W11_UDS_Forecasting Method Comparison

  1. Problem Evaluation

Sometime we face problem when we have to predict future condition. This prediction is very important to us in making decision and choosing strategy for company. Forecast methods are usually used to predict future condition.

There are two type of forecast method:

  1. Time series models; What happened in the past will happen again in the future (with consistent error)
  2. Regression Analysis; One variable is affected by other variables


  1. Development of feasible alternatives

This small research will evaluate forecast method focusing in time series models. Time series model are common model in forecasting because user friendly use and useful in determine future condition. There are three Time series models that will compare:

  1. Simple Moving Average
  2. Weighted moving average
  3. Exponential smoothing


  1. Development the outcome for each alternative

This time series model comparison will evaluate using two forecast error measurements: Mean Squared Error (MSE) and Mean Absolute Deviation (MAD). Using MSE and MAD approach we can get difference between actual data and forecast calculation. Comparing the value of MSE and MAD value each model are the good way to know which the best alternative is.

  1. Selection of criteria

The best model is the model that has the lowest error. Error formula is actual data minus forecast result. Based on those criteria, we will choose model that have lowest MSE and MAD. 

  1. Analysis and comparison of the alternative

To simulate this comparison we will use one of the product demand data. Those data consist of 70 periods.

Table 1. Alternatives data

Next step is data calculating forecast prediction using three methods formula (Simple Moving Average, Weighted moving average, and Exponential smoothing).

Equation 1. Simple Moving Average, Weighted moving average, and Exponential smoothing Formula

The Calculation result of three forecasting methods are in table below:

Table 2. Simple Moving Average calculation result

Table 3. Weighted Moving Average calculation result

Table 4. Exponential smoothing calculation result

Table 5. Comparison Result

  1. Alternative selection

As can be seen above there is no dominant model in MSE and MAD calculation result. The lowest result value of MSE is Exponential Smoothing model with alpha 0,5 and the lowest result value of MAD is three period Moving Average model.

Based on analysis above there is no the best model of forecasting, but we can conclude the appropriate model of each variances model (lowest error index in same variance). The appropriate model of each variances are list below :

  1. 3 period moving average
  2. 135 weighted moving average
  3. Exponential smoothing with alpha 0.5
  1. Performance monitoring & Post Evaluation Result

To get the best result of forecasting we have to find most appropriate model of forecasting. We should check the error index first (MSA and MAD) of our historical data. When you find the lowest error index in some forecast model, it should be the most appropriate model of your data. We should do the step above because every data have different characteristic.


  1. Harvard Business Review (2017). How to Choose the Right Forecasting Technique. Retrieved from https://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique
  2. com (2017). JD Edwards EnterpriseOne Applications Forecast Management Implementation Guide. Retrieved from https://docs.oracle.com/cd/E16582_01/doc.91/e15111/und_forecast_levels_methods.htm#EOAFM00177
  3. Data Science Central (2017). Selecting Forecasting Methods in Data Science. Retrieved from https://www.datasciencecentral.com/profiles/blogs/selecting-forecasting-methods-in-data-science

W13_ABM-Preparing Recovery Schedule for Emerald Group

Problem Definition

Emerald Group AACE 2017 is now facing serious issues with cost overruns and schedule delays when compared to original baseline.

Based on week 12 report, the group has recorded the following key progress indicators;

The programme is now critically delayed with only 39.8% complete despite 58.9% of total schedule time lapsed. SPI and BEI and figures further support this at status. This data translates to a 5 week delay on the completion of all deliverables unless action is taken to recover.

Key deliverables which are now considered as critical are summarised in below table;

In addition to the poor SPI and CPI figures, BEI metrics which compare planned and actually completed tasks indicate serious delay to the commencement of cheat sheet and problem solving projects. This delayed commencement is likely to be the result of delayed completion to 2500 word papers as well as time sunk in rectifying weekly reporting.

Based on the above, and the groups assessment of a 5 week programme delay, this represents a difference greater than 10% of the overall duration and CFH has now requested that the works be rescheduled indicate how the balance works are to be completed by the contract date.

In developing a recovery programme, the group can utilise 2 different methods. this blog will look at each method and decide which is most suitable for the recovery schedule.

Feasible Alternatives

There are 2 methods to create a new baseline or recovery schedule when using EVM. These include;

Option 1:Leave ACWP and BCWP to date unchanged. Change BCWS(early) and BCWS(Late) date curves showing the impact of changes against revised dates.

Option 2:Set BCWS (early) and BCWS(late) dates to ACWP date. Reschedule remaining works to original dates or revised dates

Development of the Alternatives

When considering Option 1, the method would generally rely on a specific event or change order to be agreed with CFH. This event(s) would provide the basis for amending the original baseline and may reflect omission or additional scope, acceleration efforts, etc.

Option 2, primarily focuses upon the remaining works from the date of rebaselining and the way in which it will be executed.

At this stage, there has been no agreement to revise completion dates. As such all deliverables must be completed by week 24 (**Jan 2018) implying that BCWS (early & late) curves to target 100% completion by the original dates.

Selection Criteria

The following criteria must be satisfied;

  • Planned progress is realigned with actual progress to date to eliminate current reporting delay
  • Allows for accurate tracking and monitoring of remaining works and costs to compete all deliverables by week 26

Comparison of the alternatives

Option 1 is only really suited to situations where there has been a series of changes in scope or definable events that have caused the BCWS to change i.e. variations

The delay and cost overrun observed within Emerald Team programme is a result of delayed execution of the activities by the group members,  including inefficient use of time and resources such that activities have not been completed within the planned durations and budgets. Initial estimates for time and cost may have also been insufficient leading to overspend and schedule delays.

They are not the result of scope changes.

Option 2 however requires lowering /realignment of the BCWS to meet the actual ACWP allowing for the return of the group’s reporting status to a neutral position.

The future BCWS curves would then be rescheduled by the team members to support stipulated completion dates

Selection of Alternatives

Option 2 appears to satisfy all criteria and will allow for the immediate elimination of perceived delay within the works as well as focusing upon the rescheduling of works to achieve original completion dates. Option 1 is not considered suitable in this instance.

Performance Monitoring

Group Members when preparing their recovery schedules should complete the following tasks

  1. Revise BCWS figures from week 13 onwards such that BCWS (early and late) are equal to ACWP
  2. Retain original completion dates as per CFH and re estimate time and cost budgets (BCWS) from week 13 onwards.
  3. Include both a early and late curve
  4. Assess and consider remaining work scope compared to remaining time and evaluate potential for scope reductions to support achievement of project objectives
  5. Review duration estimates previously used and update where observed to be incorrect
  6. Assess ability to increase working hours and potential to reduce vacation time previously planned.


1. Chapter  9.5 – Performance Monitoring Progress – Guild of project controls compendium and reference (CaR) | Project Controls – planning, scheduling, cost management and forensic analysis (Planning Planet).  Retrieved from http://www.planningplanet.com

2. Humphreys, G.C 2011 Project Management Using Earned Value Humphreys associates, Management Consultants. Second Edition, pp 599-610

3. GAO (December 2015). GAO Schedule Assessment Guide, Best Practices for Project Schedules pages 135-145.
Retrieved from http://www.gao.gov/new.items/d093sp.pdf