Embed Size (px)
Citation preview
international journal of
production economics
ELSEVIER Int. J. Production Economics 37 (1994) 101 114
Forecasting - bridging the gap between sales and manufacturing
Lorike Hagdorn-van der Meijden *, Jo A.E.E. van Nunen, Aad Ramondt
Erasmus UnicersitJ Rotterdam, P.O. Bar 1738, 3000 DR Rotterdmn. The Netherlundi
Abstract
Conflicts frequently occur between demand forecasts that help sales and marketing to reach their targets and the demand forecasts that help manufacturing to produce the right amounts of products at the right time at a minimum cost level. This gap is a serious problem especially in industrial firms with production lead times that are (much) longer than customer order lead times: production to order is not possible, so a close coordination w.r.t. forecasts between sales and manufacturing is needed. In many companies, this coordination has not been given much attention and, as a conse- quence, separate sales and manufacturing forecasts are used. This results in high inventories and in spending (too) much effort in trying to reach the customer service level required by the market. This paper is based on experiences within several practical situations, but in particular on an extensive study for an international producer of a specific type of consumer goods (like detergents, fashion, foods and electronics).
Within this large international company “Rahanu”, forecasting plays an important role for many years. In this period, each of the Operating Companies (OCs) of Rahanu has developed her own forecasting procedure. The experiences and difficulties with forecasting in these OCs was the basis of the development for one demand forecasting strategy for both sales and manufacturing. This strategy will further improve the quality of the demand forecasts and therefore reduce costs and improve customer service.
Based on this case study, we put forward some suggestions to bridge the gap between the different demand forecasts of sales and manufacturing. This is done, not by improving statistical forecasting techniques, but by combining relatively simple statistical techniques with improved cooperation and coordination between the sales department and the manufacturing department. In this way one common demand forecast is created, that is accepted by the production as well as the sales department.
1. Introduction
In this article we will discuss forecasting pro- cesses to support manufacturing, distribution and sales operations of producers. By means of this
* Corresponding author.
forecasting process the expected demand should be approximated. On the basis of the forecasts for the demand, the manufacturing, distribution and sales operations can be planned. To prevent the associ- ation with sales only, we will not refer to this forecast as a sales forecast but as a demand fore- cast. Although one might strive after production to order, this can be very expensive for some products
0925-5273,94.;$07.00 0 1994 Elsevier Science B.V. All rights reserved SSDI 0927-5273(94)00053-D
or even impossible because long lead times are inherent in the technology of the specific produc- tion process.
In such situations, forecasting demand proves to be increasingly difficult for producers. Some reasons are:
~ shorter product life-cycles, ~ increasing dynamics and competition in markets, ~ increasing promotional activities, ~ increasing spatial distance to the customer.
Since demand forecasts are used to plan both sales and manufacturing -operations, the conflict between sales and manufacturing comes to the sur- face in the demand forecasting process. This con- flict between sales and manufacturing can be found in all producing organizations [l]. The conflict is well known and does often result in separate organizations, that are located in different build- ings and hardly communicate. Since this conflict is important for the demand forecasting process it will be discussed in this introduction. In this article, we will develop some suggestions to bridge this
gap. The main sources of the conflict are the different
goals and interests of sales and manufacturing. The sales goal is to maximize sales in terms of turnover or market share, whereas manufactur- ing aims to produce efficiently in terms of minimum costs. Some important areas of conflict are:
Product runge: Sales prefers a wide variety in the product range, regular introductions of new prod- ucts, action products etc. Manufacturing prefers a limited product range which results in more stable production plans.
Promotional activities: Sales frequently uses dis- counts or promotional activities to reach its sales- targets. Manufacturing dislikes these and regards them as irritating distortions of the production pattern.
Customer service: Sales promises clients good delivery terms to accomplish sales. Manufacturing often cannot or does not want to meet these prom- ises because of the (too) high production costs in- volved.
These conflicts of interest lead to a lack of coord- ination between manufacturing and sales. This lack of coordination results in the dangers for a
company outlined below: - Inventory is used to uncouple manufacturing
and sales: this gives an increased cost level. In many companies it is not clear what the real inventory costs are and who is responsible for them.
~ Planning problems occur in the execution of promotional activities, which increase coordina- tion costs.
- Sales promises to customers are not met by manufacturing; this reduces customer satisfac- tion and might lead to the loss of customers and/or market share. In this article we will argue that good procedures
for demand forecasting can restrict these dangers and moreover control the conflict between manu- facturing and sales.
2. Forecasting
2.1. Critical characteristics qf‘,forecusts
A lot of confusion with respect to forecasting is caused by the different meanings the word forecast represents. A forecast can be categorized along some critical characteristics. We will discuss three of them.
The first characteristic is the.functional areu the forecast is supporting. In this way we can identify different forecasts: manufacturing forecast to sup- port production, market forecasts to support mar- keting, sales forecasts to support sales and financial forecasts to support finance.
The second characteristic is the time horizon of the forecast. The time horizon of the forecast is closely related to the level of planning the forecast supports. In many companies forecasts are found with time horizons that correspond to the levels of planning: long-term forecast (up to 5 yr) for stra- tegic planning, medium-term forecast (up to 1 yr) for tactical planning and short-term forecast (up to 3 months) for operational planning.
The last characteristic we will discuss is, what we will term the status of the forecast. This character- istic needs some further explanation. The status of a forecast is evolving in the forecasting process and is determined by: (a) the phase in the forecasting
L. Hagdorn-van der Mrijden er al.ilnr. J. Production Eommics 37 (1994) 101-114 103
process, (b) the connection to organizational goals, (c) the level of acceptance and (d) the opportuni- ties for adjusting the forecast.
To illustrate these characteristics: the demand forecast that we will discuss in this article supports sales and manufacturing, will have a horizon of at most one year and we will argue that it should have the status of a more or less fixed target for the whole organization.
2.2. Use qf demand,forecasting
Demand forecasting is necessary for almost all types of industrial companies. A demand forecast should be used to enable manufacturing to produce the goods according to a schedule that meets the required delivery dates of sales as well as building up inventory to cover up for anticipated shortages in production capacity. This means that demand forecasts should be used to: 1. schedule manufacturing operations with a long
lead-time and procurement (short-term demand forecast),
2. build up intermediate inventories to cover up for piques in demand (medium-term demand fore- cast),
3. make sure that the capacity is balanced with the demand (long-term demand forecast). As we have seen demand forecasts have different
uses, depending on the horizon of the forecast. This horizon corresponds to the lead-time needed to adjust capacity. In this article we focus on the short-term demand forecast. This short-term de- mand forecast is very important for companies with a production order lead time that is much longer than the required customer order lead time. In this type of company, the customer order decoupling point is located at the end or after the production process. Therefore, production has to be based on demand forecasts.
2.3. Forecast quality
The extent to which forecasting will be able to meet its principal goal of improving manufacturing and sales coordination, is dependent on the quality
of the forecast. The quality of the forecast is hard to measure. The reason for this is that, depending on the status of the forecast, the forecast might influ- ence the actual result. If a forecast has the status of a goal or target, then the quality of the forecast is hard to measure because the organisation will try to reach the forecast. This implies that the differ- ence between forecast and actual result is not only a measurement for the forecast quality but for the activities of the company to reach the goal as well.
To represent this, it is important to recognize that the quality of a forecast has two components:
1. Forecast accuracy [2]. Forecast accuracy is related to the forecast error. The forecast error is the difference between actual value and forecasted value. The smaller the forecast error is, the better the forecast quality. However, this is a relative measure. For products that are difficult to forecast (e.g. products with a demand pattern with a large standard deviation) the same forecast error repres- ents a better forecast quality than for products that are easy to forecast. Many possible indicators exist to measure the forecast accuracy.
2. Forecast acceptance. The forecast should be accepted. If the forecast is not accepted, the organ- ization will not react to it and then it is useless. Acceptance can be gained by credibility and by involvement. The credibility of a forecast is high if people in the organisation believe the forecast is a good one. Reasons for credibility are trust in the forecasting procedure, improved efficiency of manufacturing by good forecasts in the past and trust in the people who execute the forecasting process. Acceptance of a forecast can also be gained by involvement: if people are involved in the fore- casting process they will be more likely to accept the forecast. Moreover, a clear accountability for the forecasting process would help.
2.4. Importance qf,forecast quality
Forecast quality has an important effect on pro- duction. A higher quality of the forecast enables better planning of operations. Forecast quality has a direct influence on customer service and stock levels. If the forecast is more accurate and it is accepted as well, it will mean that production can
104 I.. Hagdorn-van der Meijdun et al./Int. J. Production Economics 37 (1994) 101~114
better anticipate the customer demand. Customer service elements like delivery reliability and order completeness can be improved and a better overall customer service level can thereby be achieved. On the other hand, a better forecast quality can de- crease the investment in inventory, since there will be less uncertainty as to future demand. Other costs that can be reduced are costs of outdated products, components or materials, costs of adjusting, res- cheduling, reshipments, rush-orders, extra set up costs, etc.
For a simplified situation the effects on safety stock and customer service are illustrated in Exhibit 1. Although reality is more complex than this example, the relations between forecast quality with safety stock and customer service are valid in practice and can result in considerable cost savings and significant improvements in customer service.
2.5. Improving ,forecast quality
Complicated statistical techniques prove to be insignificant in improving forecast accuracy. In the case that is described in the following section no significant differences were found between a rela- tively simple forecasting technique (Holt-Winters seasonal exponential smoothing) and complicated techniques (Lewandowski’s FORSYS-system) [3]. Recent research suggests that combining human judgement with statistical techniques is a more promising approach [4].
These conclusions indicate that a good and accepted forecasting procedure is very important in improving forecast quality. Such a procedure should include the use of human judgement wher- ever possible, and should take into account in- formation on the factors that influence sales. In practice it appeared that a procedure is in general
Exhibit 1 Effects of forecast quality on or safety stock level or customer service level
A company has a product with a weekly sales value of $100000. The customer service level is defined as the percentage of periods
without an out of stock occasion. At the beginning of each period the inventory equals the expected sales value increased with the safety
stock-level.
E&t qf the forecast accuracy on customer service lecel
Assuming the company has a fixed safety stock level of $15000 then the relation between forecast accuracy and customer service level is
as follows.
Mean absolute percentage error
Customer service level
(%) 7 8 IO 14 20
(%) 98.4 91.0 93.3 X5.8 17.3
.Effect qf the forecast awuracy on scrfety stock leoel
If we suppose the company strives for a constant customer service level of 97.5% then the relation between forecast accuracy and safety
stock level is given below.
Mean absolute percentage error (“h) 7 8 IO 14 20
Safety stock level (S) 13720 15680 19600 27 440 39 200
Explanation
We assumed that the average forecast error is 0, and that the forecast error follows the normal distribution.
The forecast accuracy is evaluated with the mean absolute percentage error. This evaluation criterion is related to the standard
deviatton of the forecast error. The mean average percentage error (MAPE) is defined as
MAPE = ,;, 1(X, - F,);Xt)I x 100%.
n
where X, = actual value in period t, F, = forecasted value for period t, n = number of forecasts evaluated
L. Hqdorn-can der Mrijden et al.lInt. J. Production Economics 37 (1994) 101~114 105
more important than the statistical technique that is used, since factors like promotional activities influence the forecasts considerably. Therefore a relatively simple but understandable statistical technique like Holt-Winters seasonal exponential smoothing [S] might be sufficient.
3. Case-study: “Rahanu”
In this section, a case-study will be discussed to illustrate the improvements that can be made in demand forecasting. The case study deals with the short-term demand forecasting process in six Euro- pean Operating Companies (OCs) of Rahanu. Cha- nges are suggested to further improve the quality of the forecasts. The goal of the study is to suggest a framework for a new cyclic forecasting process that can be used to improve the quality of forecast- ing in these six OCs. This framework should lead to further reduction of inventories and to further im- provement of customer service.
3.1. Introduction
Rahanu is a multinational production company with relatively independent national Operating Companies (OCs). The main activity of the OC is unit production and selling products. The basic structure of the business process is described in Fig. 1.
The combined lead-time of unit production and assembly is several weeks, depending on the prod- uct. The unit production and assembly time can hardly be varied since a number of weeks are neces- sary before the required quality is reached. In the packaging process, the lead-time is practically neg-
ligible. Flexibility to react on demand fluctuations
is provided for by changing the packaging type. This can be done as long as it concerns the same type of products.
Within a Rahanu OC, a rigid distinction exists between manufacturing and sales. This is expressed in the organisational structure in which there is a technical manager responsible for production, logistics, materials, purchasing, etc. and a commer- cial manager responsible for sales, marketing and distribution. A typical organizational structure might look as shown in Fig. 2.
This organisational structure indicates that the demand forecast is very important for the coord- ination between sales and manufacturing. Other characteristics of Rahanu that are important fac- tors for demand forecasting are: _ a limited number of brands and packaging types
(50-300 end-products per country), ~ demand shows a strong seasonal pattern, _ climatological circumstances (e.g., temperature,
sunshine) strongly influence demand, ~ obsolescence and perishability of products re-
strict opportunities and increase risk for seasonal stock,
~ a rather long production lead time, caused by the unit production and assembly of products,
~ aggregate production capacity is constrained, but since the packaging type can be varied till the last moment, a lot of flexibility is allowed,
~ the consumer is reached via at least one inter- mediate echelon (like warehouses, depots, whole- salers, etc.); this means that in the logistics process, there is hardly any direct contact with the final consumers. Nevertheless, marketing cam- paigns often offer a high customer service level (e.g. short order lead times, no out-of-stocks).
Fig. 1. Simplified business process for a Rahanu OC
genera/YiIce, pynnel etc.
lechnicdmanaget commercial manager
I I 1 I I I purchasing malerials mrgmnt prcdtin sales marketing dkbibtion
Fig. 2. Typical organizational structure of a Rahanu OC
As will be clear from all these factors, forecasting plays a strategic role in the logistics process within
the Rahanu OCs. For many years, much attention has been payed to forecasting and in several OCs a high professionalism in forecasting has been reached. Based on this high professionalism, effort is put in further improvements of the forecasting process, which should lead to further cost reduc- tions and higher customer service. Both will contribute to further improvement of Rahanu’s corporate image and to a stronger competitive ad- vantage.
3.2. Present situation with respect to $orecasting
3.2.1. Role in plunning hierarchy Within Rahanu, the short-term forecast (with
a horizon of about 3 months) is related to the coordination of different operational plans. In this coordination the annual plan is linked to the opera- tional plans. This means that the annual goals and budgets should be translated into operational goals. It is not a very formalized level of planning and therefore it was referred to as “coordination”, often between only two parties at once. The planning hierarchy is illustrated in Fig. 3. After the operational plans have been made, operational adjustments will still occur.
3.2.2. Forecasting process The forecasting process includes the generation
of the short-term forecast, but it also includes an important part of the coordination: establishing consensus on a common sales and production goal.
OPERATIONAL
Fig. 3. Planning hierarchy for a Rahanu OC.
The input of the short-term forecasting process consists of:
One year lead-time forecasts as well as annual goals and budgets. Restrictions and planned activities for the differ- ent operational plans such as: - planned discounts for the sales plan, - planned promotional activities from the mar-
keting plan, - capacity restrictions (including, e.g. planned
maintenance) for the production and assem- bly plans,
_ existing and planned stock levels for the distri- bution plan.
In the forecasting process the planned activities in the operational plans are still negotiable, which is necessary to reach consensus on a com- mon sales goal. Historic demand, information on the effects of marketing campaigns and promotional activ- ities and recent market information like cus- tomer orders, competitors actions, calendar influences (number of delivery days, holidays), etc. The ultimate output of the forecasting process
can be defined as the expected sales volume,
L. Hagdorn-van der Meijden et al./Int. J. Production Economics 37 11994) 101~114 107
aggregated to the national level and specified per family of end-products. This output is used to sup- port planning decisions.
The most important decisions supported by the short-term demand forecast are: 1. scheduling unit production and assembly, 2. planning seasonal stocks to cover up for peaks
in demand, 3. (re)planning of promotional activities, 4. short-term capacity management.
In all OCs, the short-term demand forecast plays an important role and much effort is put in produ- cing reliable demand forecasts. The activities to produce this output, using the input, as specified above, differ within the distinctive OC. In Table 1 the most important characteristics of the processes for the different European OCs are described.
3.2.3. Observations In observing the different forecasting process in
the OCs, some observations are common. The most important observations, which are starting points for further improvements of the quality of demand forecasts, will be summarized below. We emphasize that, although the observations show some failures and therefore might give a negative impression of the forecasting process within Rahanu, high profes- sionalism in forecasting already exists and based on this professionalism, further improvements can be made. - The involvement of the responsible manager for
forecasting is often limited. The responsible man- ager (normally a sales manager) delegates the task and since he is in many cases not conse- quently held responsible for his forecasts, his commitment is limited.
~ During the forecasting process communication tends to be limited. In the forecasting process, communication is mainly found between the de- partments of the commercial organisation. Real communication with logistics only starts after the forecast has been produced.
~ There is a lack of consensus on the forecast. Forecasts are frequently adjusted by production before use. Sometimes this results in different forecasts in the commercial and the production departments.
- Forecasts of effects of promotional activities are less professionalised than forecasts of demand. The effects of promotional activities, like special campaigns by marketing and discounts by sales, are often only roughly estimated. This results in difficulties for the coordination of manufacturing with sales. There are no appropriate procedures for forecasting promotional activities, and the estimation of the effect is evaluated on an ad hoc basis afterwards.
~ Appropriate procedures to evaluate the quality of the forecast are not used by most OCs. Goals are often defined in a qualitative way (“provide an accurate sales forecast in a timely manner”). Therefore, the goals are difficult to measure and to evaluate.
~ In most OCs no use is made of software tools to support the forecasting process. These observations indicate that there are several
suggestions to improve the quality of the forecasts, which will lead to a higher customer service level and at the same time reduction of inventories. Based on the observations in the different OCs of Rahanu, Rahanu decided to cooperate in develop- ing a framework for forecasting processes to improve the quality of the short-term demand forecast and with this bridge the gap between sales and manufac- turing. Therefore, several important changes are sug- gested, which will be discussed in the next section.
3.3. Recommended changes
The proposed changes will be described by discussing all characteristics that were used in Table 1 to describe the forecasting process in the OCs (see Table 2).
The proposed changes of Table 2 result in a cyclic forecasting process as will be described in Section 4. This process is built up from separate process steps that are connected through feed- back loops.
3.4. Experiences and results at Ruhunu
At the moment of writing this article, in one European OC the newly developed framework has
Tab
le
1 D
escr
iptio
n of
the
sh
ort-
term
fo
reca
stin
g pr
oces
s in
6 E
urop
ean
OC
s of
R
ahan
u
Cha
ract
eris
tics
OC
A
O
C
B
oc
c O
C
D
OC
E
O
C
F
Exi
stin
g sh
ort-
term
dem
and
fore
cast
s
Use
of
for
ecas
t
Furt
her
proc
essi
ng
Res
pons
ible
depa
rtm
ent
Oth
er
depa
rtm
ents
invo
lved
Prod
uct
aggr
egat
ion
or
disa
ggre
gatio
n?
1 na
tiona
l fo
reca
st,
late
r on
tr
ansl
ated
in
to
a lo
gist
ics
fore
cast
1 fo
reca
st
(reg
iona
l,
sum
mar
ized
to
natio
nal)
for
prod
uctio
n an
d fo
r di
stri
butio
n
dist
ribu
tion
plan
ning
pl
anni
ng
tran
slat
ed
into
logi
stic
s fo
reca
st
per
fam
ily
of e
nd-
prod
ucts
tran
slat
ed
into
wee
kly
outp
ut
per
plan
t
Mar
ketin
g
only
in
form
al
com
mun
icat
ion
with
in
the
com
mer
cial
orga
nisa
tion
prod
uct
grou
p fo
re-
cast
di
sagg
rega
ted
into
m
arke
ting
item
s
Sale
s
no,
fore
cast
s pe
r
fam
ily
of e
nd-
prod
ucts
I fo
reca
st
(nat
iona
l.
tran
slat
ed
to
regi
onal
)
2 fo
reca
sts:
na
tiona
l
and
regi
onal
2 fo
reca
sts:
na
tiona
l
and
regi
onal
1 fo
reca
st
(reg
iona
l,
sum
mar
ized
to
natio
nal)
for
prod
uctio
n
plan
ning
tran
slat
ed
into
fore
cast
s pe
r re
gion
and
per
plan
t
Sale
s
none
no,
fore
cast
s pe
r
fam
ily
of e
nd-
prod
ucts
for
sale
s pl
anni
ng
sale
s pl
an
is m
ade,
whi
ch
is u
sed
as
inpu
t fo
r pr
oduc
tion
plan
ning
Sale
s (b
otto
m-u
p)
and
phys
ical
di
stri
-
butio
n (t
op-d
own)
Mar
ketin
g an
d,
late
r on
L
ogis
tics
for
prod
uctio
n
plan
ning
disa
ggre
gate
d pe
r
pack
agin
g ty
pe
for
pack
agin
g pl
an
and
per
prod
uct
gro
up
for
a pr
oduc
tion
plan
com
mer
cial
m
anag
er
only
de
part
men
ts
with
in
com
mer
cial
orga
nisa
tion
for
prod
uctio
n
plan
ning
tran
slat
ed
into
a w
eek
fore
cast
for
prod
uctio
n
plan
ning
sale
s
none
no,
fore
cast
s pe
r
fam
ily
of e
nd-
prod
ucts
no,
fore
cast
s pe
r
fam
ily
of e
nd-
prod
ucts
no,
fore
cast
s
per
fam
ily
of
end-
prod
ucts
Reg
iona
l ag
greg
atio
n
or
disa
ggre
gatio
n?
Proc
edur
e de
fine
d?
Soft
war
e to
ols
Stor
ed
info
rmat
ion
Goa
ls
and
eval
uatio
n
-
natio
nal
fore
cast
disa
ggre
gate
d in
to
regi
ons
defi
ned
and
docu
men
ted
(not
alw
ays
follo
wed
)
yes
5 yr
. de
man
d hi
stor
y,
deliv
ery
days
,
tem
pera
ture
, so
me
prom
otio
nal
activ
ities
eval
uatio
ns:
yes,
ad
ho
c no
sp
ecif
ic
goal
s
area
s ag
greg
ated
to
regi
ons,
ag
greg
ated
to
na
tiona
l le
vel
defi
ned
and
docu
men
ted
(not
al
way
s fo
llow
ed)
no
no
spec
ific
in
form
a-
tion
stor
ed
for
fore
cast
ing
eval
uatio
ns:
mon
thly
com
pari
son
with
actu
al
sale
s
no
spec
ific
go
als
no,
fore
cast
s
per
regi
on
and
per
chan
nel
defi
ned
and
docu
men
ted
no
regi
onal
de
man
d
hist
ory,
m
arke
ting
pric
es,
polic
ies
and
som
e pr
omot
iona
l
activ
ities
eval
uatio
ns:
year
ly
com
pari
son
of
budg
eted
sa
les
with
actu
al
sale
s no
spec
ific
go
als
no,
natio
nal
fore
cast
defi
ned,
no
t
docu
men
ted
(not
alw
ays
follo
wed
)
yes
5 yr
. de
man
d hi
stor
y,
prom
otio
nal
activ
ities
,
deliv
ery
days
,
exce
ptio
nal
clim
ate
circ
umst
ance
s.
eval
uatio
ns:
yes,
ad
hoc
no
spec
ific
goal
s
no
natio
nal
fore
cast
s
defi
ned,
no
t
docu
men
ted
no
dem
and
hist
ory,
kept
by
Pr
oduc
tion
eval
uatio
ns:
no
no
spec
ific
go
als
regi
onal
fo
reca
st
aggr
egat
ed
to
natio
nal
fore
cast
s
defi
ned
and
docu
men
ted
(not
alw
ays
follo
wed
)
no
no
spec
ific
info
rmat
ion
stor
ed
for
fore
cast
ing
eval
uatio
ns:
yes,
ad
hoc
goal
: er
ror
F
< 5%
110 L. Hagthrn-uan der Mrijdm ct crl.:‘Int. J. Procluc~tion Economics 37 (1994) 101-114
Table 2
Recommended changes
Charactertstics Proposal
Existing short-term demand
forecasts
Use of forecast
Further processing
Responsible department
Other departments involved
Regional aggregation or disagregation?
Product aggregation or
disaggregation?
Procedure defined
Software tools (Standard) software to support the forecasting process has to be selected.
Stored information Information should be stored on all relavent factors that influence demand: Besides the demand
history and the forecast history, information should be stored on promotional activities, climatologi- cal influences, calendar (delivery days, holidays, festivities), external events (competitors actions, new
regulations, etc.), internal events, (strikes, delivery problems, etc.)
Goals and evaluation
Only one single short-term forecast should be produced for the whole OC.
The forecast should be used to support all operational planning processes for sales, purchasing, unit production. assembly, packaging, distribution, etc.
The result of the forecasting process should be a negotiated sales goal. On the basis of this sales goal
all departments should plan their operations. No individual changes or own interpretations of this
goal are allowed.
Sales should be responsible for the short-term demand forecast because
they are responsible for sales on short term
they have the best insight in the development of sales on short term
they are able to influence sales on a short term.
Although a sales manager is responsible for forecasting, he could delegate the coordination to a staff
department, which is more independent or has more knowledge of forecasting (logistics, planning
department).
At least the departments of marketing (promotional activities, market trends), sales (sales trend,
discount actions), production (production limitations), distribution (distribution requirements) and
logistics (stock positions, stock needed, customer service considerations) should be involved. They
should be involved during the forecasting process, not afterwards.
Aggregation from regional level to national level will be necessary if regional sales managers have
their own responsibility for a sales volume. Aggregation should exactly represent the responsibilities
for sales in the organisation. Aggregation is to be preferred to disaggregation.
For product aggregation the same is valid as for regional aggregation.
A formal procedure should be defined. This procedure should provide for communication between
the commercial and the production organisation. In this procedure the responsibility of different
departments to contribute to the forecasting process should be specified.
Measurable goals should be defined for forecast accuracy. A well defined procedure should be used
for regular evaluation of forecast against sales. The short-term demand forecasting process itself
should be evaluated as well, in this way the forecast acceptable can be evaluated on a qualitative basis.
been tested. Some of the other OCs are expected to follow soon. In the pilot study, a new forecasting procedure was defined on the basis of the frame- work that is described and software is implemented to support the forecasting process. In this OC the new procedure will be fully operational before the end of 1993. Preliminary results show that forecast
quality has already improved considerably, by just introducing some simple statistical techniques. The most important effect is that the average forecast error has been considerably reduced, as is demon- strated in Fig. 4. This indicates that the forecast is more objective and less “political”. However the standard deviation of the forecast error is still
L. Hagdorn-van der Meijden et al./Int. J. Production Economics 37 11994) 101-114 111
revised
Fig. 4. Effect of new procedure.
relatively large, which shows that forecasting the demand of products is difficult and will demand continual attention of all parties involved. This attention will further increase when the complete forecasting procedure for this OC is implemented. Further improvements of the forecast quality are expected when closer coordination and coopera- tion between sales and manufacturing is realized.
The improved forecast has already shown a re- duction of costs for (a) inventories, (b) reshipment of products that are needed at other depots and (c) obsolete products.
4. Forecasting systems and improving forecast quality
In this paragraph a more generalized analysis of the demand forecasting process is presented than in the case description of the previous section. We will define a forecasting system in which the forecasting process can be organized in order to improve fore- cast quality.
4.1. A forecasting system to bridge the gap between
sales and logistics
A forecasting system can be defined as the pro- cedures, processes, people, statistical techniques, information and tools needed to produce a demand
forecast with a good forecast quality. In both fore- casting and logistics literature little attention is paid to forecasting systems [6]. All the different elements of the forecasting system are to some extent crucial to reach accurate and accepted fore- casts.
In the previous section we developed an im- proved forecasting system at Rahanu. Some essen- tial elements of the forecasting system that were also briefly referred to in the case, will be elabor- ated here. Solving the conflict between sales and manufacturing can be done using a cyclic forecast- ing system as illustrated in Fig. 5. In this cyclic forecasting system the communication and co- operation between the different parties in the fore- casting process is intensified. We suggest several improvements in the forecasting process:
(ii)
(iii)
(iv)
(v)
It is necessary to set goals- for the forecast quality. On the basis of these goals the forecast performance can be evaluated. Measurable goals can be set on forecast accuracy. The result of the forecasting process should be a sales goal.This goal should be the result of a negotiation process between the different de- partments involved. It should represent the market potential on the one hand and the planned promotional activities and logistical opportunities and constraints on the other hand. By discussing the different internal re- strictions, plans and insights, a goal should be reached that is a more reliable input for plann- ing than the old forecast. Moreover, by negoti- ating a common goal, all parties should be more committed to the same goal. Responsibilities should be defined clearly. To get commitment and involvement from the responsible managers a defined evaluation procedure is necessary in which the results of the evaluation should be used to address the managers on their performance. Procedures to deal with deviations on the plan should be clearly defined. Effects of promotional activities should be forecasted by the responsible department. These forecasts should be evaluated and there- fore it is necessary to store information on the effect of promotional activities and the fore- casts of these activities. This information may
64
Operational Planning, Adjustment and Execution of plans
Fig. 5. Cyclic forecasting process
be used as a knowledge base to improve fore- casting in the future. Moreover, the most suc- cessful promotional activities can be selected for the future. Forecasting software can be an important aid in supporting the forecasting process. It can be used to store information, to calculate fore- casts, and to present the forecast (sub)results.
(vii) After the execution of plans, understanding should be gained by evaluating what hap- pened and why. Only through communication between parties this understanding can be gained. Questions should be raised to find out why there is a difference between goals and actual results. In this way the processes of forecasting, setting goals and evaluation of plans can be improved continuously.
The benefits that can be expected from a fore- casting system as we described, can be summarized as follows:
(1) The sales goal that is a result of the negoti- ation process between departments will have many benefits. It will be a more realistic goal as a basis for
planning. It will be a better accepted goal because all relevant parties are involved. And finally it will increase the insight in limitations and problems of other departments and therefore stimulate coord- ination.
(2) Clearly defined goals for forecast quality allow for a critical evaluation of forecast quality, addressing of responsible managers and support striving for continuous improvement.
4.2. Organizational changes
To implement the cyclic forecasting process we presented, the decision making process should be strongly embedded in the planning process of the organizational unit. This can cause a redesign of the business planning process. This business pro- cess redesign often takes into account many other changes which are closely connected to forecasting, like the development of a market oriented logistics organizational structure instead of a product oriented logistics organizational structure, shifting
L. Hagdorn-uan der Me!jden et al.: ht. J. Production Economics 37 i 1994) 101--l 14 113
from a national oriented logistics organizational structure towards a international organizational structure, changing the customer order penetration point, centralizing the mid-term planning of pro- duction allocation to the different plants, etc.
The willingness of the business unit management to set up a central developed forecasting procedure (as in the Rahanu case) at their business units, strongly depends on the relationship between the central management and decentral management. In case of strongly autonomous business units, the attractiveness and potential savings and improve- ments that can be caught by the new forecasting concept should be made clear to these business units. Moreover, the guidance and support from the central management in the implementation of the new forecasting procedure at the business unit should be strong to prevent decreasing interest in the implementation.
4.3. Applicnbility,for other industries
A forecasting system as described for Rahanu requires a lot of investment in time and energy from the organisation. The benefits of such an approach might not match the investments for every com- pany. However, characteristics can be identified which determine whether the investment in a forecasting system as suggested may be rewarded. We identified the following market, product and production process characteristics:
Market characteristics: (a) irregular sales pat- terns (seasonal fluctuations), (b) many factors in- fluencing demand, (c) frequent use of promotional activities, (d) distance to the market through use of intermediaries, (e) many different customers and (f) high customer service level (e.g. short delivery time).
A wide range of producers will fulfil many of these criteria and therefore benefit from such a fore- casting system. Not only companies that produce completely on stock, but also companies that pro- duce partly on order, or who assemble to order but produce components to stock, could benefit from a forecasting system as described.
5. Final comments
We realize that the suggested framework to im- prove the short-term demand forecast is not a ready made solution that works out immediately in a satisfying way for any organization. Firstly, the framework should be adapted to the specific needs of a specific company. Secondly in many com- panies, a lot of effort should be put in convincing especially the sales people to the need for coordina- tion with the logistics departments. Thirdly, a lot of time and energy have to be spent on implementing, evaluating and improving the company specific framework.
Based on the presented case-studies and based on experiences in other companies, we are convinc- ed of the necessity of statistical tools to support the forecasting process, but - as the framework shows ~ also the structured coordination and cooperation between the logistics departments and the sales departments are of high value for further improve- ment of the quality of the short-term demand fore- cast.
Market reactions and customer behaviour are very difficult to predict. So, to reduce the forecast error to “zero” seems an illusion. Nevertheless, we hope that our framework increases the added value of statistical tools and therefore supports in bridg- ing the gap between sales and manufacturing.
Pioduct characteristics: (a) products that can become obsolete or perishable products and (b) short product life cycle(s).
Production process characteristics: (a) the cus- Acknowledgements
tomer order penetration point is located within or downstream the production process, (b) lead times for production steps or for procurement of mater- ials, which are longer than the required customer order lead times and (c) capacity constraints.
Although Rahanu is a cover name for the com- pany the research was carried out for, we would like to thank the involved managers at Rahanu’s de- partment of Corporate Distribution and Logistics for their cooperation. Their suggestions for
114 L. Hagdorn-van dw Meijdm et al.tInt. J. Production Economics 37 (1994) 101~114
improvements of the several concepts of this article were of high value.
References
[l] Bertrand, Wijngaard and Wortmann, 1990. Production
control: A structural and design oriented approach.
[2] Mahmoud, 1987. The evaluation of forecasts: in: Mak-
Elsevier, Amsterdam.
ridakis and Wheelwright (Eds.), The Handbook of Fore-
casting. 2nd ed., Wiley, New York.
[3] Ramondt,
[5] Makridakis, Wheelwright and McGee, 1983. Forecast-
1992. Demand forecasting and logistics
control (in Dutch). Thesis, Erasmus University Rotterdam,
ing: Methods and Applications. 2nd ed., Wiley,
Department of Business Administration, The Netherlands.
[4] Blattberg and Hoch, 1990. Database models and mana- gerial intuition: 50% model + 50% manager. Management
New York.
Science, 36(8): 887-899.
[6] Magee, Copacino and Rosenfield, 1985. Modern Logistics
Management: Integrating Marketing, Manufacturing and Physical Distribution. Wiley, New York.
