Data Science Assignment on Workforce Planning- HR Analytics

Question

Task: Data Science Assignment on HR Analytics (Data Science Use Case):
• Think of an unconventional or a novel people/HR analytics use case (Data Science Use Case)
• Outline classical Data Science/Machine Learning Steps that will be followed for this Use Case
• Assume data, tools & technology are available
• Apply Design Thinking Principles for this Use Case

Answer

Introduction
As per the sources used to prepare this data science assignment, it is evident that data science, machine learning and business intelligence have grown tremendously in recent years. Due to the growth of these technologies, analysis has become important in sectors like finance, marketing, production, etc. Business analysis concentrates on analyzing organizational data generated over several years to support present and future decisions. Analysis based on employment is also vital as quality of human resources directly adds much higher value to business. It impacts the overall effectiveness of business. Human Resources Analytics is quite prevalent these days in which people issues are solved by making good use of data relate to human resources of the organization. The HR analytics as a data science use case is widely adapted since 2018. HR analytics using data science techniques and tools have provided HR managers an extensive view to identification of people roles and their impact on organization goals. HR analytics can be useful in making managerial decisions, workforce planning, appraisal, promotions, branding, etc. The manual work for the same was easy earlier when employees were less; these days when a company has thousands of employees; it becomes difficult to handle this manually. Many data mining and artificial intelligence algorithms and techniques gave been applied to HR data so that meaningful information can be found out in relation to HR analysis (Christos and Bussin, 2019). This report is organized as: details of workforce planning with it machine learning steps and tools available are discussed. Later, thinking principles are presented as per the selected use case i.e. workforce planning.

Workforce Planning – An HR analytics use case
There are many use cases including workforce planning, appraisal, promotions, branding, etc. This report focuses on workforce planning; its significance and machine learning steps. In order to achieve long term business goals, organizations should understand that they need right people to fit in job roles at the best suited time. In order to accomplish this, HR leaders need to hire employees who can adapt company culture. The candidates should be highly potential adhering to skills required for the job role. Also, staffing should be sufficient enough so that customer requirements are met on time.

In order to stick with the above mentioned objectives of human resources, workforce planning is important. However, many organizations do not plan their personnel and if some do, they depend on untrustworthy methods that affect business in severe manner. If workforce planning is absent or weak, it directly impacts company value. The workforce planning is important with future point of view as well as on the basis of past records; it helps managers to take future decisions wisely.Workforce planning allows organization to take decisions as per current and past employees. It empowers business if proper planning of workforce is done which can help business grow substantially. The sound workforce planning helps in proper talent acquisition as well where HR managers could hire suitable candidates as per plan (DiClaudio, 2019).

Data Science steps to create workforce plan
The process of workforce planning with the help of machine learning should undergo following process (Timms, 2020).

1. Understand the business
There are ever changing business behaviors within the organization hence HR managers need workforce planning so that the change should be noticed and proactive response is ready. In order to understand the business, planners should not lookup into business data as numbers and figures can be quite confusing. To proceed with workforce planning, it is necessary to understand the business and its goals first. After understanding the business goals, contingency plans and different workforce plans should be created and compared so that best one should be adapted as per organization goals.

2. Requirement Elicitation
The stakeholders’ meeting could help in gathering all the requirements so that no information is left out while planning a workforce. The questionnaire must be prepared that reflect overall business insights. The stakeholders are expected to answer all the important questions in right manner since any false information could impact the business.How many forecasts are needed? These can be daily, weekly, monthly and yearly. Such questions related to the business can be asked to the stakeholders that can help in preparing workforce plan well. The gathered information could help planners to dig well all the important organizational aspects.

3. Using Analytics
The workforce planners can help HR managers to support their hiring process. Once the employee is appointed at the company, throughout the span of his serving workforce plan can keep track of his skills, his participation in programs and discussions, they key roles he has played in the team, etc. till his retirement. The data built could be helpful in making employees work more in the company. Machine learning methods can be applied in this stage as discussed later in this report. At the end of this stage, workforce plan should be ready.

4. Contingency planning
The contingency planning is necessary and is brought into action when change is experienced. The events planned in contingency will be executed on change mentioned. With contingency plan, multiple possibilities of events can be recorded and their solutions or plan of action is decided so that at the peak point, least amount of time is required to act on the change.

5. Constant monitoring
The workforce plan once created should be monitored continuously. Many organizations prepare plan once in a year and do not review or plan it again as per change in business goals. This makes no meaning of preparing it and hence wastes of time and efforts.

Tools and Technology
This section describes tools and technologies required in the process of workforce planning. Tools are as listed below.

1. Strategic map – The workforce map can offer detailed view of activities involved in workforce planning as a part of company plan.

2. 9-Box grid –This is a matrix that helps in presenting employees performance in a model so that his potential can be visualized in broader and detailed model. This matrix is easy to model and understand.

3. HR dash boarding – This tool is used by many companies currently for its effectiveness of showing workforce capabilities.

4. Scenario planning – This tool can help in forecasting future needs of the workforce. The scenario includes technological, policy related, changes in company profiles, etc. These planning affect workforce in many different ways and hence scenario planning is majorly followed by many companies.

Along with tools, workforce planning is also based on machine learning techniques such as neural network and decision trees. The step three of data science steps i.e. using analytics makes use of artificial intelligence and machine learning algorithms, methods and techniques discussed next.

Neural Network
Neural network has proven to be successful in decision making and forecasting future needs related to manpower. Neural network consists of artificial neurons forming a layered network. It contains minimum of three layers: input, one (or more) processing and output. The processing layer needs to be at least one in artificial neural network. Neurons in each layer are intended to evaluate function, output of which acts as an input to next layer. Neural network also helps in accumulation of clients and also in further decision making (Brattin et al., 2019).

Decision Trees
In workforce planning, estimations and predictions are necessary to know future trends based on available company data. Decision trees arealso useful in predicting employee needs in future as per appropriate skills needed for the projects. A decision tree contains a root node and two or more children nodes. The last node of each branch is called as leaf node i.e. the node that does not have child node. The branching is done from root node based on a condition on the basis of what decision to make according to available attributes (Harper, 2008). In this manner each unique row in the database can be included in the decision trees so that can be referred in future.

Design Thinking Principles
Design thinking refers to application of known techniques so that user requirements are met which in turn enhances business value (Safarishahrbijari, 2018). Design thinking helps people to identify issues in the business and offer possible resolutions by applying the ideas at the right place.

For workforce planning, design thinking principles can be applied in following steps listed.

1. The current and future projects undertaken by the company should be noted first. This will help in understand the required staff with skills required to accomplish the projects.

2. Staffing management is important as fewer employees in the company could fail to catch up with the strict delivery deadlines. Similarly, more employees than required could lead to monetary loss to the company as they will be paid for least work in their part which can harm organization in long run.

3. Employer-employee relations are important and hence decision trees can be useful to identify which employee glues well with its higher level personnel. The employees who cannot go well with others can be refrained from forming a team. This means, if a project is signed, the HR managers can find who should be in a team so that they can be productive with their favorite people in a team.

References
Brattin, R., Sexton, R. S., Yin, W., & Wheatley, B. (2019).A neural network solution for forecasting labor demand of drop-in peer tutoring centers with long planning horizons.Education and Information Technologies : The Official Journal of the Ifip Technical Committee on Education, 24(6), 3501–3522. https://doi.org/10.1007/s10639-019-09939-7

Christos, D., &Bussin, M. (2019).Business analytics the new friend of hr. Hr Future, 2019(3), 38–39.

DiClaudio, M. (2019). People analytics and the rise of hr: how data, analytics and emerging technology can transform human resources (hr) into a profit center.Data science assignmentStrategic Hr Review, 18(2), 42–46. https://doi.org/10.1108/SHR-11-2018-0096

Harper, P., &LeiteJr, E. (2008).Treeworks: advances in scalable decision trees. International Journal of Healthcare Information Systems and Informatics, 3(4), 53–68. https://doi.org/10.4018/jhisi.2008100104

Safarishahrbijari, A. (2018). Workforce forecasting models: a systematic review. Journal of Forecasting, 37(7), 739–753. https://doi.org/10.1002/for.2541

Timms, L. (2020). Why people analytics is the hr professional’s secret weapon. Strategic Hr Review, 19(2), 85–87. https://doi.org/10.1108/SHR-04-2020-178