OM EXAM 1

Here’s a detailed explanation of each point from Chapter 1:

1. Operations as One of the Three Main Functional Concerns of Most Organizations

Most organizations have three primary functional areas:

• Operations: Responsible for producing goods or delivering services.

• Finance: Manages financial resources, including budgeting and investments.

• Marketing: Focuses on customer needs, sales, and product promotion.

Operations are critical because they determine the efficiency and effectiveness of producing goods and services. A well-managed operations function ensures cost control, quality, and timely delivery, directly impacting customer satisfaction and profitability.

2. The Role and Job of the Operations Manager as a Planner and Decision-Maker

The operations manager is responsible for overseeing production and ensuring that processes run smoothly. Their key roles include:

• Planning: Deciding on production schedules, resource allocation, and workflow design.

• Decision-Making: Addressing problems related to production, quality, efficiency, and supply chain issues.

• Staffing: Hiring and managing employees involved in production or service delivery.

• Monitoring and Control: Ensuring that operations meet performance goals and customer expectations.

Operations managers must balance cost, quality, and speed to maximize organizational performance.

3. Different Ways of Classifying (and Understanding) Production Systems

Production systems can be classified based on various criteria, including:

• Nature of Output:

  • Manufacturing (physical goods) vs. Service (intangible products).

• Production Volume:

  • Job Shop (customized, small batches), Batch Production, Mass Production, and Continuous Production.

• Customer Involvement:

  • High involvement (e.g., restaurants) vs. Low involvement (e.g., manufacturing plants).

• Process Flow:

  • Process-focused (e.g., hospitals) vs. Product-focused (e.g., assembly lines).

Understanding these classifications helps businesses choose the right strategy for efficiency and effectiveness.

4. System Design versus System Operation

• System Design: Focuses on the long-term aspects of production, such as:

  • Facility layout

  • Equipment selection

  • Process design

  • Capacity planning

• System Operation: Deals with the day-to-day management of production, including:

  • Scheduling production runs

  • Managing workforce

  • Ensuring quality control

  • Addressing supply chain issues

Both aspects are essential for a smooth and efficient operation.

5. Major Characteristics of Production Systems

Production systems have key characteristics that define their efficiency and effectiveness:

• Inputs: Resources such as labor, raw materials, and technology.

• Processes: The transformation of inputs into finished goods or services.

• Outputs: Final products that meet customer demand.

• Feedback Mechanism: Evaluates performance to improve efficiency.

• Capacity and Scalability: Ability to handle varying levels of demand.

• Flexibility: The ability to adapt to changes in customer preferences and market conditions.

6. Contemporary Issues in Operations Management

Operations management is constantly evolving due to technological advancements and global challenges. Some key contemporary issues include:

• Sustainability: Reducing waste, energy consumption, and environmental impact.

• Globalization: Managing international supply chains and competition.

• Technology Integration: Using automation, AI, and data analytics to improve efficiency.

• Lean and Agile Manufacturing: Minimizing waste while being responsive to customer needs.

• Quality Management: Implementing Six Sigma and Total Quality Management (TQM) practices.

7. Operations as Managerial (Planning, Staffing, etc.)

Operations management involves several managerial functions:

• Planning: Forecasting demand, setting goals, and developing strategies.

• Organizing: Structuring teams, allocating resources, and defining roles.

• Staffing: Hiring and training employees for production and service delivery.

• Directing: Supervising and guiding employees to meet performance targets.

• Controlling: Monitoring efficiency, quality, and cost control measures.

Effective operations management ensures a smooth workflow and customer satisfaction.

8. The Historical Evolution of Production/Operations Management

Operations management has evolved significantly over time:

• Industrial Revolution (18th–19th Century): Introduction of mechanized production and factories.

• Scientific Management (Early 20th Century): Frederick Taylor’s time and motion studies to improve efficiency.

• Mass Production (20th Century): Henry Ford’s assembly line approach.

• Lean Manufacturing (Mid-20th Century): Toyota’s Just-in-Time (JIT) production to minimize waste.

• Digital Transformation (21st Century): Use of AI, IoT, and automation in manufacturing and services.

Understanding this evolution helps organizations adopt modern best practices.

9. Manufacturing Operations versus Service Operations

Operations differ significantly between manufacturing and services:

• Manufacturing Operations:

  • Produces tangible goods.

  • Can store inventory.

  • Focuses on efficiency and cost reduction.

• Service Operations:

  • Delivers intangible products (e.g., healthcare, consulting).

  • High customer interaction.

  • Quality depends on service delivery and customer experience.

While different, both require efficient operations management to succeed.

10. The Need to Manage the Supply Chain

Supply chain management (SCM) involves overseeing the entire flow of materials, information, and finances from suppliers to customers. Key aspects include:

• Sourcing and Procurement: Selecting reliable suppliers.

• Logistics and Distribution: Ensuring timely delivery of goods.

• Inventory Management: Balancing stock levels to avoid overproduction or shortages.

• Customer Satisfaction: Ensuring high-quality products and on-time delivery.

Managing the supply chain effectively reduces costs, enhances efficiency, and improves customer satisfaction.

This detailed explanation provides a strong foundation for understanding the fundamentals of operations management. Let me know if you need further clarifications or examples!

Chapter 2: Competitiveness, Strategy, and Productivity

1. Ways That Business Organizations Compete

Businesses compete in several ways to attract customers and achieve market success. The main competitive factors include:

• Cost Leadership: Offering products at lower prices than competitors (e.g., Walmart, McDonald’s).

• Differentiation: Providing unique or high-quality products/services (e.g., Apple, Tesla).

• Innovation: Introducing new products, services, or business models (e.g., Netflix, Amazon).

• Quality: Consistently delivering high-quality goods/services (e.g., Toyota, Rolex).

• Customer Service: Ensuring a great customer experience through responsiveness and support (e.g., Zappos, Ritz-Carlton).

• Speed & Flexibility: Delivering products quickly and adapting to market changes (e.g., FedEx, Zara).

• Technology & Efficiency: Using automation and data-driven decision-making to improve operations (e.g., Google, Tesla).

2. Reasons That Business Organizations Fail

Organizations fail for various reasons, often due to poor management, financial issues, or lack of adaptability. Common reasons include:

• Lack of Strategy: No clear vision, mission, or competitive strategy.

• Poor Financial Management: Excessive costs, poor cash flow management, or lack of funding.

• Failure to Adapt: Ignoring market trends and technological advancements (e.g., Blockbuster vs. Netflix).

• Low Productivity & Efficiency: High waste, low employee output, or inefficient processes.

• Poor Customer Service: Negative customer experiences leading to a bad reputation.

• Weak Supply Chain Management: Issues with sourcing, inventory, or logistics.

• Poor Quality Control: Product defects leading to customer dissatisfaction and recalls.

3. Mission and Strategy: Definitions and Importance

• Mission: The organization’s purpose and reason for existence. It answers, "What do we do?" and "Who do we serve?"

  • Example: Google’s mission is "to organize the world’s information and make it universally accessible and useful."

• Strategy: The plan or approach a company uses to achieve its mission and compete effectively. It includes long-term goals and actions.

  • Example: Amazon’s strategy includes cost leadership, technology integration, and customer-centric services.

Why They Are Important:

• Provide direction for decision-making.

• Align company efforts toward a common goal.

• Help businesses gain a competitive edge.

4. Organization Strategy vs. Operations Strategy

• Organization Strategy: The overall plan to achieve long-term business goals. It includes areas like marketing, finance, and operations.

  • Example: Expanding into international markets or focusing on sustainability.

• Operations Strategy: The plan to optimize production, supply chain, and efficiency to support the overall strategy.

  • Example: Adopting automation, implementing lean manufacturing, or outsourcing production.

Why Linking Them Is Important:

• Ensures all departments work toward the same goals.

• Improves efficiency and competitiveness.

• Aligns operational capabilities with business needs.

For example, if an organization’s strategy is to be a cost leader (like Walmart), its operations strategy must focus on low-cost production, supply chain optimization, and inventory management.

5. Definition and Importance of Productivity

• Productivity = Output / Input

  • Measures how efficiently resources are used to produce goods and services.

Importance:

• For Organizations: Higher productivity lowers costs, increases profits, and improves competitiveness.

• For Countries: Economic growth, higher wages, and improved living standards.

6. Factors That Affect Productivity

Several factors influence productivity, including:

• Technology: Automation, AI, and advanced machinery improve efficiency.

• Workforce Skills: Skilled employees work more efficiently.

• Management Practices: Effective leadership, workflow optimization, and lean principles enhance productivity.

• Capital Investment: Investment in better equipment increases output.

• Process Design: Efficient layouts and workflows reduce waste.

• Quality Control: Reducing defects and rework improves overall efficiency.

• Supply Chain Management: Effective inventory and logistics management prevent delays.

Productivity Calculations

1. Single-Factor Productivity (SFP)

   • Measures productivity using one input factor.

   • Formula: SFP=OutputSingle Input\text{SFP} = \frac{\text{Output}}{\text{Single Input}}

   • Example: If a factory produces 500 units using 250 labor hours, then: SFP=500250=2 units per labor hourSFP = \frac{500}{250} = 2 \text{ units per labor hour}

2. Multi-Factor Productivity (MFP)

   • Measures productivity using multiple input factors.

   • Formula: MFP=OutputTotal Inputs (Labor + Capital + Materials)\text{MFP} = \frac{\text{Output}}{\text{Total Inputs (Labor + Capital + Materials)}}

   • Example: If a company produces 1,000 units using 300 labor hours, $500 in materials, and $200 in overhead, then: MFP=1000300+500+200=10001000=1.0MFP = \frac{1000}{300 + 500 + 200} = \frac{1000}{1000} = 1.0

3. Productivity Change

   • Measures the percentage change in productivity over time.

   • Formula: Productivity Change=New Productivity−Old ProductivityOld Productivity×100\text{Productivity Change} = \frac{\text{New Productivity} - \text{Old Productivity}}{\text{Old Productivity}} \times 100

   • Example: If last year’s productivity was 2.0 units per labor hour and this year’s is 2.5 units per labor hour, then: Productivity Change=2.5−2.02.0×100=25%\text{Productivity Change} = \frac{2.5 - 2.0}{2.0} \times 100 = 25\%

This breakdown covers all key points from Chapter 2 with detailed explanations and example calculations. Let me know if you need further clarifications! 🚀

Chapter 3: Forecasting

Forecasting is the process of predicting future events based on historical data and analysis. It is critical for decision-making in business operations, supply chain management, and strategic planning.

1. Features Common to All Forecasts

All forecasts share the following characteristics:

• Future-Oriented: Forecasts predict what will happen in the future based on past data.

• Uncertainty: Forecasts are never 100% accurate due to unpredictable variables.

• Reliance on Historical Data: Past trends and patterns are used to project future events.

• Involves Assumptions: Forecasts depend on assumptions about stability, trends, and external factors.

• Accuracy Varies by Time Horizon: Short-term forecasts are generally more accurate than long-term forecasts.

• Aggregate Forecasts Tend to Be More Accurate: Forecasts for broader groups (e.g., total industry sales) are more accurate than for individual items (e.g., a single product).

2. Why Forecasts Are Generally Wrong

Forecasting is an estimate, meaning errors and uncertainties always exist. Common reasons for forecast inaccuracies include:

• Unpredictable External Factors: Economic changes, political events, or natural disasters can disrupt predictions.

• Incorrect Model Selection: Using an unsuitable forecasting method leads to errors.

• Fluctuations in Data: Seasonality, unexpected demand shifts, and irregular patterns reduce accuracy.

• Poor Data Quality: Incomplete, outdated, or inaccurate data negatively impact forecasts.

• Human Bias: Over-optimism, reliance on intuition, or misinterpretation of data lead to errors.

3. Elements of a Good Forecast

A good forecast should have the following elements:

• Accuracy: It should minimize errors and deviations from actual values.

• Reliability: The forecast method should produce consistent results.

• Simplicity: Easy to understand and apply.

• Timeliness: Should be available in time for decision-making.

• Cost-Effectiveness: The benefits of the forecast should outweigh the costs of creating it.

• Flexibility: The forecast should be adaptable to new data or changing conditions.

4. Steps in the Forecasting Process

The forecasting process involves several structured steps:

1. Determine the Purpose: Define why the forecast is needed and how it will be used.

2. Gather and Analyze Data: Collect historical data and identify trends or patterns.

3. Select a Forecasting Method: Choose between qualitative or quantitative techniques based on data availability and requirements.

4. Generate the Forecast: Apply the chosen model to produce the forecast.

5. Validate and Test the Forecast: Compare forecast results to actual data and adjust the model if necessary.

6. Monitor and Update the Forecast: Continuously update the forecast based on new information.

5. Qualitative Forecasting Techniques

Qualitative forecasting is used when data is limited or unavailable, relying on expert judgment and subjective analysis. The four main qualitative techniques are:

1. Delphi Method: A panel of experts provides forecasts independently and iteratively refines them until consensus is reached.

2. Market Research: Surveys and focus groups collect customer opinions to predict demand.

3. Executive Opinions: Senior managers use their experience and intuition to make forecasts.

4. Salesforce Estimates: Sales teams provide insights on future demand based on customer interactions.

Forecasting Calculations

Now, let’s go over key forecasting methods and error measurement calculations.

1. Naïve Forecasting Methods

(a) Basic Naïve Forecast

• Assumes that the demand in the next period will be the same as the demand in the most recent period.

• Formula: Ft+1=AtF_{t+1} = A_t

• Example: If demand last month was 200 units, then the forecast for this month is 200 units.

(b) Trend-Adjusted Naïve Forecast

• Adjusts the naïve forecast by adding the observed trend.

• Formula: Ft+1=At+(At−At−1)F_{t+1} = A_t + (A_t - A_{t-1})

• Example:

  • Demand in January: 200

  • Demand in February: 220

  • Forecast for March: 220 + (220 - 200) = 240

(c) Seasonally Adjusted Naïve Forecast

• Accounts for seasonality by using the demand from the same season in the previous cycle.

• Example:

  • Demand in Q1 last year: 500 units

  • Forecast for Q1 this year: 500 units

2. Moving Average Forecasts

• Formula: Ft=∑(Demand in Previous n Periods)nF_t = \frac{\sum \text{(Demand in Previous n Periods)}}{n}

• Example (3-Month Moving Average):

  • Demand in Jan: 100, Feb: 120, Mar: 110

  • Forecast for April: (100+120+110)/3=110(100 + 120 + 110) / 3 = 110

3. Weighted Moving Average (WMA)

• Assigns different weights to recent data points.

• Formula:

Ft=(W1A1+W2A2+W3A3)F_t = (W_1 A_1 + W_2 A_2 + W_3 A_3)

(where W are weights and A are actual values)

• Example:

  • Weights: 0.5, 0.3, 0.2 (most recent has highest weight)

  • Data: Jan = 100, Feb = 120, Mar = 110

  • Forecast for April: (0.5×110)+(0.3×120)+(0.2×100)=55+36+20=111(0.5 \times 110) + (0.3 \times 120) + (0.2 \times 100) = 55 + 36 + 20 = 111

4. Exponential Smoothing

• Weights recent observations more heavily than older ones.

• Formula:

Ft=αAt−1+(1−α)Ft−1F_t = \alpha A_{t-1} + (1 - \alpha) F_{t-1}

(where α is the smoothing constant, typically 0.1 to 0.5)

• Example (α = 0.3, Previous Forecast = 100, Actual = 110):

0.3(110)+0.7(100)=33+70=1030.3(110) + 0.7(100) = 33 + 70 = 103

5. Linear Trend Forecasting

• Uses regression to predict trends over time.

• Formula: Ft=a+btF_t = a + bt (where a is the intercept and b is the slope)

6. Forecast Error Measures

(a) Mean Absolute Deviation (MAD)

MAD=∑∣At−Ft∣nMAD = \frac{\sum |A_t - F_t|}{n}

(Average of absolute forecast errors)

(b) Mean Squared Error (MSE)

MSE=∑(At−Ft)2nMSE = \frac{\sum (A_t - F_t)^2}{n}

(Squares the errors to penalize large deviations)

(c) Mean Absolute Percentage Error (MAPE)

MAPE=∑(∣At−Ft∣At)×100nMAPE = \frac{\sum \left( \frac{|A_t - F_t|}{A_t} \right) \times 100}{n}

(Measures errors as a percentage of actual values)

This chapter covers key forecasting concepts, qualitative techniques, and various forecasting calculations. Let me know if you need specific calculations or examples! 🚀

Supplemental notes:

What is the smoothing constant Alpha (in Exponential Smoothing)? It can range between 0 and 1.

Alpha is the smoothing constant for the level of the series. The limits of this value are zero and one. Usually, a value between 0.1 and 0.3 are used. As the value gets closer to one, more and more weight is given to recent observations.

Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average where past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.

Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations.

Exponential smoothing: requires less data storage, gives more weight to recent data, and is easier to change to make it more responsive to changes in demand.

The primary difference between an EMA and an SMA is the sensitivity each one shows to changes in the data used in its calculation. ... More specifically, the exponential moving average gives a higher weighting to recent data prices, demand, etc., while the simple moving average assigns equal weighting to all values.

Poor forecasting leads to poor planning. This could result in offering products and services that customers do not want. Poor forecasting and planning can negatively affect budgeting and planning for capacity, sales, production and inventory, labor, purchasing, energy requirements, capital requirements, and materials requirements.

There is always going to be a certain amount of random variation about the forecast. The amount of this random variation about the forecast (actual vs. forecast) will increase as the forecasting horizon is extended.

Chapter 4: Product and Service Design

1. Strategic Importance of Product and Service Design

Product and service design is crucial because it determines an organization’s success in the market. Well-designed products and services:

• Attract customers and differentiate from competitors.

• Enhance profitability by optimizing cost, quality, and efficiency.

• Improve sustainability by reducing waste and energy consumption.

• Support business strategy, aligning with market demand and company goals.

For example, Apple’s sleek product designs contribute to its premium brand image, while Toyota’s efficient vehicle designs focus on reliability and cost-effectiveness.

2. What Product and Service Design Does

Product and service design focuses on:

• Determining customer needs and transforming them into specifications.

• Developing new products/services or improving existing ones.

• Ensuring quality and efficiency in production or delivery.

• Balancing cost, aesthetics, functionality, and sustainability.

3. Key Questions of Product and Service Design

When designing a product or service, organizations consider:

1. What is the target market?

2. What are the customer needs and expectations?

3. What features should the product/service have?

4. What materials and processes should be used?

5. What will be the cost and pricing strategy?

6. How will the product/service be maintained and supported?

4. Reasons for Design or Redesign

Products and services may need to be designed or redesigned due to:

• Market demand changes (e.g., increased preference for electric cars).

• Technological advancements (e.g., AI-powered assistants).

• Competitive pressure (e.g., lower-cost alternatives emerging).

• Regulatory requirements (e.g., environmental laws).

• Obsolescence of existing designs (e.g., outdated software).

• Quality or performance issues (e.g., product recalls).

5. Sources of Design Ideas

Innovation can come from multiple sources:

• Customer feedback & complaints (e.g., product reviews).

• Market research & trends (e.g., sustainability-driven products).

• Competitor analysis (e.g., benchmarking best practices).

• R&D departments (e.g., pharmaceutical companies).

• Employee suggestions (e.g., frontline workers’ insights).

• Supplier input (e.g., material improvements).

6. Legal, Ethical, and Sustainability Considerations

• Legal: Compliance with safety, environmental, and consumer protection laws.

• Ethical: Fair labor practices, avoiding deceptive marketing.

• Sustainability: Using eco-friendly materials, reducing waste, and designing energy-efficient products.

For example, Tesla’s electric cars focus on sustainability by reducing carbon emissions.

7. Purpose and Goal of Life Cycle Assessment (LCA)

• LCA evaluates the environmental impact of a product throughout its entire lifecycle, from raw material extraction to disposal.

• Goals:

  • Minimize waste and pollution.

  • Improve energy efficiency.

  • Promote sustainable resource use.

Example: LEED-certified buildings use LCA to reduce environmental impact.

8. The 3 Rs: Reduce, Reuse, Recycle

• Reduce: Minimize material and energy use.

• Reuse: Extend product life by repurposing.

• Recycle: Convert waste into new products.

Example: Coca-Cola using recycled plastic in bottles.

9. House of Quality and Kano Model

• House of Quality (HOQ): A tool in Quality Function Deployment (QFD) that translates customer needs into technical requirements.

  • Uses a matrix to align design specifications with customer expectations.

• Kano Model: A framework categorizing product features into:

  • Basic Needs (expected, must-haves).

  • Performance Needs (desired, impact satisfaction).

  • Excitement Needs (unexpected, delight customers).

Example: Smartphones adding face recognition as an excitement factor.

10. Key Issues in Product or Service Design

• Cost vs. quality trade-off

• Balancing customization with standardization

• Sustainability & environmental impact

• Technological integration

• Regulatory compliance

11. Two Key Issues in Service Design

1. Customer Involvement: Services are often co-created with the customer.

2. Variability & Consistency: Service experiences can vary based on staff, customer expectations, or external factors.

Example: Fast-food chains standardizing procedures to ensure consistent service.

12. Phases in Service Design

1. Idea Generation: Identifying customer needs and trends.

2. Concept Development: Defining service processes.

3. Design & Testing: Prototyping and refining.

4. Implementation: Launching the service.

5. Evaluation & Continuous Improvement: Monitoring performance.

13. Characteristics of Well-Designed Service Systems

• User-friendly

• Efficient and cost-effective

• Reliable and consistent

• Flexible for different customer needs

• Integrated with technology

• Scalable as demand changes

Example: Amazon Prime’s seamless, user-friendly delivery system.

14. Guidelines for Successful Service Design

• Involve customers and employees in design

• Define clear service standards

• Use technology to enhance service

• Ensure reliability and efficiency

• Continuously improve based on feedback

Main Topics Breakdown

1. Reasons, Trends, and Objectives of Product and Service Design

• Changing consumer preferences.

• Increased focus on sustainability.

• Rapid technological evolution.

• Competitive differentiation.

2. The Design Process

• Mass Customization: Allowing customization while maintaining efficiency (e.g., Nike ID shoes).

• Reliability: Ensuring long-term functionality.

• Robust Design: Creating products that perform well under varying conditions.

3. Research and Development (R&D)

• Basic Research (long-term knowledge development).

• Applied Research (specific product innovation).

• Development (prototyping and testing).

Example: Pharmaceutical companies investing in drug research.

4. Standardization

• Reducing variability to enhance efficiency.

• Lowering costs through mass production.

Example: McDonald’s standardized menu items worldwide.

5. Product Design

• Concurrent Engineering: Cross-functional collaboration in design.

• Computer-Aided Design (CAD): Digital modeling for efficiency.

• Remanufacturing: Rebuilding used products (e.g., refurbished laptops).

6. Service Design

• Includes defining processes, customer interactions, and experience optimization.

• Example: Uber’s real-time tracking and automated payments enhance user experience.

7. Quality Function Deployment (QFD)

• Structured approach to translating customer needs into product specifications.

• Uses House of Quality to align design features with customer preferences.

8. Operations Strategy

• Aligning product/service design with operational capabilities.

• Integrating sustainability, efficiency, and market responsiveness.

Example: IKEA’s flat-pack furniture minimizes shipping costs and environmental impact.

Conclusion

Product and service design is fundamental to business success. Effective design processes incorporate customer needs, technology, sustainability, and efficiency while balancing cost and quality. Businesses must continuously evolve their design strategies to remain competitive.

Supplemental notes/comments:

Organizations redesign their products and services for a variety of reasons. Among them are customer dissatisfaction, government regulation, competition, liability claims, technological innovation (products and methods), and changes in costs and availability of such inputs as materials, labor, and energy.

The main advantages of Standardization are:

a.             Less variety of parts to deal with.

b.             Permits standardized training, purchasing, inspection, and material handling. It may also permit automation.

c.              Enables production to stock, which allows filling orders from inventory, and potentially long production runs.

Among the main disadvantages of standardization are the following:

a.             Designs may be “frozen” with too many imperfections remaining.

b.             The high cost of design changes increases resistance to improvement.

c.              Decreased variety may lessen consumer appeal.

Modular design refers to viewing a product (and sometimes a service) as being composed of a number of “chunks” or sections instead of a collection of individual parts. In effect, it is one form of standardization. Among the advantages of modular design are ease of diagnosis and repair of failures; standardization of manufacturing; and more routine purchasing, inventory control, and training. The disadvantages of modular design include a decrease in possible variety of the product, the possibility of not being able to disassemble a module to replace a faulty part, and possible resistance to design improvements, particularly minor ones, if they cannot be readily incorporated into an existing configuration.

Some of the competitive advantages of Concurrent Engineering are:

a.             Manufacturing personnel are able to identify production capabilities and capacities. Very often, they have some latitude in design in terms of selecting suitable materials and processes. Knowledge of production capabilities can help in the selection process. In addition, cost and quality considerations can be influenced greatly by design, and conflicts during production can be reduced greatly.

b.             Early opportunities for design or procurement of critical tooling, some of which might have a long lead-time. This can result in a major shortening of the product development process, which could be a key competitive advantage.

c.              Early consideration of the technical feasibility of a particular design or a portion of a design. Again, this can avoid serious problems during production.

d.             More effective resource allocation.

e.              The emphasis can be on problem resolution instead of conflict resolution.

Differences between service design and product design:

a.             Products are generally tangible; services are generally intangible. Consequently, service design often focuses more on intangible factors (e.g., peace of mind, ambiance) than does product design.

b.             Services are often produced and received at the same time (e.g. a haircut, a car wash). Thus, there is less latitude in finding and correcting errors before the customer has a chance to discover them. Consequently, training, process design, and customer relations are particularly important.  

c.              Services cannot be inventoried. This poses restrictions on flexibility and makes capacity design very important.

d.             Services are highly visible to consumers and must be designed with that in mind; this adds an extra dimension to process design, one that usually is not present in product design.

e.              Some services have low barriers to entry and exit. This places additional burden on service design to be innovative and cost-effective.

f.               Location is often important to service design, with convenience as a major factor. Hence, design of services and choice of location often are closely linked.

Quality function deployment (QFD) is a structured approach for integrating the “Voice of the Costumer” into the product development process. The purpose is to ensure that customer requirements are factored into every aspect of the process from product planning to the production floor. Listening to and understanding the customer is the central feature of QFD

Reverse engineering is dismantling and inspecting a competitor’s product to discover product improvements. Yes, it is ethical to look but not to copy, in most cases.

The 3 R’s are reduce (through value analysis), reuse (through remanufacturing), and recycle. They relate to sustainability by avoiding or reducing the impact on the environment that would accompany new production or, in the case of recycling, a reduction in the waste stream.