application of Predictive Analytics

Real life situations, the predictive analytic models can be used in real life situations and events. They help identify risk and predict possible outcome in the current problems faced by organizations and society. Firstly, it helps with direct marketing research where predictive analytics allows the company to identify potential grounds in the market niches. Knowing the competitive atmosphere in the market will help a firm progress cautiously with their products concerning branding, labeling, supplying and pricing (Schoenherr, 2015).

 Another application of predictive analytics is fraud detection. This exploits returns in the capital assets that needs assessment to predict accurate forecasts where seen. Monitoring the financials matching it with assets and liabilities will help the company identify loopholes in the department and solve it promptly. Heath care is viewed as an applicable critical case where patients who are in risk of developing certain diseases are identified using the models to risks of deaths and recurrence. Clinical support is compulsory to ensure the patients are categorized in the systematic portion to allow efficiency in checkup and monitoring them. (Ruminski, 2018)

Merits and demerits of predictive analytics

According to (Marr, 2018), the various advantages and disadvantages that come with this predictive analytics. The first advantage helps to identify new places for revenue by checking past buying patterns and come up with the best decisions based on the assumptions that there is a need for promotions, offers, and customer discounts. Discovery of sights in the customer profile data by delivering customer experience using any predictive analytic model. Business lead is improved immensely hence gaining a competitive advantage.

Improvement in production when using predictive analytics to help companies predict inventory and production rates to see whether there is possibility of failure. Rise in marketing promotions and campaigns in order to increase your revenue; the predictive model can forecast what customers’ needs and what you expect. Scam detections are measures that the predictive analytic track behavioral changes and patterns in the network. Screening of modified data of a person to predict whether he or she is a risk or asset.

Achievement on control, trends in the company’s management systems. This is beneficial in optimizing performances in the organization. The application of various techniques does not matter only the metrics that are operational. When you hire unqualified persons, overpayment in salaries and incentive even policy exit during interviews are the downfall of a good firm. Having the right team and a motivated HR department will ensure the metrics are effective to them. Prediction of organizational goals and targets changes the perspective of how you are viewed externally.

Eventually, the administrations have to see the productive analysis negatively on how it affects them internally and externally. Some of the demerits of using such analytic technique are lack of data skilled professional to forecast outcomes. Another disadvantage is integrating the analytic system to the business operations are hard since it influences application prediction outcomes. The manager gets a difficult task in controlling.  Incomplete data affects the prediction of the data hence no viable and possible outcome that will mislead the organization. Firms lack the understanding about the behavior of people that affects the HR metrics like the early hire. These cons can be seen to have a negative impact on the running of the business. (Wawer, 2018)

Conclusion:

Companies and startup businesses are growing towards the competitive era where there will be predictive analytics used in education sectors. Managers should be able to use such techniques to improve their revenue and maximize profitability. Human Resource metrics give guidelines to firms on what is to be improved in the HR data. HR managers are to come up with systematic measures to monitor, evaluate and motivate employees. Employees will take the company forward or destroy; it all depends on effective metric and the correct predictive analytics technique.

References:

Cohen, P. a. (2014). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press.

Edwards, M. R. (2018). HR metrics and analytics. Routledge.

Iwu, C. G.-D. (2016). Strategic human resource metrics: a perspective of the general systems theory. Acta Universitatis Danubius. {OE}conomica.

Krishnaswamy, P. a.-D. (2018). A Predictive Analytics Methodology to Assess and Optimize Readmission Risk in Heart Failure Patients.

Marr, B. (2018). Data-Driven HR: How to Use Analytics and Metrics to Drive Performance.

Ruminski, C. M.-M. (2018). Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of clinical monitoring and computing, 1-9.

Schoenherr, T. a.-P. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 120-132.

Sen, A. a. (2016). HR Metrics and the Financial Performance of a Firm: A Case-Based Approach. Journal of Management Research, 177-184.

Wawer, M. (2018). The Use of HR Metrics in Human Resources Management. Przedsi{k{e}}biorczo{'s}{'c} i Zarz{k{a}}dzanie, 303--317.