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Operations Management: Decision Making, Modeling, and Supply Chain Notes

Decision Making in Operations

  • Marketing group develops a new product (market selection, basic design) and hands it to the operations manager to determine the execution plan.
  • Operations manager's decision questions cover:
    • What, when, where, how, and who will do the work
    • Resources needed and how much, where and when each resource is needed
    • A series of "whens": scheduling of work, ordering of materials, and the design sequence
  • Core decision areas illustrated by a simple product example (a doll):
    • Whether to make components in-house or contract out
    • If in-house, whether to dedicate machines for left arm vs right arm
    • Eyes, clothes, and other parts: whether to buy or produce in-house
    • Level of hand finishing and the required quality control
  • Takeaway: The basic product idea comes from marketing; the operational plan is about how to realizethe product efficiently and at desired quality and cost.

Modeling and Models as Decision Tools

  • A model is an abstraction of reality and can take several forms:
    • Physical model: e.g., a miniature house
    • Schematic model: blueprints or charts
    • Mathematical model: abstract formulas and computations (e.g., queuing theory)
  • Example of a mathematical model: queuing theory can model the probability of a customer not waiting given the number of customers in the line.
  • In the doll example, the appropriate model type is a schematic of the processes needed to reach the end product (process flow diagram).
  • Why understand how a model works:
    • To trust and effectively use the model for decision making
    • To recognize when something in the model is out of sync, rather than blindly following the computer’s output
    • Partial quote paraphrase: it’s not enough to say, “The computer is telling me the answer”; you must understand the processes behind it.
  • Key questions to develop a model:
    • What is the purpose of the model? (objective)
    • How will the model generate results? (mechanism)
    • How will results be interpreted and used? (actions)
    • What are the model’s assumptions and limitations? (scope and constraints)
  • Garbage In, Garbage Out (GIGO): bad data lead to invalid results. The data quality directly affects model validity.
  • Real-world data quality examples:
    • Experian study: bad data directly impacts the bottom line for about 90% of American companies
    • IBM report: US organizations believe an average of 32% of their data is inaccurate
    • Estimated revenue impact: average loss of about 12% due to bad data
    • NASA Mars Orbiter Mission (1999): imperial units vs metric units caused a $125,000,000 error due to data inconsistency
  • Trusting models requires understanding data quality and the model’s function; otherwise, decisions may be flawed even if the model seems authoritative.
  • The value of models:
    • Cheaper and easier than testing in real systems
    • Forcing users to quantify information
    • Allows scenario analysis and "what if" questions (e.g., impact of a Bucks win vs. loss on Tampa police operations)
  • Caution with models:
    • If a model’s prediction is too perfect, there is likely a mistake
    • The black box problem: users may not understand what is happening inside the model, leading to unreviewed mistakes
    • Always strive to know what data are being used and how the model operates; avoid relying solely on outputs
  • Summary: models help structure problem solving, enable mathematical reasoning, and support repeatable analysis across similar scenarios, but require good data, transparent workings, and critical validation.

Quantitative Approaches to Decision Making

  • In addition to models, quantitative methods seek mathematically optimal solutions and include:
    • Linear programming
    • Forecasting techniques
    • Queuing theory
    • Inventory models
    • Statistical models for reliability
  • These quantitative tools enable rigorous analysis and optimization of operations, but rely on correct assumptions and accurate data.
  • Performance metrics used to manage and control operations include:
    • Profits
    • Flexibility
    • Costs
    • Inventory levels
    • Quality
    • Schedules (timeliness)
    • Productivity
    • Forecast accuracy
  • Forecast accuracy example:
    • Compare forecasted units sold to actual units sold to assess model validity
  • Trade-offs: decision-making often involves giving up one objective to gain another (e.g., holding more inventory to improve service levels but incurring higher costs).
  • Inventory considerations in trade-offs:
    • Higher inventory costs can be offset by improved customer service and reduced stockouts, accommodating random variability in demand or supply
  • Systems thinking: a holistic approach where interrelated parts must work together; the whole is greater than the sum of its parts.
    • Subsystems include marketing, operations, and finance
    • Changes in one area impact the entire organization (e.g., self-checkout affects customer experience, staffing, pilferage, software, and add-on sales)
  • Pareto principle (the 80/20 rule):
    • A few factors account for the majority of effects; focus on the critical few factors that drive 80% of outcomes
    • Expressed as: ext{Pareto principle: } 80 ext{\% of effects come from } 20\ ext{\% of causes.}
  • The six approaches will be used across the course to analyze diverse problems.

Key Issues for Operations Managers Today

  • Global economic conditions and rapid innovation: need to adapt quickly to changing markets
  • Quality assurance and quality control: customers have many options; poor quality leads to returns and customer switching
  • Risk management: ensuring processes are reliable and resilient against disruptions
  • Economic conditions and global competition: customers can source from anywhere; competitive pressures are high
  • Ethical considerations in operations:
    • Corporate finances, worker safety, product safety, environmental sustainability, workers' rights, and hiring/firing practices
    • Ethical issues arise across operations, including finance, safety, product quality, and sustainability
  • Sustainability: using resources in ways that do not harm ecological systems; sustainability practices can enhance brand value and profitability
  • Supply chain considerations: moving beyond internal operations to manage suppliers and their suppliers
    • Problems upstream can trigger downstream disruptions (the supply chain domino effect)
    • Example of supply chain risk: a silicon shortage impacts semiconductor delivery
  • Supply chain management realities:
    • Need to improve operations and enable easy interchangeability of similar resources during shortages
    • Increasing outsourcing and transportation/logistics costs
    • Heightened globalization and the growth of e-business (global market access)
  • Supply chain complexity: multiple moving parts across borders and stages must coordinate to deliver products on time
  • Inventory management goal: ensure customers can obtain goods when they want them, avoiding stockouts while controlling carrying costs
  • Final takeaway: Operations management and supply chain management involve navigating global dynamics, ethical considerations, and complex interdependencies to sustain customer satisfaction and organizational performance

Doll-Factory Analogy and Process Design Notes

  • The doll example demonstrates decision points about process design and outsourcing:
    • Decide on in-house manufacturing vs. outsourcing for parts (left arm, eyes, clothes, etc.)
    • Determine the extent of post-production finishing and quality control requirements
  • This analogy helps illustrate how process decisions feed into the overall operations strategy and cost structure

Practical Implications and Real-World Relevance

  • Understanding models helps managers explain, defend, and adjust decisions in marketing, operations, accounting, finance, and technology
  • Data quality is critical across domains; inaccurate data can lead to suboptimal or damaging decisions
  • A systems approach fosters cross-functional collaboration and aligns incentives and outcomes across departments
  • The Pareto principle helps prioritize improvement efforts on the factors that have the most impact on performance
  • Global supply chain awareness is essential for risk mitigation, cost control, and customer satisfaction in a worldwide marketplace