THEORY OF CONSTRAINTSAND CAPACITY PLANNING

Matt ArcheyKevin Drakes

Gurpreet KingraNitin Sharma

Theory of Constraints (TOC)

An overall management philosophy introduced by Dr. Eliyahu M. Goldratt in his

1984 book titled The Goal, Geared to help organizations continually

achieve their goal. TOC seeks to identify the constraint and

restructure the rest of the organization around it

Five Focusing Steps

Assuming the goal of the organization is to "Make money now and in the future," the steps are:

1. Identify the constraint (the resource or policy that prevents the organization from obtaining more of the goal)

2. Decide how to exploit the constraint (make sure the constraint's time is not wasted doing things that it should not do)

3. Subordinate all other processes to above decision (align the whole system or organization to support the decision made above)

4. Elevate the constraint (if required or possible, permanently increase capacity of the constraint; "buy more")

5. If, as a result of these steps, the constraint has moved, return to Step 1. Don't let inertia become the constraint.

Implementing the Theory of Constraints (TOC) in Production

Job Shops  A process where similar machines, or

people who are doing similar operations, are grouped together.

Work moves in a sequence between these specialist areas, sometimes flowing back the way it came to a previous area before continuing on in the process.

Flow Shops

  Machinery/people are sequenced throughout the plant in the

order that most work will require. Work might be unique or a repetition of a standard design. Each job could consist of a single unit or a batch of many

units.Examples Furniture companies that produce for the retail trade are

usually a flow shop. Tool bit manufacturers are another example. Electronics Automotive, however, as the diversity of products decreases

and the batch size increases the flow in parts of these flow shops becomes more and more continuous. Ultimately theses parts may become a dedicated flow shop – one flow or process for one product or product family.

Mass Production

Production of large amounts of standardized products, including and especially on

assembly lines. Various kinds of products:Fluids and particulates handled in bulk

(such as food, fuel, chemicals, and mined minerals)

Discrete solid parts (such as fasteners) Household appliances and automobiles.

Capacity Planning and its Goal

Capacity planning is the process of determining the production capacity needed by an organization to meet changing demands for its products or in other words it is defined as systematic approach that focuses on actively managing constraints that are impeding progress.

The goal of capacity planning is to provide satisfactory service levels to users in a cost-effective manner.

Examples of Capacity Measures

Type of Measures of CapacityOrganization Inputs Outputs

Manufacturer Machine hoursper shift

Number of unitsper shift

Hospital Number of beds Number ofpatients treated

Airline Number of planesor seats

Number ofseat-miles flown

Restaurant Number of seats Customers/timeRetailer Area of store Sales dollarsTheater Number of seats Customers/time

CAPACITY PLANNING Expansionism “Wait-and-See”

Steps for Capacity Planning

1. Estimate future capacity requirements2. Evaluate existing capacity3. Identify alternatives4. Conduct financial analysis5. Assess key qualitative issues6. Select one alternative7. Implement alternative chosen8. Monitor results

Forecasting Methods

Forecast

Formal

Quantitative

Causal

Multiple Regression

Econometric

Box Jenkins

Time Series

Simple Regression

Moving Average

Exponential

Smoothing

Qualitative

Life Cycle Analysis

Delphi Method

Expert Opinion

Consumer Panels

Test Marketing

Informal

MOVING AVERAGES

11 t

Ntiit D

nF

Period Demand Forecast MA(3) Forecast MA(6)January 15February 17March 20April 18 17May 20 18June 21 19July 17 20 19August 20 19 19September 22 19 19October 19 20 20November 22 20 20December 18 21 20

MOVING AVERAGES (CONT’D)

UN

ITS

TIME

EXPONENTIAL SMOOTHING

Period Demand Forecast (α=.2) Forecast (α=.5)January 150 160 160February 170 158 155March 200 160 164April 180 168 180May 200 171 174June 210 177 185July 170 183 193August 200 181 177September 220 184 190October 190 192 202November 220 191 191December 180 197 206

11 1 ttt FDF

EXPONENTIAL SMOOTHING (CONT’D)

UN

ITS

TIME

TECHNIQUE COMPARISON

MOVING AVERAGES EXPONENTIAL SMOOTHING

SEASONALITY

1. Compute sample mean of all data2. Divide each observation by sample mean to find seasonal factors3. Find average of seasonal factors for like seasons4. Multiply sample mean by average seasonal factor to obtain forecast

Year 1 Year 2January 80 100 90 94 0.96 86February 85 75 80 94 0.85 68March 80 90 85 94 0.90 77April 110 90 100 94 1.06 106May 115 131 123 94 1.31 161June 120 110 115 94 1.22 141July 100 110 105 94 1.12 117August 110 90 100 94 1.06 106September 85 95 90 94 0.96 86October 75 85 80 94 0.85 68November 85 75 80 94 0.85 68December 80 80 80 94 0.85 68

AverageDemand

MonthlyDemand

SeasonalAverage

MonthSales Demand

Forecast

OTHER CONSIDERATIONS

Loss of sale penalties Public image Profit

Holding Costs Storage Set-up costs

Transportation Limitations

Unable to keep up with demand Insufficient capacity among raw material suppliers Unable to transport materials to required locations Often under-produced by factor of 10, or

overproduced by factor of 2 Implemented quick response corrections

Asked customers to place orders in advance Implement air freight delivery systems

Turned to forecasting to meet future demands

Team of employees research market Reduced stock-out and markdown costs from 10.2%

to 1.8%

Capacity Planning for Services

Large fluctuations in demand Inventory often not an option Problem often is to match staff

availability with customer demand May attempt to shift demand to off-peak

periods Can measure capacity in terms of inputs

Example Using Decision Trees: A restaurant owner has determined that she needs to expand her facility. The alternatives are to

expand large now and risk smaller demand, or expand on a smaller scale now knowing that she might need to expand again in three

years. Which alternative would be most attractive?

The likelihood of demand being high is .70 The likelihood of demand being low is .30 Large expansion yields profits of $300K(high dem.) or $50k(low

dem.) Small expansion yields profits of $80K if demand is low Small expansion followed by high demand and later expansion yield

a profit of $200K at that point. No expansion at that point yields profit of $150K

Evaluating the Decision Tree

At decision point 2, choose to expand to maximize profits ($200,000 > $150,000)

Calculate expected value of small expansion: EVsmall = 0.30($80,000) + 0.70($200,000) = $164,000

Calculate expected value of large expansion: EVlarge = 0.30($50,000) + 0.70($300,000) = $225,000

At decision point 1, compare alternatives & choose the large expansion to maximize the expected profit: $225,000 > $164,000

Choose large expansion despite the fact that there is a 30% chance it’s the worst decision: Take the calculated risk!

What is a Healthcare Surge?

……an excess in demand over capacity in hospitals, long-term care facilities, community care clinics, public health departments, other primary and secondary care providers, resources and/or emergency medical services

Surge Capacity Basics

Staffing: Licensed healthcare professionals and support staff

Medical supplies and equipment: pharmaceuticals, personal protective equipment, portable and fixed decontamination systems, isolation beds, ventilators, masks

Measuring the Gap: 2006 California Healthcare Surge Capacity Survey

Standardized definitions for crisis care: Nurse-to-patient staffing ratio of 1:5 for Critical

Care Beds and 1:20 for Other Medical-Surgical Beds

  Assume self-sustainment within the facility for a minimum of 72 hours without re-supply of equipment, supplies or staff

Assume 30% of staff will not report to work

Measuring the Gap (cont)

Response: 324 hospital: 80 percent of California’s 73,000

operating licensed acute care hospital beds California’s local health departments 172 community clinics

Identified 19,963 beds California hospitals said they can surge Bed shortages:

Los Angeles area Pediatric beds

Greater Gaps in Meeting Needs of a Catastrophic Event

Using CDC’s Flu Surge 2.0 computer modeling program for pandemic influenza:

California needs 58,723 surge beds for a moderate pandemic influenza

38,760 beds or 194% above the 19,963 surge beds California hospitals said they can surge.

Analysis of 2006 Survey Results

California has sufficient surge capacity for a Moderate event: regional earthquake, fire, flood, or bioterrorist attack.

California’s surge capacity is insufficient for a Catastrophic event: statewide impact, e.g., Katrina-like event or pandemic influenza.

2006 Healthcare Surge Initiative

The State of California spent $172 million to improve its medical surge

capacityItem Need Bought Cost

Masks (N95 Respirators) 100 million 50.9 million $19.9 million

Ventilators 24,000 2,400 $30.6 million

Antiviral medicines -- enough to treat 25 percent of California's population 8 million courses

3.7 million courses90% Tamiflu,10% Relenza $54.6 million

Mobile Field Hospitals 3 - 200 bed $18.3 million

21,000 extra beds with supplies for alternate care sites 42,000 21,000 $33.4 million

Developing new hospital guidelines and standards $5 million

Types of constraints in services

Broad categories of constraints:1. Market constraints: defined by demographic and

socioeconomic factors. For example, a retired community is highly unlike to use products involving advanced computer technology.

2. Capacity constraints: created by inadequate labor and equipments.

3. Logistical constraints: do not play major role but related to movement of supporting material, such as papers.

4. Managerial constraints: are the strategies and policies that adversely affect system performance.

5. Behavioral constraints: involve employee action aimed at pleasing management that may adversely affect the customer.

TOC in surgery center Four stations for patient processing:

a) Administrative intakeb) The blood labc) X-ray (constrained department)d) Administrative discharge

Based on past experience a buffer of 3 patients is allowed in the X-ray waiting room, in order to keep X-ray doing at all time.

If buffer is full, new patient will not be admitted to administrative intake.

Surgery centre find that patients are less disruptive in administrative intake than in the X-ray waiting room.

Benefits for Surgery centre Surgery centre used this experience to make

appointment for X-ray and blood work test. Patient satisfaction: any delay in patient

testing and treatment become personally intolerable. By eliminating these delay, the patient can be discharged sooner and the patient's overall satisfaction is increased.

Throughput vs. Cost Control: system generate high throughput by exercising effective cost control.

TOC in Banks Banking industry in the US has changed dramatically

in recent years. Banks operate in local and regional level.

Banks must develop formal technology acquisition strategies that focus on process improvement that enable them to better satisfy customer needs.

Competitors that have world class capabilities can readily and quickly duplicate any product offering.

Banks compete for customers using such dimensions:

a) Cost- to effectively compete on price, banks have to offer low price by cutting cost. Banks require to offer customers attractive return on deposits.

b) Flexibility- involves the ability of bank’s systems to introduce new products in a cost effective manner.

c) Time- how quickly new products can be delivered. d) Quality- involves aspects related to process that deliver the

product and post delivery service( relationship banking)

Continue… In the banking business, the three measures of TOC

are exercised as follow:a) Throughput: is the rate at which banking system

generates revenue for service provided in a way consistent with goal. Bank generate money by offering a variety of service such as wire transfers, foreign exchanges, and cashier’s check.

b) Operating expenses: all the money the bank spends in the process of generating throughput. These expenses include all direct and indirect expenses except for the cost of obtaining money in the market.

c) Inventory investment: amount of money spent by a bank to raise capital necessary to generate throughput. It consists principle amount and interest.

Benefits of TOC Throughput can be enhanced by increasing

the number of borrowers, investment projects, and both the number and amount of transactions.

Operating expenses can be reduced by making operations more efficient (e.g., through investment in technology).

Inventory investment can be reduced by reducing the cost of borrowing. Banks offers low interest on short term and small deposits, it is beneficial for bank to attract small term depositors which increases the number of transactions.

TOC in Restaurant

Burger King produces 34227 sandwiches per 172 hours. Average production is 198 sandwiches per hour.

Burger king experience higher than normal volume on Friday and at 12:00 PM

Burger King need to produce 256 sandwiches during peak hours.

Constraints: a. Assembly step can produce 200 sandwiches per hour.b. Upstream processes produces more than 200

sandwiches.c. Maximum through put is 200 sandwiches per hour.

How they managed constraints?

1. Eliminate defects at the constraint: means waste is eliminated.

2. Improve quality steps in front of constraints: made sure parts entering the assembly steps are free of defects.

3. Support the constraints: added labor to the constraint

4. Appropriately used buffer: reduced variation in front of the constraint.

Key assumption

Organizations can be measured and controlled by variations on three measures: throughput

Throughput is money (or goal units) generated through sales.

operating expense Operating expense is money that goes into the

system to ensure its operation on an ongoing basis. Inventory

Inventory is money the system invests in order to sell its goods and services.

REFERENCES Nahmias, Steven. Production and

Operations Analysis, 6th edition. Lyman, B. Planning for Surge Capacity in

Health Care Services. Department of Public Health.

“Managing Constraints Under Peak Volumes”. Shmula.

“Theory of Constraints Production.” <http://www.dbrmfg.co.nz/Productioin.htm>

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