Planning without Productivity Data

 

The planning cell of any project has to go through several stages of Preliminary Planning, Phasing, Zoning, Strategy for Procurement of Material and vendors, Logistics Planning, Strategy for deployment of Plants & Machinery & several such considerations.

 After all these initial stages, next comes the Decomposition of the Scope of Works i.e. the Work Breakdown Structure (WBS) and Activities; Sequencing of works, Recourse Allocation and duration estimate for the activities.

 The allocation of resources and duration estimate is the crux of the matter in scheduling and this depends mostly – rather completely on the productivity of resources for the various items of works.  Once the schedule is ready (off course with some iterations, adjustments, smoothening, levelling etc.) it has to be updated with actuals and most important item thereafter is the ‘controlling’ of schedule. Here again it mostly depends on productivity of resources.

 Basically, to get a correct schedule you must know productivity of resources for each and every item of work and you must also know the quantity of work to be executed for each of the activity. It’s a rare chance in India to come across a schedule in which you find both? It’s also rare in India to find a resource loaded schedule?

 During planning the productivity figure is assumed and while updating schedule the planning cell should must calculate the ‘Actual Productivity’. The performance of the resources and thereby the schedule is basically the ratio of Actual Productivity to assumed Productivity provided the resources are deployed as per the plan. If the resources deployment is altered then not only the time but the cost is also affected. This is also rare to find a planning cell doing such an exercise.

 What’s the implication?

 Schedules are erroneous. Monitoring is limited. Control Measures are tentative & irrational.

 This is further taking us to a vicious circle:

Productivity data is assumed,

Timeline decided based on this data

Productivity not ratified during monitoring phase,

Control measures falling flat as there is no basis of actual productivity

Schedule failed – Blame is on the timeline considered

Timeline was considered on the Productivity Assumed

Blame Productivity Data – Pad it up further

We end up with wrong productivity data.

The gross mistake is that we are not checking and correcting data but just padding up the estimates.

 Serious requirement to change this attitude and start collecting details to make Standard Productivity Data for Scheduling.

ADGP                         www.decodingcm.com               https://decodingcm.blogspot.com

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