Management of Change (MOC) Effective Use of Reminders
The presence of an “Approvals” state in the Management of Change (MOC) business process may mislead someone to believe that approvals/signatures/sign-offs only occur during this state. That is incorrect—signatures/sign-offs may occur at any point, as indicated by the icons in the diagram. The Approvals state is highlighted, since this is the approval to “go build”, or actually implement the change. Prior approvals dealt with aspects of assessing feasibility. Subsequent approvals validate that the change was performed correctly. Also the Approvals state typically involves multiple approvals, in order to cover the technical specialties demanded the nature of the specific MOC.
Figure 1. Full, permanent, normal MOC lifecycle, showing an Approval state plus supplementary approvals.
In an electronic MOC system, approvals (whether during the Approvals state, or otherwise) are normally requested by an email notification. The approver is supposed to accept or reject the item within some timeframe, often a few days. The approver’s activity, or more correctly, the approver’s lack of activity is monitored by the eMOC system. Once the approval timeframe is exceeded, without an approval, the eMOC system issues a reminder to nudge the approver into completing the approval task.
The use of reminders is common in electronic systems, including purpose-built eMOC applications, as well as ECM systems upon which eMOC applications are often built.
There are several questions to ask about reminders: do they actually work? How well? Under what circumstances?
MOC Reminders Case Study (Part 1)
Suppose an eMOC system tracks approvals. For each approval, the system:
- assigns a unique identifier
- captures the name of the approver
- captures the timestamp when the approval is requested
- captures the timestamp when the approval is granted
The difference between the “requested” and “granted” timestamps is termed the approval duration. Suppose we consider a sample of 100 approvals, of which the first few are shown in Table 1. Note that, in calculating the duration, each day is deemed to have 8 working hours.
|MOC Number||Approval Start||Approval End||Duration [hr]|
|MOC-09-1001||1/1/09 10:41 AM||1/3/09 8:24 AM||13.70|
|MOC-09-1002||1/1/09 1:27 PM||1/5/09 8:20 AM||26.88|
|MOC-09-1003||1/1/09 3:42 PM||1/7/09 9:14 AM||41.54|
|MOC-09-1004||1/2/09 9:51 AM||1/6/09 10:00 AM||32.15|
|MOC-09-1005||1/2/09 11:10 AM||1/2/09 11:10 AM||24.89|
|MOC-09-1006||1/2/09 11:13 AM||1/4/09 12:13 PM||16.99|
|MOC-09-1007||1/2/09 12:28 PM||1/4/09 11:13 AM||14.76|
|MOC-09-1008||1/2/09 12:47 PM||1/6/09 11:04 AM||30.29|
|MOC-09-1009||1/2/09 1:32 PM||1/4/09 3:05 PM||17.54|
|MOC-09-1010||1/2/09 1:52 PM||1/5/09 11:48 AM||21.93|
|MOC-09-1100||1/20/09 3:53 PM||1/23/09 2:03 PM||22.17|
Table 1. Time for a single approval, without intervention.
A useful perspective can be gleaned if the dataset is sorted. The first four columns of Table 2 represent the same data as in Table 1, except that the data is sorted by duration.
Hoff, R., Quantifying the Effectiveness of Interventions in Workflows, submitted for publication in ASME Journal of Computing & Information Science, Dec. 15, 2008.
If we are going to look at the impact of reminders, we’ll need to represent the data in Table 2 using appropriate statistics. From a practical perspective, that’s where things get difficult, since:
- this leads to debates about which probability distribution is appropriate for the data,
- even when there is agreement on which distribution is appropriate, there’s still the problem of fitting a set of data to the distribution; except for the normal distribution, this can be time-consuming and/or complex.