H16
Problem Solving and Decision Making
Total Quality Management (TQM) emphasizes not only reactive problem solving, but also proactive prevention.
The focus lies in eliminating root causes, not just symptoms.
Decision Making vs Problem Solving
• Decision Making: Choosing between multiple alternatives, often proactively. Good decisions require
both good outcomes and good processes.
• Problem Solving: Reactive, triggered by a discrepancy between actual and desired performance. Involves understanding and eliminating root causes.
The PDCA Cycle
The PDCA (Plan–Do–Check–Act) cycle is central to continuous improvement.
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Toyota Practical Problem Solving
A structured method that clarifies the issue, identifies root causes, and implements lasting improvements.
Widely used in lean environments.
1. Perceive the problem: Recognize issues based on facts or complaints.
Example: Customers complain about inconsistent quality (overcooked, undercooked, etc.).
2. Clarify the problem: Observe the situation, analyze the gap, and set a measurable objective.
Example: Analyze variance vs. standard; aim to reduce inconsistency.
3. Locate point of cause (PoC): Identify where and when the problem occurs.
Example: Manual shaping station during doughnut formation.
4. Investigate root cause: Use the 5 Whys method to dig deeper.
Example: No shaping guideline → relies on employee judgment → no tool → quality impact ignored →
focus only on final product.
5. Develop and implement countermeasures: Address root causes, not symptoms.
Example: Introduce shaping tools, provide instructions and training.
6. Check results: Evaluate the impact of countermeasures.
Example: Compare consistency before and after implementation.
7. Standardize successful practices: Update procedures and train staff.
Example: Adjust SOP (standard operation procedures) — size check becomes mandatory; train new staff
accordingly.
Decision Making as a Process
A structured approach supports better quality decisions:
• Identify the problem or need
• Gather relevant data
• Generate and evaluate alternatives
• Select and implement the best available option
(not necessarily the theoretical optimum, but the
most feasible and effective under current constraints)
• Monitor and adjust
Decisions can be:
• Objective: Based on data, time, and freedom of choice
• Subjective: Influenced by intuition, experience, or context
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Scientific Decision-Making
Scientific decision-making involves basing decisions on data rather than on intuition or hunches.
Complexity in operations refers to nonproductive, unnecessary effort and confusion that arises when decisions
are made without a structured approach. It often results in inefficiency, errors, delays, and inconsistent
outcomes.
Helps target and eliminate:
• Errors, defects, breakdowns, delays
• Inefficiencies and unwanted variation
• Common cause vs special cause variation (see SPC)
The goal is to make sound, repeatable decisions that lead to long-term quality improvements.
Employee Involvement in Decision-Making
Involving employees can improve decisions, but also introduces challenges.
Advantages:
• More accurate and complete view of the problem
• Greater variety of ideas and potential solutions
• Increased ownership and buy-in (“acceptance”)
Disadvantages:
• Slower decision process
• Distraction from normal duties
• Possible interpersonal conflict
• Risk of groupthink or groupshift
• “Democratic” outcome is not always the best outcome
Common techniques include brainstorming, Nominal Group Technique (NGT), and team-based analysis. Managers should stay alert to group dynamics and facilitate effectively.
From Data to Action
Effective decisions rely on transforming raw data into actionable knowledge:
• Data: Raw, unprocessed facts
• Information: Contextualized and structured data
• Knowledge: Interpreted information with insight
• Action: Decisions and implementation
Too much unfiltered data leads to information overload and poor choices.
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Information Needs by Management Level
Based on Anthony’s hierarchy:
•
Strategic: Summary-level, external, unstructured,
long-term decisions.
Example: Setting long-term quality goals based on
market trends and competitive analysis.
•
Tactical: Mix of detailed and summarized information, semi-structured.
Example: Allocating resources to a training program
based on quality audit findings.
•
Operational: Detailed, internal, structured, realtime.
Example: Inspecting products on the production line
or scheduling workers to meet daily quality targets.
1
Creativity in Quality Management
Creativity is not contradictory to structure — it enhances it.
• Preparation: Involves gathering information, learning, and
building experience. It requires time and effort to understand the
context and frame the problem correctly.
• Incubation: A subconscious process where ideas are left to develop in the background. This phase benefits from detachment,
rest, or switching focus.
• Insight: The “aha” moment when a solution suddenly becomes
clear. Although it feels spontaneous, it is often the result of deep
prior work.
• Verification: The idea is tested for feasibility, usefulness, and
alignment with constraints. Techniques such as cost-benefit analysis and risk assessment may be used here.
Other aspects of creativity include:
Barriers (Inhibitors)
Stimulators (Strategies)
Looking for just one right answer
Idea vending
Being too logical
Listening
Avoiding ambiguity
Idea attribution
Avoiding risk
Openness
Forgetting how to play
Playfulness
Fearing rejection
Ambiguity tolerance
Saying “I’m not creative.”
Brainstorming techniques
Idea vending refers to feeding employees with a wide range of example ideas to stimulate their thinking.
Idea attribution means giving employees full credit and ownership for further developing a seed idea.
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SCAMPER: A Creative Thinking Technique Prof: know the abbreviation and give
examples! SCAMPER is a structured technique that helps generate new ideas by systematically transforming
existing ones. It is particularly useful when trying to innovate or improve a product. Below, each SCAMPER
step is illustrated using the example of a travel backpack:
• Substitute: Replace components or materials with alternatives to improve function or experience.
Example: Use magnetic closures instead of traditional zippers to make the backpack quicker and easier to
open.
• Combine: Merge functions or features for added convenience or performance.
Example: Integrate a water bottle holder with a phone charger, creating a dual-purpose feature ideal for
travelers.
• Adapt: Adjust the product for different use cases or environments.
Example: Add a built-in folding laptop desk to the backpack, allowing users to work comfortably on the
go.
• Modify / Magnify / Minify: Change the size, appearance, or properties to enhance functionality.
Example: Modify the backpack straps by adding built-in LED lights for increased safety during nighttime
travel.
• Put to other uses: Reimagine how the product could be used in different contexts.
Example: Design the backpack to unfold into a small picnic table for use during outdoor activities.
• Eliminate: Remove unnecessary parts to simplify and improve usability.
Example: Eliminate traditional shoulder straps and instead use a hip-attachment system for better weight
distribution.
• Reverse / Rearrange: Change the order, layout, or orientation of components to create flexibility.
Example: Rearrange the internal pockets and compartments into a modular system that users can customize to fit their personal needs.
SCAMPER encourages designers and teams to explore possibilities beyond the obvious, helping to overcome
creative blocks and generate fresh, applicable ideas.
TRIZ
jwz, ook 6hats enz voor techniek
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H17
Quality Function Deployment (QFD)
QFD is a structured method for translating customer needs into product or service specifications. It ensures
that development efforts are guided by the “voice of the customer” (VOC), leading to higher satisfaction and
better market alignment.
QFD originates from value engineering — a method developed during WWII at General Electric — where
value is defined as:
Function
Value =
Cost
The goal is to deliver only those functions that matter to the customer, avoiding over-engineering.
House of Quality
The most well-known QFD tool is the House of Quality (HoQ) — a matrix that links customer requirements
to technical specifications. It is constructed step-by-step as follows:
• 1. Customer Requirements (“Whats”): Gather and prioritize customer needs using tools like
affinity or tree diagrams. These form the left side of the house.
– Affinity diagrams are used to group raw customer statements into meaningful categories based
on similarity. This helps structure chaotic input from interviews, surveys, or brainstorms.
– Tree diagrams then decompose broad customer needs into sub-needs and specifics, making them
easier to translate into design requirements.
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• 2. Planning Matrix: Quantifies how well current products (own and competitors’) meet these needs,
and sets planned satisfaction levels.
– CS Rating Our Textbooks: Customer satisfaction (CS) score (1–5) for our product on each
customer requirement.
– CS Rating Competitor A & B: CS scores (1–5) for competing products. Used for competitive
benchmarking.
– Our Planned CS Rating: The target CS score (1–5) for each CR. A score of 5 is ideal but not
always feasible due to constraints.
– Improvement Factor: Indicates the relative improvement target. Calculated as (with 0.2 being
the global improvement factor here):
Improvement Factor = (Planned CS Rating − Current CS Rating) × 0.2 + 1
Example: (5 − 4) × 0.2 + 1 = 1.2
– Sales Point (Strategic Marketing Factor): Value from 1.0 to 1.5 that reflects the marketing
importance of the need for promotion. Estimated by the team.
– Overall Weighting: Represents the strategic weight of the CR, combining customer importance,
required improvement, and marketing relevance:
Overall Weighting = Importance × Improvement Factor × Sales Point
Example: 5 × 1.2 × 1.1 = 6.6
– % of Total Weight: Normalized percentage contribution of each CR:
Overall Weighting
%= P
× 100
Overall Weightings
6.6
Example: 51.3
× 100 ≈ 13%
CR
1
2
3
4
5
6
7
8
9
CS Rating Our Textbooks
CS Rating Competitor A
CS Rating Competitor B
Our Planned CS Rating
Improvement Factor
Sales Point
Overall Weighting
% of Total Weight
4
5
4
5
1.2
1.1
6.6
13
3
4
5
3
1.4
1.0
4.2
8
4
5
4
5
1.1
1.4
4.8
9
4
4
5
4
1.4
1.2
5.0
10
3
5
4
4
1.2
1.3
5.5
11
4
3
5
3
1.4
1.2
4.8
9
4
4
4
4
1.2
1.3
4.7
9
3
3
3
3
1.3
1.4
5.2
10
4
5
4
5
1.4
1.4
5.9
12
• 3. Technical Requirements (“Hows”)
• 4. Relationship Matrix
• 5. Correlation Matrix (Roof )
• 6. Technical Priorities and Benchmarks: Final section includes:
– Technical priority=rij · Overall weighting
– Competitor benchmarking (if data available)
– Design targets (can be values, categories, yes/no)
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Kano Model
The Kano model is a framework for identifying which product or service features most influence customer
satisfaction. The goal is to prioritize what matters most to users and avoid wasting resources. It is particularly
useful for designing a minimum viable product (MVP): get essential features to market quickly, and refine or
add others later.
The method is named after Professor Noriaki Kano, who developed it in 1984. Kano is not an acronym — it
simply refers to his surname.
The model classifies features based on their impact on satisfaction into five categories:
• Basic (Must-Be): Expected and assumed. If absent, cause dissatisfaction; if present, neutral impact.
E.g. working brakes on a car.
• Performance (One-Dimensional): The more you offer, the more satisfaction you generate.
E.g. better fuel economy.
• Excitement (Delighters): Unexpected features that generate delight when present, but don’t cause
dissatisfaction if absent.
E.g. self-parking on a basic car.
• Indifferent: Have little or no effect on satisfaction.
E.g. aesthetic changes that users don’t notice.
• Reverse: Features that reduce satisfaction if included.
E.g. overly complex controls for simple users.
The model helps prioritize which features to include in a product or service, based on customer segment. What
is a delighter for one group (e.g. vacationers) may be a must-have for another (e.g. business
travelers).
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Functional vs. Dysfunctional Questions
A Kano questionnaire uses two complementary questions per feature:
• The functional question asks: “How do you feel if the feature is present?”
• The dysfunctional question asks: “How do you feel if the feature is absent?”
This dual-question format helps uncover not only what users explicitly want, but also what they would miss if
absent — which reveals more subtle categories like delighters or basic needs.
AOMIRQ and Kano Mapping
In Kano analysis, customer preferences are categorized using the AOMIR coding scheme, which maps directly
to the five Kano quality types:
• A: Attractive ⇒ Excitement
• O: One-dimensional ⇒ Performance
• M: Must-be ⇒ Basic
• I: Indifferent ⇒ Indifferent
• R: Reverse ⇒ Reverse
• Q: questionable, considered a mistake, when the customer says I want it to be dysfunctional and
functional.
Differences between QFD and Kano:
• QFD translates explicit needs into design specs; Kano captures unconscious preferences.
• QFD delivers design targets; Kano guides strategic feature selection.
• Both aim to integrate customer input — but use different logic and deliverables.
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H18
Statistical Quality Control (SQC)
Statistical Quality Control (SQC) is a key discipline in TQM and includes:
• Statistical Process Control (SPC) — monitoring and improving the process
• Acceptance Sampling — deciding on acceptance or rejection of lots
Definition:
SPC is a statistical method of separating special-cause variation from natural variation to eliminate the
special causes and establish and maintain consistency in the process, enabling process improvement.
SPC is essential today to elevate the quality of products and services while lowering costs in order to compete
successfully in world markets.
Core Messages of SPC
• Variation in any process is the enemy of quality
Reducing variation — especially eliminating special causes — is essential to improving quality.
• SPC is more than control charts
Control charts are just one part of SPC. Effective implementation also requires:
– Management involvement
– Clear process understanding
– Use of supporting quality tools
Types of Variation
• Common (natural) causes: random, inherent variation; expected
• Special causes: non-random, external influences; must be identified and removed
Special causes disturb the process — they must be eliminated before improvement is possible.
SPC Benefits
• Continual improvement is a key principle of total quality.
Before a process can be improved, it must first be understood. External factors that cause special variation
must be identified and eliminated. Once only natural variation remains, control charts can be used to
monitor the process for trends and reoccurring issues. Only then can true improvement start — leading
to better products and services.
• Predictability of processes is crucial in today’s competitive markets.
Organizations must aim for processes that are:
– Predictable — statistically stable and under control
– Consistent — repeatable performance over time
SPC provides the structure to track and maintain this consistency. Customers value reliability: “making
commitments and living up to them.”
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• Elimination of waste is both a quality and a cost concern.
Reducing variation helps reduce:
– Scrap
– Rework
– Unnecessary inspection
– Non-conformance issues
Waste elimination leads to lower production cost and higher quality. SPC is therefore a cornerstone in
Lean Thinking.
• Product inspection should focus on in-process checks.
It is normal to inspect both in-process and final products. However, earlier detection of problems is
better:
– The further a product progresses, the more value it accumulates
– Rejecting or scrapping late-stage products is costly
Inspections require skilled operators and expensive equipment. Although 100% inspection may seem
ideal, it is not foolproof — defects may still be missed due to human error or instrument drift. Therefore,
inspections are often done on a sampling basis, guided by acceptance schemes.
Variables vs Attributes
• Variable data — measurable/continuous (e.g. length, weight, time)
• Attribute data — countable/discrete (e.g. number of defectives)
The focus depends on what information is needed:
• Count late deliveries → attribute
• Measure delivery time → variable
”Not what you are looking at but what you want to know”
Standard Deviation vs Variance
The variance measures the average squared deviation from the mean:
n
1X
σ =
(xi − µ)2
n
2
i=1
The standard deviation is the square root of the variance:
√
σ = σ2
This means:
• Variance is in squared units (e.g. mm2 , s2 )
• Standard deviation is in original units (e.g. mm, s) and thus easier to interpret
The standard deviation indicates how much the data typically deviates from the mean. It reflects the natural
variability in a process.
• Small σ ⇒ low variation, data is tightly clustered
• Large σ ⇒ high variation, data is widely spread
In a normal distribution:
• 68% of the data lies within ±1σ
• 95% within ±2σ
• 99.7% within ±3σ
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Variable Data
Definitions and Formulas:
• n: number of samples per subgroup (subgroup size) ex: #products checked per subgroup
• k: total number of subgroups collected ex: 4 days, 3 times per day =⇒ k=12
1 Pn
xij
n j=1
• X̄i : mean of the i-th subgroup
⇒
X̄i =
• Ri : range of the i-th subgroup
⇒
Ri = max(xij ) − min(xij )
¯ : mean of all subgroup means
• X̄
⇒
¯ = 1 Pk X̄ (this will be the centerline in X̄ chart)
X̄
i
k i=1
1 Pk
Ri (this will be the centerline in R chart)
⇒ R̄ =
k i=1
• R̄: average of all subgroup ranges
Procedure:
1. Define subgroup size (n) and number of subgroups (k)
2. Collect k subgroups of n samples each
3. For each subgroup:
• Compute X̄i and Ri
4. Compute overall averages:
¯ = 1 P X̄
• X̄
i
k
1P
• R̄ =
Ri
k
5. Use standard table values (based on n) to find A2 , D3 , D4
6. Determine control limits: (represents ±3σ of the process)
¯ + A · R̄
• UCLX̄ = X̄
2
¯
• LCLX̄ = X̄ − A2 · R̄
• UCLR = D4 · R̄
• LCLR = D3 · R̄
(LCL cannot be negative)
7. Plot the values and interpret the charts:
• Any point outside control limits ⇒ special cause likely
• Look for non-random patterns: trends, cycles, shifts, or runs
Additional notes:
• The variation is assumed to be due to “natural causes” and its distribution is expected to be normal
(bellshaped)
• Use frequent enough sampling to detect shifts, but not so frequent that trends are masked.
• Always base calculations on actual process data over a representative period.
• Charts should not be recalculated continuously — update only when the process has changed or improved
significantly.
• If LCL < 0, set LCL = 0 (e.g. for length, mass, time — negatives are not meaningful).
charts:
• X̄-chart: plots x̄ for every subgroup (so k= x-axis)
• R-chart: plots R for every subgroup
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Attribute Data
Attribute data is used when outcomes are counted rather than measured. The data consists of discrete events
such as defectives (items that fail inspection) or defects (number of faults per item). Four common control
charts are used, each depending on the type and size of the data.
Defects vs Defective Items
A product can have one or more defects — minor imperfections — without being considered defective.
A product is only considered defective if the defects are severe enough to:
• prevent the product from functioning properly, and/or
• make the product unsaleable
For example, a new car may have several small defects that the customer may never notice — it is not defective.
This distinction is important when choosing between C/U charts (defects) and P/NP charts (defectives).
Underlying distributions:
• P- and NP-charts use the binomial distribution, which models yes/no outcomes across n trials. It
is appropriate when each item is either acceptable or defective.
• C- and U-charts use the Poisson distribution, which models the number of events (e.g. defects)
occurring in a fixed space, time, or item — assuming these events occur independently.
Chart Types:
• P-chart: monitors the fraction of defective items in a sample
Use when sample sizes vary
• NP-chart: monitors the number of defective items per sample
Use when sample size is constant
• C-chart: monitors the number of defects per unit
Use when inspecting one unit or constant-size areas
• U-chart: monitors the number of defects per unit
Use when sample size varies (e.g. different surface areas, batch sizes)
P-chart:
Monitors the proportion pi of defectives in each subgroup. (y = pi ; x = k)
We start from the proportion of defectives p in a sample of size n. By multiplying both, we obtain the expected
number of defectives:
np = p · n
number of defectives in sample i
npi
=
sample size ni
ni
P
1 Pk
np
• p̄ =
, (center line)
i=1 pi = P
k
n
r
r
p̄(1 − p̄)
p̄(1 − p̄)
• U CLp = p̄ + 3 ·
,
= σ =⇒ p̄ + 3 · σ
ni
ni
r
p̄(1 − p̄)
• LCLp = p̄ − 3 ·
(set to 0 if negative)
ni
• pi =
74
NP-chart:
Monitors the number of defectives in each subgroup (fixed sample size n). (y = npi ; x = k)
• npi = number of defectives in sample i
P
np1 + np2 + . . . + npk
np
=
, (center line)
• np̄ =
k
k
p
• U CLnp = np
¯ + 3 · n · p̄(1 − p̄)
p
• LCLnp = np
¯ − 3 · n · p̄(1 − p̄)
C-chart:
Monitors the number of defects (not defectives) per unit (for constant inspection size). (C for count)
(y = ci ; x = sample number)
Defects may also be logged by type, but the c-chart data are the simple sum of all defects (can be different
types of defects)
• ci = number of defects in sample i
• c̄ =
1P
ci ,
k
(k=#samples)
• U CLc = c̄ + 3 ·
• LCLc = c̄ − 3 ·
√
√
(center line)
c̄
c̄ (set to 0 if negative)
U-chart:
Monitors the number of defects per unit (also called non-conformity’s) when sample sizes differ.
(U for unit) (y = ui ; x = sample number i)
ci
, where ci = number of defects, ni = size (e.g. area, units)
ni
P
ci
total number of defects
• ū = P =
(center line)
total sample inspected
ni
r
ū
• U CLu = ū + 3 ·
(dependent on ni so it will vary for the sample number in the chart)
ni
r
ū
• LCLu = ū − 3 ·
(set to 0 if negative)
ni
• ui =
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Time-Weighted Charts
Time-weighted control charts are used to detect small, gradual shifts in a process that may not be visible
on traditional Shewhart charts. They give more weight to recent observations and are especially useful for
processes with tight tolerances or high stability.
Two main types:
• EWMA (Exponentially Weighted Moving Average)
• CUSUM (Cumulative Sum Control Chart)
EWMA chart
• Tracks a weighted moving average of past data, where recent values get more weight.
• Formula:
EWMAt = λ · Yt + (1 − λ) · EWMAt−1
where:
– Yt = measured value at time t
– λ = smoothing constant (0 < λ ≤ 1)
• Small λ ⇒ more memory (smoother curve)
Large λ ⇒ more reactive to recent data
• Control limits:
UCLt = µ + k · σEWMA
where:
– µ = target process mean
– k = width multiplier (typically 3)
r
λ
– σEWMA = σ ·
2−λ
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LCLt = µ − k · σEWMA
CUSUM chart
• A CUSUM (Cumulative Sum) chart monitors the cumulative sum of deviations from a target mean over
time.
• It is especially effective for detecting small, sustained shifts in the process mean — even as small as
1σ.
• Unlike Shewhart charts (which only consider the current sample), the CUSUM chart incorporates the
full sequence of historical values.
Suppose we collect n ≥ 1 samples, and let X̄j be the mean of the j-th sample. The CUSUM chart plots the
cumulative statistic Ci :
i
X
Ci =
(X̄j − µ0 )
j=1
where:
• µ0 = target process mean
• Ci = cumulative deviation from the target up to sample i
Interpretation:
• If the process is in control, then Ci fluctuates around zero (random variable with mean 0)
• If the mean shifts upward to µ1 > µ0 , Ci will drift upward over time
• If the mean shifts downward to µ1 < µ0 , Ci will drift downward over time
Supplementary Detection Rules
In addition to checking whether a point exceeds the control limits, additional rules have been developed to
detect non-random patterns that indicate special causes of variation — even when all points lie within the
limits.
These rules improve sensitivity to:
• Small shifts
• Sustained trends or drifts
• Cyclical or systematic patterns
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The best-known example is the set of rules introduced by the Western Electric Company, known as the
Western Electric Company (WECO) rules. These were published in the Western Electric Statistical
Quality Control Handbook and are widely used in industry.
The Nelson Rules are a similar set that overlap significantly with WECO in logic and purpose, though
naming and structure may differ.
These rules help identify special causes earlier than standard 3σ limits would, but should be applied with
process knowledge to avoid unnecessary false alarms. Zones refer the levels of standard deviation.
Control vs Capability
A process that is in statistical control is predictable — it only varies due to common causes and behaves
consistently over time. However, this does not necessarily mean that the process is producing good parts.
A process is capable when it can consistently meet the specification limits set by the customer or design. In
other words, a capable process produces within tolerance.
Important distinction:
• A process can be in control but not capable
(e.g. stable but centered incorrectly or with too much variation)
• A process can appear capable but be out of control
(e.g. hitting specs by luck, not by predictability)
For proper functioning, a process must be:
• Under control: consistent and stable performance over time
• Capable: meets customer requirements within specification limits
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Process capability can be quantified using specific indices that compare the natural variation of the process to
the customer-defined specification limits.
1. If the process mean coincides with the average of the specification limits, we use the capability
index Cp :
U SL − LSL
R̄
Cp =
with
σ̂ =
6σ̂
d2
where:
• U SL = upper specification limit
• LSL = lower specification limit
• σ̂ = estimated process standard deviation
• R̄ = average range of subgroups
• d2 = control chart constant (from tables)
The reason the formula for Cp uses the denominator 6σ is that this represents the full spread of a stable process:
±3σ around the mean. Similarly, the numerator (U SL − LSL) represents the total width of the specification
limits — also often defined as a 3σ interval on each side. Capability compares these two spreads directly:
process width vs. tolerance width.
2. If the process mean is not centered on the specification midpoint, we use the index Cpk :
U SL − X̄ X̄ − LSL
Cpk = min
,
3σ̂
3σ̂
where:
• X̄ = process average
• σ̂ = estimated process standard deviation
Interpretation of capability index:
• Cp = 1 or Cpk = 1 ⇒ specification limits coincide with the process ±3σ spread
• < 1 ⇒ process spread is larger than specification limits
→ defect rate > 3 per 1,000
• > 1 ⇒ process spread is tighter than specification limits
→ defect rate < 3 per 1,000
• Note: Cpk is always taken as the smaller of the two values
Preferred minimum: Cpk ≥ 1.33
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Control Chart Pitfalls
Control charts are only reliable when the underlying assumptions and data integrity are respected. Several
common pitfalls can undermine their validity.
Common issues to avoid:
• Using the wrong chart type:
Each chart assumes a specific data distribution (normal, binomial, Poisson). Applying a chart that does
not match the data leads to flawed analysis and false conclusions.
• Inappropriate data set size:
Very small datasets lead to unstable parameters; very large datasets may inflate sensitivity and generate
false alarms.
• Invalid data sets:
Control charts require sequential, complete, and consistent data. Removing points, mixing data from
different subprocesses, or reordering values breaks the assumptions.
• Incorrect adjustment of control limits:
Limits should only be updated when the process truly changes. Updating limits every time new data
arrives (especially when based on small initial samples) undermines statistical control.
Always check the assumptions behind the control chart and validate the quality and structure of the data
before interpreting results.
Understanding of the process is also important =⇒ first step is Flow chart the process
Line Stoppages: Traditional vs Total Quality
The approach to stopping production lines differs strongly between traditional factories and total quality
environments.
Traditional Factory
• Line stops because: mechanical failure, wrong parts, or operator error
• Reaction: apply a quick fix to restart production; root cause analysis is postponed
• Result: defective products continue; recurrence likely
• Attitude: stoppages are costly and should be avoided at all costs
Total Quality Factory
• Line stops because: the operator detects a process anomaly (e.g. control chart signal)
• Reaction: stop production, identify and eliminate the root cause before restarting
• Result: avoids further defects and strengthens process robustness
• Attitude: line stops are valuable opportunities for learning and improvement
In a total quality setting, the operator actively takes responsibility for process monitoring and quality,
not just execution. This empowerment is essential for continuous improvement.
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Case: Hi-Sport Manufacturing Co. Inc.
Hi-Sport is a small company known for high-quality sporting jackets, historically achieved through rigorous
final inspection. However, this approach led to:
• High rejection rates at end-of-line
• Excessive cost due to scrap and rework
• Some defective items still reaching customers
• Poor competitiveness due to pricing pressure
With external guidance, the company began addressing special causes of variation, including:
• Poorly maintained machines
• Inadequate operator training
• Missing or unclear work instructions
Within six weeks, visible improvement occurred. Management then introduced a control chart system to
sustain control.
Since the process was evaluated on non-measurable (attribute) data, three charts were considered:
• p-chart for percentage of defective jackets
• np-chart for number of defective jackets per sample
• c-chart for number of defects per jacket
They selected the c-chart, because it allowed them to track the number of individual defects on each jacket
inspected.
For the initial data collection:
• One jacket was sampled per hour over 30 working hours
• A total of 46 defects were recorded across those 30 jackets
This case illustrates the importance of first stabilizing the process, then choosing the appropriate control chart
— in this case, for discrete defect counts.
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Acceptance Sampling
Acceptance sampling is an inspection method used to decide whether to
accept or reject an entire lot (quantity of material) based on a sample. It is typically used when 100% inspection
is not feasible or would be too costly.
Key characteristics:
• Involves both the producer (supplier) and the consumer (buyer)
• The consumer typically specifies the plan parameters
• No screening: only the sample is inspected, the rest of the lot is not
Procedure:
1. A random sample is taken from the lot
2. If the sample passes, the entire lot is accepted
3. If the sample fails, then:
(a) Either the entire lot is 100% inspected and reworked, or
(b) The entire lot is rejected and returned
Quality Levels and Risks
Acceptable Quality Level (AQL):
• The quality level that is considered acceptable by the consumer.
• Defined as the maximum proportion of defective items that can still result in lot acceptance.
• Typical contractual value (e.g. AQL = 0.0001 means max. 1 defect per 10,000 units).
• Represents a target for good quality.
Lot Tolerance Proportion Defective (LTPD):
• The worst quality level that the consumer is willing to tolerate.
• If the actual proportion of defectives equals the LTPD, the probability of lot acceptance should be low.
• LTPD defines the boundary for bad quality.
Associated Risks:
• Producer’s risk α (Type I error):
– Probability that a good-quality lot (at AQL level) is rejected.
– Often set at 5%.
– This risk is typically borne by the supplier.
• Consumer’s risk β (Type II error):
– Probability that a bad-quality lot (at LTPD level) is accepted.
– Often set at 10%.
– This risk is typically borne by the buyer.
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Sampling Plans
Sampling plans are designed to achieve specific α and β values while minimizing the cost of inspection. Average
number of inspected items (ANI) are best kept to a minimum. The most commonly used types are:
Notation:
• n = sample size
• c = acceptance number
Single-sampling plan
• Take a single sample of n items
• If number of defectives ≤ c: accept the lot
• If number of defectives > c: reject the lot or inspect all items
Double-sampling plan
• Define two sample sizes n1 and n2 , and two decision thresholds c1 and c2
• Step 1: Take a sample of n1 items
– If defectives ≤ c1 : accept the lot
– If defectives > c2 : reject the lot
– If c1 < defectives ≤ c2 : take an additional n2 items
• Step 2: Add results from both samples (n1 + n2 )
– If combined defectives < c2 : accept the lot
– If combined defectives ≥ c2 : reject the lot
Sequential-sampling plan
• Items are randomly selected and inspected one by one
• After each item, the cumulative number of defectives is compared to two decision boundaries:
– If defectives < c1 : accept
– If defectives > c2 : reject
– If c1 ≤ defectives ≤ c2 : continue sampling
• The total number of inspected items depends on when a clear decision is reached
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Operating Characteristic Curve (OC)
An OC curve shows how well a sampling plan can distinguish between good and bad quality lots. It plots the
probability of acceptance of a lot against its proportion defective.
In an ideal world, a perfect plan would accept every lot better than
the AQL with 100% certainty, and reject every lot worse than the
LTPD with 0% acceptance. This is shown as the ideal OC curve
(see top figure). However, this performance can only be achieved
through 100% inspection.
A typical OC curve, as seen in the red line, shows a smooth transition — better lots have a higher probability of acceptance, worse
lots a lower one. The steepness of this curve is a measure of the
plan’s ability to discriminate.
• At the AQL, the curve should pass near the 95% acceptance
probability line. This corresponds to the producer’s risk α
— the risk of rejecting a good lot.
Mathematically: α = 1 − Pa , where Pa is the probability of
acceptance when p = AQL.
• At the LTPD, the curve should drop near the 10% acceptance probability line. This corresponds to the consumer’s
risk β — the risk of accepting a bad lot.
Mathematically: β = Pa , where Pa is the probability of acceptance when p = LTPD.
The shape of the OC curve depends on:
• Sample size n: A larger sample makes the curve steeper and the plan more discriminating. (See figure
bottom left: n varies, c constant)
Increasing n improves the ability to detect bad lots — the same acceptance number c is now applied over
more inspected items, which makes it less likely that a poor-quality lot will escape detection. As a result,
the LTPD point shifts leftward (better protection for the consumer).
• Acceptance number c: Increasing c makes the plan more lenient (higher acceptance probabilities across
the curve). (See figure bottom right: c varies, n constant)
A higher c allows more defectives before rejecting the lot, which increases the probability of accepting
bad lots. This shifts the LTPD to the right (worse consumer protection).
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Drawing the OC Curve
The OC curve for a single-sampling plan is based on the binomial distribution, since each inspected item is
either defective (failure) or non-defective (success).
The probability of accepting a lot equals the probability of observing c or fewer defective items in a sample of
size n taken from a lot with defect probability p.
For small p and large n (specifically, when n > 20 and p < 0.05), the binomial distribution can be approximated
using the Poisson distribution. This simplifies calculations and allows the use of precomputed Poisson tables.
Average Outgoing Quality (AOQ)
The Average Outgoing Quality (AOQ) represents the expected proportion of defective items that
remain in accepted lots after sampling inspection.
The AOQ is calculated as:
AOQ =
p · Pa · (N − n)
N
where:
• p = true proportion defective in the lot: this represents the actual fraction of items in the lot that are
nonconforming or defective. It is an unknown property of the lot before sampling, and it determines how
“good” or “bad” the incoming lot truly is. The sampling plan does not know p in advance — instead,
AOQ is evaluated over a range of p values to predict the outgoing quality performance under various
incoming quality levels.
• Pa = probability of accepting the lot
• N = total lot size
• n = sample size used for inspection
This formula assumes that:
• All defective items in rejected lots are identified and removed (100% inspection).
• Any defective items found in the sample are also removed before acceptance.
• This approach is known as rectified inspection.
The AOQ thus reflects the average quality level after applying the sampling plan. It is useful for assessing
whether the plan provides sufficient consumer protection in the long run.
If we vary the true defect level p from 0 to 1, we obtain a curve of AOQ values. The maximum point on this
curve is known as the:
Average Outgoing Quality Limit (AOQL):
• AOQL = highest possible AOQ across all values of p
• This value indicates the worst-case outgoing quality under the plan
• If AOQL is considered too high, the plan must be modified (e.g. increase n, reduce c)
AOQL is particularly useful for regulatory or contractual guarantees of quality — it provides a ceiling for how
bad the outgoing quality can be under worst-case incoming conditions.
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H19
Continuous Improvement
Continuous Improvement (CI) aims to enhance competitiveness by adapting to evolving customer needs.
It requires ongoing, structured effort and leadership commitment.
Key Management Roles:
• Provide visible leadership and personal involvement
• Ensure communication, problem correction, and root cause analysis
• Promote documentation and progress monitoring
Scientific Approach: Data-driven improvements using root cause identification, planning, change implementation and follow-up.
Tools for Improvement
• Classic: Five Whys, Pareto analysis, Ishikawa (fishbone) diagram
• Multi-criteria: Pugh matrix, decision matrices
A Pugh matrix is a structured decision-making tool used to compare multiple options against a set of
weighted criteria. One option is chosen as the baseline, and all others are rated relative to it using symbols (e.g. +, 0, – or numerical scores). This helps identify the best-balanced alternative by highlighting
strengths and weaknesses objectively. (DM lol)
• Role clarity: RACI / RASCI matrix (Responsible, Accountable, Consulted, Informed, Supporting)
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• Risk tools: Bowtie diagram (causes, escalation, recovery controls)
A Bowtie diagram is a visual risk assessment tool that illustrates how a central unwanted event (the
“knot”) can occur, and how it can be prevented or mitigated. The left side shows threats and preventive barriers, while the right side shows consequences and recovery controls. It helps structure
thinking around both proactive and reactive risk management.
Idea Prioritization: Multivoting and brainstorming for problem scoping and solution selection.
Kaizen and Problem Structuring
Kaizen is the philosophy of continuous, incremental improvement. It emphasizes small, ongoing changes
made by everyone in the organization, rather than large-scale overhauls imposed from above.
• Contrast: Kaizen focuses on gradual, step-by-step improvements through employee involvement, while
BPR (Business Process Reengineering) aims for radical redesign of processes, often involving disruptive change.
• Kaizen approach: Problems are seen as improvement opportunities. The focus lies on identifying
root causes, eliminating waste, and standardizing solutions to prevent recurrence.
• Kaizen tools:
– Checklists: Ensure consistency and completeness in task execution
– Five W’s and One H: Who, What, Where, When, Why, How — useful for clarifying the problem
context
– Five M’s: Man, Machine, Material, Method, Measurement — used to categorize potential causes
in root cause analysis
Kaizen encourages structured problem-solving cycles such as PDCA (Plan-Do-Check-Act) and reinforces a
culture of daily improvement at all levels.
To promote continual improvement, consider the following factors every day and ask:
“How can this be improved?”
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Process Mapping
• Define boundaries and visualize the real process (flowcharts)
• Distinguish between: work as imagined, prescribed, done, and disclosed
• Use for identifying improvement opportunities
Lean Thinking
Goal: Create more value with less resources.
The Lean Approach is derived from the principles of the Toyota Production System (TPS), which
emphasizes efficiency and waste reduction through just-in-time operations.
A Lean operation aims to deliver better products or services by using less of everything — less human
effort, less space, less capital, and less time — while still achieving high quality and responsiveness.
Five Lean Principles (process steps):
• Identify value: Determine what the customer truly values and is willing to pay for.
• Map the value stream: Analyze all steps in the process to distinguish value-adding from non-valueadding activities.
• Create continuous flow: Organize the process so that products or services move smoothly without
interruptions or delays.
• Establish pull: Produce only in response to actual customer demand, not forecasts.
• Pursue perfection: Continuously improve processes to eliminate waste and better serve the customer.
The Three M’s in Lean: Muda, Mura, Muri
Lean aims to eliminate three root causes of inefficiency:
• Muda (Waste): Activities that do not add value to the product or service.
– The 7 (or 8) classic types of muda are summarized by acronyms like TIM WOODS or DOWNTIME:
∗ Transportation
∗ Inventory
∗ Motion
∗ Waiting
∗ Overproduction
∗ Overprocessing
∗ Defects
∗ Skills (unused creativity)
– These wastes are non–value-adding and should be reduced or eliminated.
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• Mura (Unevenness): Inconsistency in workflows, demand, or production schedules.
– Mura causes inefficiencies such as idle equipment, fluctuating workloads, or irregular material flow.
– Example: unpredictable shift output, leading to idle machines during some hours and bottlenecks
during others.
• Muri (Overburden): Pushing people or equipment beyond safe or effective capacity.
– This causes breakdowns, errors, or safety risks.
– Example: forcing operators to meet unrealistic takt times without adequate rest or automation
support.
In Lean, these three concepts are addressed together, because:
Mura and Muri often lead to Muda.
Eliminating only waste (muda) without addressing overburden (muri) or unevenness (mura) results in limited
improvements.
Lean Tools and Strategies
• SMED (Single-Minute Exchange of Die): This method was developed in Japan as part of the
Toyota Production System to reduce equipment changeover time — especially in press and die-changing
operations. The name “Single-Minute” refers to the ambition to reduce setup time to fewer than 10
minutes, not literally one minute. The term “Exchange of Die” comes from its original application in
stamping presses, where tool changeovers (dies) could take several hours.
In Western manufacturing environments, the term QCO (Quick Changeover) is often used as an
adaptation of SMED. Both share the same principles and objectives: minimize production downtime,
enable smaller batch sizes, and increase process flexibility.
The SMED methodology involves analyzing the changeover process and separating it into:
– Internal steps — tasks that can only be performed when the equipment is stopped
– External steps — tasks that can be performed while the machine is still running
The core idea is to convert as many internal steps as possible into external ones, and then streamline
or eliminate the remaining internal steps. This is typically done using a structured improvement ladder,
including: EXAM 2022
1. Separate internal and external setup activities
2. Standardize external activities
3. Convert internal activities to external
4. Improve internal setup activities
5. Improve external setup activities
6. Mechanize setup activities
7. Completely eliminate unnecessary steps
• Jidoka (Autonomation): Equipment or operators stop the process immediately when an abnormality
occurs. Only once the problem is understood and fixed does production resume. This prevents defects
from moving downstream and supports quality at the source.
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• Poka-Yoke (Error Proofing): Physical or procedural mechanisms are built into the process to prevent
mistakes. This can involve shape constraints, order restrictions, alarms, or sensors that ensure tasks are
performed correctly by design.
• Pull systems: Work is only initiated when a signal is received from the next step in the process, often
using Kanban cards or bins. This avoids producing more than is needed, reducing overproduction and
inventory waste.
• TPM (Total Productive Maintenance): Operators are trained to carry out daily preventive maintenance tasks on their own equipment. This improves machine uptime, avoids breakdowns, and builds
shared responsibility for reliability.
• SOPs (Standard Operating Procedures): Tasks are documented in step-by-step, visual instructions
to ensure every operator performs them in the same safe and effective way. This reduces variability and
supports training and quality control.
• Visual Workplace (Visual Management): Information such as status, abnormalities, instructions,
or locations is displayed visually through color codes, labels, floor markings, boards, and signage. This
makes problems and priorities immediately visible to everyone.
• Value Stream Mapping (VSM): The entire process flow is mapped from raw material to finished
product, including time spent in each step and delays in between. The result reveals where value is
created and where waste occurs, enabling a redesigned “future state” map.
• Point-of-use storage: Tools and materials are stored directly at the location where they are used, not
in central stockrooms. This reduces walking, searching, and waiting.
• One-piece flow: Instead of batching, items are processed one by one through sequential steps. This
exposes issues immediately, reduces lead time, and improves responsiveness.
• Cellular layout: Equipment is arranged into small, self-contained units based on product families.
Operators can complete multiple steps within a compact area, enabling better flow and fewer handoffs.
Six Sigma
Goal: Reduce process variation and defects to improve quality and performance.
• Target: Achieve a maximum of 3.4 defects per million opportunities (DPMO), which corresponds to a long-term process performance of six standard deviations between the process mean and
the nearest specification limit.
• Motorola correction: The original Six Sigma model by Motorola
includes a 1.5σ shift to account for long-term process drift. This
means that although a process may perform at a 6σ level in the
short term, it is expected to degrade to 4.5σ over time.
DMAIC: The core structured problem-solving methodology in Six Sigma, used for process improvement.
• Define: Clearly identify the problem, project goals, customer needs, and deliverables. Use project
charters and high-level process maps.
• Measure: Collect data on current performance to quantify the problem. Define key metrics (e.g.,
DPMO, cycle time, defect rate).
• Analyze: Identify root causes of variation or defects using tools like cause-and-effect diagrams, Pareto
charts, and hypothesis testing.
• Improve: Develop and implement solutions that address root causes. Use design of experiments (DoE),
pilot runs, or simulations to test improvements.
• Control: Sustain the gains by implementing process controls, control charts, and response plans to
monitor ongoing performance.
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SIPOC Diagram: A high-level process mapping tool used in the Define phase to provide a structured
overview of the process.
• Suppliers — entities providing input to the process
• Inputs — materials, data, or information entering the process
• Process — high-level steps involved in the transformation
• Outputs — results or deliverables produced by the process
• Customers — recipients of the output, internal or external
SIPOC helps to define scope, align stakeholders, and ensure that the team understands the upstream and
downstream elements affecting the process.
Theory of Constraints (TOC)
TOC is a management philosophy that focuses on continuously improving system performance by identifying
and addressing the most limiting factor (the constraint). The goal is to maximize throughput while minimizing
investment and operating expenses.
Key Concepts:
• Throughput: Rate at which the system generates money through sales.
• Investment: All assets used to generate throughput (e.g. equipment, inventory).
• Operating Expenses: Costs required to turn investment into throughput.
Performance is improved by systematically managing the weakest link in the chain. The constraint may be a
physical resource (e.g. a slow machine), a policy, or even market demand.
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5 Focusing Steps:
1. Identify the constraint: Find the process step that limits overall performance.
2. Exploit the constraint: Maximize use of the constraint with no additional investment.
3. Subordinate other processes: Align all other activities to support the constraint.
4. Elevate the constraint: Take further action to increase capacity (e.g. invest).
5. Repeat: Once the constraint is broken, go back to step 1. Continuous improvement.
Benefits:
• Increased profit: By maximizing throughput at the constraint.
• Faster improvement: Narrow focus leads to quicker results.
• Improved capacity: Constraint improvement enables higher output.
• Reduced lead times: Smoother flow with fewer delays.
• Lower inventory: Bottlenecks removed ⇒ less WIP.
Example: If a production process has three steps with capacities of 20, 14, and 18 units/hour respectively,
the system’s throughput is limited by the finishing step (14 units/hour). This step is the constraint and
should be the focus of improvement efforts.
The iTLS approach (Integrated TOC, Lean, Six Sigma) is a combined improvement strategy that leverages
the strengths of three complementary methodologies. First, Theory of Constraints (TOC) helps organizations
identify the critical bottlenecks that limit overall performance, ensuring that improvement efforts are focused
where they matter most. Next, Lean thinking is applied to eliminate waste and non-value-adding activities
across the process, making operations more streamlined and efficient. Finally, Six Sigma tools are used to
reduce variation and defects, providing statistical control and stability to the improved process. By integrating
these three methods, iTLS enables organizations to improve faster, more effectively, and with lasting impact
— focusing on what to improve (TOC), what to remove (Lean), and how to control it (Six Sigma).
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H20
Benchmarking
Definition: Benchmarking is a structured process in which an organization compares its processes and performance metrics to those of another organization—typically best-in-class peers—in order to identify improvement
opportunities. It is focused on practices and processes, not products, and should not be confused with competitive analysis or illegal copying.
Goals:
• Identify best practices that lead to superior performance.
• Stimulate both incremental and breakthrough (“quantum leap”) improvements.
• Learn from others – embracing “proudly found elsewhere” rather than “not invented here”.
Types of Benchmarking
• Internal benchmarking: Compare different units or locations within the same organization. Easier
due to common language and shared systems but limits learning from external innovations.
• External benchmarking: Compare with similar functions in other companies. More difficult due to
confidentiality and inconsistent terminology but brings more valuable insights.
• Functional benchmarking: Compare similar functions across different industries. Encourages creative
thinking and out-of-the-box insights.
• Generic benchmarking: Compare against a theoretical best-practice model. Difficult to apply in
practice but helpful for identifying ideal performance.
Benefits of Benchmarking
• Improved understanding of current performance
• Better alignment with changing customer needs
• Encouragement of innovation and renovation
• More effective and realistic goal-setting
• Improved productivity measurement
Prerequisites and Obstacles
Prerequisites:
• Top management commitment and strategic alignment
• Willingness to adopt new ideas
• Understanding and documentation of own processes
• Clear benchmarking goals and team-building skills
Obstacles:
• “Not invented here” attitude
• Unrealistic expectations or timelines
• Lack of ambition or poor team composition
• Difficulty finding good external partners
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The Benchmarking Process
The process is structured in three phases:
• Preparation: Identify areas to benchmark, secure management support, form teams, define KPIs
• Execution: Select partners, collect and compare data, identify performance gaps
• Post-execution: Analyze feasibility, evaluate resources, develop action plans
Note: the 14-step model from the slides emphasizes planning and team involvement. You should understand
the rationale behind the steps, even if not required to memorize them.
Critical Reflection (Oakland’s View)
The handbook is optimistic about benchmarking’s impact. Oakland provides a more realistic perspective:
• Only implement benchmarking results that align with strategic goals
• Always evaluate resource requirements (people, budget)
• Consider interactions with ongoing improvement activities
• Think carefully before translating benchmarking into action
Benchmarking is not a one-time activity; it must be repeated to stay aligned with external changes (markets,
customer needs, disruptions).
Best Practices and Cautions
• Always include employees familiar with the actual processes in the team
• Management should actively support and resource the benchmarking study
• Use both quantitative and qualitative insights from benchmarking
• Be aware of data quality, missing information, and partner reliability
• Do not expect to simply copy solutions (“no copycat behavior”) – every organization must adapt ideas
to its context
Capability and Maturity Models
Capability models offer a structured way to assess how advanced an organization is in managing a specific area
(e.g. quality, IT, risk, etc.).
• Maturity levels are often shown in pyramids, staircases, or matrices.
• Help identify where an organization stands and what steps are needed to progress to a higher level.
Example: Crosby’s Quality Management Maturity Grid
• Defines 5 stages: Uncertainty, Awakening, Enlightenment, Wisdom, Certainty
• Assesses key dimensions such as management attitude, cost of quality, and problem-handling behavior.
• Organizations move from reactive problem-solving to prevention and integration of quality as a strategic
concern.
These models support strategic planning, change management, and long-term improvement initiatives.
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H21
Just-in-Time (JIT) and Lean Manufacturing
JIT and Lean are intertwined philosophies aiming to improve efficiency, reduce waste, and enhance product
quality. The full system is often referred to as the Toyota Production System (TPS).
• Goal: Higher quality at lower cost simultaneously.
• Philosophy: Eliminate all types of waste (Muda), improve flow and flexibility.
• Method: Replace traditional push production with pull production.
Push vs. Pull Systems
• Push system: Production based on forecast ⇒ risk of overproduction, large inventories.
• Pull system (JIT): Triggered by actual customer demand, internal or external.
• JIT emphasizes small lots, low inventories, short setup times, and frequent deliveries.
Integration with Total Quality Management (TQM)
• JIT and Lean work best as part of a total quality system.
• Continuous improvement (Kaizen), Statistical Process Control (SPC), and Total Productive Maintenance
(TPM) are essential enablers.
Total Productive Maintenance (TPM)
TPM ensures machines are reliable and available, especially when working with small lot sizes. It focuses on
Overall Equipment Effectiveness (OEE), composed of:
• Availability: Uptime (maintenance responsibility)
• Performance: Output speed (production responsibility)
• Quality: Scrap and rework (quality responsibility)
Six classical losses in TPM:
• Breakdowns (failures)
• Setup and adjustments
• Small stops and idling
• Reduced speed
• Process defects
• Startup losses (reduced yield)
Cycle Time and Flow Efficiency
• Cycle time: Total time from raw material to finished good (incl. processing, waiting, inspection).
• JIT reduces cycle time by eliminating non-value-adding activities.
• Exposing and addressing bottlenecks improves flow and predictability.
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Lot Size and Flexibility
• JIT favors small lot sizes to reduce WIP and improve responsiveness.
• Requires short setup times (see SMED) and reliable machines (TPM).
Level Scheduling (Heijunka)
Production leveling smooths variation:
• By volume: Distribute production evenly across days.
• By type: Mix product variants to avoid batching and rigid schedules.
• Goal: Combine flexibility with predictability and reduce need for buffer stock.
Kanban Systems
• Visual signal (card, bin, or electronic) to authorize production or replenishment.
• Helps regulate pull-based workflows.
• e-Kanban is the digital successor to physical Kanban.
Automation and Lean
JIT and Lean are compatible with automation, but do not require it. Automation can support efficiency
but is not a prerequisite for implementing lean principles.
The Hidden Factory
The hidden factory refers to lost production capacity due to inefficiencies not captured in official output figures.
These losses can be grouped as follows:
• Schedule Loss: Production could run but is not scheduled (e.g. nights/weekends). Often solved with
overtime or added shifts.
• Availability Loss: Production should run but doesn’t due to setups or failures.
Tools: SMED (planned stops), TPM (unplanned stops)
• Performance Loss: Line is running, but too slowly or with frequent small stops. Often hidden and
underestimated.
• Quality Loss: Output is produced, but defective.
Tools: Error-proofing (Poka-Yoke), standardized work
Case Example (Avionics Production)
A factory making avionics test equipment was falling short on daily production goals. The solution was not to
push harder, but to reduce cycle time via JIT implementation:
• Reduced cycle time from 12 to 8 to 5 weeks
• Eliminated non-value-added activities
• Improved delivery performance and customer satisfaction
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Impact of IT–OT Convergence on TQM
This topic was raised during the discussion session and highlights how the integration of Operational Technology (OT) and Information Technology (IT) transforms the execution of Total Quality Management
(TQM).
The convergence enables a more integrated and data-driven approach to quality management by:
• Real-time data collection: OT devices (sensors, machines, PLCs) can stream process data directly
into IT systems (ERP, MES, cloud dashboards), enabling immediate quality monitoring and feedback
loops.
• Faster detection & response: Quality deviations can be identified earlier, often automatically, triggering alerts or even process corrections without human intervention.
• Better root cause analysis: IT systems can aggregate data across the value chain. Combined with
OT input, this improves traceability and enables statistical or AI-driven root cause analysis.
• Increased visibility & transparency: Managers can access cross-functional quality dashboards that
merge machine-level data (OT) with operational KPIs (IT), supporting better decision-making.
• Support for predictive quality: Integration supports advanced tools like machine learning, allowing
proactive detection of quality drift and optimization of control parameters.
• Digital transformation of quality culture: TQM evolves from paper-based inspections and audits
toward a digital-first, real-time quality mindset that is embedded in every operator and system.
In summary, IT–OT convergence enhances TQM by making quality more visible, measurable, and controllable
across all levels of production and management.
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H22
Implementing Total Quality Management
This closing chapter serves both as a summary roadmap for students and as a practical how-to guide
for organizations implementing TQM. It links back to the foundational concepts from earlier chapters and
consolidates the main insights across the course.
Ten Key Points for TQM Implementation
1. Rationale for Change: The need for a long-term focus, customer orientation, employee involvement,
and leadership. Quality does not mean higher cost — rather, improved quality leads to lower cost.
2. Requirements: Success depends on top management commitment, resource availability, clear planning, and a supporting infrastructure (see Chapter 5). This includes vision, mission, strategic and tactical
objectives.
3. Role of Management: Leadership must come from the top, but middle management plays a crucial
enabling role. They must support and not resist empowerment, even if that means letting go of some
control.
4. Stakeholder View: All stakeholders must be considered — from top managers and employees to
customers. This perspective should guide TQM activities from the start and throughout the process.
5. Common Pitfalls: Lack of training, rushing implementation, unclear delegation, or insufficient preparation can lead to failure. Be mindful of these when planning change.
6. Structured Approach: The 20-step roadmap from the handbook is useful for planning. Steps include
preparation, planning, execution, and feedback. It’s not for memorization, but may appear on the exam
as a prompt for reflection.
7. No One-Size-Fits-All: Every organization must tailor its implementation. Use general guidelines,
underlying concepts, and tools — but adapt them to your specific context (products, culture, services,
etc.).
8. Use of Consultants: Consultants are not always necessary, but can bring valuable expertise. Use them
to build in-house knowledge (e.g., through train-the-trainer programs), not for perpetual dependence.
9. Critical Success Factor: Commitment. Without sustained commitment, implementation will fail.
This has been a consistent message throughout the course.
10. Recognition & Assessment: Consider ISO 9000 and quality awards (e.g., Malcolm Baldrige National
Quality Award) as structured assessment frameworks. Criteria include leadership, planning, customer
focus, knowledge, workforce, operations, and results — which connect directly with the EFQM model.
Final Notes
Chapter 22 is a capstone for the entire TQM course. It brings together earlier concepts (from vision setting to
quality tools) and reflects on practical implementation. It emphasizes critical reflection over memorization
and encourages customized, organization-specific deployment strategies — supported by awareness, stakeholder
inclusion, and continual improvement.
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