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Forecasting and Supply Chain Post Pandemic in Operations

Suresh

By Dr Suresh Vidyasagar Menon-Chief Consultant & Business Advisory for Six Sigma, Operations, Strategic Management and Information Security

  1. The forecast should be timely, usually a certain amount of time is needed to respond to the information contained in a forecast. For example, capacity cannot be expanded overnight, nor can inventory levels be changed immediately. Hence the forecasting horizon must cover the time necessary to implement possible changes.
  2. The forecast should be accurate, and the degree of accuracy should be stated. This will enable users to plan for possible errors and will provide a basis for comparing alternative forecasts.
  3. The forecast should be reliable; it should work consistently. A technique that sometimes provides a good forecast and sometimes poor one will leave users with the uneasy feeling that they may get burned every time a new forecast is issued.
  4. The forecast should be expressed in meaningful units. Financial planners need to know how may dollars will be needed, production planners need to know how may units will be needed.
  5. The forecast should be in writing.
  6. The forecast technique should be simple and easy to understand.
  7. The forecast should be cost effective

Forecasting and the Supply Chain

Accurate forecasts are very important for the supply chain. Inaccurate forecasts can lead to shortages and excesses through ought the supply chain. Both shortages and excesses in the supply chain have a negative impact not only on customer service but also on profits. Furthermore, inaccurate forecasts can result in temporary increase and decrease in orders to the supply chain.

Steps in the forecasting process post pandemic

  1. Determine the purpose of the forecast if it is post corona, we have to do a vulnerability assessment, business continuity planning procedures to complement the forecast we are going to make. Further our forecasts should take into account the comeback possibilities of corona or any other natural disaster. Disaster management and vulnerability assessment go hand in hand. An organization can take help of experts in disaster management, vulnerability assessment and risk management. Ideally consultants are available in these specialized areas.
  2. Establish a time horizon the forecast must indicate a time interval, keeping in mind that accuracy decreases as time increases.
  3. Obtain, clean and analyze appropriate data.
  4. Selecting a forecasting technique
  5. Make the forecast
  6. Monitor the forecast errors.

Forecast error is the difference between the value that occurs and the value that was predicted for a given time period. Hence error = actual-forecast.

ERP systems can also forecast with some precision but then it more reliant on the data input in the ERP system thus it is not very much feasible

Forecasting based on Time-Series Data

A time series is a time ordered sequence of observations taken at regular intervals (e.g. Hourly, daily, weekly, monthly, quarterly, annually).

Analysis of time series data requires the analyst to identify the underlying behavior of the series.

  1. Trend referrers to a long term upward or downward movement in the data. Population shifts, changing incomes and cultural changes often account for such movements
  2. Cycles are wavelike variations for more than one year’s duration these are often to variety of economic, political and even agricultural conditions.

Linear regression forecasting can also be used in short term; it is a statistical equation of a straight line and there are software’s which can be used to draw a linear regression graph to help you in forecasting.

Finally, Time series graphs are available in Statistical Software Minitab; if we provide the data to this software, it will draw the graph so also Microsoft power BI a powerful data visualization and business intelligence software which can show your sales spread geographically as well as what are the future trends in sales.

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