Key Performance Indicators

One of the most useful tools in any business is Key Performance Indicators (acronym KPI). To find the effectiveness of any process, some criteria needs to be identified to check how successful the process is. In this article we are going to have a brief look on what is key performance indicators, its benefits, how to setup and how to present it.

Key performance indicator is a business tool used to help businesses to understand to what extent their performance is acting out in reference to their strategic and financial objectives. In a widespread sense, KPI delivers the most central performance data to stakeholders to recognize if the business is on the right track or not. Furthermore, it assists to simplify the complex performance data of a business into a handy number of indicators that will help to make the right decision. The hint behind KPI is summarizing and presenting meaningful technical data using appropriate language that can be understood by ordinary stakeholders. Worthy key performance indicators are clear, obtainable, generate opportunities and initiate actions.

The importance of key performance indicators comes from the ability to show business leaders where are they compared to where they want to be. KPI can help to,

·         Assessment of the current position and how far it is from the desired one.

·         Cutting through existing oceans of data and providing simple and vital piece of information to support decision making.

·         Accurately measuring current performance and continuous learning from it to improve future results.

·         Ensuring compliance with internal and external regulations and requests.

Key performance indicators setup is a simple process, but we need to consider it first as a SMART indicator which means to be a specific, easy to be measured, easy to be achieved, real, and definite a time period. The smart KPI might be either an average of a quantitative data, i.e. average order value per month, and average production quantity per month, or a rate of qualitative data based on a selected criteria, i.e. rate of on-time deliveries, and rate of rejected purchase orders. The process itself starts with defining what is needed to be measured and find realistic, timely, and logical ways to measure it. They result needs to be compared to a preset standard which might be based on old performance or a near reality random value if KPI culture is adopted for the first time.

For more illustration, if we need to setup a KPI for measuring shipping capacity in a distribution center, the KPI used here for example is “number of trucks loaded per day”. This indicator is blind since there is several kinds of trucks which will have different time lapse to load. So we need to make sub KPIs related to the main one for every truck type to reflect the actual performance   

The presentation of the key performance indicators need to be easy to understand. The hint behind that is to choose the appropriate chart type to display, i.e. pie charts for percentages, bar charts for comparison, and line charts for trends. Also, it can just a number screen to display a single value which changes dynamically when new data is added.

Forecasting

One of the critical business tool that have been growing in recent days with growth of the business intelligence through big data science is forecasting. According to a simple rule of statistical analysis is that all data are following the pattern of normal distribution, the need to analyze the old data to predict the short and mid-term future one. In this article we are going to have a brief overview on what is forecasting, the different types and what is the best method according to the data type.

Forecasting can be simply known as the process of predicting future data based on historical data. Businesses apply forecasting methodologies to decide how to distribute their budget lines for projected expenses for any forthcoming period of time. The term forecasting in supply chain usually correlated to the demand planning as a predictive analytics field.  This is typically start with what is the expected demand for the goods and services they offer; then proceeding with the quantities that should be produced to cover the market demand and offset any controllable variance in demand.

The central pointers to the forecasting process are uncertainty and risks; the uncertainty about the future and any unexpected events might incur the risk of supply chain and market sustainability like what happened in some countries during the beginning of COVID-19 epidemic when market was shorted in medical supplies, like face masks and alcohol disinfectant sprays, due to high unpredictable consumption.

The major types of forecasting techniques are

·         Qualitative forecasting which is concerned about limited scope forecasts as they are very much dependent on surveys and opinions of experts.

·         Quantitative forecasting which is concerned about quantitative data like quantities sold or dollar sales. 

The use of quantitative forecasting method is the common in the world of data. There is multiple common quantitative methodologies to perform forecasting such as moving averages, exponential smoothing, and trend projection. The selection of a method depends on several factors like the framework of the forecast, the wanted degree of accuracy, the accessibility of historical data, and the forecast’s time horizon.

The quantitative forecasting methods can be grouped into the moving averages, exponential smoothing, and trend projections. The difference between moving averages and exponential smoothing is that the moving average is giving an equal weight to all observations in the study. While in exponential smoothing, the weight is assigned in a decreasing way over time. The trend projection is used when there is obvious style in the data over a specific time period either linear or exponential.

As a demand planner, the method selection is depending on the behavior of the data. For example, for a monthly sales of a specific product, does it sold every month with high coefficient of relation to the average; then the moving average method seems to be suitable there. The same is for seasonal products, to compare current season with the previous season using the same time frame, i.e. weeks, months, or quarter. On the other hand, the data with low coefficient of relation to the average; we can use trend projection methods such as linear regression, or exponential regression methods.

Cost Benefit Analysis

One of the most important tools for any supply chain or business folks to know is the Cost Benefit Analysis (acronym CBA). This tool will help how to reach a rational decision based on given criteria. In this article we are going to discuss the theory behind this analysis and the term of opportunity cost and how it can help to make a coherent decision.

Cost benefit analysis is a systematical methodology to find the benefits and costs of multiple potential alternatives to make an ending judgment for what is suitable for a business. The benefits are listed for every option in terms of qualifying criteria. Then these criteria are converted to a money value to measure its final cost. The option with highest benefits and lower cost will be the rational one to choose.

The application of Cost benefit analysis is confined in determining if the decision’s benefits are outweigh its cost and to provide assessment basis between expected costs and benefits. CBA is often used by companies to evaluate the attractiveness of a given strategy.

Common cost benefit analysis procedure is consisted mainly of the following steps,

·         Goals Definition: To define objectives of the analysis and the desired outcome of the comparison.

·         Alternatives Listing: Citation of available alternatives that will lead to the predefined goals.

·         Measurements selection: choosing of measurement tools that will assist in comparison of alternatives. These gears will assist in define the possible outcomes of each alternative.

·         Outcomes prediction: Identify expected benefits and costs for each one of the alternatives. Benefits and costs can be either a qualitative or quantitative.

·         Monetizing costs and benefits: after outcomes detection, it will be shown in monetary terms to make a standard base for comparison; transfer qualitative outcomes to quantitative outcomes.

·         Sensitivity analysis: this will measure the aptitude for every alternative to be changed due some factors and how this will affect its predicted benefits and costs.

·         Adoption: final selection of the best alternative that achieve the highest benefits and lowest cost.

After outlining the costs and benefits for all alternatives, Benefits Cost ratio needs to be calculated for every alternative. That is what we call cost benefit analysis formula. This ratio for a single alternative is computed as the sum of present value of future benefits divided by the sum of the future costs. The target will be choosing alternative that will give the highest ratio.

One of the factors needs to be considered in this analysis is the Opportunity Cost which can be defined as “the cost incurred from not choosing the benefits of the next best option”. In other words, it is other benefits that could have been recognized when choosing one alternative over another.

Cost benefit analysis is appropriate for short and medium size capital expenditure alternatives in short and medium time intervals. When it comes to large size of capital expenditure and long term projects, CBA might be unsuccessful to consider other financial concerns such as interest rates, inflation, and the present value of money.