Ch. 2: Human Resources Planning and Analytics

Learning outcomes

1. Discuss why HR planning matters in relation to strategy.
2. Describe the HR planning process.
3. Discuss how companies estimate the supply of and demand for human capital.
4. Explain what parts make up a human resources information system (HRIS).
5. Describe how HRIS data and HR analytics have increased the effectiveness of delivering HR services to employees.

2.1 Human resources planning and strategy

With HR planning a firm can anticipate the future, assess what it needs, estimate how many candidates for particular jobs are available on the market and choose when and how to adjust and deploy its staff. Since markets shift quickly, the HR plan lets companies react efficiently. As the well-known phrase goes, a firm must have suitable people in suitable places when it needs them, backed by a plan, practices and policies.

Example. A company growing in visual imaging must update its technology, but its employees resist change, have not upgraded their visual imaging skills for three years and doubt the company will survive. HR must build a plan that (1) attracts and hires skilled new employees, (2) upgrades the skills of existing employees and (3) builds trust and loyalty so current staff feel secure.

The strategic plan is shaped by internal and external forces. The HR plan must be aligned with the strategic plan and focus on the company's long-term success. HR starts by predicting how many employees the expansion requires and which skills they need.

2.2 The HR planning process

HR planning is identifying the company's current and future "people" needs: hiring the right people, creating an environment that supports their work and building capacity for success. The Treasury Board of Canada describes five steps:

The Treasury Board's Human Resources Planning Guide for Executives adds questions HR managers should consider at each step.

2.3 Forecasting demand for labour

Predicting how much labour will be needed is difficult, yet an HR plan lowers the chance of facing a labour shortage or an excess supply of workers. Several factors carry weight in the forecast:

2.4 HR demand forecasting methods

• Nominal group method: experts gather as a focus group, in person or online, and receive a question to work on, for example which factors will shift our need for staff. Everyone notes down ideas, these are shared and written up, put in order of priority, and the group then votes on the top 3 to 5.
• Delphi method: a group of experts predicts specific future events through surveys and opinions. HR summarizes the results in a report and the experts keep answering new survey rounds until their responses converge and there is agreement on future trends.
• Trend projections, with two techniques:
  • Extrapolation uses past change: we hired 10 carpenters last year, so we will likely need 10 more this year as the company grows.
  • Indexation links growth in employment to particular indices (in the book's example, sales relative to the number of pieces produced).

HR must choose methods that are reliable and valid. Data can be gathered with purchased software or by outside experts who report to the company. The results are then converted into staffing tables: lists of projected employees for each type of position. They support short- and long-term planning of HR demand and help HR set objectives. The book's partial staffing table (Table 2.4.1) for a retail chain:

Reading the table: the columns show the current number per position and the expected outflow (quits, attrition, retirements) plus the extra positions needed for new stores, which together indicate how many people HR must find for each level.

2.5 Human resources supply

After predicting demand and building staffing tables, HR must decide how to fill the need. There are two sources:

Estimating internal supply

HR must know the talents of current employees and track their constant movement through the company. Methods:

• Markov analysis: forecasts future HR needs with transitional probability matrices based on past data. It projects the future from the current numbers and follows the internal movement of employees. Example: with a population of 50 to 60 year olds and 5 retirements last year, the firm predicts 5 retirements this year and therefore 5 replacements.
• Human resources inventories: made through HR audits that summarize employees' knowledge, skills and abilities (KSAs): years of experience, education, training, successes and accomplishments. Replacement summaries track each employee's history so HR can decide whether they can fill gaps. They need constant updating.
• Leadership and management inventories: like HR inventories (KSAs recorded) but they also monitor each employee's potential for promotion to management. Also need constant updating.
• Replacement charts: a "picture", similar to a hierarchical organizational chart, showing jobs, the status of each employee and whether they are promotable or have potential. They show quickly who can replace whom when a job opens. HR can support promotion decisions with leadership and management tests, psychological tests and interviews about employees' interest in promotion.

Estimating external supply: the labour market

• Labour market analysis: studies the present and future availability of employees. In Canada, unemployment rates quickly show how many people are actively seeking work and in which occupations. Firms can also target people employed elsewhere.
• Community attitudes: local, national and international attitudes affect the labour market. Employers may move to another region or country because of attitudes; social justice shifts regarding minority groups also change hiring practices.
• Demographics: governments track labour market trends years ahead. Useful sources are Statistics Canada, Employment and Social Development Canada (ESDC) and the Conference Board of Canada, which report on demographics, occupations, income, census results, population statistics and regional labour market trends.

2.6 Planning HR's response to supply and demand

HR needs strategies ready to either reduce the workforce (over-supply) or increase it (labour shortage).

Plan for reducing employees

Plan for labour shortages

2.7 Human Resources Information Systems (HRIS)

Relational database management systems and database programming from the mid-1980s moved HRM from manual, transaction-based bookkeeping to semi-automated processes. Example: onboarding can be tailored automatically using data from recruitment: a recruit with experience in a specific POS (point of sale) system skips the software orientation, while someone who has never worked in a team joins the teamwork session. Twenty years ago such customization was unthinkable.

Every HR process now has a technological element and is basically about using information to make decisions. Example: hundreds of resumes are screened by recruiters. With the data (resume content, decisions, who decided and when) one could detect that a recruiter becomes more lenient later in the day as they tire, is biased against graduates of a particular university, or rates applicants with Asian-sounding names less favourably. Doing this in Excel is possible but inefficient; the data must already be collected and organized.

With a few clicks an HR manager can find the average salary of junior sales associates in a store, the last person promoted in the Saskatoon plant, how often employees looked at their performance feedback, or how much was invested in leadership training in 2020.

Selecting an HRIS. An HRIS is expensive and time-intensive, so the organization should do its due diligence and involve the right stakeholders. Options differ in price and functionality. Pricing models: per employee (roughly $1 to $20 per employee per month) or per user (the people who actually use it, mostly HR managers). The package should meet current needs and be flexible enough to grow with the organization, within constraints of budget, hardware and time.

Benefits of HRIS:

2.8 HR analytics

The HRIS collects and organizes data; HR analytics is the process of analysing that data to improve workforce performance. It is also called talent analytics, people analytics or workforce analytics. The book distinguishes:

Data. Thanks to high-performance HRIS and employee tracking, the data available has grown exponentially. Commonly collected metrics:

Analytics and the law. HR data collection is heavily governed by compliance laws. Legal considerations:

1. Employee privacy and anonymity.
2. Employees agree to how much data is gathered and what kind.
3. Establishing the goal of data collection and informing employees.
4. IT security when using third-party software.
5. Location of the analytics vendor (where data is stored) and its compliance with local laws.

Example: Sociometric Solutions offers electronic badges that record conversation length, tone of voice, interruptions and empathy. A major bank found that its best call-centre workers took breaks together to let off steam; after introducing group breaks, performance rose 23% and stress fell 19% (Kuchler, 2014). Such technology raises legal and ethical questions because it is increasingly intrusive.

2.9 HR analytics application

HR analytics is analysing and using data to make informed business decisions. In recruitment it can show which qualities matter for a position and which do not: if analysis of top salespeople shows college GPA does not predict sales performance, the firm can widen its pool to all degree holders instead of only high-GPA graduates. Applications by HR area:

• Job design: data pinpoints what helps or hinders motivation and performance. During the pandemic Rabobank, Merck and National Australia Bank used quick surveys on coping with remote work, support needs and return-to-work preferences. Text analytics (software that turns words and word frequency into emotional sentiment or psychological traits) on comments and discussion boards gives insight while avoiding survey fatigue and preserving anonymity, so interventions can be precise in nature and timing, even tailored to individuals.
• Recruitment: aims to generate the maximum number of quality applications and borrows from marketing science: segmentation, statistical analysis and optimal people models (ideal candidates). Programmatic advertising defines target groups and reaches them through multiple online sources, monitoring and adjusting spend (per click or per thousand impressions) and testing different ads on different segments to optimize conversion and lower cost. Example of out-of-the-box recruiting: Nissan and Sony's GT Academy turns the best racing gamers into real racing drivers; past winners are all still racing.
• Talent acquisition: finding the best person for a job is complex and prone to bias. Analytics can profile top performers (e.g. MBA, high-level athletics, introversion) so staffing processes can be aligned with those characteristics.
• Training: adaptive learning technology personalizes courses, segments, activities and test questions to the learner's pace and method. Self-paced online learning is more cost-effective than sending people on courses and integrates development into daily routines. Example: Danone Campus 2.0, an online platform for development and sharing best practices.
• Compensation: data supports fact-based decisions on salary expectations and pay equity. When an employee gets a competing offer, the manager's instinct to match it can be a costly mistake; instead, compare the employee with the team and with market pay for the role.
• Performance management: handheld computers help UPS drivers work out the most efficient order of deliveries, and each truck has more than 200 sensors (seatbelt use, reversing, U-turns). Through monitoring, feedback and training the company cuts 85 million miles and 8.5 million gallons of fuel each year, while daily stops went from fewer than 100 to roughly 120. There is little backlash, since UPS pays some of the highest wages, has to inform drivers which data it gathers and may not discipline a driver on the basis of data alone.
• Health and safety: traditional analysis of past incidents uses lagging information: it shows what happened, not why. Predictive modelling identifies factors behind incidents (weather, job site, maintenance schedules, production measures) so firms can prevent them, e.g. by adjusting maintenance scheduling, machine placement or task timing. Example: Seeing Machines tracks mining truck drivers' eyes (camera, GPS, accelerometer; works with sunglasses). If the eyes close longer than 1.6 seconds, a sound and seat vibration alarm goes off; a second alarm alerts a dispatcher or supervisor who contacts the driver by radio.

2.10 Key terms

Absenteeism, buyout, contingent workers, Delphi method, external supply, extrapolation, forecast, HR analytics, HR planning, HRIS, human resources inventories, indexation, internal supply, involuntary turnover rate, labour market analysis, labour shortage, leadership and management inventories, Markov analysis, nominal group method, offshoring, offer acceptance rate, phased retirement, people analytics, recall list (list of laid-off employees who are called back by seniority or need), replacement charts, replacement summaries, revenue per employee, staffing tables, time to fill, time to hire, training efficiency, training expenses per employee, voluntary turnover rate. All are explained in the sections above.

2.11 Summary

HR planning is a strategic process that looks ahead to future staffing needs in five steps: review the goals, scan the environment, find the gaps, build the plan and track progress. Forecasting relies on the nominal group method, the Delphi method and trend projections, and the outcomes are turned into staffing tables. HR assesses internal and external supply and reacts with strategies to shrink or grow the workforce. An HRIS structures people data used in recruiting and selecting, training, health and safety and managing performance, while HR analytics applies metrics like time to hire, training expenses and time until promotion to make better decisions and raise performance.