Human Resource Management System

Human Resource Management Systems (HRMS, EHRMS), Human Resource Information Systems (HRIS), HR Technology or also called HR modules, shape an intersection in between human resource management (HRM) and information technology. It merges HRM as a discipline and in particular its basic HR activities and processes with the information technology field, whereas the planning and programming of data processing systems evolved into standardised routines and packages of enterprise resource planning (ERP) software. On the whole, these ERP systems have their origin on software that integrates information from different applications into one universal database. The linkage of its financial and human resource modules through one database is the most important distinction to the individually and proprietary developed predecessors, which makes this software application both rigid and flexible.

The HR function's reality

All in all, the HR function is still to a large degree administrative and common to all organizations. To varying degrees, most organizations have formalised selection, evaluation, and payroll processes. Efficient and effective management of the "Human Capital" Pool (HCP) has become an increasingly imperative and complex activity to all HR professionals. The HR function consists of tracking innumerable data points on each employee, from personal histories, data, skills, capabilities, experiences to payroll records. To reduce the manual workload of these administrative activities, organizations began to electronically automate many of these processes by introducing innovative HRMS/HCM technology. Due to complexity in programming, capabilities and limited technical resources, HR executives rely on internal or external IT professionals to develop and maintain their Human Resource Management Systems (HRMS). Before the "client-server" architecture evolved in the late 1980s, every single HR automation process came largely in form of mainframe computers that could handle large amounts of data transactions. In consequence of the high capital investment necessary to purchase or program proprietary software, these internally developed HRMS were limited to medium to large organizations being able to afford internal IT capabilities. The advent of client-server HRMS authorised HR executives for the first time to take responsibility and ownership of their systems. These client-server HRMS are characteristically developed around four principal areas of HR functionalities: 1) "payroll", 2) time and labour management 3) benefits administration and 4) HR management.
The payroll module automates the pay process by gathering data on employee time and attendance, calculating various deductions and taxes, and generating periodic paycheques and employee tax reports. Data is generally fed from the human resources and time keeping modules to calculate automatic deposit and manual cheque writing capabilities. Sophisticated HCM systems can set up accounts payable transactions from employee deduction or produce garnishment cheques. The payroll module sends accounting information to the general ledger for posting subsequent to a pay cycle.
The time and labor management module applies new technology and methods (time collection devices) to cost effectively gather and evaluate employee time/work information. The most advanced modules provide broad flexibility in data collection methods, as well as labour distribution capabilities and data analysis features. This module is a key ingredient to establish organizational cost accounting capabilities.
The benefit administration module permits HR professionals to easily administer and track employee participation in benefits programs ranging from healthcare provider, insurance policy, and pension plan to profit sharing or stock option plans.
The HR management module is a component covering all other HR aspects from application to retirement. The system records basic demographic and address data, selection, training and development, capabilities and skills management, compensation planning records and other related activities. Leading edge systems provide the ability to "read" applications and enter relevant data to applicable database fields, notify employers and provide position management and position control.
Typically, HRMS/HCM technology replaces the four core HR activities by streamlining them electronically; 1) payroll, 2) time and labour management, 3) benefit administration and 4) HR management. While using the internet or corporate intranet as a communication and workflow vehicle, the HRMS/HCM technology can convert these into web-based HRMS components of the ERP system and permit to reduce transaction costs, leading to greater HR and organizational efficiency. Through employee or manager self-service (ESS or MSS), HR activities shift away from paper based processes to using self-service functionalities that benefit employees, managers and HR professionals alike. Costly and time consuming HR administrative tasks, such as travel reimbursement, personnel data change, benefits enrollment, enrollment in training classes (employee side) and to instruct a personnel action, authorise access to information for employees (manager's side) are being individually handled and permit to reduce HR transaction time, leading to HR and organizational effectiveness. Consequently, HR professionals can spend fewer resources in managing administrative HR activities and can apply freed time and resources to concentrate on strategic HR issues, which lead to business innovation.

EHRMS vendors

A wide variety of other software vendors provide various subsets of functionality. For example, basic time and attendance software packages provide employee timekeeping functionality while other vendors focus primarily on payroll processing.
Open Source EHRMS' are also available, however they still lack end-to-end processes, functionalities and integration with common or open source ERP systems.

Business Performance Management

Business performance management (BPM) is a set of processes that help organizations optimize their business performance. It is a framework for organizing, automating and analyzing business methodologies, metrics, processes and systems that drive business performance.[1]
BPM is seen as the next generation of business intelligence (BI). BPM helps businesses make efficient use of their financial, human, material and other resources.[2]

History

An early reference to non-business performance management occurs in Sun Tzu's The Art of War. Sun Tzu claims that to succeed in war, one should have full knowledge of one's own strengths and weaknesses and full knowledge of one's enemy's strengths and weaknesses. Lack of either one might result in defeat. A certain school of thought draws parallels between the challenges in business and those of war, specifically:
collecting data
discerning patterns and meaning in the data (analyzing)
responding to the resultant information
Prior to the start of the Information Age in the late 20th century, businesses sometimes took the trouble to laboriously collect data from non-automated sources. As they lacked computing resources to properly analyze the data they often made commercial decisions primarily on the basis of intuition.
As businesses started automating more and more systems, more and more data became available. However, collection remained a challenge due to a lack of infrastructure for data exchange or due to incompatibilities between systems. Reports on the data gathered sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making continued to rely on intuition.
In modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have set up repositories to store this data. Improved ETL and even recently Enterprise Application Integration tools have increased the speedy collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sieving through large amounts of data, extracting useful information and turning that information into actionable knowledge.
In 1989 Howard Dresner, a research analyst at Gartner (until 2005, now Chief Strategy Officer at Hyperion Solutions Corporation), popularized "Business Intelligence" as an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems. BPM is built on a foundation of BI, but marries it to the planning and control cycle of the enterprise - with enterprise planning, consolidation and modeling capabilities. As CSO at Hyperion, Dresner has become a champion for BPM and has suggested that it is subsuming BI.
The term "BPM" is now becoming confused with "Business Process Management", and many are converting to the term "Corporate Performance Management" or "Enterprise Performance Management".

What is BPM?

BPM involves consolidation of data from various sources, querying, and analysis of the data, and putting the results into practice.
BPM enhances processes by creating better feedback loops. Continuous and real-time reviews help to identify and eliminate problems before they grow. BPM's forecasting abilities help the company take corrective action in time to meet earnings projections. Forecasting is characterized by a high degree of predictability which is put into good use to answer what-if scenarios. BPM is useful in risk analysis and predicting outcomes of merger and acquisition scenarios and coming up with a plan to overcome potential problems.
BPM provides key performance indicators (KPIs) that help companies monitor efficiency of projects and employees against operational targets.

Metrics / Key Performance Indicators

For business data analysis to become a useful tool, however, it is essential that an enterprise understand its goals and objectives – essentially, that they know the direction in which they want the enterprise to progress. To help with this analysis key performance indicators (KPIs) are laid down to assess the present state of the business and to prescribe a course of action.
More and more organizations have started to speed up the availability of data. In the past, data only became available after a month or two, which did not help managers react swiftly enough. Recently, banks have tried to make data available at shorter intervals and have reduced delays. For example, for businesses which have higher operational/credit risk loading (for example, credit cards and "wealth management"), A large multi-national bank makes KPI-related data available weekly, and sometimes offers a daily analysis of numbers. This means data usually becomes available within 24 hours, necessitating automation and the use of IT systems.
Most of the time, BPM simply means use of several financial/nonfinancial metrics/key performance indicators to assess the present state of the business and to prescribe a course of action.
Some of the areas from which top management analysis could gain knowledge by using BPM:
Customer-related numbers:
New customers acquired
Status of existing customers
Attrition of customers (including breakup by reason for attrition)
Turnover generated by segments of the Customers - these could be demographic filters.
Outstanding balances held by segments of customers and terms of payment - these could be demographic filters.
Collection of bad debts within customer relationships.
Demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers.
Delinquency analysis of customers behind on payments.
Profitability of customers by demographic segments and segmentation of customers by profitability.
This is more an inclusive list than an exclusive one. The above more or less describes what a bank would do, but could also refer to a telephone company or similar service sector company.
What is important is:
KPI related data which is consistent and correct.
Timely availability of KPI-related data.
Information presented in a format which aids decision making
Ability to discern patterns or trends from organised information
BPM integrates the company's processes with CRM or ERP. Companies become able to gauge customer satisfaction, control customer trends and influence shareholder value.

Application software types

People working in business intelligence have developed tools that ease the work, especially when the intelligence task involves gathering and analyzing large amounts of unstructured data.
Tool categories commonly used for business performance management include:
OLAP — Online Analytical Processing, sometimes simply called "Analytics" (based on dimensional analysis and the so-called "hypercube" or "cube")
Scorecarding, dashboarding and data visualization
Data warehouses
Document warehouses
Text mining
DM — Data mining
BPM — Business performance management
EIS — Executive information systems
DSS — Decision support systems
MIS — Management information systems
SEMS — Strategic Enterprise Management Software

Designing and implementing a business performance management programme
When implementing a BPM programme one might like to pose a number of questions and take a number of resultant decisions, such as:
Goal Alignment queries: The first step is determining what the short and medium term purpose of the programme will be. What strategic goal(s) of the organization will be addressed by the programme? What organizational mission/vision does it relate to? A hypothesis needs to be crafted that details how this initiative will eventually improve results / performance (i.e. a strategy map).
Baseline queries: Current information gathering competency needs to be assessed. Do we have the capability to monitor important sources of information? What data is being collected and how is it being stored? What are the statistical parameters of this data, e.g., how much random variation does it contain? Is this being measured?
Cost and risk queries: The financial consequences of a new BI initiative should be estimated. It is necessary to assess the cost of the present operations and the increase in costs associated with the BPM initiative? What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.
Customer and stakeholder queries: Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers / stakeholders will benefit directly from this initiative? Who will benefit indirectly? What are the quantitative / qualitative benefits? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customer benefits be monitored? What about employees, shareholders, and distribution channel members?
Metrics-related queries: These information requirements must be operationalized into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can be used to track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
Measurement Methodology-related queries: One should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will data be collected? Are there any industry standards for this? Is this the best way to do the measurements? How do we know that?
Results-related queries: The BPM programme should be monitored to ensure that objectives are being met. Adjustments in the programme may be necessary. The programme should be tested for accuracy, reliability, and validity. How can it be demonstrated that the BI initiative, and not something else, contributed to a change in results? How much of the change was probably random?

Decision Suppot Systems

Making decisions concerning complex systems (e.g., the management of organizational operations, industrial processes, or investment portfolios; the command and control of military units; the control of nuclear power plants) often strains our cognitive capabilities. Even though individual interactions among a system's variables may be well understood, predicting how the system will react to an external manipulation such as a policy decision is often difficult. What will be, for example, the effect of introducing the third shift on a factory floor? One might expect that this will increase the plant's output by roughly 50%. Factors such as additional wages, machine weardown, maintenance breaks, raw material usage, supply logistics, and future demand also need to be considered, however, because they will all affect the total financial outcome of this decision. Many variables are involved in complex and often subtle interdependencies, and predicting the total outcome may be daunting.

There is a substantial amount of empirical evidence that human intuitive judgment and decision making can be far from optimal, and it deteriorates even further with complexity and stress. In many situations, the quality of decisions is important; therefore, aiding the deficiencies of human judgment and decision making has been a major focus of science throughout history. Disciplines such as statistics, economics, and operations research developed various methods for making rational choices. More recently, these methods, often enhanced by various techniques originating from information science, cognitive psychology, and artificial intelligence, have been implemented in the form of computer programs, either as stand-alone tools or as integrated computing environments for complex decision making. Such environments are often given the common name of decision support systems (DSSs). The concept of DSS is extremely broad, and its definitions vary, depending on the author's point of view. To avoid exclusion of any of the existing types of DSSs, we define them roughly as interactive computer-based systems that aid users in judgment and choice activities. Another name sometimes used as a synonym for DSS is knowledge-based systems, which refers to their attempt to formalize domain knowledge so that it is amenable to mechanized reasoning.

Decision support systems are gaining an increased popularity in various domains, including business, engineering, the military, and medicine. They are especially valuable in situations in which the amount of available information is prohibitive for the intuition of an unaided human decision maker, and in which precision and optimality are of importance. Decision support systems can aid human cognitive deficiencies by integrating various sources of information, providing intelligent access to relevant knowledge, and aiding the process of structuring decisions. They can also support choice among well-defined alternatives and build on formal approaches, such as the methods of engineering economics, operations research, statistics, and decision theory. They can also employ artificial intelligence methods to heuristically address problems that are intractable by formal techniques. Proper application of decision-making tools increases productivity, efficiency, and effectiveness, and gives many businesses a comparative advantage over their competitors, allowing them to make optimal choices for technological processes and their parameters, planning business operations, logistics, or investments.

Although it is difficult to overestimate the importance of various computer-based tools that are relevant to decision making (e.g., databases, planning software, spreadsheets), this article focuses primarily on the core of a DSS, the part that directly supports modeling decision problems and identifies best alternatives. We briefly discuss the characteristics of decision problems and how decision making can be supported by computer programs. We then cover various components of DSSs and the role that they play in decision support. We also introduce an emergent class of normative systems (i.e., DSSs based on sound theoretical principles), and in particular, decision-analytic DSSs. Finally, we review issues related to user interfaces to DSSs and stress the importance of user interfaces to the ultimate quality of decisions aided by computer programs.