Module 2 Section A

CPFR is a nine-step process:

1.Developing a collaboration agreement 2.Creating a joint business plan 3.Creating a sales forecast 4.Identifying exceptions for the sales forecast 5.Resolving/collaborating on exception items 6.Creating an order forecast 7.Identifying exceptions for the order forecast 8.Resolving/collaborating on exception items 9.Generating orders

3-Month Moving Average

3-Month Moving Average = (M1 + M2 + M3)/3

3-Month Weighted Moving Average

3-Month Weighted Moving Average = ((1*M1)+(2*M2)+(3*M3))/6

options

A choice that must be made by the customer or company when customizing the end product. In many companies, the term option means a mandatory choice from a limited selection.

Outlier

A data point that differs significantly from other data for a similar phenomenon. For example, if the average sales for a product were 10 units per month, and one month the product had sales of 500 units, this sales point might be considered an outlier.

Upside supply chain flexibility

A discrete measurement of the amount of time it takes a supply chain to respond to an unplanned 20 percent increase in demand without service or cost penalty.

time series forecasting

A forecasting method that projects historical data patterns into the future. [It] involves the assumption that the near-term future will be like the recent past.

Adaptive smoothing

A form of exponential smoothing in which the smoothing constant is automatically adjusted as a function of forecast error measurement.

master planning of resources

A grouping of business processes that includes the following activities: demand management (the forecasting of sales, the planning of distribution, and the servicing of customer orders); sales and operations planning (sales planning, production planning, inventory planning, backlog planning, and resource planning); and master scheduling (the preparation of the master production schedule and the rough-cut capacity plan).

customer relationship management (CRM)

A marketing philosophy based on putting the customer first. Involves the collection and analysis of information designed for sales and marketing decision support (in contrast to enterprise resources planning information) to understand and support existing and potential customer needs. Includes account management, catalog and order entry, payment processing, credits and adjustments, and other functions.

standard deviation

A measurement of dispersion of data or of a variable. The standard deviation is computed by finding the differences between the average and actual observations, squaring each difference, adding the squared differences, dividing by n - 1 (for a sample), and taking the square root of the result. standard deviation = square root( the sume of(actual - average forecast)^2/(n-1))

Second-order smoothing or double smoothing

A method of exponential smoothing for trend situations that employs two previously computed averages, the singly and doubly smoothed values, to extrapolate into the future.

decomposition

A method of forecasting where time series data is separated into up to three components—trend, seasonal, and cyclical—where trend includes the general horizontal upward or downward movement over time; seasonal includes a recurring demand pattern such as day of the week, weekly, monthly, or quarterly; and cyclical includes any repeating, nonseasonal pattern. A fourth component is random—that is, data with no pattern. The new forecast is made by projecting the patterns individually determined and then combining them.

Monthly

A monthly forecast is not too detailed, and it gives an adequate level of precision. Most commonly used by forecasters, this forecast interval allows detection of seasonal patterns that are hidden in quarterly forecast intervals.

Normal distribution

A particular statistical distribution where most of the observations fall fairly close to one mean, and a deviation from the mean is as likely to be plus as it is to be minus. When graphed, the normal distribution takes the form of a bell-shaped curve.

Sample

A portion of a universe of data chosen to estimate some characteristics about the whole universe. The universe of data could consist of sizes of customer orders, number of units of inventory, number of lines on a purchase order, and so forth.

seasonality

A predictable repetitive pattern of demand measured within a year where demand grows and declines. These are calendar-related patterns that can appear annually, quarterly, monthly, weekly, daily and/or hourly.

Supply Chain Operations Reference (SCOR) model

A process reference model developed and endorsed by the APICS Supply Chain Council (SCC) as the standard cross-industry diagnostic tool for supply chain management. The SCOR model describes the business activities associated with satisfying a customer's demand, which include plan, source, make, deliver, and return. Use of the model includes analyzing the current state of a company's processes and goals, quantifying operational performance, and comparing company performance to benchmark data. SCOR has developed a set of metrics for supply chain performance, and APICS SCC members have formed industry groups to collect best practices information that companies can use to evaluate their supply chain performance.

sales and operations planning (S&OP)

A process to develop tactical plans that provide management the ability to strategically direct its businesses to achieve competitive advantage on a continuous basis by integrating customer-focused marketing plans for new and existing products with the management of the supply chain. The process brings together all the plans for the business (sales, marketing, development, manufacturing, sourcing, and financial) into one integrated set of plans.

Delphi method

A qualitative forecasting technique where the opinions of experts are combined in a series of iterations. The results of each iteration are used to develop the next, so that convergence of the experts' opinions is obtained.

First-order smoothing or single exponential smoothing

A single exponential smoothing; a weighted moving average approach that is applied to forecasting problems where the data does not exhibit significant trend or seasonal patterns.

demand filter

A standard set to monitor sales data for individual items in forecasting models. Usually set to be tripped when the demand for a period differs from the forecast by more than some number of mean absolute deviations.

base series

A standard succession of values of demand-over-time data used in forecasting seasonal items. This series of factors is usually based on the relative level of demand during the corresponding period of previous years. The average value of the base series over a seasonal cycle is 1.0. A figure higher than 1.0 indicates that demand for that period is higher than average; a figure less than 1.0 indicates less-than-average demand. For forecasting purposes, the base series is superimposed upon the average demand and trend in demand for the item in question.

Probability distribution

A table of numbers or a mathematical expression that indicates the frequency with which each of all possible results of an experiment should occur.

Line manufacturing process

A type of manufacturing process used to produce a narrow range of standard items with identical or highly similar designs. Production volumes are high, production and material handling equipment is specialized, and all products typically pass through the same sequence of operations.This involves a low to medium number of parts, a high production rate, high volume, and low variety. Work processes are arranged according to the steps involved in making the product, and discrete parts are made by moving from workstation to workstation at a controlled rate (assembly rate), repeating the sequence as many times as necessary to build the number of products needed. Cars or refrigerators in an assembly line are examples.

exponential smoothing forecast

A type of weighted moving average forecasting technique in which past observations are geometrically discounted according to their age. The heaviest weight is assigned to the most recent data. The smoothing is termed exponential because data points are weighted in accordance with an exponential function of their age. The technique makes use of a smoothing constant to apply to the difference between the most recent forecast and the critical sales data, thus avoiding the necessity of carrying historical sales data. The approach can be used for data that exhibits no trend or seasonal patterns. Higher order exponential smoothing models can be used for data with either (or both) trend and seasonality.

Objectives for customer service levels include

Acceptable lead times Product that is shipped complete and arrives on time without substitution Product of the right quality and at the right price.

actual demand

Actual demand is composed of customer orders (and often allocations of items, ingredients, or raw materials to production or distribution). Actual demand nets against or "consumes" the forecast, depending upon the rules chosen over a time horizon. For example, actual demand will totally replace forecast inside the sold-out customer order backlog horizon (often called the demand time fence) but will net against the forecast outside this horizon based on the chosen forecast consumption rule.

several types of exponential smoothing

Adaptive smoothing First-order smoothing or single exponential smoothing Second-order smoothing or double smoothing

Agility

Agility metrics evaluate the ability of a supply chain to deal reliably with demand variation in the near and long term.

qualitative forecasting techniques

An approach to forecasting that is based on intuitive or judgmental evaluation. It is used generally when data is scarce, not available, or no longer relevant.

quantitative forecasting techniques

An approach to forecasting where historical demand data is used to project future demand. Extrinsic and intrinsic techniques are typically used.

moving average

An arithmetic average of a certain number (n) of the most recent observations. As each new observation is added, the oldest observation is dropped. The value of n (the number of periods to use for the average) reflects responsiveness versus stability in the same way that the choice of smoothing constant does in exponential smoothing. There are two types of moving average: simple and weighted.

aggregate forecast

An estimate of sales, often time-phased, for a grouping of products or product families produced by a facility or firm. Stated in terms of units, dollars, or both, the aggregate forecast is used for sales and production planning (or for sales and operations planning) purposes.

Cash-to-cash cycle time

An indicator of how efficiently a company manages its assets to improve cash flow. Calculated as inventory days plus accounts receivable days minus accounts payable days.

time series analysis

Analysis of any variable classified by time in which the values of the variable are functions of the time periods. Time series analysis is used in forecasting. A time series consists of seasonal, cyclical, trend, and random components.

Batch manufacturing

Batch processing involves a medium number of parts, medium to high volume, low to medium variety, and a medium to high production rate. Products are either made-to-order or assembled-to-order based on the production flow rate of the manufacturing cell, which is tied to customer orders. An example is clothing that is first dyed and then cut, sewn, and packaged.

resource planning

Capacity planning conducted at the business plan level. The process of establishing, measuring, and adjusting limits or levels of long-range capacity. Resource planning is normally based on the production plan but may be driven by higher-level plans beyond the time horizon of the production plan (e.g., the business plan). It addresses those resources that take long periods of time to acquire. Resource planning decisions always require top management approval.

So how does one choose the best forecast method?

Complete versus incomplete data Stable versus unstable data

Continuous manufacturing process

Continuous processes involve a low number of parts, high volume, low to medium variety, and a high rate of production. Products are made-to-stock using production flow rates that are tied to demand forecasts when it's necessary to achieve high capacity utilization and economies of scale. Examples of types of organizations that use continuous processes are petrochemical and water and gas companies.

The primary components of a customer service policy include

Customer focus Service levels Performance measurement Systems support Customer interface Culture Top management support Integration with strategic goals.

Lifetime customers provide value by

Decreasing the total cost of marketing Making it easier to know and satisfy their needs over time Supplying opportunities for increased revenue and profit.

Abnormal demand

Demand in any period that is outside the limits established by management policy. This demand may come from a new customer or from existing customers whose own demand is increasing or decreasing. Care must be taken in evaluating the nature of the demand: Is it a volume change? Is it a change in product mix? Is it related to the timing of the order?

Dependent demand

Demand that is directly related to or derived from the bill-of-material structure for other items or end products. Such demands are therefore calculated and need not and should not be forecast. A given inventory item may have both dependent and independent demand at any given time. For example, a part may simultaneously be a component of an assembly and sold as a service part.

five major issues must be addressed in order to meet customer demand:

Directing the flow of raw materials from suppliers, to work in process through production, and to finished product and delivery channels to customers Identifying the delivery lead time customers are willing to accept Deploying and using production resources: labor, equipment, technology Having the right design and process to support the transformation process and/or service being offered Identifying appropriate relationships with other parties in the supply chain, including outsourcing, arm's-length relationships, partnerships, alliances, and vertical integration

While the item forecast is what the actual replenishment timing and quantity is based on, the aggregate product family forecast is important for two reasons:

During S&OP, the product family forecast is used to ensure that resources are available to transport, handle, and store replenishment inventory within major distribution channels. The process that addresses channel resource needs during S&OP is LRRP, which we will address later in this section. During DRP, which is an input to master production scheduling, product family forecasts by channel are disaggregated to the item-mix level and then are allocated down through the BOD to plan the replenishment of inventory stocking locations.

Extrapolation

Estimation of the future value of some data series based on past observations. Statistical forecasting is a common example.

Forecast Accuracy

Forecast Accuracy = 1 - Forecast Error as a Percentage

Forecast Accuracy Equation

Forecast Accuracy = Sum of (Number of Hits) / Sum of (Number of Hits * Number of Misses) * 100%

mix forecast

Forecast of the proportion of products that will be sold within a given product family, or the proportion of options offered within a product line. Product and option mix as well as aggregate product families must be forecasted. Even though the appropriate level of units is forecasted for a given product line, an inaccurate mix forecast can create material shortages and inventory problems.

Some organizations invest in demand management software that can effectively do many tasks simultaneously:

Gather market information. Generate forecast information. Screen and monitor performance. Supply detailed action instructions. Provide accurate product delivery information for customers.

Complete versus incomplete data

If all the required sales data for a particular item as well as the causal variables are available, then the data are considered complete. Incomplete data would have limited—or even an absence of—sales data for a particular product or not have the causal variables identified.

smoothing constant

In exponential smoothing, the weighting factor that is applied to the most recent demand, observation, or error. In this case, the error is defined as the difference between actual demand and the forecast for the most recent period. The weighting factor is represented by the symbol α. Theoretically, the range of α is 0.0 to 1.

Available-to-promise (ATP)

In operations, the uncommitted portion of a company's inventory and planned production maintained in the master schedule to support customer-order promising. The ATP quantity is the uncommitted inventory balance in the first period and is normally calculated for each period in which an MPS receipt is scheduled. In the first period, ATP includes on-hand inventory less customer orders that are due and overdue. Three methods of calculation are used: discrete ATP, cumulative ATP with look-ahead, and cumulative ATP without look-ahead. (2) In logistics, the quantity of a finished good that is or will be available to commit to a customer order based on the customer's required ship date. To accommodate deliveries on future dates, ATP is usually time-phased to include anticipated purchases or production receipts. ATP = On-Hand + Supply - Demand

A high level of forecast accuracy provides a number of overall benefits:

It supports customer satisfaction by ensuring that the organization can deliver the product desired to the customer in a timely manner. It reinforces customers' loyalty to the organization's products that meet their needs and makes customers less likely to purchase the products from a competitor. It reflects a more complete understanding of customers' future product demands, thereby helping support future revenue streams.

Objectives for the costs of production include

Low amount of work in process, or WIP High turnover of inventory Faster throughput.

Conversion of standard deviation to MAD

MAD = Standard Deviation / 1.25

Mean Absolute Percent Error (MAPE)

MAPE = Sum of (|actual demand - forecast demand|/actual demand)(%)/n If MAPE is greater than a certain percentage, additional review is required.

Mean Squared Error (MSE)

MSE = Sum of (Errors for Each Period)^2/Number of Forecast Periods

potential causes to consider by environment type:

Make-to-stock—material capacity Make-to-order—machine and/or labor hours Assemble-to-order—lack of raw materials or components Engineer-to-order—lack of available components or subcomponents Retail environment for consumers—stocking schedule, shipments delayed, installation schedule delays, or lack of parts or equipment needed for installation

Safety Stock Probability

Mathematically, a number between 0 and 1 that estimates the fraction of experiments (if the same experiment were being repeated many times) in which a particular result would occur. This number can be either subjective or based upon the empirical results of experimentation. It can also be derived for a process to give the probable outcome of experimentation.

The right goods and the right services

Meeting or exceeding customer expectations requires designing and making products that meet or exceed customers' needs and desires. This ability is dictated by how well sales and marketing identifies and selects products and services that customers want to buy.

trend forecasting models

Methods for forecasting sales data when a definite upward or downward pattern exists. Models include double exponential smoothing, regression, and triple smoothing.

Objectives for the investment in inventory include

Minimizing the number of setups Longer production runs and high utilization Low cost of materials.

New Forecast

New Forecast = (alpha * Last Period's Demand) + [(1 - alpha) * Last Period's Forecast]

When developing forecasts, planners need to take into consideration that forecasts are

Not as accurate as one might hope More on target the larger the group measured Best when used along with a forecast error measuring technique More accurate the shorter the time period.

Perfect order fulfillment

Perfect order fulfillment = Number of perfect orders / Total number of orders

Some of the most important criteria for customer segmentation are based on

Profitability Strategic importance to the business Special needs.

Project manufacturing process

Project manufacturing involves a high number of parts, a very low production rate or production level, very low volume, and high variety. A project plan is based on work breakdown and network analysis techniques to create an optimum schedule of activities to minimize lead time and costs. An example would be the construction of a stadium.

Process types

Project manufacturing process Work center manufacturing process Batch manufacturing Line manufacturing process Continuous manufacturing process

To build and communicate accurate forecasts, the demand information needed contains data on

Promotion requirements Pipeline build-ups Spare parts Pilot project or exhibition requirements Quality assurance needs Charitable donations of products Inter- and intraplant transfers.

Quarterly

Quarterly forecasts are appropriate in a few industries with long production lead times, as in engineer-to-order environments. They are also appropriate in the later years of a multiyear forecast but may hide seasonal demand patterns.

There are four key performance attribute categories of delivery metrics.

Reliability Responsiveness Agility Cost

Responsiveness

Responsiveness focuses on the average cycle time that a manufacturer requires to respond to and deliver customer orders Responsiveness = Sum of Actual Cycle Times for All Orders Delivered / Total Number of Orders Delivered

Simple regression

Simple regression (also called linear regression) uses the least-squares method. y = alpha + beta * x

Aside from developing forecasts, demand management entails a number of other critical activities. One such activity is identifying sources of demand, including

Specific demands from various existing customer segments, including new customers, new items, new color options, or regional or culture-specific variations Unmet demands, such as spare parts, demonstration stock, or inventory changes Requests for discontinued products or obsolete replacement parts Difficult-to-forecast products with low and/or erratic demand patterns and low, discontinuous sales.

Stable versus unstable data

Stable data have a distinct pattern such as seasonality or trends. There's a randomness and no distinct pattern to unstable data.

The following steps will demonstrate how decomposition can be used to create a forecast when there are both trend and seasonal components in a time series

Step 1: Plot the seasonal data and calculate the trend line. Step 2: Calculate the trend-adjusted seasonal factor. Step 3: Calculate the trend-adjusted seasonal forecast.

Individual business strategic changes

Steps 1 and 2—developing a collaboration agreement and creating a joint business plan—are often the most time-consuming, and they involve strategy and planning. For many organizations, this requires a change in how they do business with suppliers. This culture shift to a more collaborative relationship could take months and requires support from the top down to be successful. Most organizations do not begin with the level of transparency needed in order share the sensitive information required to be successful. The organization needs to decide not only what information will be shared but also how it will be shared. Technology can be a hurdle when beginning this process. If these hurdles can be addressed, then both organizations can benefit from better forecasting and demand management. The success of both organizations in executing steps 1 and 2 are essential in creating the collaboration needed to execute steps 3 through 9.

Day-to-day activity

Steps 3 through 9 include day-to-day forecasting and tracking activities. The success or failure of these activities depends on regular communication, organized as part of a regular cycle of conference calls and meetings. An example can be seen at Motorola, which used CPFR to align its mobile device division and retail distribution centers. A meeting was scheduled once a week throughout the month, involving all the appropriate parties. Each week the focus of the meeting shifted to a different objective, including Operations review—to discuss the last month's performance Forecasting—to develop the collaborative forecasts Process improvement—to examine issues from operations review and assign actions Financial implications—to assess financial implications on period-end goals.

Mean Error (Bias)

Sum of (Actual Demand - Forecast Demand) / n

Mean

The arithmetic average of a group of values.

mean absolute deviation (MAD)

The average of the absolute values of the deviations of observed values from some expected value. MAD can be calculated based on observations and the arithmetic mean of those observations. An alternative is to calculate absolute deviations of actual sales data minus forecast data. This data can be averaged in the usual arithmetic way or with exponential smoothing. MAD = Sum of (|actual demand - forecast demand|)/number of preiods

mass customization

The creation of a high-volume product with large variety whose manufacturing cost is low due to the large volume, allowing customers to specify an exact model out of a large volume of possible end items. An example is a personal computer order in which the customer specifies processor speed, memory size, hard disk size and speed, removable storage device characteristics, and many other options when PCs are assembled on one line and at low cost.

The right place

The delivery of products is dependent upon the company's sales and distribution channels. Sales channels are the internal functions and external parties (wholesale distributors and retailers) that support the marketing, promotion, and sale of products and services to supply chain customers.

Sampling distribution

The distribution of values of a statistic calculated from samples of a given size.

Two important things to note about forecasts:

The forecast model selected should not be any more complicated than necessary. Simpler is better. Data put in, and the forecast that comes out, should be monitored on a routine basis for appropriateness and quality.

Demand management

The function of recognizing all demands for goods and services to support the marketplace. It involves prioritizing demand when supply is lacking. Proper demand management facilitates the planning and use of resources for profitable business results.

Median

The middle value in a set of measured values when the items are arranged in order of magnitude. If there is no single middle value, the median is the mean of the two middle values.

Mode

The most common or frequent value in a group of values

customer segmentation

The practice of dividing a customer base into groups of individuals who are similar in specific ways relevant to marketing. Traditional segmentation focuses on identifying customer groups based on demographics and attributes such as attitude and psychological profiles.

Safety Stock Stockout probability

The probability of not having a stockout in any one ordering cycle, which begins at the time an order is placed and ends when the goods are placed in stock.

Order entry

The process of accepting and translating what a customer wants into terms used by the manufacturer or distributor. The commitment should be based on the available-to-promise (ATP) line in the master schedule. This can be as simple as creating shipping documents for finished goods in a make-to-stock environment, or it might be a more complicated series of activities, including design efforts for make-to-order products.

Capable-to-promise (CTP)

The process of committing orders against available capacity as well as inventory. This process may involve multiple manufacturing or distribution sites. Used to determine when a new or unscheduled customer order can be delivered. Employs a finite-scheduling model of the manufacturing system to determine when an item can be delivered. Includes any constraints that might restrict the production, such as availability of resources, lead times for raw materials or purchased parts, and requirements for lower-level components or subassemblies. The resulting delivery date takes into consideration production capacity, the current manufacturing environment, and future order commitments. The objective is to reduce the time spent by production planners in expediting orders and adjusting plans because of inaccurate delivery-date promises.

rough-cut capacity planning

The process of converting the master production schedule into requirements for key resources often including labor, machinery, warehouse space, suppliers' capabilities, and, in some cases, money. Comparison to available or demonstrated capacity is usually done for each key resource. This comparison assists the master scheduler in establishing a feasible master production schedule. Three approaches to performing RCCP are the bill of labor (resources, capacity) approach, the capacity planning using overall factors approach, and the resource profile approach.

forecast management

The process of making, checking, correcting, and using forecasts. It also includes determination of the forecast horizon.

Consuming the forecast

The process of reducing the forecast by customer orders or other types of actual demands as they are received. The adjustments yield the value of the remaining forecast for each period.

master scheduling

The process where the master schedule is generated and reviewed and adjustments are made to the master production schedule to ensure consistency with the production plan. The master production schedule (the line on the grid) is the primary input to the material requirements plan. The sum of the master production schedules for the items within the product family must equal the production plan for that family.

tracking signal

The ratio of the cumulative algebraic sum of the deviations between the forecasts and the actual values to the mean absolute deviation. Used to signal when the validity of the forecasting model might be in doubt. Tracking Signal = Algebraic Sum of Forecast Errors / MAD

correlation

The relationship between two sets of data such that when one changes, the other is likely to make a corresponding change. If the changes are in the same direction, there is positive correlation. When changes tend to occur in opposite directions, there is negative correlation. When there is little correspondence or changes are random, there is no correlation.

focal points of customer service

The right goods and the right services The right price The right quality The right quantity The right time The right place

The right time

The speed, flexibility, quality, and dependability of production and distribution are critical to meeting the on-time delivery required by customers.

The right price

The speed, quality, and flexibility of production and distribution processes dictate the price point that customers pay for a product or service.

Cost

The supply chain cost attribute category includes supply chain management cost and cost of goods sold. The final row details cost metrics.

distribution of forecast errors

The tabulation of the forecast errors according to the frequency of occurrence of each error value. The errors in forecasting are, in many cases, normally distributed even when the observed data does not come from a normal distribution.

Work center manufacturing process

The work center process type involves a medium to high number of parts, a low to medium production rate, low volume, and high variety. Products are made-to-order for customers and are validated by rough-cut capacity planning. A yacht might be manufactured using a work center process

demand forecasting

There are many ways in which demand forecasts are used by manufacturers and service providers—to strategically guide their decision making (for new product line development and merger or acquisition decisions), for shorter-term scheduling, and in master production scheduling. The focus here is on the latter.

Reliability

This attribute category focuses on quality of product and service. It is measured by perfect order fulfillment and demonstrates the degree to which a supplier is able to serve its customers within the promised delivery time

Weekly

This forecast is necessary for master scheduling and can be achieved by dividing monthly product family forecasts into weekly buckets for individual products. A weekly level of detail is not always necessary. A weekly forecast interval increases data management and can impart a false sense of precision.

The right quality

This includes conformance of products and services to regulatory or other standards as well as specification quality in terms of the design, manufacture, and delivery of products and services that provide the functionality, aesthetics, and value that customers want.

The right quantity

This involves the integration and execution of purchasing, order management, production, and logistics so that materials are in the right quantity and are available for delivery.

Often the more profitable customers receive differentiated treatment in the areas of

Volume discounts Priority attention from sales and marketing staff and other functions such as product development.

Distribution requirements planning (DRP) addresses the following issues:

When, where, and how much inventory is needed at the distribution centers within a manufacturer's distribution network in order to fulfill customer orders. What resources are needed, and when, to ensure that replenishment inventory can be delivered, handled, stored according the replenishment plans.

collaborative planning, forecasting, and replenishment (CPFR)

a collaboration process whereby supply chain trading partners can jointly plan key supply chain activities from production and delivery of raw materials to production and delivery of final products to end customers.

product group forecast

a forecast for a number of similar products

pyramid forecasting

a forecasting technique that enables management to review and adjust forecasts made at an aggregate level and to keep lower-level forecasts in balance.

multiple regression models

a form of regression analysis where the model involves more than one independent variable.

work in process

a good or goods in various stages of completion throughout the plant

panel consensus

a judgmental forecasting technique by which a committee, sales force, or group of experts arrives at a sales estimate

customer service level

a measure of delivery performance of finished goods or other cargo, usually expressed as a percentage.

customer service life cycle

a model that describes the customer relationship as having four phases: requirements, acquisition, ownership, and retirement

life cycle analysis

a quantitative forecasting technique based on applying past patterns of demand data covering introduction, growth, maturity, saturation, and decline of similar products to a new product family.

Econometric model

a set of equations intended to be used simultaneously to capture the way in which dependent and independent variables are interrelated.

regression analysis

a statistical technique for determining the best mathematical expression describing the functional relationship between one response and one or more independent variables.

focus forecasting

a system that allows the user to simulate the effectiveness of numerous forecasting techniques, enabling selection of the most effective one.

Historical analogy

a technique based on identifying a sales history that is analogous to a present situation, such as the sales history of a similar product, and using that past pattern to predict future sales.

judgmental/expert opinion

a technique in which juries of executives, salespersons, independent market analysts, marketing committees, and others use their detailed knowledge of products and customers, along with their knowledge of the differences between prior forecasts and actual results, to generate a forecast or, more often, to adjust a quantitative forecast. Tracking these adjustments separately from any quantitative component will help in determining whether these modifications are increasing or decreasing in accuracy and in showing whether they are introducing bias by being consistently high or low. One judgmental technique used is panel consensus.

Curve fitting

an approach to forecasting based on a straight line, polynomial, or other curve that describes some historical time series data.

weighted moving average

an averaging technique in which the data to be averaged is not uniformly weighted but is given values according to its importance.

Engineer-to-order (ETO) tradeoffs

complex structures and customer-specified projects that were never built before and cannot be handled with standard variations

information needs for Engineer-to-order

demand forecasts based on similar products previously created, with the focus on engineering hours, for S&OP and MPS; delivery lead time for customers

information needs for Make-to-stock

demand forecasts for S&OP; actual demand for MPS; next inventory replenishment for customers

information needs for Make-to-order

demand forecasts, engineering detail for S&OP; final configuration for MPS; design status, delivery date for customers

information needs for Assemble-to-order

demand forecasts, product family mix for S&OP; mix forecasts and actual demand for MPS; configuration issues, delivery date for customers

types of uncertainty in Engineer-to-order

hiring difficult-to-find design engineers who demand high pay for their services

value perspective

holds that quality must be judged, in part, by how well the characteristics of a particular product or service align with the needs of a specific user

The nine steps can be grouped into two categories:

individual business strategic changes and day-to-day activity

least-squares method

method of curve fitting that selects a line of best fit through a plot of data to minimize the sum of squares of the deviations of the given points from the line.

Conversion of MAD to standard deviation

standard deviation = MAD * 1.25

Make-to-stock (MTS) tradeoffs

standard product made to a forecast before any committed orders come in

Make-to-order (MTO) tradeoffs

standard product not held in inventory and made after a committed order comes in

Assemble-to-order (ATO) tradeoffs

standard product where some components are held in stock and the finished product is finished after the order comes in

order fulfillment lead time

the average amount of time between the customer's order and the customer's receipt of delivery; this includes every manufacturing or processing step in between.

forecast error

the difference between actual demand and forecast demand, stated as an absolute value or as a percentage. Forecast Error = |actual demand - forecast demand| Forecast Error as a Percentage = |actual demand - forecast demand|/actual demand

distribution channel

the distribution route, from raw materials through consumption, along which products travel.

types of uncertainty in Make-to-order

the level of company resources necessary to finish the engineering and produce the product per the specifications

decoupling points

the locations in the product structure or distribution network where inventory is placed to create independence between processes or entities.

forecast horizon

the period of time into the future for which a forecast is prepared

distribution planning

the planning activities associated with transportation, warehousing, inventory levels, materials handling, order administration, site and location planning, industrial packaging, data processing, and communications networks to support distribution.

customer satisfaction

the results of delivering a good or service that meets customer requirements

forecast interval

the time unit for which forecasts are prepared, such as week, month, or quarter.

substitution

the use of a nonprimary product or component, normally when the primary item is not available.

setups

the work required to change a specific machine, resource, work center, or line from making the last good piece of item A to making the first good piece of item B

types of uncertainty in Make-to-stock

variations in demand stated in forecasts for each inventory location

types of uncertainty in Assemble-to-order

variations in quantity, customer order timing, and product mix