Inventory forecasting with a multiple criteria decision tool

international journal of

production economics

ELSEVIER

Inventory

forecasting Jukka

with a multiple Korpcla”.*,

Markku

criteria decision tool

Tuominenb

Abstract The strategic role of logistics is incrcasingl) being rccognised. and inventory stratcg! k an essential clcmcnt of logistics stratcgq. Demand forecasting is one of the most crucial issucs ofinventory managcmont as forecasts form the basis for the planning of production. transportation and inventory lcvcls. The traditional methods used for forwasting include time series methods and causal methods. In the paper. \c’t‘present an Analytic IHierarchy Pmccss-based approach to demand forecasting. The proposed decision support system oli’ers many improvements compared to traditional methods. such as the possibility to include both tangible and intangible factors in the forecasting process. and the ability to make predictions about the future dcvclopmcnt of the cnvironmcntal factors. Ke_wwtl.s: Ikmand

Forecasting;

Decision

support

systems: Analytic

1. Introduction

The role and importance of logistics as a key elcmcnt of an organisation’s business strategy has steadily increased during the last 20 years [ 11. During the 1970s. logistics was approached in an opcrational nay with the focus on discrete functions. In the 1980s. a more holistic approach to managing logistics \vas adopted and many of the discrete functions were grouped under the label distribution. Kccently. the distribution function has more and more been elevated to a strategic level. As Copacino and Koscnfield [I] have stated. “logistics has been recogniscd not only as a group of important

* C‘orrssponcling aullwr.

Hierarchy

Process

functions. but as functions that have important strategic impacts as well”. The issues leading to a more strategic approach to managing logistics are numerous. LaLondc and Mason [3] have idcntitied factors related both to the external and internal settings of a company that have increased the strategic importance of logistics. The external factors include the high cost of money. technological changes cspccially in the nature and cost of information. and the changing nature of the competitive environment. The internal prossurcs affecting logistics are the ne\v decision support environmcnt for the logistics executive. the ranpc of logistics organisational options cwrcised by the firm. and the increasing performance cxpcctations for the logistics function. Bovct [4] explains the increased significance of logistics strategy with the following global changes in the competitive

environment: (1) time-to-market competition is tightening as companies strive to beat competitors into new markets, (2) global sourcing is becoming more common, (3) globalization of markets and competition are accelerating, (4) product proliferation is increasing, and (5) a demand for “zero defects” is mushrooming. Inventory strategy is an important element of the overall distribution logistics strategy with significant impacts on both customer service and total logistic costs. Forecasting the demand for products both in the immediate future and over longer time periods is one of the most crucial issues in inventory management [S]. The aim of demand forecasting is to estimate the amount of product and accompanying service that customers will require at some point in the future [6]. Based on the forecasts, the logistics management can decide how much of each product must be transported to the various markets served by the company, how much of each product must be placed or stored in each market area, and how the resources should be allocated to activities in order to satisfy the demand [6]. In Section 2 of this paper, we review the basics of demand forecasting, and explore methods and techniques that have been used to support the forecasting procedure. In Section 3, we present an Analytic Hierarchy Process-based decision support system for demand forecasting and prove its applicability with an illustrative example.

2. Inventory forecasting The role of forecasting as a logistics activity is further emphasised as companies go through the Table 1 The evolution

of logistics

organisation

I

evolution stages in their approach to logistics management and organisation. McGinnis and Kohn [7] divide the evolution of logistics into three main phases. In stage 1, the logistics organisation is responsible only for the most basic logistics activities. During the stages 2 and 3, the responsibilities are expanded as additional activities are added to the logistics organisation. Demand forecasting is one of the activities that are added to the responsibilities of the logistics organisation in stage 3. The evolution of logistics responsibilities is illustrated in Table 1. According to Bowersox et al. [S] the fundamental input to planning and co-ordinating logistical activities is a forecast of customer demand. By forecasting, a company aims to anticipate future uncertainty on operations. The demand forecast links a company to its market environment. Stock and Lambert [9] define two basic justifications for forecasting. First, forward planning is needed for effective logistics system control and, in turn, forecasts are required for forward planning. Second, forecasting is the driving force behind all planning activities as it enables management to approximate the future with some reasonable accuracy. Gattorna et al. [lo] clarify the linkage between demand forecasting and distribution logistics planning with an open system concept. In the context of a company’s logistics system, the input include elements like finished goods inventory. transport modes, and facilities. The output of the logistics system is customer service and the process is the way the input is transformed into customer service. The process is often affected by restrictions which are factors external to the logistics system. Thus, the efficiency and effectiveness of the system depends on the effects of the restrictions as well as

[7] Stage 2

Stage 3

Outbound transportation Logistics management lntracompany transportation Logistics control

Order processing Customer service Finished goods plant warehousing Finished goods inventory management

Finished-goods field warehousing Logistics system planning

Inbound

Logistics engineering Production planning Purchasing Raw materials.’ work-in inventory management Sales forecasting International logistics

Stage

transportation

process

J. Korpela. M Tuominrn~ht.

J. Production

the internal organisation of the system. The feedback loop is the means by which the output is related to input for planning and control purposes. The link between demand forecasts and distribution planning is established by the feedback loop as the forecasts form the basis for production and procurement plans, and transportation and facility decisions. The formulated plans are usually rather inflexible in many companies which means that the accuracy of the forecasts has a significant impact on the level of customer service. Schary [S] defines four basic requirements for inventory forecasting: (1) projecting requirements during the order cycle ~ because of the time interval between order placement and delivery, sufficient stock level must be maintained which means that requirements for both cycle and safety stock need to be defined by forecasting, (2),forecasting hy time period - subjects to be covered in inventory and production planning are anticipation of future demands, planning of facility and equipment requirements, and material and production capacity, (3) indicating changes in demand ~ forecasting should reveal and signal changes in demand patterns so that inventory requirements can adapt, and (4) projecting ,for multiple items ~ logistics forecasting should involve anticipating requirements for the entire product line. According to Bowersox et al. [S], logistical forecasting covers the projection of customer demands by location, product, and time period. Logistical forecasting is based on analysing data, such as historical demand patterns. customer intelligence, and scheduled promotions and programs. Logistical forecasts consist of the following components which all need to be considered by the forecaster: (1) the seasonal component which is a generally recurring upward or downward movement in the demand pattern, (2) the trend component which is a long-range general movement in periodic sales over an extended period of time, (3) the cycle component which refers to wide swings in the demand pattern lasting typically a year or more, and (4) the irregular component which covers completely unpredictable or random events. The two basic approaches to forecasting are the decomposition method and the aggregation method. The decomposition approach starts by developing a high-

Economics 45 (1996) ISY- 11%

161

level forecast which is then spread across markets or products whereas the first step in the aggregation method is to develop detailed forecasts for each product and market which are then combined into an aggregate forecast. The basic types of forecasts identified by Magee et al. [lo] are long-term, medium-term, and shortterm forecasts. The long-term forecasts cover a time span of 3310 years and they are used in the analysis of fixed commitments and requirements for new plant or warehouse capacity. The medium-term forecasts are made for one year to support production planning in the face of highly cyclical demand or raw material supply. The short-term forecasts cover a time period of 1 week to 3 months and they are used to control manufacturing levels and stock replenishment in the face of short-term demand variation. The number and variety of available forecasting methods and techniques makes the choice of the most appropriate method a complex process. Makridakis and Wheelwright [11] (see [S]) have identified the following criteria for the evaluation of the applicability of a certain method: (1) accuracy, (2) the forecast time horizon, (3) the value of forecasting, (4) the availability of data, (5) the type of data pattern, and (6) the experience of the practitioner at forecasting. Boldt [ 121 (see [6]) has defined seven basic steps for selecting and implementing the appropriate forecasting technique: (1) identify the problem or purpose to be addressed by the forecast, (2) gather available factual data covering both the internal and external environments of the company, (3) determine which forecasting method is most compatible with the objectives of the company and the type of data available, (4) generate good assumptions concerning each of the forecast elements with as high accuracy as possible, (5) compare the forecast to expectations which means reviewing the initial forecast and comparing its outcome with the results expected or with the actual result, (6) analyse variance, and (7) adjust the forecast in order to make it a more accurate reflection of reality. Forecast techniques can be categorised in several ways. The categories identified by Bowersox et al. [S] are (1) qualitative methods, (2) time series methods, and (3) causal methods.

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The qualitative methods are based on qualitative data, such as expert opinion, and special information to forecast the future. The past may or may not be considered in a qualitative forecast. An example of the qualitative methods is the Delphi technique where estimates are obtained from experts in an iterative process and new estimates are developed by each expert after having seen a summary of the group’s previous results [6]. The second category of forecasting techniques, i.e. time series methods, are statistical techniques that are used when historical sales data are available with relatively clear and stable relationships and trends [8]. Time series analysis can be used to identify (1) systematic, seasonal variations in the data, (2) cyclical patterns, (3) trends, and (4) growth rates of the trends. The basic assumption that the future will be similar to the past limits the effective use of time series analysis to short-term forecasting. The actual forecasting techniques included in the category time series methods are moving averages, exponential smoothing, extended smoothing, and adaptive smoothing. Schary [S] identifies autoregressive moving average as one of the most sophisticated time series approaches. The rationale behind the causal methods is to use refined and specific information concerning variables to develop a relationship between a lead event and the event being forecasted [8]. A typical example of causal methods is regression analysis. By using regression, the demand forecast is based on a correlation of one event to another. No causal relationship is needed between the demand and the independent event if a high degree of correlation can be discovered. However, the reliability of regression-based forecasting can be increased by using cause-effect relationships. The use of regression analysis requires a large amount of data for the forecast variable and the causal variables. The limitations of regression analysis include the pitfall that it may not be possible to discover a causeeffect relationship that has an acceptable coefficient of correlation. In addition to forecasting methods and techniques, management should put emphasis on the process of forecasting [lo]. The process of forecasting consists of the procedures used in developing and using a forecast, such as the assignment of

Economics

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(1996J

159-168

responsibility for forecasts, the reconciliation of aggregate and individual item forecasts, the approval of forecasts by different levels of management, the adjustments of forecasts by managers to reflect conditions not captured in a forecasting model, and the agreement on a single forecast by the different functional groups within an organisation.

3. A multiple criteria tool for inventory forecasting The Analytic Hierarchy Process (AHP) is a theory of measurement for dealing with quantifiable and intangible criteria that has been applied to numerous areas, such as decision theory and conflict resolution [13]. AHP is a problem-solving framework and a systematic procedure for representing the elements of any problem [14]. AHP is based on the following three principles: decomposition, comparative judgements, and the synthesis of priorities. AHP starts by decomposing a complex, multicriteria problem into a hierarchy where each level consists of a few manageable elements which are then decomposed into another set of elements [El. The second step is to use a measurement methodology to establish priorities among the elements within each level of the hierarchy. The third step in using AHP is to synthesise the priorities of the elements to establish the overall priorities for the decision alternatives. AHP differs from conventional decision analysis methodologies by not requiring decision makers to make numerical guesses as subjective judgements are easily included in the process and the judgements can be made entirely in a verbal mode [16]. According to Saaty [17], the AHP forms a systematic framework for group interaction and group decision making. Dyer and Forman [IS] describe the advantages of AHP in a group setting as follows: (1) both tangibles and intangibles, individual values and shared values can be included in an AHP-based group decision process, (2) with AHP, the discussion in a group can be focused on objectives rather than on alternatives, (3) with AHP, the discussion can be structured so that every factor relevant to the decision is considered in turn, and (4) in a structured analysis, the discussion continues

J. Korpela, M. TuominenJlnt. J. Production

until all relevant information from each individual member in the group has been considered and a consensus choice of the decision alternative is achieved. A detailed discussion on conducting AHP-based group decision making sessions including suggestions for assembling the group, constructing the hierarchy, getting the group to agree, inequalities of power, concealed or distorted preferences, and implementing the results can be found in [17,19]. Dyer and Forman [20] suggest three primary areas in forecasting where AHP can be applied. First, AHP can be used as an expert-opinion forecasting tool. The second use is to use AHP in the selection of the most appropriate forecasting method or technique. Third, AHP can be used to combine the results of several forecasting techniques to produce a single, composite forecast. Applications of AHP for forecasting and prediction can be found in [ 17,211. Wolfe [22] and Wolfe and Flores [23] have applied AHP for adjusting earnings forecasts of companies. Using AHP, we develop a decision support system for demand forecasting. The applicability of the developed DSS is demonstrated with an illustrative example. The process consists of three basic steps: (1) identify the factors affecting the demand level and structure the AHP-hierarchy, (2) assign priorities to the elements in the hierarchy, and (3) synthesise the priorities to obtain the overall priorities for the elements, calculate the composite demand forecast, and examine the outcome of the forecast with sensitivity analysis.

3.1. Structuring

the hierarchy

Corporation A is involved in a processing industry, and it sells its products to other industries. Most of the corporation’s products are exported and logistics is a key competitive factor in the business. The logistics organisation is responsible for preparing a demand forecast for a certain market area covering a time period of one year. The demand forecast is a basic input for the tactical planning of production and inventory levels. The logistics executives of the corporation use an AHPbased approach to forecasting in order to be able

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to include qualitative factors in the forecasting process. The first step is to identify the factors that have a significant impact on the demand level of the corporation’s products. The logistics executives define the following major actors and environmental forces to be considered in the forecasting process: (1) the development of the national economy of the market area, (2) the major competitors, (3) the present and potential customers, (4) the development of ecological factors in the market area, and (5) corporation A itself. In order to reach a level of sufficient detail in the analysis, the actors and environmental forces are divided into sub-components. The main factors related to the national economy that have an effect on the demand for the corporation’s products are the inflation rate, the unemployment level, and the GDP growth rate. The three factors describe the overall state of economic activity in the market area and they have proved to have a strong correlation with the product demand level. With regard to the major competitors, the following factors are to be considered: sales promotion activities, product features, pricing policy, and customer service policy. As the aforementioned elements are the critical success factors of the business, the way the competitors perform with respect to them has a significant effect on the demand level of corporation A’s products. If, for example, some competitors use a very low pricing policy, they are expected to gain market share. The key elements related to the present and potential customers are the growth rate of their business and the requirements they set on logistics and product features. The business growth has a direct positive effect on the demand as the customers use the products offered by corporation A and its competitors as supplies for their own production processes. If the customers tighten their logistical or product-related requirements, corporation A has a good chance of increasing sales as the corporation is the competitive leader in terms of logistics and product quality. There is a strong ecological movement in the market area in question. The government has already defined strict ecological requirements for the

164

J. Korpelcr, M. Tuominen,iInt. .I Production

products and possible changes in the laws and regulations have a significant effect on the total sales of the products. Furthermore, the general attitude towards the products offered by corporation A and its competitors has a strong impact on the demand level. The factors to be considered with respect to corporation A are the same as with respect to the major competitors. The performance level achieved by the corporation in terms of sales promotion, pricing, customer service, and product features has a clear effect on the demand. If the corporation can outperform its competitors with regard to the critical success factors. it can significantly increase the sales level. The hierarchy (Fig. 1) is constructed according to the basic structure defined by Dyer and Forman [20] for forecasting enrollment growth of certain educational programs. The goal for the forecasting process is located on the highest level and the actors and environmental forces on the second level. The factors describing the actions and subcomponents of the elements on the second level are placed on the third level. Scenarios defining the possible development paths of the third level elements are located on the fourth level of the hierarchy. The decision alternatives defining the possible demand growth rates are placed on the lowest level of the hierarchy. The scenarios on the fourth level of the hierarchy are verbal descriptions about the possible development paths of the elements on the third level. By using scenarios, the uncertainty about the actual development of the third level elements can be included in the forecasting process. For example, the scenarios related to the national economy factors are labelled high, medium, and low. Correspondingly, the competitors’ actions can be more aggressive than normally, normal, or weaker than normally. The scenarios enable decision makers to estimate the probabilities of the future development of each factor, and estimate the demand growth level for the products under each of the scenarios. The elements on the last level of the hierarchy define the possible change rates in the demand for corporation A’s products compared to the sales estimate for the present year. The logistics executives have identified four possible demand change

Econonzics 45 (1996) 159-168

rates: (1) strong decline (from - 10 to - 5%), (2) weak decline ( - 5-O%), (3) weak growth (s-5%), and (4) strong growth (5510%).

3.2. Assigning priorities to the elements in the hierarchy The next step in the forecasting process is to derive priorities for the elements in a hierarchy. The priorities are set by comparing each set of elements in a pairwise fashion with respect to each of the elements in a higher stratum [15]. A verbal or a corresponding 9-point numerical scale can be used for the comparisons and the comparisons can be based on objective, quantitative data or subjective, qualitative judgements. In a group setting, there are several ways of including the views and judgements of each person in the priority setting process. There are four basic ways that can be used for establishing the priorities: (1) consensus, (2) vote or compromise, (3) geometric mean of the individuals judgements. and (4) separate models or players [18]. The primary method used in the presented case is to try to achieve consensus based on extensive debate and discussion. However, if consensus cannot be established, the geometric mean of the group members’ judgements is used as it is the uniquely appropriate rule for combining judgements since it preserves the reciprocal property of the judgement matrix [24] (see [lS]). With the hierarchy illustrated in Fig. 1, the priority setting procedure is started by comparing the actors and environmental forces in a pairwise fashion with regard to the overall goal of the forecasting process (what is the importance of each element on the second level of the hierarchy with regard to the demand growth rate). Next, the importance of the factors on the third level of the hierarchy is determined with regard to the second level elements. The third step is to make judgements about the likelihood of the scenarios for each specific factor (for example, what are the likelihoods that the competitors’ pursue a normal, a more aggressive, or a weaker pricing policy). The last step in the priority setting procedure is to assign likelihoods to each of the possible growth rates with regard to each scenario (for example, if

J. Korpela, M. Tuorninen,iInt. J. Production

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FORECAST THE DEMAND

GOAL

ACTORSAND ENVIRONMENTA

NATIONAL ECONOMY

FACTDR9

--:

COMPETITORS

CUSTOMERS

CO_RAT,ON

FACTORS

A

I\

?

FACTOFS

5

HIGH

MORE AGCRESSNE

BETTERITIGHTER

POSITIVE CHANGE

MORE EFFECTIVE

MEDIUM

NORMAL

NORHAL

PRESENT STATE

EXPECTED EFFECT

NEGATIVE CHANCE

LESS EFFECI-NE

SCENARIOS

mw_

DEMAND GROWTH RATE%

STRONG DECLINE -5.10 76

WEAK DECLINE o-4

Fig. 1. The AHP-hierarchy

Table 2 The overall

priorities

of the factors

Customers 0.285

National 0.220

WEAK GROWTH

%

o-5

for the demand

STRONG GROWTH

%

forecasting

510

%

process

on the third level of the hierarchy Competitors 0.193

economy

Corporation 0.153

Business growth Logistical requirements

0.141 0.089

GDP-Growth Inllation

0.109 0.068

Pricing policy Customer service policy

0.070 0.058

Product requirements

0.056

Unemployment

0.043

Product features Sales promotion

0.044 0.020

the customers’ business growth stays at the “normal” level, what are the likelihoods of the possible demand growth rates). Consulting the corporate planning staff and the marketing executives, the logistics executives assess the customers to have the greatest impact on the demand growth rate. The importance of customers is 0.285 while the development of the national economy in the market area has a priority of 0.220. As corporation A has the largest but not a clearly dominant market share in the market area, its overall impact (0.153) on the demand growth rate is still

A

Pricing policy Customer service policy Product features Sales promotion

Ecological 0.149 0.054 0.050

factors

Lawsjregulations General attitude

0.097 0.052

0.030 0.019

assessed to be lower than that of all the major competitors together (0.193) i.e., corporation A is a major player in the market but the competitors’ actions must be taken into account in the forecasting process. The ecological factors are also important with a priority of 0.149. The priorities of the factors on the third level are shown in Table 2. The priorities present the overall impact of a certain factor on the demand growth rate. The business growth is evaluated to be the most important customer-related factor affecting the demand growth rate. Of the factors related to the

J. Korpela, A-4.TuomineniInt.

166

J. Production Economics

s C

A

R 1 0

4 0.000 0.100 0.200 0.300

0.400

0.500

LIKELIHOOD Fig. 2. The likelihoods business growth.

of the scenarios

related to the customers’

development of the national economy, the GDPgrowth rate has the greatest impact on the demand level. The pricing policy is the main element of the competitors’ strategy to gain market share from corporation A, whereas corporation A relies on its strong ability to provide superior customer service and superior product quality. The laws and regulations concerning ecology are more important among the two ecological factors. An example of the likelihoods assigned to the scenarios on the fourth level of the hierarchy is presented in Fig. 2. The figure illustrates the estimated likelihoods of the possible development paths of the customers’ business growth. The normal level refers to the average growth rate of the customers’ turnover in the last 10 years. The estimated likelihood of the normal level growth is 0.311 but the decision makers assess the probability of a better growth rate to be even higher (0.493).

H

0.000

0.100

45 (1996) 159-168

The likelihood that the customers’ business growth does not reach the normal level is relatively low (0.196). As an example, the likelihoods of the alternative demand growth rates under the scenario “normal level customer business growth” are illustrated in Fig. 3. The weak growth has the highest likelihood but the probability of the weak decline in growth is also considerable. Based on the analysis, a strong decline in demand growth is the least probable growth rate under the scenario in question.

3.3. Calculating

the demand forecast

The first step in defining the actual demand forecast is to synthesise the priorities of the elements in the hierarchy to obtain the overall likelihoods for the alternative demand growth rates. The results of the synthesis are shown in Fig. 4. All factors in the hierarchy considered, the weak growth rate has the highest overall likelihood (0.345). However, the probability of the weak decline alternative is almost as high. The most extreme growth rate alternatives, i.e. strong growth and strong decline, have almost equal likelihoods. The illustrated results represent the probabilities that a certain growth rate will materialise. In order to define a composite forecast, the probabilities are combined by multiplying the average of each growth rate range by its overall probability (see

0.200

0.300

0.400

0.500

LIKJSLIHOOD Fig. 3. The likelihoods

of the alternative

demand

growth

rates with regard

to the normal

level customer

business

growth

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J. Korpela, M. TuomineniInt. J. Production Economics 45 (1996) 159-lfi8

H

0.000

0.100

0.050

0.150

0.200

0.250

0.300

0.350

LIKELIHOOD Fig. 4. The overall

Table 3 The calculation Growth

of the composite

rate

Strong growth Weak growth Weak decline Strong decline

likelihoods

of the demand

growth

forecast

Range

Range

(%I

(RA) (n/o)

+ 5.~10 &5 - 5-O 10--5

average

1.5 2.5 - 2.5 ~ 7.5

Total

1.000

OVERALL Fig. 5. A sensitivity

IMPORTANCE analysis

rates

OF CUSTOMERS

with regard

to the customers.

[22]). The composite forecast is presented in Table 3. Based on the AHP-supported forecasting process, the expected demand growth rate for corporation A’s products on the market area in question is only 0.14%. The rather even distribution of probabilities to both positive and negative growth rates suggests that there is a lot of instability in the

Overall Probability

Ra* OP (%)

0.181 0.345 0.295 0.179

1.3575 0.8625 - 0.7375 ~ 1.3425

1.ooo

0.1400

market and a lot of uncertainty with regard to the actors and environmental forces. The result of the forecasting process can be analysed further with sensitivity analyses as illustrated in Fig. 5. Based on Fig. 5, it can be concluded that the higher the evaluated impact of the customers on the demand, the higher is the composite demand growth rate. The result reflects the ability of corporation A to respond to the customers’ logistical and product-related requirements more effectively than the competitors. Corresponding sensitivity analyses can be performed with regard to the other elements in the hierarchy.

4. Conclusions The proposed AHP-based decision support system forms a flexible and systematic framework for demand forecasting. The presented approach

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enables decision makers to avoid the following problems inherent in classical forecasting methods [25] (see [21]): (1) traditional forecasting methods include a few explanatory variables, most of which can easily be expressed in quantitative terms, (2) traditional forecasting methods do not take into account the development of new relationships among variables and possible changes in trends, (3) a basic assumption in classical forecasting methods is that the dimension on which prediction takes place is autonomous, (4) the forecasts are based solely on past data, and (5) classical forecasting methods are both deterministic and structurally stable leading to error in forecasting because of constant change which is selectively studied, and interpreted from a special view point. The AHP-based approach to forecasting enables decisionmakers to take into account both quantitative and qualitative variables. By structuring a hierarchy, the relationships between the factors can be defined and analysed effectively. In the priority setting procedure. qualitative and subjective judgements by multiple persons as well as quantitative data from various sources can be included. Using the presented approach, decision makers are not bound to past data as assumptions and predictions about the future development of the factors involved and the future actions by the actors included in the hierarchy can be made. As a forecasting process usually involves a group of people, the AHP-based approach helps to conduct a group session in an analytical and systematic manner addressing every key factor in the hierarchy in turn. With the help of the computer software called Expert Choice, the hierarchy and the priorities can easily be updated and modified thus allowing for an iterative approach to forecasting. Furthermore, sensitivity analyses can be used to examine the outcome of the forecasting process in more detail. By using the proposed approach, the forecasting process can be documented in detail and communicated to other people in order to create consensus and understanding.

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