MOS 3330 Course Overview
1. Introduction
2. Supply Chain Management
3. Inventory Management
4. Forecasting
5. Aggregate Planning
6. Material Requirements Planning
7. Enterprise Resource Planning
8. Process and Product Design
9. Just-In-Time Systems
10. Quality
11. Statistical Process Control
12. Total Quality Management
Test 1
Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
MOS 3330: OM Introduction
Test 2
Final
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INTRODUCTION TO OPERATIONS MANAGEMENT
Learning Objectives:
1. What is Operations Management (OM)
2. Why study OM
3. Goods vs. services
4. Trends in OM
MOS 3330: OM Introduction
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1. What is Operations Management (OM)?
Organizational view
Manufacturing or Service Organization
Marketing
Sales
Advertising
Sales promotion
Market research
Operations
Facilities
Production
Inventory control
Quality assurance
Purchasing
Engineering
Finance
Credits
Disbursements
Funds management
Capital requirements
Support functions:
Human Resources, Information Technology, Administration
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Process view
Operations: Transformation of inputs into finished goods
and services
Inputs
Raw materials
Supplies
Employee skills
Facilities
Capital
Equipment
Transformation
Physical (ex. Manufacturing)
Locational (ex. Deliveries)
Exchange (ex. Retail)
Physiological (ex. Healthcare)
Psychological (ex. Entertainment)
Informational (ex. Education)
Outputs
Finished goods
Services
Operations Management: Planning, design, coordination
and execution of operations-related activities
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Strategic view
Market Analysis
Environmental Scan &
Internal Audit
Mission & Vision
Corporate Strategy
Future direction
Competitive priorities
Operations Strategy
Cost
Quality
Time
Flexibility
Low
High
Consistent
Fast
On-time
Customization
Volume
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2. Why Study OM?
At the core of all organizations
Interrelated with other areas’ activities
Contribution to the overall corporate strategy
25% of all Canadian jobs are in goods-producing sector
Operations managers manage:
 Production/service
 Technology
 People
 Projects
 Strategy
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3. Goods vs. Services
Service is:
 Usually intangible and “consumed” right after serving
 High customer interaction
 Often customized and cannot be stored for future use
Service, compared to producing goods, is more:
 Labour intensive, knowledge based, difficult to automate,
difficult to measure service quality and productivity
 Total productivity = Output / Input
 In many cases, distinction is not clear-cut
 For example, fast food, computer
 Service as a distinguishing factor for a manufacturing firm
MOS 3330: OM Introduction
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MOS 3330: OM Introduction
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SUPPLY CHAIN MANAGEMENT
Learning Objectives:
1. Supply chains
2. Supply chain related subjects
3. Bullwhip effect
4. Supply chain strategies
5. Supplier relationships
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1. Introduction
 Supply chain: A network of facilities, functions, and
activities involved in producing and delivering
products or services, from suppliers to customers
 Supply chain management: (i) coordination of the
movement of goods through the supply chain, and
(ii) control of information such as sales data, sales
forecasts, promotions, and inventory levels
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1.1 Traditional View of Supply Chains
Manufacturers
Suppliers
Wholesalers
Distributors
Information Flow
Retailers
Goods Flow
Customers
Revenue Flow
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1.2 Network View of Supply Chains
Active
Ingredients
Pharmaceutical
products
Tablets
Capsules
Chemicals
Packaging
Formulation
Printed labels
& materials
Bottles, caps
Tier 1 suppliers
(e.g., for Packaging)
= direct suppliers
Primary
Packaging
Secondary
Packaging
Blank
labels
Tier 2 suppliers
(e.g., for Packaging)
= suppliers who supply
to tier 1 suppliers
Label
Manufacturer
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Distributor
Pharma.
products
Retail
Pharmacy
Pharma.
products
Customer
12
1.3 e-Supply Chains
Logistics
Data
Supply
Data
Customers
Customer
Data
Production
Data
Logistics
Providers
Suppliers
Contract
Manufacturers
Information Flow
Goods Flow
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2. Supply Chain Subjects
 Logistics
 Purchasing
 Sourcing
 Bullwhip effect
 Sustainable
 Other subjects (mention only)
 Information management: accuracy, timing
 Globalization: foreign business practices and regulations
 e-Commerce
 Radio Frequency Identification (RFID)
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2.1 Logistics
 Shipping and delivery: transportation cost, mode, lead time,
traffic management
 Highway  flexibility
 Rail or water  high volume at low cost
 Pipeline  high volume at low cost
 Air  fast
 Distribution management
 Facility location (where/how many facilities?): proximity to
customers, business climate, quality of labour, infrastructure
 Positioning of inventory (where/how many to hold?)
 Third-party logistics (3PL)
 Outsourcing freight consolidation and distribution activities
 International business
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2.2 Purchasing
 Ordering and receiving materials: materials of correct
quality, in correct quantity, at good price, and on time
 Purchasing cycle:
1. Purchasing receives the requisition
2. Purchasing selects a supplier
3. Purchasing places the order with the vendor
4. Monitoring orders
5. Receiving orders
 Interface with accounting, engineering, & legal teams
 Develop a supplier base: select, evaluate & maintain
 Sourcing
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2.3 Sourcing
 Selecting suppliers
 Price, quality, services, location, inventory policy, flexibility
 Supplier selection strategies
Single Sourcing
Multiple Suppliers
- Quantity discount
- More responsive
- Frequent deliveries
- High quality
- Better relations
- Support just-in-time
- Competitive pricing
- Spreading risks
- Low dependence
- Volume flexibility
- Easier to test new supplier
- Not enough capacity
- Government regulations
- Order is too small
- High ordering cost
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3. Bullwhip Effect
Increasing distortion of information along the
supply chain
 Customer demand gets distorted as information reaches
suppliers
Contributing factors
 Batch ordering, high ordering cost, free return policy
 Promotions, pricing that leads to forward buy
 No visibility of end demand, inaccurate forecast
 Long lead time, localized reaction
 Mistrust, conflict of interest
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Bullwhip Effect
Source: Johnson & Pike (1999)
Grocery store order size
Distribution centre order size
Central warehouse order size
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3.1 Implications of the Bullwhip Effect
Loss of volume breaks
Higher freight costs
Higher raw material inventory
Raw material
expediting
Lower
revenues
Supplier
schedule
variability
Manufacturer
Schedule
changes
Productivity loss
Higher WIP Inventory
Customer
switching
Increased
safety stock
Retailer
Distributor
Poor
demand
planning
Higher finished goods
inventory &
warehousing costs
Stockouts
and late
orders
Poor service &
longer lead time
Small
Shipments &
Shipping b/w
Warehouses
Higher shipping
costs
Order of consequences
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3.2 Solutions to the Bullwhip Effect
Traditional “solution” = increase inventory
Modern approach
Examine the contributing factors
Improve the supply chain metrics
Understand the supply chain relationships:
interdependent, systemic and goal sharing
 Improve coordination, communication & collaboration
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4. Supply Chain Strategies
Physical proximity
 75% of Honda’s suppliers are located within 150 miles of its
Marysville plant in Ohio
Plant-direct shipping: from the manufacturer to retailer
 Pampers to Wal-Mart, Dell Computer
Cross-docking: goods move from one loading dock to
another without being stored as an inventory
 Wal-Mart distribution centres
Receiving
Put away
Replenish
Picking
Staging
Shipping
STORAGE
CROSS DOCK
DIRECT SHIP
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 Postponement: customize products as late as possible
 Distribution centres performing assembly, packaging, etc.
 Vendor-managed inventory: let the vendor manage
ordering, warehousing, shipping, and placing products
 IKEA, music CDs at department stores
 Virtual integration: allow suppliers to access critical
information in real time
 Retail Link at Wal-Mart: Point-of-Sales (POS) data shared with its
suppliers
 Vertical integration: control the most or all of a supply
chain
 McDonald’s in Russia
 CPFR
 See the next slide
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5. Supplier Relationships: CPFR
 Collaborative Planning, Forecasting, and Replenishment
 Process where trading partners share and discuss planning,
forecasting, and replenishment information in order to
work in partnership from a single forecast
 Share forecast through the CPFR process
 Share replenishment data through the supplier schedule
 Guidelines for information sharing
 Operating agreement
 Voluntary Inter-Industry Commerce Standards (VICS)
 Builds collaborative and strategic relationships
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Business Relationships
Open market
environment
- Multiple suppliers
- Short term decisions
Cooperative
- Fewer suppliers
- Longer term contracts
- Move away from price-based purchase criteria
- Win-win relationship
Collaborative
- Open exchange of information
- Joint planning
- Technology sharing - Strategic relationship
- Price based decisions
- Adversarial
Future: mass collaboration at the industry level?
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6. Sustainability in Supply
• Sustainability: Reduced use of resources, and harm to the
environment, so that the future is not threatened.
• Supply chain sustainability: refers to companies’ efforts to
consider the environmental and human impact of their
products’ journey through the supply chain, from raw
materials sourcing to production, storage, delivery and every
transportation link in between.
• Sustainable Supply Chain Management: Supply chain
management focuses on the speed, cost and reliability of
operations, while sustainable supply chain management
adds the goals of upholding environmental and societal
values. This means addressing global issues such as climate
change, water security, deforestation, human rights, fair
labor practices and corruption.
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Introduction
26
Key words in sustainability
Biodegradable: Something capable of decaying into its basic components.
Carbon emissions: Pollution released into the atmosphere from carbon
dioxide and carbon monoxide; often produced by motor vehicles.
Carbon footprint: The amount of carbon dioxide produced by your lifestyle.
Climate change: Significant change in climate including temperature,
precipitation, or wind that lasts for an extended period.
Compostable: Decomposition of organic material within a specific time
frame.
Eco-friendly: Environmentally minded actions that cause minimal harm to
the earth.
Fair trade: Principles of fair treatment, wages, and safe working conditions
for workers.
Greenwashing: Misrepresenting something as being “green” when it’s not
environmentally sound.
Recyclable: Items that can either be reused or broken down and converted
into new products.
Renewable energy: Electricity from replenishable sources such as
geothermal, hydropower, solar, and wind.
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Examples of sustainability in supply chains
• A road builder moved away from buying asphalt based only on
the price – result was a cut in shipping distance and related
carbon emissions by 40%.
• A fast-food company redesigned its packaging – eliminated
literal tons of waste.
• A grocery store in Canada launched using refillable packaging
for products such as ice cream, sauces, snacks, pet food, and
toothpaste – reducing plastic waste. And when delivering
products using route optimization to help ensure efficient
customer deliveries.
• A car producer with a plant the size of about 60 football fields
assembling some 375,000 cars each year and employing 5,500
people transformed its automotive assembly plant into a zerolandfill factory - lowering waste generation by 60% since 2000.
MOS 3330: Supply Chain Management
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Introduction
28
6. Benefits from Supply Chain Improvement
 Lower inventory
 Shorter cycle time
 Lower total cost
 Higher service levels
 Stronger relationship with supply chain members
MOS 3330: Supply Chain Management
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MOS 3330: Supply Chain Management
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INVENTORY MANAGEMENT
Learning Objectives:
1. Purposes of inventory
2. Inventory control systems
3. Inventory costs
4. EOQ models
5. Safety stock
Formula
Sheet
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1. Introduction
Inventory: A stock of items or materials held to satisfy
eventual demand
 Raw materials, purchased parts and supplies
 Work-in-process (partially completed) products
 Finished goods
 Rework items
 Tools, machinery, and equipment
 Labour
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 Types of inventory based on different purposes
 Anticipation inventory to meet demand forecast (e.g.,
seasonality)
 Safety stock  buffer to protect against uncertainties
 Lot-size inventory  result of batch ordering
 Pipeline inventory  in transit
 Hedge inventory  to protect against future events (e.g., price
increase of raw materials)
 Maintenance, Repair and Operating (MRO) inventory  to
minimize disruptions to general operations and maintenance
 Decoupling  work-in-process items waiting for the next step
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Why keep inventory?
 Buffer against expected & unexpected changes
 Faster customer service
 Economies of scale (production, purchasing)
 Not to be dependent on suppliers
Why is too much inventory bad?
 Cost (ties up working capital, may deteriorate or get stolen)
 Need for storage space
 Need for labour (material handling, transfer)
 Complacency
General objective: To keep enough inventory to meet
customer demand and be cost efficient
Main operational concerns: When to order and how many
to order
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2. Inventory Control Systems
Different ways of determining when & how many to
order
 Q system  fixed quantity
 P system  fixed time period
 ABC system
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2.1 Q System vs. P System
Q System
 Reorder a fixed quantity (Q)
whenever the inventory falls to
or below a reorder point (R)
 Continuous review system:
reviews the inventory each time
a withdrawal occurs
 Time between orders varies
P System
 Reorder after a fixed time period
(P)
 Periodic review system: reviews
the inventory periodically
 Order quantity (Q) varies
Q = (Target inventory level) –
(Current inventory level)
Compare in terms of record keeping system, administration cost,
responsiveness to demand variability, average inventory level, and
ease to combine orders to the same supplier
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2.2 ABC System
 An inventory classification system in which a small percentage
of items (A-level) account for most of the inventory value
Level
% of units
% of dollar value
(e.g., annual volume x unit cost)
A
B
C
5-15
30
50-60
70-80
15
5-10
Step 1: Classify products into ABC categories
Step 2: Apply a different inventory policy to each category
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Inventory Management Policy (Example)
A items
B items
 High priority
 Tight control with
 Moderate priority
 Moderate control
C items
 Low priority
 Simple control
 Carefully
 Order quantities or
 Large inventories,
regular review
determined Q,
frequent deliveries,
continuous review
 Very accurate and
with regular attention
order points reviewed
quarterly
 Batch updating of
inventory records
detailed inventory
records, update
monthly
MOS 3330: Inventory Management
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visual review
 Simplified
counting, annual
review
38
3. Inventory Management Costs
 Ordering (set-up) cost: Fixed cost incurred whenever a
replenishment order is placed, regardless of the quantity
 Requisition and purchase ordering, transportation and shipping,
receiving and storage, inspection, accounting and auditing costs
 Holding (carrying) cost: Cost to keep one item in inventory
for a period of time (usually one year)
 $ per unit per period or % of a unit cost/price
 Rent, heating, cooling, lighting, security, record keeping costs
 Interest on loans, depreciation, obsolescence, spoilage
 Shortage (stockout) cost: Cost of not being able to meet
customer demand
 Loss of sales, loss of future sales, loss of production, penalties
 Backorder: the order is filled from the next shipment
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4. Economic Order Quantity (EOQ) Models
 For managing anticipation inventory
 Mathematical model for determining order quantity and
when to reorder
Assumptions:
 Demand is independent, known, and constant
 Supply is certain and received all at once in a batch
 Replenishment lead time is known and constant
 Lead time: Time between order placed and order received
 Cost information is fixed and constant
 No shortages and no back orders
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Order cycle time
= (No. of days in a year) / (No. of orders)
Formula
Sheet
EOQ Inventory Model:
Annual demand (D)
Inventory
Level
Order qty (Q)
Average daily
demand (d)
Reorder point (R)
R=dL
Formula
Sheet
0
Lead
time
(L)
Order
Order
Placed
Received
Lead
time
(L)
Order
Placed
Time
Order
Received
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4.1 Basic Model
Total annual inventory management cost (TC)
= (Annual ordering cost) + (Annual holding cost)
= (Ordering cost)(No.of orders)+(Holding cost)(Average inv.)
 TC = CO (D/Q) + CH (Q/2) Formula
Sheet
EOQ objective: to minimize TC
 EOQ = (2DCO / CH)½
Formula
Sheet
Annual
cost ($)
Total Cost
Minimum
total cost
Holding Cost = CHQ/2
Ordering Cost = CoD/Q
EOQ
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Order Quantity (Q)
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Example 1: Western Jeans Company (WJC) purchases denim from Huron
Textile Mills. WJC uses 36,000 yards of denim per year. The cost of
ordering denim from Huron is $500 per order. It costs Western $0.40 per
yard annually to hold a yard of denim in inventory.
a) What is the total inventory cost if each order is for 6000 yards?
b) What is the total inventory cost if denim is ordered every month?
c) Calculate the optimal order quantity and the total annual inventory cost.
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4.2 Economic Production Quantity (EPQ) Model
An order is received gradually, not all at once
Common when inventory user is also the producer
Inventory is depleted while it is being replenished
Inventory
level
Demand
rate (d)
Replenishment
rate (p-d)
Q-(Q/p)d
Average
inventory level
Q
(1-d/p)
2
Begin
order
receipt
End
order
receipt
Time
Production
& usage
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Maximum
inventory level
Usage only
44
Total annual inventory cost (TC)
= (Annual set-up cost) + (Annual holding cost)
= (Set-up cost)  (No. of production runs)
+ (Holding cost)  (Average inventory)
 TC = CO (D/Q) + CH (Q/2)(1 – d/p)
Formula
Sheet
d = daily demand rate
p = daily production rate, where p > d
Optimal production quantity: EPQ = {(2DCO)/[CH(1 – d/p)]}½
Length of production run = Q/p
Max. inventory level = Q(1 – d/p)
Formula
Sheet
Formula
Sheet
Formula
Sheet
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Example 2: Western Jeans Company (WJC) produces and sells its own
jeans. The cost of setting up the production process to make jeans is
$150. The annual holding cost is $0.75 per pair, and the annual demand
is 10,000 pairs. The manufacturing facility operates 311 days a year and
produces 150 pairs of jeans per day.
a) What is the length of production run?
b) What is the maximum inventory level?
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5. Managing Safety Stock
Safety stock: Buffer added to on-hand inventory to protect
from demand/supply variability during the lead time
EOQ model considers only anticipation inventory and says
nothing about safety stock
General objective: to minimize shortage costs
 Method 1: % of annual demand as safety stock
 Simplistic but common
 Method 2: Satisfy a specified service level
 Service level: The probability that the inventory available
during lead time will meet demand
 Service level method includes demand behaviour and
probability of stockout in consideration
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Q
Average
demand
during LT
Safety
Stock
Safety
Stock
0
Between
receipt &
order (P)
Lead
time (L)
Lead
time (L)
Time
Don’t worry about this
MOS 3330: Inventory Management
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Reorder
point (R)
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FORECASTING
Learning Objectives:
1. Qualitative vs. quantitative methods
2. Forecasting process
3. Time series methods
4. Forecast error measures
Formula
Sheet
Formula
Sheet
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1. Introduction
 Forecasting: A method for translating past experience and
present events into predictions of the future
 Forecasting in OM
 Strategic: future products and markets
 Planning: product demand
 Forecasting horizon
 Long-range (longer than 2 years) for strategy
 Mid-range (weekly/monthly for up to 2 years) for planning
 Short-range (hourly/daily for up to several months) for scheduling
 Forecasting is important because
 It is a starting point for business planning
 All business decisions will follow the result of forecasting
 “Bad” forecast can lead to a significant increase in cost
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2. Types of Forecasting Methods
 Qualitative: based on opinions or judgement of
knowledgeable persons
 Executive opinions, panel of experts
 Market survey, focus group
 Delphi method: to develop a consensus among experts
 Quantitative: based on numerical data and
mathematical models
 Causal methods: based on a known or perceived relationship
between the factor to be forecasted and other external or
internal factors  linear regression
No calculation
 Time series methods
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Qualitative vs. Quantitative
Qualitative
Quantitative
 Subjective
 Objective
 Can incorporate a variety of
 Can incorporate large a volume
 Do not require numerical
 Do not have to rely on few
 Results may be biased
 Numerical data may not be
information
of information
individuals
data
 Results may be conflicting
available
 Mathematical models may be
too simplistic
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1. Identify the purpose
3.Forecasting
Process
2. Collect historical data
Data
3. Examine data (plot)
4. Select appropriate models
Quantitative
5. Compute forecasts for historical
data and check forecast accuracy
6. Is accuracy acceptable?
No
Yes
7b. Adjust
parameters
or select new
model
7a. Forecast over planning horizon
8. Include qualitative information
Qualitative
9. Monitor results
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4. Time Series Forecasting Methods
 Based on statistical analysis of historical data
 Time series: a set of observed values measured over successive
time periods
 Assumptions: (i) past demand is a predictor of future
demand, and (ii) record of past demand is available
 Time series demand behaviour
 Average  stable over time
 Trend  general increase or decrease in average demand
 Seasonality  short-term periodic behaviour due to time of day,
day of week, month, season, etc.
 Cycle  long-term periodic behaviour due to product life cycles
 Random variation  any remaining variability that cannot be
explained; virtually unpredictable
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Cycle
Demand
Demand
Seasonal Pattern
Demand
Average
Time
Time
Time
Trend with Seasonal Pattern
Demand
Demand
Trend
Random
movement
Time
Time
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5. Time Series Forecasting Models
Naïve
Simple moving average
Weighted moving average
Exponential smoothing
Adjustment for seasonality
Data used in Examples 1-5:
Period (t)
Jan(1)
Feb(2)
Mar(3) Apr(4) May(5) Jun(6) Jul(7) Aug(8)
Demand (Dt)
12
18
22
20
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24
20
18
16
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5.1 Naïve Model
Relies only on demand in the current period
Short-term
Sensitive to random variation
Ft+1 = Dt
Formula
Sheet
Ft+1 = forecast for period t+1 (or for any future period)
Dt = actual demand in the most current period t
Example 1: Use the naïve model to forecast demand for September. How
about for October?
 When would be a good idea to use the naïve model?
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5.2 Simple Moving Average
 Relies on the recent past
 Smooth out random variations
 Useful for stable demand
Ft+1 = (Dt + Dt–1 +  + Dt–(N–1))/N
Formula
Sheet
N = total number of periods used in calculation
 Sensitivity to random variation depends on N
 N is subjective
Example 2: Use a 3-period moving average to forecast demand for Sept.
 What happens to a seasonal effect if this model is used?
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5.3 Weighted Moving Average
 Weights represent the varying amounts of influence of past
demand on forecast
 Weights also reflect fluctuations in the demand data
Ft+1 = Wt Dt + Wt–1 Dt–1 +  + Wt–(N–1) Dt–(N–1)
Formula
Sheet
Wt = weight applied to period t’s demand
 Weights are non-negative and sum to 1
 Most common: most current demand has most influence
 Weights and N are subjective
Example 3: Suppose W8 = 0.7, W7 = 0.2, and W6 = 0.1. Use a weighted 3period moving average to forecast demand for September.
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5.4 Exponential Smoothing
 A special case of weighted moving average
 Requires minimal amount of data (but includes all past
data)
Ft+1 =  Dt + (1 – ) Ft
Formula
Sheet
 = smoothing parameter (0<1); commonly 0.01–0.5
 Low more smoothing; high more responsive to changes
  is subjective
Example 4: Compute forecasts for Sept and Oct using exponential
smoothing with  = 0.1. Assume F8 = 18.75, D8 = 16, and D9 = 14.
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5.5 Adjustment for Seasonality
 Multiply unadjusted forecast (Ft+1) by a seasonal factor (Si)
Ft+1,i = Si Ft+1
Formula
Sheet
ATTENTION
Ft+1,i = forecast for season i in period t+1
Ft+1 = forecast for total demand in period t+1
Si = seasonal factor for season i = Di/Di
Di = actual total past demand for season i
Formula different
from textbook
Example 5: Forecast demand for each quarter in year 4 (Fyear4 = 30.4).
Year 1
Year 2
Year 3
Quarter
1 2
5.0 4.6
6.0 5.2
6.0 5.8
3
6.0
6.8
7.6
4
6.6
7.4
8.0
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5.6 Guidelines for Selecting Appropriate Models
Hypothesize based on demand patterns observed
If the underlying average is not changing:
 Moving average with large N
 Weighted moving average with large N
 Exponential smoothing with small 
If a trend exist:
 Weighted moving average with large weight on recent demand
 Exponential smoothing with large 
If the underlying average exhibits seasonal patterns:
 Seasonally-adjusted model
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6. Forecast Accuracy
 Forecasts are rarely perfect
 Internal factors, external factors
 Modern issues  more product choices, faster introduction of
new products
 Small business issues  lack of data, lack of expertise, time
constraint
 Forecasting objective: to minimize forecast error

Forecast error = (Actual demand) – (Forecast) = D – F
 To minimize error
 Select the best possible model
 Select the best possible value for a parameter
 Forecasts are more accurate for a shorter term
 Forecasts are more accurate for aggregate demand
 Minimize forecast error measures: MAD, MAPD, MSE, CE
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Forecast Error Measures
Dt = actual demand in period t
Ft = forecast for period t
N = number of periods under consideration
Mean
Absolute
Deviation
Dt – Ft / N
Mean
Absolute
Percentage
Deviation
Dt – Ft /  Dt
Mean
Squared
Error
Cumulative
Error
Formula
Sheet
Formula
Sheet
 (Dt – Ft)2 / N
Formula
Sheet
 (Dt – Ft)
Formula
Sheet
Measures general
variability
Smaller
the better
Forecast error
relative to demand
magnitude
Smaller
the better
Reveals large
errors
Smaller
the better
Measures bias
Large+, underestimate
Large, overestimate
Closer to 0
the better
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Example 6: Evaluate the following forecast model.
t
Dt
1
Ft
MAD
MAPD MSE
CE
170 200
30
17.7% 900
–30
2
230 195
32.5
16.3% 1063
5
3
250 210
35
16.2% 1242
45
4
200 220
31.3
14.7% 1031
25
5
185 210
30
14.5%
950
0
6
180 200
28.3
14%
858
–20
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7. Machine Learning (ML) in Demand Forecasting
• In recent years, machine learning has become an essential
component of reliable demand forecasting.
• Machine learning is a discipline within artificial intelligence (AI)
that uses advanced algorithms to learn from data. More
importantly, it uses data to automatically learn and improve
without human intervention.
• In demand forecasting, machine learning algorithms can
identify historical sales patterns and relationships in data,
allowing the computer to make predictions about future trends.
• While traditional methods use a set of predefined rules to
make predictions, machine learning is able to learn and adapt
from any amount of data.
• In 1997, IBM’s Deep Blue Chess (DBC) system managed to
defeat the World Chess Champion, Garry Kasparov, using
algorithms that were perfected through Machine Learning.
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7.1 Machine Learning Forecasting Process
1. Data gathering
• Historical data
• Information from social media
• Information about competitors products
• Information about future events
• Information from experts
2. Data pre-processing
• Remove noisy data
• Transforming data
• Standardizing data
3. model training
• ML algorithm is trained
• Selecting a model type and parameter values (done by the system)
4. model evaluation
• The performance of the model is then evaluated by comparing its
predictions against actual outcomes.
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7.2 Machine Learning Forecasting Example
Grocery retailers often place manual reorders, using “gut instinct” to
decide how much to order of fresh, perishable goods — such as fruit and
vegetables, meat, dairy and baked goods.
Freshflow: AI-powered forecasting platforms to help retailers optimize
stock replenishment, minimize food waste, and maximize revenue.
(https://freshflow.ai)
•
Freshflow’s premise is that ML can do a far better and less wasteful job of
restocking fresh food than the human eye, nose and gut by being able to weight
a variety of factors that may influence demand (for example, weather, season,
local events) and by evaluating available retailer data to create probabilistic
models and forecasts (for example, to predict the shelf life of various products),
to more accurately match overall supply and demand.
• Retailers saw a 30% reduction in food waste and a 16.7% increase in revenue
after around eight months of using the AI-powered system to automate fresh
produce restocking.
• The platform is designed to sit as a layer on top of the retailer’s existing
information system.
• The produce team uses an iPad app to inform them of recommended restock
quantities per product.
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7.3 Benefits of Machine Learning
1. ML can identify patterns that are too complex for humans to
observe.
2. ML can make predictions based on a much larger data set
than traditional methods.
3. ML is not easily influenced by human emotions or subjective
opinions.
4. ML can quickly adapt to changes.
5. ML is not easily manipulated by users as traditional methods.
6. ML uses human resources more efficiently than traditional
methods.
7. ML is more accessible than traditional methods.
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Start of Test 2
AGGREGATE PLANNING
Learning Objectives:
1. What is an aggregate production plan
2. Aggregate planning strategies
3. Generating basic aggregate plans
4. Modifying/improving the basic plans
5. Capacity strategies
Formula
Sheet
Formula
Sheet
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1. Introduction
Business Plan
e.g., new products, facility location
Aggregate Plan
Production plan to meet
aggregate demand
Master Production Schedule
Production plan for individual products
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Long term
2-10 years
Medium term
6-18 months
Short term
Weekly, daily
72
Aggregate demand: Demand for product lines or
product families
Aggregate production plan: A period-by-period plan of
production, workforce and inventory levels
Main concern: Do we have enough capacity?
 Capacity shortage  need to get more
 Excess capacity  how to effectively use the existing capacity
and resources
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2. Aggregate Planning Strategies
Active strategy: by influencing demand
 Incentives, sales promotions, advertising campaigns, pricing
 Products with countercyclical demand
Reactive strategy: by managing supply and capacity
 Inventory, back orders, subcontracting
 Overtime, undertime, hiring, firing
2 main approaches to the reactive strategy
 Level  constant production rate by no hiring/firing
 Chase  varying production rate by hiring/firing
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Chase Approach
 Production “chases” demand
 Constant production rate
 Change production rate by
 Demand variability dealt by inventory,
hiring/firing workers
back orders, overtime, undertime,
 Good for make-to-order, service
subcontracting
 Good for minimizing inventory
 Good for make-to-stock
 Bad for employee morale
 Good for workforce stability
Level Approach
 Bad for inventory buildup or stockouts
Demand
Production
Units
Units
Demand
Production
Time
Time
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3. Generating Aggregate Plans
Step 1: Choose level or chase approach
Step 2: Based on the approach chosen, determine the
following to satisfy aggregate demand
 Production rate (regular, overtime, subcontracting)
 Inventory level and back orders
 Workforce level (hiring and firing)
 Total cost = production cost + inventory cost +
workforce-related cost
Step 3: Modify or improve the plan
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Example 1: Develop an aggregate production plan for the following
aggregate demand:
Month
1
2
3
4
5
6
Demand
50
70
100 120 60
60
Cost and Production Information:
Production: Regular production cost = $10 per unit
Overtime cost = $15 per unit
Subcontracting cost = $20 per unit
Overtime production limit = max of 20 units per month
Production rate = 2 units per worker per month
Inventory: Holding cost = $2 per unit per month
Backorder cost = $5 per unit per month
Opening inventory level = 10 units
Labour:
Invt = Invt–1 + Prodt – Demt
(assumed to be computed
at the end of each period)
Formula
Sheet
Hiring cost = $200 per worker hired
Termination cost = $100 per worker terminated
Opening workforce level = 30
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Example 1a: Level approach based on the average demand
 Average demand = (Total demand  opening inventory) / (# of periods)
= (50+70+100+120+60+60  10)/6 = 75
Formula
Sheet
 May use inventories and back orders
Level@
average
demand
Use cheapest first
TP = Reg + OT + Sub Formula
Sheet
Level
Opening
Inventory
formula
Reg = #WF
 prod. rate
Formula
Sheet
Match WF
Level
Level
Opening
302=
7560=
Formula
TC = (production cost) + (inventory cost) + (workforce-related cost) Sheet
= (10360 + 1590 + 200) + (290 + 545) + (2000 + 1000) = $5355
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Example 1b: Level approach based on the highest demand
 Highest demand in this example = 120 (period 4)
 Guarantee no shortages in any period but build inventory
TC = (3600+1800+4800) + 1980 + 0 = $12180
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Example 1c: Level approach based on the max of cumulative average demand
 Calculate the cumulative average (C.A.) for each period and choose the max
 C.A. = (Total cumulative demand  Open. inv.)/(# of cumulative periods)
Formula
Sheet
C.A. for period 1 = (Demand1  Open. inv.) / 1
C.A. for period 2 = [(Demand1 + Demand2) Open. inv.] / 2
C.A. for period 3 = [(Demand1 + Demand2 + Demand3)  Open. Inv.] / 3
...and so on. Then choose the largest number among all the C.A.s calculated.
 Guarantee no shortages with less inventory build-up
TC = (3600+1800+360) + 426 + 0 = $6186
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Example 1d: Chase approach
 Production “chases” demand
Chasing
demand
TP/
prod.rate
Inventory
formula
Use cheapest first
TP = reg + OT + Sub
Reg = #WF
 prod. rate
Match WF
5010=
TC = 4500 + 0 + (8000+4000) = $16500
If each worker produces 3 units per month, how would the plan change?
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4. Modifying or Improving the Aggregate Plan
 Add organization policies, physical constraints, etc. to
a basic level/chase plan
 For example, add min. or max. requirements, desired levels
 Hybrid approach
 3 specific “techniques” (Examples 2a to 2c)
 2a: Keep the initial workforce but use overtime/subcontract
as needed
 2b: Limit the use of overtime and subcontracting
 2c: Desired level of ending inventory
 May also improve objectives other than cost
 Maximize customer service
 Minimize changes in workforce levels
 Minimize changes in production output
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Example 2a: Modify 1a  keep the initial workforce level and use overtime
and subcontracting only when needed
 Start from Example 1a
 Check each period, starting from period 1, to see if regular production +
inventory is enough to meet demand; if enough, do not use OT/Sub; if not
enough, then use OT/Sub as needed
 Period 1: (Reg + Inv = 60+10 = 70) > (Dem = 50)  Do not use OT/Sub
 Revised TP = 60+0+0 = 60  Revised Inv = 10+6050 = 20
TC = (3600+600+1000) + 60 + 0 = $5260
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Example 2b: Modify 1b – use hiring/firing to eliminate overtime and
subcontracting
 Start from Example 1b
 Determine the level of workforce that would eliminate the need for
overtime and subcontracting
 For TP = 120 (from Example 1b), the workforce level of 120/2 = 60 would
eliminate the need for OT/Sub
TC = 7200 + 1980 + 6000 = $15180
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Example 2c: Modify 1c – the target inventory level at the end of period 6 is
10 units
 Start from Example 1c, where the ending inventory for period 6 = 48
 To achieve the target = 10, we need to reduce inventory by 38 units
 In order to reduce inventory, we need to reduce production, starting from
period 6 and work your way up
 Period 6: eliminate 3 from Sub & 20 from OT
 Period 5: eliminate 3 from Sub & 12 from OT
 Need to revise TP and Inv in periods 5&6
Total reduction in production
= 3+20+3+12 = 38 units
TC = (3600+1320+240) + 320 + 0 = $5480
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5. Capacity Planning
 Do we have enough capacity?
 If enough, are we operating at the best operating level (for example,
volume of output that results in the lowest average unit cost)?
 Measuring capacity
 Design capacity: Maximum output rate under ideal conditions
Utilizationdesign =(actual output rate)/(design capacity)
 Effective capacity: Maximum output rate under normal conditions
Utilizationeffective =(actual output rate)/(effective capacity)
 Capacity shortage
 Should we outsource or expand?
 If expanding, when to increase capacity and by how much?
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Capacity expansion strategies:
Average capacity
strategy
Capacity
Capacity
Time
Capacity
Time
Time
Units
By how much?
Capacity lag
strategy
Units
Capacity lead
strategy
Units
Units
When to increase capacity
One-step
expansion
strategy
Incremental
Expansion strategy
Time
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87
88
MATERIAL REQUIREMENTS PLANNING (MRP)
Learning Objectives:
1. What MRP is and does
2. Inputs and outputs of MRP
3. Scheduling basics
4. Scheduling sequencing rules
Formula
Sheet
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89
1. Introduction
Items
Production Planning
Capacity Planning
Resource level
Product lines or
families
Aggregate
Production Plan
Resource
Requirements Plan
Plants
Individual
products
Master Production
Schedule (MPS)
Rough-Cut
Capacity Plan
Critical work
centers
Components
MRP
Capacity Req’ts
Plan (CRP)
All work
centers
Manufacturing
operations
Shop Floor
Schedule
Input/Output
Control
Individual
machines
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Aggregate plan provides a framework for shorter-term
production and capacity decisions
 Disaggregation: The process of breaking an aggregate plan into
more detailed plans
Material Requirements Planning (MRP): Computerized
inventory and production control system that determines
the requirements of dependent demand inventory
 Introduced in the 1960s
 Dependent demand inventory = parts, components, raw materials
 Determine what is required, how much is required, and when it is
required
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2. MRP System Overview
Master Production
Schedule (MPS)
Inventory
Master File
Product
Structure File
1. Explosion: disassembly of the end product into its
components
2. Netting: (Net requirements) = (Gross reqts) – (Onhand inventory) – (Quantity on order)
3. Offsetting: order release is offset by production or
delivery lead time
4. Lot sizing: determine the batch size to be purchased
or produced
Formula
Sheet
Planned Order Releases
Work Orders
Purchase Orders
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Action Notices
92
2.1 Master Production Schedule (MPS)
 States the requirements for individual end items by date and quantity
Aggregate Production Plan
Month
Days
Plan
Jan
21
21,000
Feb
19
19,000
Mar
23
23,000
Apr
20
20,000
2
4,000
1,000
5,000
3
5,000
4
2,500
2,500
5,000
MPS for April
Week
Prod X
Prod Y
Totals
1
2,000
3,000
5,000
5,000
MPS is Not
 a sales forecast
 a wish list
 a final assembly schedule
MPS should be
 anticipated build schedule
 realistic and achievable
 may not be feasible
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2.2 Inventory Master File
Description
Item
Item no.
Item type
Product/sales class
Value class
Buyer/planner
Vendor/drawing
Phantom code
Unit price/cost
Pegging
LLC3
Physical Inventory
On hand
Location
On order
Allocated
Cycle
Difference
Board
7341
Manuf.
Ass’y
B
RSR
07142
N
1.25
Y
Policy code
100
W142
50
75
3
-2
Inventory Policy
Lead time
Annual demand
Holding cost
Ordering/setup cost
Safety stock
Reorder point
EOQ
Minimum order qty
Maximum order qty
Multiple order qty
3
Usage/Sales
YTD usage/sales
MTD usage/sales
YTD receipts
MTD receipts
Last receipt
Last issue
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2
5,000
1
50
25
39
316
100
500
100
1,100
75
1,200
0
8/25
10/5
94
2.3 Product Structure File
Clipboard
Clip Assembly
(1)
Rivet
(2)
Top Clip
(1)
Bottom Clip
(1)
Pivot
(1)
Spring
(1)
Sheet
Metal
(8 in2)
Sheet
Metal
(8 in2)
Spring
Steel
(10 in.)
Iron
Rod
(3 in.)
Board
(1)
Pressboard
(1)
Finish
(2oz.)
Bill of material (BOM): lists which and how many items that go
into a product
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Example 1: Based on the product structure diagram on the previous slide:
a) How much spring steel is needed to fill an order of 500 clipboards if on-hand
inventory = 0? (Explosion & netting)
b) How much sheet metal is needed to fill an order of 1000 clipboards if on-hand
inventory = 7000? (Explosion & netting)
c) In order to produce 1000 clipboards, subassembly takes 1 day and final assembly
takes 1 day. For b), when should an order for sheet metal be placed if 1000
clipboards are needed by April 11? Order lead time = 5 days. (Offsetting)
d) In b), if the order lot size is 2000, how much would you order? (Lot sizing)
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3. MRP as an Analysis Tool
 MRP is not a demand planning tool
 Input quantities are production quantities, not demand
 Production plan derived from aggregate production planning
 MRP is deterministic
 All input numbers are known
 Problems show up as the action notices
 Main MRP problem = shortage of components/finished goods
 MRP is a simulation tool
 Can estimate the production outcome  identifies potential
shortage before actual production
 Does not solve problems on its own  requires human input
 Solution examples: to purchase or produce more ahead of time; to move some
jobs forward (expediting) or backward (de-expediting)
 Can show how the change in MRP input (i.e., a potential solution)
affects MRP output  shows whether or not the solution would
work
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4. Production Scheduling
 Allocation of resources to accomplish specific tasks
 Last stage of planning before production
 Scheduling objectives
 Meet customer due dates
 Minimize job lateness, response time, completion time, overtime,
idle time, and work-in-process inventory
 Maximize labour or equipment utilization
 Scheduling performance measures
 Job flow time: total time a job spends in the shop incl. waiting,
setup
 Makespan: total time to finish a batch of jobs
 Average number of jobs in the system: measure of WIP inventory
 Job lateness: ahead of, on, or behind the schedule
 Job tardiness: how long after the due date the job is completed
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4.1 High Volume vs. Low Volume
High Volume Production
Low Volume Production
 Length of a production run,
 Coordination difficulty from
having a variety of orders, tasks
& process requirements
flow schedule, line balancing
 Bottleneck: an operation with
the lowest effective capacity
 Loading: assigns jobs to work
centres according to
performance efficiency, skill
requirements, and job priority
 Optimized Production
Technology (OPT): a technique
used to schedule bottleneck
systems
 Sequencing: determines the
 Theory of Constraints (TOC): a
management philosophy that
extends the concepts of OPT
sequence in which jobs
assigned to a work centre are to
be processed
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4.2 Sequencing Rules
 First come first served (FCFS)
 Last come first served (LCFS)
 Earliest due date first (EDD): min. tardiness
 Shortest processing time first (SPT): min. average flow time
 Longest processing time first (LPT)
“Formulas” for Scheduling Performance Measures:
Job flow time = (waiting time) + (processing time)
Makespan = finish time for the last job
Avg.# jobs in the system=(all jobs job flow ime)/(makespan)
Job lateness = all jobs (finish time  due time)
Job tardiness = only late jobs (finish time  due time)
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Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
10
0
Example 2: A hospital lab has one MRI, and 5 patients need scheduling.
Job:
A
B
C
D
E
Processing time (hrs):
4
7
2
6
3
Pickup time (hrs from now): 6
12 7
16 8
Sequence
Flow time
=wait+process
Tardiness
=finish-due
Avg.
Tardiflow
ness
time
FCFS
LCFS
EDD
SPT
LPT
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4.3 Other Types of Operations Scheduling
Scheduling in services
 Complicated because demand is often variable and difficult to
forecast
 Service scheduling: appointments, reservations, posted
schedules, backlogs
 Staff scheduling: peak demand, floating, on-call, seasonal, parttime
Maintenance scheduling
 Repair scheduling
 Preventative maintenance
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ENTERPRISE RESOURCE PLANNING (ERP)
Learning Objectives:
1. What is ERP
2. Historical development of ERP
3. ERP enablers
4. Implementation issues
5. Project management basics
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103
1. Introduction
• Enterprise Resource Planning (ERP): Software with
integrated modules for controlling and coordinating
business processes of an entire enterprise
• Manufacturing information systems as the origin:
• MRP
• MRP + CRP
• MRP II
• ERP
1960s
1970s
1980s
1990s to present
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2. Information Systems
 A collection of components that work together to provide desired
information in the proper format at an appropriate time
 ERP is one type of IS
People
Software
Data
Hardware
Information
Procedures
Information Technology
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105
 ERP is one type of transaction processing systems
 Maintain a huge volume of transaction records  sales, purchases,
customers, creditors, banking
 ERP is one type of information reporting systems
 Provide various reports
 Provide information for managers  Management Information
Systems (MIS)
 ERP is not a functional (departmental) information system;
it consists of all key functional modules
 Accounting  accounts receivable/payable, payroll records,
budgeting, financial planning
 HR  personal data, job skills, training history, pay rates, insurance,
vacation days, sick leave time
 Marketing  sales orders, buying trends, customer databases,
product information, advertising records
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106
3. MRP + CRP (1970s)
MRP ensures that material requirements are met
Capacity Requirements Planning (CRP): checks for the
availability of labour and/or machine hours
 CRP identifies capacity overload and underload
 CRP does not solve problems on its own
Human intervention required for deciding:
 Overtime or reduce work load
 Push back a job or pull ahead a job
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4. MRP II (1980s)
 Manufacturing Resource Planning (MRP II): Extension of
MRP that plans all resources needed for running a business
 MRP + CRP + Finance + Marketing
 Finance  access to accurate reliable operations numbers
 Sales  less trouble shooting, more selling, able to promise
 HR  easier to assess performance measures
 Limitations
 Limited functional integration
 Often limited to one location
 No supply chain capabilities
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5. ERP (Since 1990s)
 To create the information linkages that integrate the
processes and structures within a supply chain
 MRP II + Supply Chain Management + Global Presence
Operations
& Logistics
ERP
Sales &
Marketing
Customers
Suppliers
Finance
HR
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5.1 ERP Technological Enablers (Building Blocks)
 Advancement in electronic communication
 For example, technology behind e-mails and e-bulletin boards
 Electronic data
 Bar codes and electronic scanning
 Advancement in computer-to-computer data sharing
 Electronic Data Interchange (EDI): Computer-to-computer
exchange of business documents in a standard format
 Electronic Funds Transfer (EFT): Computer-to-computer
exchange of funds
 Data conversion software
 Internet
 e-commerce; shared databases; cloud storage
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5.2 ERP Mixed Results in the 1990s
• Popularity of ERP systems soared after an introduction of
SAP’s ERP software, R/3, in 1994
• Other big ERP vendors: Oracle, Microsoft, Deltek, and Sage
Success/Benefits
Nightmare in the 1990s
Cisco Systems, Kodak
Lower inventory levels
Lower workforce costs
On-time completions
Better coordination among
different functional areas
 Better communication
 Better data integrity
 FoxMeyer Drug bankruptcy
 Law suits (Boeing, Dow Chemical,
Mobil Europe, Hershey, Kellog’s)
 Dell Computer scrapping SAP’s
ERP software
 1999 survery  40% partial
implementation, 20% scrapped





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5.3 ERP Implementation Issues
Technical:
 Handling large amounts of data
 Inaccurate data, loss of data
 Insufficient training
 Lack of understanding of what ERP is or does
Behavioural:
 Lack of support by management
 Lack of commitment by employees
 Coordination difficulty among different functions
 Ownership claim problem (too much vs. too little)
 Unrealistic expectations, unrealistic promises
 Not understanding the underlying needs
 Changing habits
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6. Project Management
 Project: one-at-a-time product exactly to customer
specifications
 Project management: application of knowledge, skills,
and techniques to project activities in order to meet
stakeholder needs
 Project manager must
 Balance scope, time, cost and quality
 Manage multiple stakeholders with differing needs
 Satisfy identified requirements (needs) & unidentified requirements
(expectations)
 Project life cycle
1. Concept  identify the need for the project
2. Feasibility analysis  evaluate costs, benefits, and risks
3. Planning  decide who does what and how long
4. Execution  do the project
5. Termination  end the project
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Topics Covered By Project Management Institute:
 Scope
 Human resources
 Time
 Cost
 Quality
 Risk
 Procurement
 Communications
 Integration
See next 3 slides
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6.1 Project Scope Management
 To ensure that the project includes all the work required,
and only the work required, for successful completion
 Project scope  work that must be done for delivering a product
 Product scope  features and functions of a product or service
 Scope planning
 Product description, formal recognition of business need
 Constraints and assumptions
 Scope definition
 Cost, time and resource estimates, how to measure performance,
and clear responsibility assignments
 Scope change control
 How to deal with scope changes, corrective action, and lessons
learned
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6.2 Project Human Resource Management
 Organizational planning
 Project roles, responsibilities, and reporting relationships
 Communication planning
 Distribution structure, record keeping structure
 Status and progress reporting, expectations
 Staff acquisition
 What skills are required from which individuals or groups, when
and how long
 Team development
 Training, meetings, “war room”
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6.3 Project Time Management
Time Tracking Tools
 Gantt chart: visual representation of a schedule over time
 Load chart: shows the planned workload and idle times
 Progress chart: shows planned and actual progress
 Project management software (for example, Microsoft Project)
Time Estimate Techniques
 Critical Path Method (CPM): identify the critical path using a
network diagram with deterministic time estimates
 Critical path: a set of activities that would cause an overall project
delay if any of them is late
 Program Evaluation and Review Technique (PERT): probabilistic
time estimates
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End Here for Test 2
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Final Exam Start Here!
PROCESS AND PRODUCT DESIGN
Learning Objectives:
1. Process types
2. Facility layout types
3. Manufacturing technology
4. Product design basics
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1. Introduction
Process: How to transform input into output
Classification by
 Type of product flow (fixed position, jumbled flow, line flow,
continuous flow)
 Approach to customer orders (make-to-order, make-to-stock,
assemble-to-order)
 Amount of customization
 Our focus  demand volume vs. product standardization
(product variety; the level of customization)
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2. Classification by Volume and Standardization
Product Standardization
Intermittent
operations
Low
Project
Repetitive
operations
Batch
process
Mass
process
Continuous
process
High
Low
High
Product Volume
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 Project: one-at-a-time product exactly to customer specifications
 Batch: small quantity of products in batches based on actual or expected
customer orders
 Mass: large volume of a standardized product, assembly line format
 Continuous: continually produce a very high volume of a fully
standardized product
Product variety
Approach to customer orders
Product flow
Type of equipment
Degree of automation
Critical resources
Throughput time
WIP inventory
Intermittent
Repetitive
Great
Small
Make-to-order
Jumbled
Make-to-stock
General purpose
Line
Specialized
Low
Labour
Longer
More
High
Capital
Shorter
Less
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3. Process Type and Facility Layout
 For intermittent operations
 Process layout: groups resources based on similar processes
 Warehouse layout, office layout
 For repetitive operations
 Product layout: groups resources based on products
 Arranges resources in sequence (assembly lines)
 Hybrid between process and product layouts
 Cell layout: grouping of products based on similar requirements
(processes)
 Fixed-position layout: used when the product is too big and
cannot easily be moved (e.g., bridge construction)
 Retail layout: allocates space based on customer behaviour
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Process layout
Radiology
Laboratory
Lobby
Exam room
Surgery
Physical
therapy
Product
layout
A
A, B, C
1
A
1
4
B
2
5
C
3
6
2
1
3
2
Cell
layout
3
5
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4
124
Product Standardization
4. Process Type and Technology Adoption
Intermittent
operations
General
Low purpose
Group
Technology
Repetitive
operations
FMS
Focused
automation
Dedicated
automation
High
Low
High
Product Volume
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Manufacturing Technologies
 Group technology
 Grouping of dissimilar automated machines to produce a family
of parts
 Flexible manufacturing system (FMS)
 Consists of numerous programmable machine tools connected
by an automated material handling system
 Robotics
 Controlled by a computer; can perform complex tasks
 Computer-aided manufacturing (CAM)
 Controlling manufacturing through computers
 Computer-integrated manufacturing (CIM)
 Integration of product design, process planning, and
manufacturing through computers
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5. Process Selection
Cost, volume, product maturity level, labour/technology
availability
Matching process and corporate strategy
Intermittent
Repetitive
Corporate strategy
Customized products
Mass market
Operations strategy
Low volume, customized service High volume
Competitive priorities
Volume flexibility, customization Low cost
Matching process and product  product design
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6. Product Design
Process of defining all of the product’s characteristics
General steps in product design
1. Idea development (customer driven, reverse engineering, R&D)
2. Product screening (feasibility study)
3. Preliminary design and testing
4. Final design
Faster introduction of new products
 Concurrent engineering: Multifunctional team approach to
simultaneously design the product and the process
 Computer-aided design (CAD): Use of computer graphics to design
new products
 3D printing: Method of making an object by adding thin layers of
materials layer-by-layer based on CAD
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JUST-IN-TIME SYSTEMS
Learning Objectives:
1. JIT origin and philosophy
2. JIT key elements
3. Implementation issues
4. Job design basics
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1. Introduction
Just-in-time (JIT) manufacturing: To produce only what
is needed, when it is needed
 JIT inventory, JIT purchasing, lean manufacturing
JIT philosophy: Eliminate all waste in the organization
JIT system: A management system that aims to improve
the manufacturing or service process by eliminating waste
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1.1 Origin of JIT Systems
 In the 1950s, the Japanese were short on capital and space
 For Toyota (Taiichi Ohno), to improve performance meant to
reduce inventory
 Inventory hides problems
 Reducing inventory led to reducing all kinds of waste
Machine
breakdowns
Poor
quality
Poor
design
Poor
vendors
Inefficient
layout
Long
setups
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1.2 Types of Waste
Waste: Anything other than the minimum amount of resources that is
essential to add value to the product
 Process
 Waiting time
 Unplanned
 Planned queue
 Waiting for other parts in batch
 Scrap
 Non-value-added cost
 Wrong tools/equipment
 Over-production
 Extra inventory
 Inappropriate use of resources
 Inventory
 Storage
 Capital costs
 Product defects
 Interrupted flow
 Lost capacity
 Wait for replacement
 Methods
 Searching for tools
 Poor layout
 Walking
 Movement




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Material handling
Receiving
Storing
Retrieving
132
2. Key Elements of JIT Systems
Work cells  efficiency within a process, multi-functional
workers
Pull system  coordination between processes
 To improve the pull system, reduce the variability associated
with supply and demand
 To control supply variability
 Small lot sizes
 Preventive maintenance
 High quality
 Reliable suppliers
 To control demand variability
 Uniform loading
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2.1 Work Cells
 Eliminate worker inefficiency  increase worker
productivity
 Operation of a number of different machines  highly
utilized, multi-functional workers
 Cellular facility layout: work is moved within a cell (mostly
U shaped) according to a prescribed path
 Different from traditional automobile production, which is mass
production of standardized products using assembly lines
 Flexible machines
 Automated, general purpose machines
 Quick setup time
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Work Cell Example
Machines
Product
route
Worker
2
Worker
1
Worker
3
Exit
Cell: grouping of products based on similar requirements
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2.2 Pull Production System
 Improve poor coordination between processes  eliminate
the need for large inventory
 Major problem in automobile manufacturing
 Push system: Each workstation produces according to a
schedule and “pushes” its completed work to the next
workstation
 Traditional approach to production; builds inventory
 Pull system: Each workstation requests, or “pulls”, items
from the previous workstation only when needed
 Prevent overproduction and underproduction
 Force coordination between processes  kanban system
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 Kanban system: A visible production control system which
authorizes the production or movement of the next batch
of material only when needed
 Kanban: a Japanese word for “signal” or “visible record”
 Most common form: card, container
 Kanbans are not schedules
 Work the same way as a fixed-quantity inventory system
where order quantity (Q) equals to the reorder point (R)
 Only inventory maintained is the amount needed to cover usage
until the next order arrives
 MRP vs. Kanban
 Schedule vs. cue
 Complex vs. repetitive
 Higher-level vs. shop-level control
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2.3 Supply Variability #1: Lot Sizes
Each kanban “container” represents the production or order
lot size
Small lot sizes provide many benefits
 Better coordination
 Better material handling
 Reduce average inventory level
 Reduce inventory space
 Avoid buildup of defective items
 Flexibility in reacting to problems
Quick set-up is crucial
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2.4 Supply Variability #2: Machine Breakdowns
 Maintenance: To keep facilities and equipment in good
working order
 Machine downtimes are waste  fix fast  fixing is
also waste
 Preventive maintenance: Periodic inspection and
maintenance designed to avoid breakdowns
 Preventive vs. breakdown maintenance
 Necessary due to small inventory and high automation
 Work environment: Five S’s
 Seiri (housekeeping), Seiton (workplace organization), Seiso
(clean-up), Seiketsu (keep clean), Shitsuke (discipline)
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2.5 Supply Variability #3: Quality
Consistently high quality products are necessary due to
small inventory
Quality at the source: uncover the root cause
 Andon lights to signal problems
 Empowerment: authority given to workers to stop the
production line if a problem occurs
Preventive
 Poka-yoke: devices that are designed to prevent
mistakes/defects from happening
 Standard parts, modular design: reduce variability
Continuous improvement (“kaizen”)
 Quality Circle: team-based process improvement
 Under-capacity scheduling to deal with problems daily
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2.6 Supply Variability #4: Suppliers
 Need reliable suppliers for on-time, frequent deliveries
 Must build long-term, close relationships with few suppliers
 Single sourcing: entire family of parts provided by one supplier
 JIT II: supplier working in the manufacturer’s plant
 Physical proximity is preferred but not necessary
 From the supplier’s point of view:
Guaranteed, steady demand
Advanced notice of volume changes
Minimal design changes
Lots of requirements
Competitive vs. cooperative dilemma
“All eggs in one basket”
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2.7 Demand Variability
Cells can deal with changes in product mix and volume
to a certain extent
 Volume adjustment by a number of workers
 Volume adjustment by integrating or separating cells
Kanban system can absorb 10% variability in demand
by manipulating the number of kanbans
 The fewer kanbans, the less production
 At Toyota, container size is at most 10% of daily demand
Uniform loading: Arrange daily production mix in the
same ratio as monthly demand
 Always have some quantities of each product
 Steady demand on components
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3. Before and After JIT
Before
 Inventory to protect against
problems & uncertainty
 Assembly lines
 Push manufacturing
 Tolerate defects
 Tolerate setup times
 Emphasize work of individuals,
following manager instructions
 Treats suppliers as independent
entities
After
 Reduce inventory
 Cells
 Pull manufacturing
 Zero defects
 Reduce setup times
 Emphasize team-oriented
employee involvement
 Suppliers as partners
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4. Benefits of JIT Systems
 Reduced inventory
 Reduced space
requirements
 Improved quality
 Improved process
 Better use of human
resources
 Better relations with
suppliers
 Increased employee
participation






Shorter cycle time
Shorter lead time
Greater flexibility
More product variety
Increased productivity
Increased machine
utilization
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5. JIT Implementation Issues
 All key elements must be present and integrated for the
system to work well
 Must balance technical and behavioural aspects
 Each organization must mould a JIT system to suit its own
environment
 JIT system requires a fundamental change in the
organization
 Not suited for
 Very high volume
 Very low volume, unique products
 Highly fluctuating demands or true make-to-order products
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6. Job Design
 Job design: specifies the contents of the job
 Automation
 Good for repetitive, computational, precise or physical tasks
 Not good for people interaction, creativity, multiple variables
Management
Readily available labour
Minimal training
Reasonable wage
High productivity
High absenteeism
High turnover rates
High scrap rates
Grievances filed
Workers
 Specialization: performing single or limited tasks
Minimal credentials
Minimal responsibilities
Minimal mental effort
Reasonable wage
Boredom
Little growth opportunity
Little control or initiative
Little intrinsic satisfaction
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 Alternative job designs
 Job rotation: shifts workers to different jobs to increase
understanding of the total process
 Job enlargement: expansion of the job through increasing the
scope of the work assigned
 Job enrichment: expansion of the job through increasing the
worker responsibility
 Teams
 Problem-solving teams
 Special-purpose teams
 Self-directed teams
 Work measurement
 Method analysis: the study of how a job is done
 Time study method: sets a standard time based on timed
observations of one worker over several cycles
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147
148
QUALITY
Learning Objectives:
1. What is quality
2. Quality measures
3. Costs of quality
4. Process improvement
5. Continuous improvement
Formula
Sheet
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1. What is Quality?
Meaning of Quality: degree of excellence
Product based
Quality of
Design:
Degree to which
quality
dimensions are
designed into the
product
Producer’s perspective
Quality of
Conformance:
Degree to which a product
conforms to required
specifications
Consumer’s perspective
Fitness for use:
Degree to which a product
satisfies customer’s wants
Value-based:
Degree to which a product
provides acceptable quality at
a reasonable price
American Society for Quality:
The totality of features and characteristics that satisfy needs
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2. Dimensions Of Quality
Products
Services
standards
 Reliability: frequency of failure
 Durability: length of product’s life
 Serviceability: ease of getting
repair
 Aesthetics: appearance
 Safety: not causing injury or harm
 Perceived quality: reputation &
intangibles
convenience
 Accuracy, competence
 Completeness
 Consistency
 Courtesy
 Responsiveness to unusual
circumstances
 Communication
 Security
 Credibility
 Performance: basic characteristics
 Features: “extra” items
 Conformance: meeting pre-set
 Time and timeliness
 Accessibility and
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3. Quality Measures
Scrap, rework, number of errors, premium shipping cost
Product yield:
Yield = (#Units to produce)(%Good units)
+ (#Units to produce)(1  %Good units)(%Reworked)
Formula
Sheet
Quality index: report quality cost relative to some base
value (indexing)
 Labour index = (Quality cost) / (Direct labour hours)
 Cost index = (Quality cost) / (Production cost)
 Sales index = (Quality cost) / (Sales)
 Production index = (Quality cost) / (Product yield)
 Quality-productivity index=(Product yield)/(Production cost)
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Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
Formula
Sheet
152
4. Costs of Quality
 All costs, tangible and intangible, relating to managing the
quality of a good or service
 Costs of poor quality (failure costs)
 Internal failure: costs of scrap, rework, downtime, material losses
 External failure: costs of product returns, repairs, recalls, warranty
claims, customer complaints, and lost sales costs
 As product quality , failure costs 
 As internal failure costs , external costs may or may not  − why?
 Costs of good quality (control costs)
 Appraisal: costs of testing and inspection (equipment, operators)
 Prevention: costs of preparing and implementing a quality plan
(product/process design, training, information costs)
 As product quality , appraisal costs  but prevention costs may or
may not  − why?
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Costs of good quality vs. costs of poor quality
(source: Schneiderman, 1986)
Total cost
Total cost
Failure
costs
Failure
costs
Control costs
Control costs
0%
Optimal
quality
100%
0%
Pre-“zero defects”
mentality
“Zero defects” mentality
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100%
Optimal
quality
154
5. Quality and Profitability
Improved
quality
Higher sales
- Improved response
- Improved reputation
- Higher prices
- Increased market share
Higher
profits
Lower costs
- Lower rework & scrap
- Lower warranty & liability
- Increased productivity
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6. Quality Improvement
 Process improvement: Final product inspection is too late
 must improve the production process
 Eliminate common causes (e.g., poor design)
 Eliminate special causes (e.g., specific equipment)
 Continuous improvement: A philosophy of never-ending
pursuit of high product quality
 Innovation vs. continuous improvement: big jumps vs. small steps,
dramatic vs. not dramatic, specialists vs. everyone
 Quality of design: Built-in quality in the product or service
design
 Quality Function Deployment (QFD): a tool to translate the
customer preferences into specific technical requirements
 House of quality
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6.1 Process Improvement Tools
 Check sheet
 To initiate process improvement by recording quality problems
 Histogram, Pareto chart
 To prioritize quality problems
 Cause-and-effect (fishbone, Ishikawa) diagram
 To identify possible causes of a quality problem; good for
brainstorming
 Scatter diagram
 To narrow down to one cause
 Flow chart
 To identify the cause of a quality problem by tracing the
production process
 Statistical Process Control (SPC) chart
 Quality monitoring
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Histogram: shows
the frequency of
quality problems
Check sheet: factfinding tool; tally the
number of problems
Pareto chart: shows %
of the frequency of
problems
Problem
Late
Missing
Broken



#
%
Cause-and-effect diagram: identifies all possible
areas to which quality problems may be related
Receiving
Supplier
Shipping
Machine
Workers Material
Late
order
Flowchart: focus on
where in a process a
quality problem
might exist
Temperature
Statistical process
control chart:
monitors whether or
not the process is in
control over time
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Scatter diagram:
shows the
relationship
#
between two
variables
158
6.2 Continual Improvement Tools
 Deming Wheel: 4 stage-process (a.k.a. PDSA or PDCA cycle) for
continual quality improvement
1. Plan
Identify problem
and develop plan
for improvement
4. Act
Institutionalize
improvement
2. Do
Implement plan
on a test basis
3. Study/Check
Check to see
if the plan is
working
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Quality Circle: Team-based approach to continuous quality
improvement
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STATISTICAL PROCESS CONTROL (SPC)
Learning Objectives:
1. 4 stages/levels of quality management
2. Acceptance sampling vs. SPC
3. SPC steps
4. Attributes vs. variables
5. 4 SPC charts
6. Pattern tests
7. Process capability basics
Formula
Sheet
Formula
Sheet
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1. Quality Management
Four stages
 Quality inspection: focus on providing information
 Quality control: focus on monitoring and controlling
 SPC
 Quality assurance: management programs aimed at
ensuring good product quality by setting minimum or
desired levels of quality
 Total Quality Management (TQM): management
philosophy about ensuring and improving product
quality throughout the entire organization
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2. Quality Inspection
When
 Upon receipt of resources
 Before transformation operations (especially bottleneck)
 The first few items coming out of an automated operation
 Final inspection
 Customer complaints and returned goods
How much and how often
 Complete inspection vs. sampling
 Cost of inspection vs. cost of not detecting defects
 Cost, time and physical possibility
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3. Acceptance Sampling
 The method of randomly inspecting a sample of goods and
deciding whether to accept the entire lot based on the
results
1.
2.
3.
Take a random sample from a lot (batch) of items
Test the sample items for the specified quality characteristics
Accept all items in the lot if
(Number of defective items in the sample)
< (Maximum number of defective items allowed in a sample)
Otherwise, reject all items in the lot.
 Historical mentality: “some degree of poor quality will occur
and that is acceptable”
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4. Statistical Process Control (SPC)
To prevent poor product quality
Process improvement tool
Method of randomly inspecting a sample of goods and
deciding whether the production process is in control
 Monitor the production process (data pattern)
 Provide a statistical signal when the process (quality) changes
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4.1 General Steps in SPC
Step 1: Define the quality characteristic to measure
Step 2: Set up a control chart
Step 3: Take a random sample and plot the quality measure
Step 4: Is any sample point
outside of the control limits?
Yes
No
Step 5: Does any
pattern exist?
The process is in control
(continue monitoring)
No
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Yes
Step 6: The
process is out of
control;
stop the process
until the quality
problem is
identified and
fixed
166
Step 1: Define the quality characteristic to measure
 Attributes: characteristics that are measured qualitatively,
thus have discrete values
 For example, defective/non-defective, # of scratches or blemishes
 Count both defective and non-defective items  p-chart
 Count defective features in an item  c-chart
 Variables: characteristics that are measured quantitatively,
thus have continuous values
 For example, weight, length, volume, temperature
 Difference (range) between smallest and largest values  R-chart
 Average  x-chart
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Step 2: Set up a control chart
UCL (Upper
Control Limit)
3 Sigma Limits
Process Average
3 Sigma Limits
LCL (Lower
Control Limit)
1
2
3
4
5
6
7
8
9
10
Sample number
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Step 3: Take random samples over time. For each sample,
measure the quality characteristic and plot the result.
UCL
Process Average
LCL
1
2
3
4
5
6
7
8
9
10
Sample number
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Key ideas behind the control limits:
 In spite of inherent random variation in a production
process, the average of the distribution of the quality
characteristic should be stable if the process is in control
 The range between the UCL and LCL allows for random
variation
 Observations falling outside the UCL or LCL indicate the
existence of abnormal variation
 Control limit standard = 3-sigma limits
 Control limits too narrow  Type I error: random variation
mistaken for an abnormal variation
 Control limits too wide  Type II error: abnormal variation may
not be detected
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Step 4: Is any sample point outside of the control limits?
 Yes  the process is out of control (step 6)
 No  go to step 5
Step 5: Does any pattern exist? (Pattern tests)
 Yes  the process is out of control (step 6)
 No  the process is in control  continue monitoring
(step 3)
Step 6: Identify and correct the quality problems
 Process improvement tools
 Discard out-of-control sample observations
 May need to revise the control chart
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4.2 p-Chart: Control Chart for Attributes
 Quality to be measured: proportion of defective items
 Sample size (n): 30-100 items per sample (guideline)
Center line: p = historical average proportion of defects
UCL = p + z [ p (1 – p)/n]1/2
LCL = p – z [ p (1 – p)/n]1/2
Formula
Sheet
Formula
Sheet
Sample
standard deviation
z = number of standard deviations corresponding to the sigma limit
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Example 1: A soft drink bottler is concerned about over- and under-filled
bottles. Management desires 3-sigma control limits. 10 samples (200
bottles per sample) have been collected.
a) Is the process in control?
b) The first day on the job, a machine operator takes a sample of 30 units
and finds 5 units to be defective. Should you be concerned?
Sample
1
2
3
4
5
6
7
8
9
10
# of defects  p
12
0.06
16
0.08
8
0.04
24
0.12
20
0.10
4
0.02
16
0.08
12
0.06
28
0.14
8
0.04
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Control chart for Example 1:
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
1
2
3
4
5
6
7
8
9
10
Sample number
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4.3 c-Chart: Control Chart for Attributes
Quality to be measured: # of defective features per item
Sample size: 1 item per sample
Used when it is not possible to count non-defective items
(i.e., no proportion of defectives can be computed)
Center line: c = historical average # of defective features
UCL = c + z ( c )1/2
LCL = c – z ( c )1/2
Formula
Sheet
Formula
Sheet
Sample
standard deviation
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Example 2: An airline company wishes to set up a control chart to monitor
the number of misplaced luggage. One flight is selected at random each
hour, and the cases of misplacement are counted.
a) Is there any concern regarding misplaced luggage? Use 3 sigma limits.
b) The 7th flight had 11 cases of misplacement. Should you be concerned?
Flight
1
2
3
4
5
6
# of misplacement
6
3
9
6
9
3
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Control chart for Example 2:
18
16
14
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10
Sample number
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4.4 R-Chart: Control Chart for Variables
Quality to be measured:
R = (Largest value in the sample) – (Smallest value)
Sample size (n): 2-10 items per sample
Center line: R = historical average of R values
UCL = D4 R
Formula
Sheet
LCL = D3 R
Formula
Sheet
D3 and D4 = range value constants based on sample size
• Specifically developed to determine control limits for R-chart
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4.5 x-Chart: Control Chart for Variables
Quality to be measured: sample average
Sample size (n): 2-10 items per sample
Center line: x = historical average of x values
UCL = x + A2 R
Formula
Sheet
LCL = x – A2 R
Formula
Sheet
A2 = mean value constant based on sample size
• Specifically developed to determine control limits for x-chart
 R-chart and x-chart should be used together - why?
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Control Limit Factors
for R-chart and x-chart
with 3-sigma limits
Formula
Sheet
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Example 3: University Food Services uses SPC to monitor the length of
time from when an order is placed with a supplier to when the goods
arrive at the receiving dock (“order cycle time”). When the process is
in control, x = 1.13 days and R = 0.5 days based on samples of size 5. Is
the process in control? Use 3 sigma limits.
Sample1
1.1
1.2
1.1
1.1
1.0
Sample2
1.2
1.1
1.2
1.1
1.0
Sample3
1.0
1.0
1.8
1.0
1.0
Sample4
0.8
1.1
1.2
1.2
1.9
Sample5
1.1
1.0
1.0
1.0
1.2
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Control charts for Example 3:
R chart
x chart
1.6
1.6
1.4
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
1
2
3
4
0
5
2
3
4
5
Sample number
Sample number
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1
182
5. Pattern Tests
To detect a non-random pattern within the control limits
 Run tests: check for the runs
 Run: a sequence of observations with a certain characteristic
 Pattern 1: 8 consecutive points on one side of the center line
 Pattern 2: 8 consecutive points up (or down)
 Pattern 3: 14 points alternating up-down-up-down…
 Zone Tests: check for the zones
 3 zones for 3 sigma limit control charts
 Pattern 4: 2 out of 3 consecutive points
UCL
Formula
Sheet
Formula
Sheet
Zone width = (UCL − Center)/3
MOS 3330: SPC
Formula
Sheet
Zone A
Zone B
in zone A
 Pattern 5: 4 out of 5 consecutive points Center
in zone A or B
Formula
Sheet
Formula
Sheet
Formula
Sheet
LCL
Zone C
Zone C
Zone B
Zone A
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183
UCL
LCL
UCL
LCL
Consistently below  pattern 1? (not yet)
UCL
UCL
LCL
LCL
Upward trend  pattern 2? (no)
Alternating up&down  pattern 3? (not yet) Close to boundarypatterns 4&5? (yes)
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Example 4: Is the process in control? x=1.47, UCL=2.05, LCL=0.89
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
x
1.4
1.3
1.2
1.36
1.56
1.6
1.7
1.53
1.66
1.43
1.87
1.65
1.4
1.78
1.9
1.7
1.83
1.5
1.88
1.1
Above/Below
B
B
B
B
A
A
A
A
A
B
A
A
B
A
A
A
A
A
A
B
Up/Down
Zone
--D
D
U
U
U
U
D
U
D
U
D
D
U
U
D
U
D
U
D
C
C
B
C
C
C
B
C
C
C
A
C
C
B
A
B
B
C
A
B
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6. Process Capability
 SPC monitors natural vs. non-random variation within a
process  producer’s perspective
 Process capability: The ability of a process to satisfy a
product’s or service’s tolerances
 Tolerances: Design specifications that reflect customer
requirements
 Upper Tolerance Limit (UTL), Lower Tolerance Limit (LTL)
 Not statistically determined
 Not a result of production process
 Production process must be in control (check SPC) and
must meet design specifications (check process capability)
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 How to assess process capability: compare specification
width against process width
 Process capability index (Cp)
= (specification width) / (process width)
Formula
Sheet
 Rough guideline
 Process is capable of meeting specifications if
(UTL  LTL) = (UCL  LCL)  Cp = (UTL  LTL) / (UCL  LCL) = 1
 Process is capable of exceeding specifications if
(UTL  LTL) > (UCL  LCL)  Cp = (UTL  LTL) / (UCL  LCL) > 1
 Process is not capable of meeting specifications if
(UTL  LTL) < (UCL  LCL)  Cp = (UTL  LTL) / (UCL  LCL) < 1
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TOTAL QUALITY MANAGEMENT (TQM)
Learning Objectives:
1. What is TQM
2. Quality evolution and quality gurus
3. Similarities/differences to other concepts
4. Implementation issues
5. Six sigma quality program
6. Quality awards
7. ISO
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1. What is TQM?
 An organization-wide effort to achieve high product quality based
on 3 guiding principles:
1) Customer focus
 Quality from the customer perspective; obtain customer feedback
2) Continual improvement
 Constantly seek to improve processes, products, productivity,
effectiveness, responsiveness, etc.
3) Total participation and teamwork
 Workers are the inspectors: provide training, authority, rewards
 Cross-functional, process improvement teams
 Top management commitment
 Supplier relationship
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1.1 Stages of QM
1) Quality Inspection
2) Quality Control
Management commitment
Customer focus
Obtain management support
Choose a SPC leader
Select a process for pilot study
Empowered workers
Provide SPC training
Continual improvement
Process improvement
Construct control charts
3) Quality Assurance
Project-by-project
Process-by-process
Cross-functional
Supplier relationship
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4) TQM
Company-wide
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191
1.2 Quality Evolution
Ideas
Industrial
Revolution/
early 1900s
Quality
inspection
1920s1950s
Quality
control
Shewhart
Process improvement
Statistical methods
1960s1970s
Quality
assurance
Deming, Juran,
Crosby
ISO, JIT, Oil Crisis,
Foreign competition
Management philosophy
Continual improvement
Cost of quality
Zero defects
Feigenbaum
Ishikawa
Organizational focus
Customer driven quality
More choices and info
Higher expectations
Affordable quality
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1980s to
present
192
Events/People
TQM
1.3 Quality Gurus
 Shewhart
 Promoted SPC, Shewhart Cycle
 Deming
 Deming Wheel, 14-point quality management philosophy, promoted
quality management in Japan  Deming Prize
 Juran
 Quality Control Handbook (famous book), quality trilogy (planning,
control and improvement), cost of quality
 Crosby
 Cost of poor quality, zero defects, Quality Is Free (famous book)
 Feigenbaum, Ishikawa (cause-and-effect diagram)
 Total quality control, company-wide commitment
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1.4 TQM vs. JIT
 Customer focus
 JIT: to achieve good forecast of customer demand
 Continuous improvement (“kaizen”)
 JIT: require high quality products; undercapacity scheduling
 Total participation and teamwork
 JIT: well-trained, multifunctional, empowered employees; group
TQM
JIT
1950 1960 1970 1980
Recognition in the West
Recognition in Japan
problem solving
JIT
1950 1960 1970 1980
1990
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TQM
1990
194
1.5 TQM Implementation Issues
 Lack of a genuine quality culture
 Forgetting customer-focus
 Conflict with existing systems (e.g., compensation and promotion
systems, policies and procedures)
 Lack of top management support and commitment
 Forgetting long-term benefits (“bottom line” approach)
 Inadequate training
 Over- or under-reliance on quality tools
 Over-emphasis on teams
 Under-emphasis on individual efforts
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2. Six Sigma Quality Program
 Six sigma: A high level of product quality with 3.4 defective
parts per million
 Coined by Motorola in 1986
 Motorola won Baldrige Award in 1988
 Six sigma program: A quality management program with
clear business goals, which are achieved by
 Cutting poor quality costs
 Rewarding employees for quality improvement
 Training employees  Six sigma black belt certification
 Black belt: Employee trained and experienced in application of statistical
techniques, problem solving, project management, team leadership skills
 Applying a scientific methodology  DMAIC, statistics, computers
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Six Sigma Methodology
 DMAIC model
 Define problem/opportunity
 Measure characteristics that are critical-to-quality
 Analyze the problem using benchmarking and gap studies
 Improve by reducing variation and reducing defects
 Control performance
 Other tools
 Computer simulation
 Part standardization
 Supplier qualification, SPC
 Design of experiments
 Measurement system analysis
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3. Quality Awards
 Baldrige Award (USA): created by law in 1987
 Manufacturing, service, small business, education, health care
 Customer focus, process management, leadership, HR, strategic
planning, information and analysis, business results
 Application, initial screening, in-depth examination
 Deming Prize (Japan): created in 1951
 Company, business unit, individual
 Allowed foreign companies to apply in 1984
 Minimum application standards
Common themes
 Privilege, worthy experience, expensive
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4. Quality Standards
 ISO (International Organization for Standardization)
 Headquarter in Geneva, officially established in 1947
 Network of national standards institutes of 146 countries
 Developed over 18,000 international standards on a variety of
subjects
 Quality Management  ISO 9000 series
 First published in 1987
 To promote a uniform quality standard for cross-border transactions
 A guideline for developing an organizational system for documenting
quality management-related processes and procedures
 Certification of organizations, not products
 Common themes: Marketing strategy, customer requirement,
international trade requirement
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 ISO 9000 series include:
 ISO 9000 – Quality management systems: fundamentals/ vocabulary
 ISO 9001 – Quality management systems: requirements
 ISO 9004 – Quality management systems: managing for the
sustained success of an organization (continuous improvement)
 ISO 14000 series include:
 ISO 14001 – Environmental management systems: framework /
criteria
 ISO 14006 – Environmental management systems: guidelines for
incorporating ecodesign
 ISO 14020 – Environmental labels and declarations
 ISO 14034 – Environmental technology verification: how to verify the
performance of new environmental technologies
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ISO 9000
 Registration process
 Application  documentation review  pre-assessment 
registration audit  registration decision  surveillance audit
 Cost
 Internal: analysis, project planning, system development, system
documentation, system implementation, training, internal audit,
system modifications
 External: ISO publication and software, registrars (fees&travel),
consultants (fees&travel)
 Time: “3 months to 3 years”
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