This document are my notes and midterm review for the BU 375 Operations course taught at Wilfrid Laurier University. While studying for the midterm, I discovered my own formulae shortcuts, undoubtedly due to “blind teaching syndrome” (I made that term up). So let’s begin. I’ll add a divider to let you know what is part of the midterm.

Appendix

It’s better to have the appendix at the top rather than the bottom, so that people know where to get the values for some examples.

Control Factors

Out with the ugly table, and in with the new! It’s absurd that at $500+ per course and hundreds of students, universities do not collaborate with each other to make course curriculum/content, do not strive for perfect function (notes) and rarely place importance on aesthetics.

Operations Management Introduction

Transformation Process

• General idea: inputs -> transformation process -> output
• Feedback between inputs <-> process <-> output
• Value is created
• Physical, locational, exchange, physiological, psychological, informational
• Value = Price consumers are willing to pay - Cost to produce

Input

• Materials, machines, labor, management, and capital
• These inputs are determined by the requirements for the output

Output

• Goods and services

Major Components of a Firm

Primary Areas

• Operations
• Marketing
  • research, packaging
• Finance and Accounting
• Human Resources (HR)
• Outside Suppliers

Importance

• Middle of the firm
• Opportunity to innovate (internally)
• Results in failure

Decisions

• Design
  • long-term
  • Equipment, capacity, layout, location
• Day-to-Day Operations
  • Scheduling, quality control, labor requirements, input procurement

Strategic Planning

Globalization

• Thinking big
• Foreign markets, production, purchasing, partnerships
• Interests: cost, international, reliability

Missing Statement

• What does the firm do and sell?

Vision

Direction of the firm in 10 years

Values

• Common beliefs

Consumer Exepectations

Order Qualifiers & Winners

Price, Quality, Variety (comparing to competitors), Timeliness, Convenience, Customer Service

Order Qualifiers

Basic criteria for product candidacy (functional)

Order Winners

Differentiating criteria (aesthetic)

Strategy

• direction for achieving a mission
• consistency
• dependent on organization size
  • formal (larger)
• mission statement

Formulation

• Primary task
• Core competencies (competitive advantage)
• Order qualifiers & winners
• Positioning (how to compete)
• Deployment

Positioning / Competitiveness

• common goals
• collaboration
• expertise
• learning from past experiences
• cost
  • waste elimination
  • trades off with quality sometimes
• quality (reducing defects)
• flexibility
  • ability to change internal processes to accommodate needs
  • trades off with speed
• speed / delivery
  • based on context

Key performance Indicators (Evaluation)

1. Finances
2. Customers
3. Processes
4. Learning
5. Tools: balanced scorecard worksheet

Quality

Fitness for use, design. (PDCA) plan, act check, do.

Products

performance, features, reliability, conformance (to the design), durability, serviceability, aesthetics, safety, perceptions

Services

Time, completeness, courtesy, consistency, accessibility, accuracy, responsiveness, HARDER TO EVALUATE

Total Quality Management (TQM)

• Continuous improvement
• Customer satisfaction
• Involves employees and management
• Performance tracking

W.E. Demmings

• Statistical quality-control techniques
• Continuous improvement
• Inspect at stages before final
• Responsibility lies with management and employees
• Few suppliers & pick quality > cost

Six Sigma

• Decrease process variation

Tools

• Process Flowchart (location of problem)
• Cause-and-Effect (fishbone) diagram (find root cause)
• Check Sheet (form)
• Pareto Analysis (data collection) and Chart
• Scatter Diagram
• Control Chart

Cost of Quality

Total = cost of achieving good quality + cost of poor quality

Good

• Prevetion
• Appraisal/Measuring

Poor

• Internal failure
• External failure

Quality Index (QI)

Measures quality cost against a base value (e.g. manufacturing, sales, labor hours, quantity)

Productivity

Single-Factor Productivity

where Specific Input could be Labor, Materials, Capital.

Total Factor Productivity

To calculate multi-factor productivity, use a subset of inputs.

Yield

OR

Product Cost

For this one, I have my own simplified formulas.

This avoids calculating how many units were reworked.

Quality Productivity Ratio (QPR)

Even for this one, I have my own method, but I’ll include the original in case some information isn’t given.

Statistical Analysis

• A type I Alpha error is an error that wasn’t supposed to fall outside the control limits. False-positive.
• A type II Beta error is an error that should’ve fallen outside the control limits.
• Variations are either random or assignable.
• n refers to sample size or number of observations, and k refers to the number of samples.
• z refers to the sigma count; either 2 or 3
• UCL and LCL stand for upper control limit and lower control limit.

p-chart

A p-chart is a control limit chart with relation to the proportion of defects. It is used when there are multiple observations per sample and defects are reported.

c-chart

Similar to p-chart, except there is only 1 observation per sample.

x-chart

A chart used for checking if process variability is in control.

In the case σ is unknown, use the control factors table along with these formulas:

R-chart

A range chart, to test if variability is in control. Use the control factors table to determine factor values or to help you figure out other factor values.

Reliability

To improve reliability,

• reduce number of components in a series
• increase backup components
• increase individual component reliability

To calculate the reliability of a process, use these two functions.

Series

For components in a series (independents), multiply their reliability scores:

Parallel (Backups)

For components that are in parallel (i.e. backups exist), follow this schema,

System Availability

Process Capability Index

When design mean and process mean are the same use c_p, otherwise c_pk.

If c_p and c_pk are >= 1, then the process is capable (99.70%). If c_p = c_pk, then design mean and process mean are the same.

I tweaked the formula for c_pk. Instead of dividing both numbers by 3σ, divide only the min.

The number chosen also represents which limit the process (mean) deviated towards.

Server Model

• Poisson distribution
• First in First Out (FIFO)
• Infinite population because people served can re-join the queue

The lambda variable refers to mean arrival rate and the mu variable refers to mean service rate.

TODO: add table with formulas and descriptions

Simple Processes

Learning Curves

Capacity Planning

End of Midterm Review

Supply Chain Management Strategy & Design

• Chain of organizations and facilities, with varying activities
• Multi-tiered
• Activities
  • forecasting, purchasing, product/process design, manufacturing, quality assurance, inventory management, distribution

Supply Chain Process

1. Procurement of raw materials and services
2. Production of product and services
3. Storage of products in inventory
4. Taking orders and distribution of products to the customer

Supply Chain for Service Providers

• Focuses on human resources
• Fewer tiers

Supply Chain Management (SCM)

• Create maximum value for the end customer
• perfecting coordination and collaboration of components
• To provide enough supply at the right price, time, and place
• Systemic approach to manage entire flow of information, materials, and services from raw-materials to customers
• Competition becomes between supply chains rather than companies

Bullwhip Effect

• Supply chain members farther down the line get information after the members nearer to customers.
• importance of transparency across members
• Safety stock issues
• Lack of accurate demand information
• Order batching results in less orders but larger quantities
• Price fluctuations
• Shortage gaming (placing orders more than is required bc inventory is short supply)
• Solutions
  • Coordination by parties for pricing, transportation, and inventory management
  • Joint demand information forecast
  • Price stabilization
  • Discouraging shortage gaming

I believe a solution is for the supplier to force the orderer to tell them when the next order will be. That way, a supplier won’t have to decide whether to anticipate more orders if batch ordering occurs.

Supply Chain Uncertainty

• Inaccurate demand forecasting
• Long and Varying times for orders
• Late/Incomplete shipments
• Product changes and price fluctuations/discounts
• Batch ordering and inflated orders
• Failures cause chain reactions, leading to lag, lack of supply, and lower sales
• Inventory is used as insurance against uncertainty, however optimality needs to be determined

Supply Chain Coordination and Integration

• Sharing information
• Coordinated workflow, production, and operations, procurement
• Adopt new business models and tech
• Collaborative Planning, Forecasting, Replenishment (CPFR)
• Enterprise Resource Planning (ERP)

Information Technology

• E-Business
• Electronic data interchange (EDI)
• Barcode and point-of-sale (POS) data
• Radio Frequency Identification (RFID)
• Internet
• Blockchain

Supply Chain Performance

• inventory turnover
• weeks of supply

Average aggregate inventory value is the sum of average inventory times value for each item.

Fill Rate = fraction of orders filled within a specific time period.

• low inventory turnover indicates that a large amount of inventory is required to satisfy demand

SCOR Model

• Plan
• Source
• Make
• Deliver
• Enable
• Return

Project Management

A project is one-time and non-repetitive operational activities or efforts. Clear goal, limited time frame, budget, schedule, resources.

Project Elements

1. Objective
2. Scope
3. Contract requirements
4. Schedules
5. Resources
6. Personnel
7. Control
8. Risk and problem analysis

Project Manager

• plans, schedules, executes, controls project
• meeting requirements
• keeps project on track, within budget, and meets quality standards
• makes trade-off decisions
• expedites work when needed
• resolves conflicts
• adapts

Project Management Process

• Planning
• Scheduling
• Control

Work Breakdown Structure

1. Identify major components of the project
2. Break down the major components into sub-components
3. Break down sub-components into work packages

Responsibility Assignment Matrix (RAM)

• Organizational breakdown structure (OBS) shows which organizational units are responsible for work items used to develop RAM

Gantt Chart

• A visual aid for scheduling and control purposes

CPM & PERT

• Critical Path Method (CPM) has deterministic task times
• Program Evaluation and Review Technique (PERT) has probabilistic task times
• network path: starting node to finishing node sequence
• critical path: longest network path; determines project duration
• critical activities: activities on the critical path
• slack time: allowable slippage for a path

To calculate expected time of a single activity, use

Where a is best case time, m is most likely, and b is worst case time.

Thus the expected duration of a path (t_path) is the sum of activity expected times.

The variance of each activity time is

The variance of a path is then the sum of activity variances, and the variance of the critical path is also known as the project variance.

Like always, the standard deviation of anything is the square root of the variance.

As for calculation probabilities, the probability of completing a project by a certain time isw

Use z-table to get a probability representing p(x < deadline) where sigma is the std of the critical path.

To calculate the deadline given a probability, use the z-table to get a z value, and then use the z formula to calculate.

Crashing

With CPM and PERT some activities can be crashed. For crashing to have an effect on the project duration, the crashes must reduce the time of the critical path or paths. A single crash might not have an affect on the project duration if there was an equivalent critical path that didn’t need that activity.

Crashing information is stated as cost to crash completely (additional cost) as well as how many weeks can be crashed. Thus, implicitly, activities can be crashed by a single week rather than the entire allowable crash time.

Demand Forecasting

Projections of demand for products/services underlies strategic planning when it comes to plant or service design.

• often inaccurate
• forecasts of similar goods is more accurate than individual items
• forecast horizons increases uncertainty

Decision Based on Forecasts

• Production and Operations
  • aggregate planning
  • inventory control
  • scheduling
• Finance
  • plant/equipment investment
  • budgetary planning
• Marketing
  • new product introduction
  • sales-force allocation
  • promotions
• Human Resources
  • workforce planning
  • hiring, layoff

Making Forecasts Useful

• long enough to make it relevant
  • typically for daily, weekly, or monthly sales demand for up to approximately two years into the future, depending on the company and the type of industry.
• limitations on accuracy must be clearly stated
• forecasting method should be reliable
• operations forecasts should be expressed in units

Quantitative Forecasting Methods

• Time-Series methods
  • use historical data to project into the future
  • naive method, moving average, weighted moving average, exponential smoothing
• Associative models
  • independent casual variables

Naive Method

Assumes the demand for the next period will be the same as the previous period

Moving Average

As the MA-window increases, so too will the lag.

For MA-2, do

Weighted Moving Average

Exponential Smoothing

Adjusted Exponential Smoothing

Incorporates a trend factor on top of exponential smoothing

Where beta is a smoothing constant for the overall trend

Example

For the data given below, generate a forecast using adjusted exponential smoothing. Assume alpha = 0.5 and beta = 0.3.

An adjusted exponential smoothing forecast requires T₂ to start the computational process. For this case, assume T₂ = 0.

Sample calculation

F₃ = alpha(D₂) + (1 - alpha) F₂ = 38.5

T₃ = Beta(F₃ - F₂) + T₂(1 - Beta) = 0.45

AF₃ = F₃ + T₃

Seasonal Adjustments

Course specific: seasonal adjustment factor (S_q) relative to the total demand for some period. Use factor after a forecast to get each quarter’s demand forecast.

Forecast Accuracy

e_t = D_t - F_t

MAD = sum of | e_t | / n

mean squared error = sum (e_t^2) / n

mean absolute percentage deviation = sum (e_t) / sum (D_t)

Inventory Management

What is Inventory?

• raw materials
• work in progress
• finished products
• packaging
• replacement parts
• goods-in-transit

Important because assets are tied up in inventory. Too much inventory can increase costs and reduce efficiency while too

Inventory Control Systems

Continuous System

• economic [fixed] order quantity (EOQ) whenever the reorder point is reached
• inventory is continually monitored (costly)

Periodic System

• orders are placed on a fixed time interval
• order quantity = desired inventory level - inventory on hand
• less level of control

Economic Order Quantity (EOQ)

• single product
• constant annual demand is known
• single delivery with constant lead time smaller than the order cycle
• no discounts

Average inventory = Q / 2

Average inventory cost = Q / 2 * C_c [holding cost per unit]

Purchase orders per year = D / Q

Annual order costs = D / Q * C_O

TC = Q/2 * C_c + D/Q * C_O = Holding Cost + Ordering cost

Slope = dTC / dQ = C_C/2 - DC__O / Q² = 0

Quantity Discounts

Add PD to total cost where price is per unit and D is demand.

If quantity is feasible done, otherwise compare with the minimum for each proce discount level larger than EOQ.

Economic Production Quantity

Receive quantity incremetally while inventory is being depleted.

Where p is the production rate per day and d is the demand rate per day.

Inventory max = (Q/p)(p-d)

TC = I/2 * C_c + D/Q * C_S = Holding Cost + Setup cost per run

Reorder Point (Constant Demand)

Ensure that the time unit is the same.

Reorder Point (Variable Demand)

R = demand rate * lead time + Safety Stock

Use table for z depending on % service level. e.g. 95% -> z = 1.65. Service level is the probability that demand won’t be greater than quantity of goods in stock.

Periodic Order Quantities (Variable Demand)

Q = average demand rate * (time between orders + lead time) + z sigma (sqrt (t + L)) - I

Single Period Inventory Model

Shortage Costs = revenue per unit - cost per unit

Excess Costs = cost per unit - salvage value

SL = C_S / (C_E + C_S) (round up to closest cumulative probability in table)