Question

Write an essay of 1000+ words on the topic ‘Techniques of business statistics are a valuable tool for the enhancement of business operations and success.’  

 

Answer 1

Defining business trend in the Digital Age has been the growth in the volume and the use of quantitative data. Increasingly, decisions once based on management intuition and experience now rely on empirical evidence drawn from statistical data. As the volume of data sets grow larger, the term "big data" has become a buzzword. Statistical evidence can inform business leaders about how their companies perform, the effectiveness of their business operations and information about their customers.

Statistical research in business enables managers to analyze past performance, predict future business practices and lead organizations effectively. Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand.

Descriptive analytics look at what has happened and helps explain why. By using historical data, managers can analyze past successes and failures. This is also called “cause and effect analysis.” Some common applications of descriptive analytics include sales, marketing, finance and operations.

Predictive analytics uses a variety of statistical techniques (such as modeling and data mining) to predict future probabilities and trends based on historical data. This goes beyond reporting what has happened to create best estimates for what will happen. Some common applications of predictive analysis include fraud detection and security, risk assessment, marketing and operations.

Prescriptive analytics is the stage of determining the best course of action in a given business situation. This includes knowing what may happen, why it may happen, and how to navigate it. Constantly updating information changes prescriptive analysis, allowing managers to maintain action plans for their organizations in real-time.

Effective collection and mining of statistical data can yield valuable insight for companies about the likes, dislikes and buying habits of their customers. Online retailer Amazon.com collects and tracks data on what its customers view and buy as they browse the company's website. From this, Amazon developed algorithms to predict what books and other products customers might be interested in purchasing, according to a 2012 article in the "Harvard Business Review."McKinsey reported that statistical data analysis can enable companies to narrowly segment their customer bases, precisely tailoring their services and products to satisfy these consumers and clients, and thus, make more sales.

Companies in many industrial sectors rely on statistics for other purposes, too. McKinsey reported that some companies rely on data and statistics to enhance their abilities to compete with other firms. For other companies, statistics inform their efforts to develop better products and services. Some firms use data from sensors embedded in their products to offer such services as proactive maintenance, according to McKinsey.

The late management guru Peter Drucker once said that what gets measured in business is what gets done. With this in mind, many business leaders rely on key performance indicators, or KPIs, to measure how well their companies operate. The Balanced Scorecard Institute reported that KPIs enable companies to measure results and determine what successful operations look like. Examples of KPIs include quarterly profits, customer satisfaction, and project completion rates, all of which can be quantitatively measured. KPIs require reliable statistical data, which companies then analyze on a regular basis to determine if they are meeting success measures.

Statistics not only help measure business performance, but can also provide a means for boosting it. Management consulting giant McKinsey and Company calls statistical data a frontier for business innovation, reporting that, as companies collect and store more data, they can gain insight into such issues as employee sick days and product inventories, looking for ways to improve performance. Some firms even use data and statistics to experiment with ways to improve management decisions, McKinsey reported.

Today's good decisions are driven by data. In all aspects of our lives, and importantly in the business context, an amazing diversity of data is available for inspection and analytical insight. Business managers and professionals are increasingly required to justify decisions on the basis of data. They need statistical model-based decision support systems.

Statistical skills enable them to intelligently collect, analyze and interpret data relevant to their decision-making. Statistical concepts and statistical thinking enable them to:

• solve problems in a diversity of contexts.
• add substance to decisions.
• reduce guesswork.   

In competitive environment, business managers must design quality into products, and into the processes of making the products. They must facilitate a process of never-ending improvement at all stages of manufacturing and service. This is a strategy that employs statistical methods, particularly statistically designed experiments, and produces processes that provide high yield and products that seldom fail. Moreover, it facilitates development of robust products that are insensitive to changes in the environment and internal component variation. Carefully planned statistical studies remove hindrances to high quality and productivity at every stage of production. This saves time and money. It is well recognized that quality must be engineered into products as early as possible in the design process. One must know how to use carefully planned, cost-effective statistical experiments to improve, optimize and make robust products and processes.

Both Descriptive and Inferential statistical methods find important place in business management. To quote a few of the many applications across functions,

1. A Marketing manager needs to gather and analyze a large amount of data pertaining to market dynamics and target customers. Ideally, marketing strategy depends up on the outcomes of a Market research, which involves statistical methods for collecting and analyzing data, application of sampling techniques and evaluating the effect of various marketing strategies.
2. A production manager would ideally use Statistical Process Control techniques to improve productivity and quality. Knowledge and application of Control Charts, Sampling techniques and Probability Distributions ensures better processes and products. This also leads to the reduction in production cost and higher profits.
3. A HR manager would be interested in identifying the best approach to train employees and evaluate the impact of training. There is a need to measure attrition and understand the underlying factors.
4. For a Finance manager, crunching financial data and using financial techniques is an integral part of day-to-day job. Knowledge of Statistics enhances competency and proficiency of a manager as a researcher and therefore provides an edge.

Business Statistics is a science assisting you to make business decisions under uncertainties based on some numerical and measurable scales. Decision making processes must be based on data, not on personal opinion nor on belief.

Statistical models are currently used in various fields of business. However, the terminology differs from field to field. For example, the fitting of models to data, called calibration, history matching, and data assimilation, are all synonymous with parameter estimation.

As an example of statistical modeling with managerial implications, such as "what-if" analysis, consider regression analysis. Regression analysis is a powerful technique for studying relationship between dependent variables (i.e., output, performance measure) and independent variables (i.e., inputs, factors, decision variables). Summarizing relationships among the variables by the most appropriate equation (i.e., modeling) allows us to predict or identify the most influential factors and study their impacts on the output for any changes in their current values.

Frequently, for example the marketing managers are faced with the question, What Sample Size Do I Need? This is an important and common statistical decision, which should be given due consideration, since an inadequate sample size invariably leads to wasted resources. The sample size determination section provides a practical solution to this risky decision.

Statistics is a tool that enables us to impose order on the disorganized cacophony of the real world of modern society. The business world has grown both in size and competition. Corporate executive must take risk in business, hence the need for business statistics.

Business statistics has grown with the art of constructing charts and tables! It is a science of basing decisions on numerical data in the face of uncertainty.

Business statistics is a scientific approach to decision making under risk. In practicing business statistics, we search for an insight, not the solution. Our search is for the one solution that meets all the business's needs with the lowest level of risk. Business statistics can take a normal business situation, and with the proper data gathering, analysis, and re-search for a solution, turn it into an opportunity.

While business statistics cannot replace the knowledge and experience of the decision maker, it is a valuable tool that the manager can employ to assist in the decision making process in order to reduce the inherent risk, measured by, e.g., the standard deviation.