# OMG Operations Management Spring 1997

## Presentation on theme: "OMG Operations Management Spring 1997"— Presentation transcript:

OMG 402 - Operations Management Spring 1997
OMG Spring 1997 LN 2: Process Analysis OMG Operations Management Spring 1997 CLASS 2: PROCESS ANALYSIS Harry Groenevelt Bring: - course packets - lecture notes - Boeing article - Course Overview - gray cards - nameplates - marker

Agenda Recap Capacity Bottlenecks and Congestion
OMG Spring 1997 LN 2: Process Analysis Agenda Recap Capacity Bottlenecks and Congestion The Multi-Product Case Types of Processes and Process Strategy Conclusion

Recap Process mapping Little’s Law:
OMG Spring 1997 LN 2: Process Analysis Recap Process mapping Little’s Law: (average throughput) = (average WIP)/(average lead time) Observing throughput is not the same as observing lead time (one is flow, the other is time). information flow Data hold add value

Recap: The Toll Booth Process
OMG Spring 1997 LN 2: Process Analysis Recap: The Toll Booth Process 3 booths ‘plaza’ entrance line exit line throughput: # cars across entrance line / min. stock: # cars in plaza + # in booth lead time: time to cross plaza + time in booth (min.)

Recap Possible process flow diagram: toll booths = ‘workstation 2’
OMG Spring 1997 LN 2: Process Analysis Recap Possible process flow diagram: Toll booth Arrivals Traverse plaza toll booths = ‘workstation 2’ plaza = ‘workstation 1’

Recap Will Little’s Law ‘work’ if toll booth service time is variable?
OMG Spring 1997 LN 2: Process Analysis Recap Will Little’s Law ‘work’ if toll booth service time is variable? Will Little’s Law work if arrivals are ‘lumpy’? How can we predict the maximum possible throughput?

OMG Spring 1997 LN 2: Process Analysis Capacity: Definition capacity: the upper limit on the throughput of a process (or of a workstation within the process) For even the simplest systems, capacity estimates can vary with the time horizon type of demand (if there are multiple products) mix of demand (if changeovers take time)

Capacity Example: Toll Booth
OMG Spring 1997 LN 2: Process Analysis Capacity Example: Toll Booth Booths 0.2 min. 0.2 min. to process toll (raw process time), for each of 3 toll booths. 0.5 min. to cross plaza (raw process time). Arrivals 0.5 min. 0.2 min. 0.2 min.

Capacity Example: Toll Booth
OMG Spring 1997 LN 2: Process Analysis Capacity Example: Toll Booth If items are handled one at a time, then: capacity = 1/(raw process time). (This is just Little’s Law, applied to a server who is assumed to be always busy) Capacity of one toll booth = ______ Capacity of three toll booths = _______ Is the capacity of the plaza 1/(0.5 min.)? Why or why not?

Bottlenecks and Congestion: Definitions
OMG Spring 1997 LN 2: Process Analysis Bottlenecks and Congestion: Definitions bottleneck: any workstation with capacity less than or equal to the demands placed on it or - the bottleneck is the limiting constraint on the entire process as demand increases. By definition, the capacity of the bottleneck determines the capacity of the entire process.

Bottlenecks and Congestion: Definitions
OMG Spring 1997 LN 2: Process Analysis Bottlenecks and Congestion: Definitions Utilization of a workstation = throughput / capacity For random systems, congestion builds at the bottleneck as throughput approaches capacity (utilization approaches 1)…. capacity number in system Throughput: Utilization: 0.2 0.4 0.6 0.8 1.0

Bottlenecks and Congestion: A Deterministic (Non-Random) Example
OMG Spring 1997 LN 2: Process Analysis Bottlenecks and Congestion: A Deterministic (Non-Random) Example Production of precision aluminum panels Milling is continuous with capacity 12 ft2/hour Shot-peening is done in batches in a chamber: a batch of 24 ft2 takes 2 hours Loading of batches is essentially instantaneous Assume both processes are deterministic (not random) raw materials buffer milling shot-peening (S-P) - describe shot-peening… Squares are “processes” or “activities”; triangles are storage. This is a rudimentary “bottlneck analysis”; S-P is the bottlneck. Hhow fast can milling go? If S-P at 20, is nothing in the buffer? No - if S-P is a batch process.

Capacity: A Deterministic Example
OMG Spring 1997 LN 2: Process Analysis Capacity: A Deterministic Example What is the capacity of the S-P chamber? What is the capacity of the line? Where is the bottleneck? What do we see in the milling/SP buffer…? raw materials buffer milling shot-peening (S-P)

Capacity: A Deterministic Example
OMG Spring 1997 LN 2: Process Analysis Capacity: A Deterministic Example inventory in buffer (ft2) Find the average inventory in the buffer: Find the avg. time spent waiting in the buffer: What happens if batches are cut in half? Would it help to expand milling capacity (say, to 24 ft2/hour)? 1 2 3 4 hours

Capacity and Bottleneck Insights
OMG Spring 1997 LN 2: Process Analysis Capacity and Bottleneck Insights Insight 1: Bottlenecks determine the overall capacity of a process. Insight 2: In a system with randomness, stock and lead time explode as utilization at the bottleneck approaches 1. Insight 3: Even in a deterministic system, large batches increase stock and extend lead times.

The Multi-Product Case
OMG Spring 1997 LN 2: Process Analysis The Multi-Product Case Things are more complicated when multiple products each have different processing times on the same machine. For such a system, we’ll consider: What is capacity? What is a bottleneck?

Bottlenecks in the Multi-Product Case
OMG Spring 1997 LN 2: Process Analysis Bottlenecks in the Multi-Product Case Consider a retail bank offering two products: Home equity line Activities Resources ‘consumed’ credit check research staff rate assignment research staff line approval underwriter Home Mortgage Activities Resources ‘consumed’ credit check mortgage sales staff appraisal mortgage sales staff package design mortgage sales staff mortgage approval underwriter

Multiple Products: Lines and Mortgages
OMG Spring 1997 LN 2: Process Analysis Multiple Products: Lines and Mortgages Credit research capacity = 30/day credit line: arrival rate =10/day Underwriters For credit line: capacity = 30/day For mortgage: capacity = 20/day Mortgage research capacity = 15/day mortgages: arrival rate = 10/day

Multiple Products: Calculating Utilization
OMG Spring 1997 LN 2: Process Analysis Multiple Products: Calculating Utilization utilization of resource i example: utilization of the underwriters = _______________________

Multiple Products: A Capacity Constraint
OMG Spring 1997 LN 2: Process Analysis Multiple Products: A Capacity Constraint Utilization of underwriters (UW) must be less than 1: 20 Underwriters constraint: lc/50 + lM/20 < 1 when throughput mix is close to the ‘capacity constraint’, the underwriters are a bottleneck. 15 Mortgage Thruput (lm) (jobs/day) 10 5 20 40 60 Credit Line Thruput (lc) (jobs/day)

Multiple Products: Capacity Constraints
OMG Spring 1997 LN 2: Process Analysis Multiple Products: Capacity Constraints mortgage sales 20 underwriters 15 10 Mortgage Thruput (jobs/day) 5 credit research feasible production region 10 20 30 40 50 Credit Line Thruput (jobs/day)

Multiple Products: Capacity Constraints
OMG Spring 1997 LN 2: Process Analysis Multiple Products: Capacity Constraints Increase mortgage throughput from 10 to 15. Where is the bottleneck (the ‘binding capacity constraint’)?

Multiple Products: Capacity Constraints
OMG Spring 1997 LN 2: Process Analysis Multiple Products: Capacity Constraints mortgage sales 20 underwriters 15 10 Mortgage Thruput (jobs/day) 5 credit research feasible production region 10 20 30 40 50 Credit Line Thruput (jobs/day)

Multiple Products and The Value of Capacity
OMG Spring 1997 LN 2: Process Analysis Multiple Products and The Value of Capacity Suppose we keep this arrival rate: throughput of credit lines = 10 jobs/day throughput of mortgages = 15 jobs/day Consider new products: product X uses mortgage sales staff product Y uses credit research staff Accounting measures indicate: X and Y have equal unit cost and profit contribution Are the products equally costly?

Multi-Product Bottleneck Insights
OMG Spring 1997 LN 2: Process Analysis Multi-Product Bottleneck Insights Insight 1: bottlenecks are binding capacity constraints, resources with utilization close to 1 Insight 2: the identity of the bottleneck is determined by product mix as well as resource capacity Insight 3: Time on a bottleneck is an opportunity cost. Time spent on the bottleneck is more expensive than time on an under-utilized resource, regardless of the ‘actual’ cost

Types of Processes and Process Strategy
OMG Spring 1997 LN 2: Process Analysis Types of Processes and Process Strategy Calculation of capacity and control of bottlenecks becomes increasingly difficult as: product variety increases routings through resources become more complex arrival and process variability increase one end of spectrum: large volume mass production other end: small volume customized production.

Types of Processes Line Flow (large volume mass production)
OMG Spring 1997 LN 2: Process Analysis Types of Processes Line Flow (large volume mass production) product A product B Batch Process (more customized, small volume) product A A C product B C B A A product C B

Types of Processes continuous process: chemicals, oil, paper
OMG Spring 1997 LN 2: Process Analysis Types of Processes continuous process: chemicals, oil, paper line flows (‘mass production’): fast food assembly, automobiles, single-use cameras, batch process: auto parts, machine tools, tour guides, bookbinders job shop: auto repair, health clinic, machine shop projects: product development, consulting

Types of Processes where is The Goal’s factory on this graph? project,
OMG Spring 1997 LN 2: Process Analysis Types of Processes project, job shop process focus none batch flow assembly line product focus continuous flow none low volume, high variety one of a kind high volume, low variety standardized where is The Goal’s factory on this graph?

Types of Processes and Process Strategy
OMG Spring 1997 LN 2: Process Analysis Types of Processes and Process Strategy Process-Focused facilities typically follow a Make-to-order strategy: produce to satisfy specific, in-hand orders. Examples: Product-Focused facilities typically follow a Make-to-stock strategy: produce to replenish inventories.

Types of Processes and Process Strategy
OMG Spring 1997 LN 2: Process Analysis Types of Processes and Process Strategy Intermediate strategies: Assemble-to-order or Finish-to-order: use make-to-stock strategy for subassemblies final assembly (or finishing) of these subassemblies initiated by customer orders. Examples:

Types of Processes and Process Strategy
OMG Spring 1997 LN 2: Process Analysis Types of Processes and Process Strategy semblies Subas- Major Products Finished Material Raw Major stocking point Product A: Make to stock FG Product B: Assemble-to-order SA Width indicates the variety of items at that point in the production process Product C: Make to order RM Production

Conclusions The realities of ‘process physics’:
OMG Spring 1997 LN 2: Process Analysis Conclusions The realities of ‘process physics’: there will always be a bottleneck to limit production. in most systems, congestion builds in front of a fully loaded bottleneck. bottlenecks are binding capacity constraints. Complexity of constraints varies with the type of process. Process strategy is shaped by product complexity and strategic priorities. Jonah’s measures most appropriate for Bob Donovan’s levels, and aligned with goals at all levels. In trouble when measures encourage ‘sub-optimization’, an individual maximizing their own measure but hurting others. In general, most measures are inherited. But are they the best?