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Chapter 3

FORECASTING

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Because learning changes everything.®

Learning Objectives

Understand how forecasting is essential to supply chain planning.

Evaluate demand using quantitative forecasting models.

Apply qualitative techniques to forecast demand.

Apply collaborative techniques to forecast demand.

Role of Forecasting in Starbucks

Starbucks, the largest coffee chain in the world with over 30000 stores in 80 countries.

Variety of products beyond coffee, coffee beans, salads, sandwiches, mugs, etc.

Product offerings varies by season and some are store location-specific.

Many are perishable, some runs the risk of becoming obsolete.

Starbucks branded coffee and ice cream are sold in grocery stores.

Need for forecasting regional and global demand as well as store-specific or demand.

Far-flung Supply chain at Starbucks.

The company brings raw beans from Latin America, Africa and Asia in giant shipping containers to USA and European 6 storage locations near roasting facilities .

Roasted coffee is packaged and shipped to distribution centers (DCs) 4 in USA, 2 in Europe and 2 in Asia).

DCs also store many more items that are sold through Starbucks besides coffee.

Various types of forecasting (global, regional, store-specific) will help make decisions as to the quantity to be bought and shipped and processed through various steps in the supply chain.

Analytical exercise on Forecasting SC demand –Starbucks corp. gives a numerical illustration of this interesting forecasting challenge.

It is important to understand why decision making can be so challenging. Exhibit 3.1 illustrates several characteristics of managerial decisions that contribute to their difficulty and pressure.

Forecasting in Operations and Supply Chain Management

Forecasting is vital to every business organization and impacts every significant management decision.

Forecasting is the basis of corporate planning and control .

Finance and accounting use forecasts as the basis for budgeting and cost control.

Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation.

Production uses forecasts to select suppliers, determine capacity requirements, and to drive decisions about purchasing, staffing, and inventory.

Different activities require different forecasting approaches.

Decisions about overall directions require strategic forecasts.

Tactical forecasts are used to guide day-to-day decisions and the goal is to estimate demand in short term.

This slide repeats the planning steps.

Decoupling Points

Decoupling points occur when inventory is positioned in the supply chain to allow processes or entities to operate independently.

For example, inventory at a retail store separates (buffers) the manufacturer from the actions of individual consumers.

Forecasts of demand at these decoupling points allow inventory to be set to the proper level.

Types of Forecasting

Basic types of forecasts:

Qualitative.

Quantitative.

Time series analysis (primary focus of this chapter).

Causal relationships.

Simulation.

Time series analysis is based on the idea that data relating to past demand can be used to predict future demand.

Based on the situational analysis, the planning process should generate multiple goals that could be pursued and various plans for achieving them. This step should stress creativity and encourage managers and employees to think broadly about their work.

Goals are the targets or ends the manager wants to reach. Plans are the actions or means the manager intends to use to achieve goals.

At a minimum, plans should outline alternative actions attaining each goal, the resources required to reach the goal through those means, and the obstacles that may develop.

Components of Demand

Average demand for the period

Trend

Seasonal element

Cyclical elements

Random variation

Autocorrelation

In particular, they will pay a great deal of attention to the cost of any initiative and the predicted return on investment.

Time series forecasting overview

Trend line is the usual starting point of the time series forecasting.

Trend line is adjusted for

Seasonal effects.

Cyclical elements.

Any other expected elements that may influence the final forecast.

Estimation of autocorrelation allows us the improve forecasting accuracy.

Estimation of random effects allows us to give a range for the forecast.

Once managers have assessed the various goals and plans, they try to select the best. The evaluation process helps to identify trade-offs and decide what to do.

Scenario: a narrative that describes a particular set of future conditions.

Components of Demand – Growth & Seasonal

Exhibit 3.1

Access the text alternative for slide images.

Once managers have assessed the various goals and plans, they try to select the best. The evaluation process helps to identify trade-offs and decide what to do.

Scenario: a narrative that describes a particular set of future conditions.

Trends

Identification of trend lines is a common starting point when developing a forecast.

Common trend types include linear, S-curve, asymptotic, and exponential.

Exhibit 3.2

Access the text alternative for slide images.

Without monitoring, you will never know whether your plan is succeeding.

Time Series Analysis

Using the past to predict the future.

Short term – forecasting less than three months

Used mainly for tactical decisions (for example, replenishing inventory).

Medium term – forecasting three months to two years

Used to develop a strategy which will be implemented over the next six to eighteen months (for example, meeting demand).

Long term – forecasting greater than two years

Useful for detecting general trends and identifying major turning points.

The discussion of the social enterprise can be extended these questions.

1. Why do you think more companies don’t incorporate a TBL philosophy into their bylaws?

2. Assume you want your employer to consider adopting a TBL philosophy. How would you pitch the idea? With whom would you speak?

Model Selection

Choosing an appropriate forecasting model depends upon:

Time horizon to forecast.

Data availability.

Accuracy required.

Size of forecasting budget.

Availability of qualified personnel.

Other factors may also be considered.

Degree of flexibility (can the firm react quickly if the forecast is inaccurate?).

Consequence of a bad forecast (important or costly decisions require a good forecast).

In Chapter 1, you learned about the three major types of managers: top-level (strategic managers), middle-level (tactical managers), and frontline (operational managers). Because planning is an important management function, managers at all levels engage in it. However, the scope and activities of the planning process differ at different levels.

A strategy is a pattern of actions and resource allocations designed to achieve the goals of the organization .

Forecasting Method Selection Guide

Forecasting Method	Amount of Historical Data	Data Pattern	Forecast Horizon
Simple moving average	6 to 12 months; weekly data are often used	Stationary (i.e. no trend or seasonality)	Short
Weighted moving average and simple exponential smoothing	5 to 10 observations needed to start	Stationary	Short
Exponential smoothing with trend	5 to 10 observations needed to start	Stationary and trend	Short
Linear regression	10 to 20 observations	Stationary, trend, and seasonality	Short to Medium
Trend and seasonal models	2 to 3 observations per season	Stationary, trend and seasonality	Short to Medium

Simple Moving Average

Forecast is based on average demand over the most recent periods

Useful when demand is not growing or declining rapidly, and no seasonality is present.

Removes some of the random fluctuation from the data.

Selecting the period length is important.

Longer periods provide more smoothing.

Shorter periods react to trends more quickly.

- forecast for the coming period t.

– number of periods to be averaged.

- actual occurrence in the past period.

, , and - actual occurrences two periods ago, three periods ago, and so on up to n periods ago.

Simple Moving Average – Example (p. 52 to 54)

Note that no forecast is possible until “n” periods have passed

Exhibit 3.4

Week	Demand	3 -Week	9 -Week	Week	Demand	3-Week	9-Week
1	800			16	1,700	2,200	1,811
2	1,400			17	1,800	2,000	1,800
3	1,000			18	2,200	1,833	1,811
4	1,500	1,067		19	1,900	1,900	1,911
5	1,500	1,300		20	2,400	1,967	1,933
6	1,300	1,333		21	2,400	2,167	2,011
7	1,800	1,433		22	2,600	2,233	2,111
8	1,700	1,533		23	2,000	2,467	2,144
9	1,300	1,600		24	2,500	2,333	2,111
10	1,700	1,600	1,367	25	2,600	2,367	2,167
11	1,700	1,567	1,467	26	2,200	2,367	2,267
12	1,500	1,567	1,500	27	2,200	2,433	2,311
13	2,300	1,633	1,556	28	2,500	2,333	2,311
14	2,300	1,833	1,644	29	2,400	2,300	2,378
15	2,000	2,033	1,733	30	2,100	2,367	2,378

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Tactical planning translates broad strategic goals and plans into specific goals and plans that are relevant to a particular unit in the organization, often a functional area like marketing or human resources. Tactical plans focus on the major actions a unit must take to fulfill its part of the strategic plan.

Operational planning identifies the specific procedures and processes required at lower levels of the organization. Frontline managers usually focus on routine tasks such as production runs, customer service product updates, delivery schedules, and human resources.

Weighted Moving Average

The simple moving average formula implies all periods are equally important.

A weighted moving average allows unequal weighting of prior time periods.

The sum of the weights must be equal to one.

More recent data (periods) are given more significance (higher weights) than older data.

W1 – weight to be given to the actual occurrence for the period t – 1.

W2 – weight to be given to the actual occurrence for the period t − 2.

W1 – weight to be given to the actual occurrence for the period t − n.

n – total number of prior periods in the forecast.

Exhibit 4.4 shows a strategy map and how the various goals of the organization relate to each other to create long-term value for the firm.

Choosing Weights

Experience and trial and error are the simplest approaches.

The most recent past is the most important indicator of what to expect in the future, so weights are generally higher for more recent data.

If the data are seasonal, weights should be established appropriately.

The weighted moving average has an advantage over the simple moving average.

Because of these trends, firms often use the term strategic management to describe the process. Strategic management involves managers from all parts of the organization in the formulation and implementation of strategic goals and strategies. It integrates strategic planning and management into a single process and uses a SWOT analysis.

Exponential Smoothing

The importance of data diminishes as the past becomes more distant.

The most logical and easiest method to use.

An integral part of all computerized forecasting programs.

Well accepted for six major reasons:

Exponential models are surprisingly accurate.

Formulating an exponential model is relatively easy.

The user can understand how the model works.

Little computation is required to use the model.

Computer storage requirements are small.

Tests for accuracy are easy to compute.

The first step in strategic planning is establishing a mission, a vision, and goals for the organization. The mission is a clear and concise expression of the basic purpose of the organization. It describes what the organization does, for whom it does it, its basic good or service.

The strategic vision points to the future—it provides a perspective on where the organization is headed and what it can become. Ideally, the vision statement clarifies the long-term direction of the company and its strategic intent.

Exponential Smoothing Model

-forecast value for time period t.

-forecast value for the previous time period t − 1.

-actual occurrence for time period t − 1.

-alpha, the smoothing constant.

Stakeholders include buyers, suppliers, competitors, government and regulatory agencies, unions and employee groups, the financial community, owners and shareholders, and trade associations. The environmental analysis assesses these stakeholders and the ways they influence the organization. The environmental analysis also should examine other forces in the environment, such as economic conditions and technological factors.

Exponential Smoothing Example

Week	Demand	Forecast
1	820	820
2	775	820
3	680	811
4	655	785
5	750	759
6	802	757
7	798	766
8	689	772
9	775	756
10		760

Exhibit 4.6 is abbreviated on the slide. The important activities in an environmental analysis include the ones shown here.

Exponential Smoothing with Trend

The presence of a trend in the data causes the exponential smoothing forecast to always lag behind the actual occurrence.

This can be corrected by adding a trend adjustment.

The trend smoothing constant is delta

Both alpha and delta reduce the impact of the error that occurs between the actual and the forecast.

As managers conduct an external analysis, they also assess the strengths and weaknesses of major functional areas inside their organization. Exhibit 4.7 lists some of the major components of this internal resource analysis.

Exponential Smoothing with Trend – Example 3.1

Calculate the new forecast, assuming the following:

The previous forecast including trend (F I Tt − 1) is 110 and the previous estimate of the trend (Tt − 1) is 10.

Actual demand for period t − 1 was 115.

Choosing Alpha and Delta

Relatively small values for 𝛼 and 𝛿 are common.

Usually in the range 0.1 to 0.3.

𝛼 depends upon how much random variation is present.

𝛿 depends upon how steady the trend is.

Measurements of forecast error can be used to select values of 𝛼 and 𝛿 to minimize overall forecast error.

A resource provides advantage if it is instrumental in creating customer value—increasing the benefits customers derive from a good or service relative to the costs they incur. Second, resources are a source of advantage if they are rare and not equally available to all competitors. Even for extremely valuable resources, if all competitors have equal access, the resource cannot provide competitive advantage. Third, resources provide competitive advantage if they are difficult to imitate. Fourth, the resources must be organized.

Linear Regression Analysis

Regression - the functional relationship between two or more correlated variables, usually from observed data.

One variable (the dependent variable) is predicted for given values of the other variable (the independent variable).

Linear regression is a special case which assumes the relationship between the variables can be explained with a straight line.

An important bias that inhibits benchmarking is the Not Invented Here (NIH) syndrome: a negative attitude toward knowledge (ideas, technologies) derived from an external source.

Linear Regression Forecasting

Past data and future projections are assumed to fall around straight line.

Used both for time series forecasting and for causal relationship forecasting.

Least squares method used for linear regression forecasting.

Strengths (internal)

Weaknesses (internal)

Opportunities (external)

Threats (external)

Least Squares Method – Example 3.2

The least squares method determines the parameters a and b such that the sum of the squared errors is minimized – the “least squares.”

Quarter	Sales	Quarter	Sales
1	600	7	2,600
2	1,550	8	2,900
3	1,500	9	3,800
4	1,500	10	4,500
5	2,400	11	4,000
6	3,100	12	4,900

Exhibit 3.6

The best line to use is one that minimizes the sum of the squared errors.

Access the text alternative for slide images.

Once managers have analyzed the external environment and the internal resources of the organization, they have the information they need to assess the organization’s strengths, weaknesses, opportunities, and threats. SWOT analysis helps managers summarize the relevant, important facts from their external and internal analyses.

Calculations - Example 3.2

The forecast is then extended to periods 13 to 16.

Exhibit 3.7

t	y	t × y	t squared	y squared	Y
1	600	600	1	360,000	801.3
2	1,550	3,100	4	2,402,500	1,160.9
3	1,500	4,500	9	2,250,000	1,520.5
4	1,500	6,000	16	2,250,000	1,880.1
5	2,400	12,000	25	5,760,000	2,239.7
6	3,100	18,600	36	9,610,000	2,599.4
7	2,600	18,200	49	6,760,000	2,959.0
8	2,900	23,200	64	8,410,000	3,318.6
9	3,800	34,200	81	14.440,000	3,678.2
10	4,500	45,000	100	20,250,000	4,037.8
11	4,000	44,000	121	16,000,000	4,397.4
12	4,900	58,800	144	24,010,000	4,757.1

A corporate strategy identifies the set of businesses, markets, or industries in which the organization competes and the distribution of resources among those businesses. There are four basic alternatives for a corporate strategy, ranging from very specialized to highly diverse.

A concentration strategy focuses on a single business competing in a single industry. A vertical integration strategy involves expanding the domain of the organization into supply channels or distributors. A strategy of concentric diversification involves moving into new businesses that are related to the company’s original core business. In contrast to concentric diversification, conglomerate diversification is a corporate strategy that involves expansion into unrelated businesses.

Excel Regression Tool

Microsoft Excel includes data analysis tools, which can perform least squares regression on a data set.

Exhibit 3.8

A corporate strategy identifies the set of businesses, markets, or industries in which the organization competes and the distribution of resources among those businesses. Exhibit 4.10 shows four basic alternatives for a corporate strategy, ranging from very specialized to highly diverse. A concentration strategy focuses on a single business competing in a single industry.

Decomposition of a Time Series

Time series - chronologically ordered data.

A time series may contain one or many elements.

Trend, seasonal, cyclical, autocorrelation, and random.

Decomposition – the process of identifying and separating time series data into fundamental components:

Trend.

Seasonality.

Seasonal Variation

Seasonal variation may be either additive or multiplicative (superimposed on changing trend).

Exhibit 3.9

Trend + Seasonal factor

Trend × Seasonal index

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Each business in the corporation is plotted on the matrix based on the growth rate of its market and the relative strength of its competitive position in that market (market share). To convey additional information visually, each business can be represented by a circle whose size indicates its contribution to corporate revenues.

Determining Seasonal Index – Simple Proportion Example 3.3

The seasonal index is the ratio of the amount sold during each season divided by the average for all seasons.

Example 3.3

Season	Past Sales	Average Sales for Each Season (1,000 / 4)	Seasonal Index	Expected Demand for Next Year	Average Sales (1,100 / 4)		Seasonal Index		Next Year’s Seasonal Forecast
Spring	200	1,000 / 4 = 250	200 / 250 = 0.8		275	×	0.8	=	220
Summer	350	1,000 / 4 = 250	350 / 250 = 1.4		275	×	1.4	=	385
Fall	300	1,000 / 4 = 250	300 / 250 = 1.2		275	×	1.2	=	330
Winter	150	1,000 / 4 = 250	150 / 250 = 0.6		275	×	0/6	=	165
Total	1,000			1,100

Once corporate strategies are determined, managers must determine how they will compete in each business area. Business strategy defines the major actions by which an organization builds and strengthens its competitive position in the marketplace. A business can gain competitive advantage using one of two generic business strategies: low cost and differentiation.

Businesses using a low-cost strategy attempt to be efficient and offer a standard, no-frills product. Alternatively, an organization may pursue a differentiation strategy. With a differentiation strategy, a company attempts to be unique in its industry or market segment along some dimensions that customers value.

Computing Trend and Seasonal Indexes from a Linear Regression – Example 3.4 1

Demand for the past two years:

Quarter	Amount	Quarter	Amount
1	300	7	520
2	200	8	420
3	220	9	400
4	530	10	700

Forecast including trend: FIT = 176.1 + 52.3t

Exhibit 3.10

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The final step in strategy formulation is to establish the major functional strategies. Functional strategies are implemented by each functional area of the organization to support the business strategy.

Computing Trend and Seasonal Indexes from a Linear Regression – Example 3.4 2

Exhibit 3.10

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As with any plan, simply formulating a good strategy is not enough. Strategic managers also must ensure that the new strategies are implemented effectively and efficiently. The best executives and strategy consultants realize that clever planning techniques and a good strategy do not guarantee success .

Computing Trend and Seasonal Indexes from a Linear Regression – Example 3.4 3

Example 3.4

Forecast , including trend and seasonal index (FITS): FITSt = FIT × Seasonal.

Forecast for next year: I – FITS9 = [176.1 + 52.3(9)]1.25 = 808 II – FITS10 = [176.1 + 52.3(10)]0.79 = 552 III – FITS11 = [176.1 + 52.3(11)]0.70 = 526 IV – FITS12 = [176.1 + 52.3(12)]1.28 = 1,029

The final component of the strategic management process is strategic control. The system must encourage efficient operations that are consistent with the plan while allowing flexibility to adapt to changing conditions.

Forecast Errors

Forecast error - the difference between the forecast value and what actually occurred.

All forecasts contain some level of error.

Sources of error.

Bias – when a consistent mistake is made.

Random – errors that are not explained by the model being used.

Measures of error.

Mean absolute deviation (M A D).

Mean absolute percent error (M A P E).

Tracking signal.

This slide shows the six silent killers. The remainder of the exhibit lists how to

attack these six barriers.

Top-down or laissez-faire senior management style: The CEO creates a partnership with the top team and lower levels to develop a compelling business direction, create an enabling organizational context, and delegate authority to clearly accountable individuals and teams.

Unclear strategy and conflicting priorities: The top team, as a group, develops a statement of strategy and priorities that members are willing to stand behind.

An ineffective senior management team: Involve the top team in all steps in the change process so that its effectiveness is challenged and developed.

Poor vertical communication: Establish an honest, fact-based dialog with lower levels about the new strategy and the barriers to implementing it.

Poor coordination across functions, businesses, or borders: Define a set of businesswide initiatives and new organizational roles and responsibilities that require “the right people to work together on the right things in the right way to implement the strategy.”

Inadequate down-the-line leadership skills and development: Lower-level managers develop skills through newly created opportunities to lead change and drive key business initiatives. They are supported with just-in-time coaching, training, and targeted recruitment. Those who still are not able to make the grade must be replaced.

Measurement of Error

Ideally, MAD will be zero (no forecasting error).

Larger values of MAD indicate a less accurate model.

MAPE scales the forecast error to the magnitude of demand.

Tracking signal indicates whether forecast errors are accumulating over time (either positive or negative errors).

How does Disney’s decision to increase focus on digital content and delivery align with each step of the planning process?

Answers will vary. Students should refer to each of the six steps of the planning process.

Conduct your own situational analysis for Disney. Based on your assessment of the current media landscape and the effects of the Covid-19 pandemic, where would you suggest Chapek focus attention and resources?

Answers will vary. Students should refer to concepts in the chapter such as the SWOT analysis and BCG matrix.

Computing Forecast Error – Example (p. 69)

Exhibit 3.12

Month	Forecast	Actual	Deviation	RSFE	Abs. Dev.	Sum of Abs. Dev.	MAD	TS
1	1,000	950	−50	−50	50	50	50	−1
2	1,000	1,070	+70	+20	70	120	60	0.33
3	1,000	1,100	+100	+120	100	220	73.3	1.64
4	1,000	960	−40	+80	40	260	65	1.2
5	1,000	1,090	+90	+170	90	350	70	2.4
6	1,000	1,050	+50	+220	50	400	66.7	3.3
Overall

Causal Relationship Forecasting

Causal relationship forecasting uses independent variables other than time to predict future demand.

This independent variable must be a leading indicator.

Many apparently causal relationships are actually just correlated events – care must be taken when selecting causal variables.

Multiple Regression Analysis

Appropriate when a number of factors influence a variable of interest.

In this case, the forecast analyst may utilize multiple regression.

Analogous to linear regression analysis, but with multiple independent variables.

Multiple regression is supported by statistical software packages.

Qualitative Forecasting Techniques

Generally used to take advantage of expert knowledge.

Useful when judgment is required, when products are new, or if the firm has little experience in a new market.

Examples of techniques:

Market research.

Panel consensus.

Historical analogy.

Delphi method.

Collaborative Planning, Forecasting, and Replenishment (C P F R)

An Internet tool used to coordinate the efforts of a supply chain.

Demand forecasting.

Production and purchasing.

Inventory replenishment.

Integrates all members of a supply chain – manufacturers, distributors, and retailers.

Depends upon the exchange of internal information to provide a more reliable view of demand.

N-Tier Supply Chain

Exhibit 3.14

Access the text alternative for slide images.

C P F R Steps

Step 1

Creation of a front-end partnership agreement.

Step 2

Joint business planning.

Step 3

Development of demand forecasts.

Step 4

Sharing forecasts.

Step 5

Inventory replenishment.

This slide relists the chapter learning objectives and can be used to review the chapter highlights.

Chapter 5 will focus on ethics, corporate responsibility and sustainability.

End of Main Content

Because learning changes everything.®

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Accessibility Content: Text Alternatives for Images

Components of Demand – Growth & Seasonal – Text Alternative

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A graph is plotted for number of units demanded versus years. The years range from 0 to 4. The graph shows a horizontal line labeled average and a rising line labeled trend. A rising oscillating curve, rises along the trend line. Dots are plotted along the rising curve. The dots along the peaks of the curve are indicated as seasonal.

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Trends – Text Alternative

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The first image shows, the graphs are plotted for sales in thousands versus years. The first graph for linear trend shows a straight rising line. The second graph shows a S shaped curve which has shallow rise, then a steep rise and then flattens at the end.

The second image shows, the graphs are plotted for sales in thousands versus years. The first graph for asymptotic trend shown in inward opening rising curve with a steep slope at the beginning which flattens as time increases. The second graph shows an exponential curve.

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Simple Moving Average – Example (p. 52 to 54) – Text Alternative

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The graph shows three lines for actual, 3-week and 9-week. The actual line begins at 1, 800 and has an oscillating upward trend it rises and ends at 31, 2000. The 3-week line begins at 4, 1000 and also has a rising oscillating trend with less up and downs. The line ends at 30, 2200. The 9-week line begins at 10, 1200 and has a rising trend. The line ends at 30, 2600.

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Least Squares Method – Example 3.2 – Text Alternative

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A graph is plotted for sales ranging from 0 to 5000 dollars versus quarters ranging from 0 to 12. The graph shows a line rising from 0, 500 to 12, 4800. The line has points ranging from Y 1 to Y 12 scattered along it.

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Seasonal Variation – Text Alternative

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The graph is plotted for amount versus months. The first graph shows a rising oscillating curve which is bound within two parallel rising lines. The amplitude of each segment is same.

The second graph shows a rising oscillating curve which is bound within two diverging rising lines. The amplitude of each segment increases as time increases.

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Computing Trend and Seasonal Indexes from a Linear Regression – Example 3.4 1 – Text Alternative

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A graph is plotted for amount ranging from 100 to 700 versus quarters. The graph shows a rising line from 0, 180 to 600, quarter 4 of last year. 8 points are scattered around the line, 4 above and 4 below.

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Computing Trend and Seasonal Indexes from a Linear Regression – Example 3.4 2 – Text Alternative

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The table has 5 columns for quarter, actual amount, from trend equation F I T sub I = 176.1 + 52.3 t, ratio of actual over trend and seasonal index (average of same quarters in both years). The data in the table is as follows:

Quarter 2 years ago: 1, 2, 3 and 4. Actual amount for each quarter: 300, 200, 220, 530. From trend equation F I T sub I = 176.1 + 52.3 t: 228.3, 280.6, 335.9, 385.1. Ratio of actual over trend: 1.31, 0.71, 0.66, 1.38.

Last year: 1, 2, 3 and 4. Actual amount for each quarter: 520, 420, 400, 700. From trend equation F I T sub I = 176.1 + 52.3 t: 437.4, 489.6, 541.9, 594.2. Ratio of actual over trend: 1.19, 0.86, 0.74, 1.18. Seasonal index (average of same quarters in both years is as follows: 1, 1.25; 2, 0.79; 3, 0.70; 4, 1.28.

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N-Tier Supply Chain – Text Alternative

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In the flow diagram the solid arrows indicate the material flows and the dashed arrows indicate information flows.

The material flows from Tier n vendors till tier 1 vendors and then to fabrication and final assembly. From fabrication and final assembly it flows to the distribution center and finally to the retailer. From the retailer forecast information is relayed to the distribution center. Distribution center relays replenishment information to fabrication and final assembly. Fabrication and final assembly relays production planning and purchase information to the vendors.

opm 625

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