3. Operations management

Operations Management

Management of the transformation process that transforms inputs such as raw material and labor into outputs in the form of finished goods and services.

Is transformation processes only for goods?

No for goods and services

Goal of operations management

The essence of the operations function is to add value during the transformation process: value-added is the term used to describe the difference between the cost of inputs and the revenue that is created by outputs.

• For most companies, profitability is a good measure for this value creation: profit = revneue - costs

• Other ‘value’ measures are of course also relevant: quality, responsiveness, sustainability, resilience

Hierarchy of objectives & measures

What are the three fundamental performance measures?

• Throughput (TH): Number of good (quality) products processed per unit time, e.g. 8 bottles per second

• Work in process (WIP): The inventory (number of products) in a line (machines and buffers), not including raw materials and finished goods inventory, e.g. 25,000 bottles

• Cycle time (CT): time between the release of a product to the production line and its completion, e.g. 52 minutes

What is the correct relationship between average TH, CT and WIP?

Over the long-term, average work in process (WIP), throughput (TH), and cycle time (CT) for any stable process are related according to:

WIP = TH x CT

Known as Little’s law

How is the average computed for Little’s law?

• TH = often: data on finished products over time

• WIP = data on buffer/stock levels over time

• CT = Often easiest: Compute TH and WIP using the data, and use Little’s law, WIP = TH x CT, to compute CT.

Capacity

Maximum average rate at which products can flow thorugh a system

What is the capacity of a process/machine determined by?

• r₀: ideal rate of machine i not including detractors

• r_e: effective rate of machine i, including detractors such as failures, operator inefficiencies and stoppage

Bottleneck process

• Constrains the capacity of the system

• Bottleneck of a system: process with the highest utilization

Utilization

Throughput of machine = 8 bottles per second

Utilization

Cycle time and average WIP increase with utilization in a highly nonlinear fashion

Utilization analogy

Capacity principle

Capacity in a steady state, the average output (throughput) of a system is strictly less than the average capacity

• It cannot equal capacity due to variability

Key sources of variability in operations (processing time)

Variability in the processing time due to, e.g.:

• Product variety

• Operator speed

• Quality problems

• Failures

• Setups

Key sources of variability in operations (times between arrivals)

Variability in the times between arrivals of entities to a process due to e.g.:

• Customer decisions

• Transportation delays

• Quality problems

• Upstream processing stops

Queueing

At a single processing station with no limit on the number of entities that can queue up, the waiting time (WT) due to queuing is a function of a variability factor (V), a utilization factor (U) and average processing time (T)

Influence of variability on cycle time and WIP

• Utilization often has a much higher impact on cycle time/WIP than variability

• However, increasing cpacity is costly, therefore high utilizaiton is desirable

• Therefore variability reduction is often key to achieving high performance

Variability

Increasing variablity always degrades the performance of a (production) systme

Variability buffering

We often cannot completely get rid of variability.

Variability will then have to be buffered by some combination of:

1. Inventory (WIP)

2. Capacity

3. Time

The appropriate mix of variability buffers depends on:

• Physical characteristics of the system

• Strategy of the system

Afweging tussen variability of:

• Larger inventory (WIP) level

• Increased cycle times

• Long lead times and/or poor customer service

• Lost throughput

• Underutilized (wasted) capacity