Industry Specific Shift Planning & Challenges
Time evaluation is the integral part of Workforce evaluation. But for time evaluation, system always need a plan shift. In SAP plan shift is either drawn from info type 007 or can be overwritten by roster.
But this roster preparation or shift planning can be one of the toughest challenges. How can you be sure that the right people – are available for the right job, at the right time, and at the right cost?
Shift planning depends on following consideration and that varies for different industry –
Demand based (Planning of shift according to demand) – It is the basic approach followed by most of the industry. Here demand terms can be used for production, manufacturing process, project based element, service etc.
Employee skills & efficiency (Planning of shift according to employee skill and efficiency) – This approach is mainly used in high precision required process industry and service industry.
Cost minimization (Used for minimization of cost) – This planning is kind of dynamic and last minutes of planning to operate a process/service with minimize cost.
Now I shall try to put how Demand based shift planning mostly work for manufacturing production based industry but not a very good choice for service and utility industry.
For a manufacturing industry demand is meant for production of finished goods/sellable material for the organization. Here demand mostly occur in constant fashion for a particular period (like for a particular period of time in a year, they have constant production rate) and demand curve is more or less linear. For this case, alignment of human resources or shift planning is quite easy.
Now we have a look on demand and availability curve for steady demand.
Demand Curve – Drawn for a particular period of time throughout the day. X1, (X1+t), X2 are time stamp on curve. Here
X1 ≈ X1+t, as t -> 0
Availability Curve – S1, S2, S3 are different shift.
Here Demand and HR resources (availability) have direct proportional relation. Suppose multiple for this direct relationship is ‘N’. So in ideal scenario shift planning is fully optimized, if-
(Area under Demand Curve) ≈ N * (Area under Availability Curve),
For any particular time span t ->0. Means –
∫ F (Demand) ≈ N * ∫ F (Availability) Where t ->0
For above curves, N is almost equals to 1.
Now for Service industrydemand is meant for service order/tickets/SLA for a particular period. Here demand varies throughout the day. So pattern of Demand Curve is not liner. So planning of shift should be dynamic and tough to attain.
Here I have merged the Demand and Availability Curve (liner) and let see the problems.
So, lower utilization level from time span 0 to X1 can be mathematically derived,
D1 = N * ∫ F (Availability) – ∫ F (Demand)
Similarly lower service level for a short time span t,
D2 = ∫ F (Demand) – N * ∫ F (Availability)
This shift schedule does not consider the real demand anticipated, so there are periods of lower utilization alongside lower service. This has numerous negative implications on the organization from different perspectives.
Customer perspectives –
– Dissatisfaction of customer for missing or higher response time of service
– missing new and repeat business
Organization perspectives –
– Slower response time for service
– Missing of SLAs – result in financial penalties
– Increase in overtime and other shift dependent allowance
– Last minutes changes increases fuel cost and decrease corporate sustainability for expanded carbon footprint
Employee perspectives –
– Unpredictable shift changes or requesting existing shift employee for over time
– Employee dissatisfaction because employees appreciate predictable and reliable shift patterns as well as early visibility of future shifts
– Speaking negatively about their employers even in front of customer
– Productivity and work quality decrease as a result of their attitudes
– Eventually high attrition rate
– Increase in recruitment cost
To get optimized shift planning means D1 ≈ D2 ≈ 0, need variability in Shift start time and shift duration. This can presented using Riemann integral curve (http://en.wikipedia.org/wiki/Riemann_integral) which will reduce the area D1 and D2 both.
Here Lower service level is completely reduced and lower utilization level also reduced in an optimized way. To achieve this kind of shift planning, we need,
– past data for demand forecasting and better analysis
– variability in Shift start time and shift duration
– Cost minimization method for shift
– Part time workforce with different pay structure
– Sub contracting