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Operations Management II

Forecasting
the art and science of predicting future events. Involves taking historical data (past sales) and projecting them into the future with a mathematical model.
Three Time Horizons
A forecast is usually classified by the future time horizon that it covers. IT falls into three categories: Short-range forecast, Medium-range forecast, Long-range forecast.
Short-range forcast
This forecast has a time span of up to 1 year but is generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels. Tend to be more accurate than long-term forecasts. Employs more mathematical techniques
Medium-range forecast
A medium-range, or intermediate, forecast generally spans from 3 months to 3 years. Useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans. Tends to be less quantitative.
Long-range forecast:
Generally 3 years or more in time span, long-range forecasts are used in planning for new products, capital expenditures, facility location or expansion, and research and development. Tends to be less quantitative.
Economic Forecasts
address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators. They tend to be medium- to long-range forcasts.
Technological forecasts
are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment. They are long-term forecasts.
Demand forecasts
projections of demand for a company’s products or services. Forecasts drive decisions, so managers need immediate and accurate information about real demand.
Demand-driven forecasts
focus is on rapidly identifying and tracking customer desires.
Forecast on demand
It is the only estimate of demand until the actual demand becomes known.
Three activities affected by product demand forecast
(1) Supply-Chain Management (2) Human Resources (3) Capacity
Collaborative Planning, Forecasting and Replenishment (CPFR)
creates significantly more accurate information that can power the supply chain to greater sales and profits by combining the intelligence of multiple supply-chain partners.
Capacity
When inadequate, can result in shortages that lead to loss of customers and market share. When excessive, costs can skyrocket.
Seven Steps in the Forecasting System: Step 1
Determine the Use of the Forecast.
Seven Steps in the Forecasting System: Step 2
Select the items to be forecasted
Seven Steps in the Forecasting System: Step 3
Determine the time horizon of the forecast. Is it medium, short, or long-term?
Seven Steps in the Forecasting System: Step 4
Select the forecast model(s). Can be moving averages, econometrics, and regression analysis. Can also mean employing judgemental, nonquantitative models.
Seven Steps in the Forecasting System: Step 5
Gather the data need to make the forecast.
Seven Steps in the Forecasting System: Step 6
Make the forecast
Seven Steps in the Forecasting System: Step 7
Validate and implement the results.
Realities of Forecasts
(1) Outside factors we cannot predict often impact forecasts. (2) Most forecasting techniques assume there is some underlying stability in the system. Some firms automate predictions using computerized forecasting software, the monitor product items with erratic demand closely. (3) Product family and aggregated forecasts are more accurate than individual product forecasts.
Quantitative forecasts
Forecasts that employ mathematical modeling to forecast demand.
Qualitative Forecasts
Forecasts that incorporate such factors as the decision maker’s intuition, emotions, personal experiences, and value system.
Qualitative: Jury of executive opinion
A forecasting technique that uses the opinion of a small group of high-level managers to form a group estimate of demand.
Qualitative: Delphi method
A forecasting technique using a group process that allows experts to make forecasts. Three types of participants: Decision Makers, Staff Personnel, and Respondents.
Qualitative: Sales force composite
A forecasting technique based on salespersons’ estimates of expected sales.
Qualitative: Market Survey
A forecasting method that solicits input from customers or potential customers regarding future purchasing plans.
Quantitative: Time Series
A forecasting technique that uses a series of past data points to make a forecast. Includes Naive Approach, Moving Averages, Exponential Smoothing, & Trend Projection.
Quantitative: Associative
incorporate the variables or factors that might influence the quantity being forecast, i.e. linear regression. For example, an associative model for lawn mower sales might use factors such as new housing starts, advertising budget, and competitors’ prices.
Decomposition of a Time Series
(1) Trend, the gradual upward or downward movement of the data overtime. (2) Seasonality, a data pattern that repeats itself after a period of days, weeks, months, or quarters. (3) Cycles, patterns in the data that occur every several years. Usually tied into business cycle and are of major importance in short-term business analysis and planning. Can be difficult because of political events or international turmoil (4) Random variations, “blips” in the data caused by chance and unusual situations.
Naive Approach
assumes that demand in the next period will be equal to demand in the most recent period.
Moving Average
A forecasting method that uses an average of the n most recent periods of data to forecast the next period.
Exponential Smoothing
A weighed-moving-average forecasting technique in which data points are weighted by an exponential function.
Smoothing Constant
The weighing factor used in an exponential smoothing forecast, a number greater than or equal to 0 or less than or equal to 1.
Measuring Forecast Error
Actual demand – forecast value
Mean absolute deviation
a measure of the overall forecast error for a model.
Mean squared error (MSE)
The average of the squared differences between the forecasted observed values.
Mean absolute percent error (MAPE)
The average of the absolute differences between the forecast and actual values, expressed as a percent of actual values.
Trend Projection
A time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts. Uses the least-squares method.
Seasonal variations
Regular upward or downward movements in a time series that tie to recurring events.
Cycles
Patterns in data that occur every several years
Linear-regression analysis
A straight-line mathematical model to describe the functional relationships between independent and dependent variables.
Standard error of the estimate
A measure of variability around the regression line–its standard deviation
Coefficient of correlation
A measure of the strength of the relationship between two variables.
Coefficient of determination
A measure of the amount of variation in the dependent variable about its mean that is explained by the regression equation.
Multiple regression
An associative forecasting method with more than one independent variable.
Tracking signal
A measurement of how well a forecast is predicting actual values. Falls under monitoring and controlling forecasts.
Bias
A forecast that is consistently higher or consistently lower than actual values of a time series.
Adaptive Smoothing
An approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keeping errors to a minimum. Usually refers to computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit.
Focus forecasting
Forecasting that tries a variety of computer models and selects the best one for a particular application. Based on two principles: (1) Sophisticated forecasting models are not always better than simple ones (2) There is no single technique that should be used for all products or services.
Problems presented by Moving Average
(1) Increasing the size of N does smooth out fluctuates better, but makes it less sensitive to changes in data. (2) Moving averages cannot pick up trends well. They lag the actual values. (3) Moving averages require extensive records of past data.
Why can’t the alpha in exponential smoothing be 0 or 1?
Because it ends up falling back onto the naive model, and pretty much says the next forecast is the same as the last period.
Objective of Inventory Management
to strike a balance between inventory investment and customer service.
Functions of inventory
(1) To provide a selection of goods for anticipated customer demand and to separate the firm from fluctuates in that demand. (2) To decouple various parts of the production process. (protect against stockouts) (3) To take advantage of quantity discounts (purchase in larger quantities may reduce the cost of goods or their delivery) (4) To hedge against inflation and upward price changes.
Raw material inventory
Materials that are usually purchased but have yet to enter the manufacturing process.
Work-in-Progress (WIP) Inventory
Products or components that are no longer raw materials but have yet to become finish products.
Maintenance/repair/operating (MRO Inventories)
Maintenance, repair, and operating materials. Exists because the need for timing for maintenance and repair of some equipment are unknown.
Finished-goods inventory
completed product awaited shipment. Finished goods may be inventoried because future customer demands are unknown.
Goods in transit
Those goods on their way to warehouses or customers (pipeline inventory)
ABC Analysis
Divides on-hand inventory into three classifications on the basis of annual dollar volume. It is the inventory application of what is known as the Pareto principle. “Critical few and trivial many”
Determining annual dollar volume for ABC Analysis
measure annual demand of each inventory items times the cost per unit.
Class A items
those on which the annual dollar volume is high. Such items may represent only about 15% of the total inventory items, they represent 70-80% of total dollar usage.
Class B Items
inventory items of medium annual dollar volume. These items may represent about 30% of inventory items and 15% to 25% of the total value.
Class C items
5% of the annual dollar volume but about 55% of the total inventory items.
Downside to periodic systems in record accuracy
Periodic systems have a lack of control between reviews and the necessity of carrying extra inventory to protect against shortages.
Two-bin System
Two containers have adequate inventory to cover demand during the time required to receive another order, and places an order when the first bin is empty.
Perpetual Inventory
Tracks both receipts and subtractions from inventory on a continuing basis.
Record Accuracy Requires..
Good incoming and outgoing record keeping as well as good security.
Cycle Counting
A continuing reconciliation of inventory with inventory records. In ABC analysis, A items are counted frequently, B items will be counted less frequently, and C items will be counted once every 6 months.
Advantages of Cycle Counting
(1) Eliminates the shutdown and interruption of production necessary for annual physical inventories. (2) ELiminates annual inventory adjustments (3) Trained personnel audit the accuracy of inventory (4) Allows the cause of the errors to be identified and remedial action to be taken (5) Maintains accurate inventory needs
Shrinkage
Retail inventory that is unaccounted for between receipt and sale. Occurs from damage and theft as well as from sloppy paperwork.
Pilferage
A small amount of theft (inventory theft) Retail inventory loss of 1% of sales is considered good, with losses in many stores exceeding 3%.
Applicable techniques for control of service inventories
Good personnel selection, training and discipline. Tight control of incoming shipments, and effective control of all goods leaving the facility (bar codes, RFID, etc).
Holding Cost
The cost to keep or carry inventory in stock
Ordering Cost
The cost of the ordering process. includes cost of supplies, forms, order processing, purchasing, clerical support, and so forth.
Setup Cost
The cost to prepare the machine or process for manufacturing an order. Includes time and labor to clean and change tools or holders.
Setup Time
The time require to prepare a machine or process for production.
Three inventory models for independent demand
Basic Economic Order Quantity (EOQ) Model, Production Order Quantity Model, Quality Discount Model
Economic Order Quantity (EOQ) Model
is an inventory-control technique that minimizing the total ordering and holding costs. Based on several assumptions:
1. Demand for an item is known, reasonably constant, and independent of decisions for other items.
2. Lead time is known and consistent.
3. Receipt of inventory is instantaneous and complete. Inventory for an order arrives in one batch at a time.
4. Quantity discounts are not possible.
5. Only variable costs are the cost of setting up or placing an order (setup or ordering cost).and the cost of holding or storing inventory over time (holding or carrying cost).
6 Stockouts (shortages) can be completely avoided if orders are placed at the right time.
Robust
Giving satisfactory answers even with substantial variaion in the parameters.
Lead time
In purchasing systems, the time between placing an order and receiving it; in production systems, the wait, move, queue, setup, and run times for each component produced.
Reorder point (ROP)
The inventory level (point) at which action is taken to replenish the stocked item.

Determinants: Rate of Demand, Lead time, Extend of Demand and/or Lead time Variability, Degree of stock-out risk acceptable to management.

Under certainty: Demand Per Day * Lead time for a new order in days (d x L)

Safety stock (SS)
Extra stock to allow for uneven demand; a buffer. As amount of safety stock carried increases, risk of stock our decreases (and improves customer service level)

ROP = Expected demand during lead time + Safety Stock

Daily Demand (d)
Demand / Number of working days in a year
Production Order Quantity Model
An economic order quantity technique applied to production orders.
Average Inventory Level
Maximum Inventory / 2
Quantity Discount
A reduced price for items purchased in large quantities. IP is percent of product price(in holding costs)
Probabilistic Model
A statistical model applicable when product demand or any other variable is not known but can be specified by means of a probability distribution.
Service Level
The probability that demand will not be greater than supply during lead time. It is the complement of the probability of a stockout.
Single-period inventory model
A system for ordering items that have little or no value at the end of a sales period (perishables). The goal is to identify the order quantity that will minimize the long-run excess and shortage costs. It is the model for ordering perishables and other items with limited, useful lives.
Fixed-quantity (Q) System
An ordering system with the same order amount each time.
Perpetual Inventory System
A system that keeps track of each withdrawal or addition to inventory continuously, so records are always current.
Fixed-period (P system
A system in which inventory orders are made at regular time intervals.
Independent Demand
The demand for final products & has a demand pattern affected by trends, seasonal patterns, & general market conditions
Dependent Demand
Describes the internal demand for parts based on the demand of the final product in which the parts are used. Subassemblies, components, & raw materials are examples of dependendent demand items.
Deterministic Demand
when uncertainty is not included in its characteristics
Stochastic demand
incorporates uncertainty by using probability distributions.
Static demand
Stable demand
Dynamic Demand
varies over time.
Management roles in inventory
1. Establish a system for tracking items in inventory.
2. Make decisions regarding when and how much to order.
Reasons for using the FOI Model
1. Supplier’s policy may encourage its use
2. Grouping orders from the same supplier can produce savings in shipping costs.
3. Some circumstances do not lend themselves to continuously monitoring inventory position.
Shortage level
Shortage Cost Per Unit Divided by the Shortage Cost Per Unit – Excess Cost Per Unit

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