Download Supply chain redesign employing advanced planning systems

Production Planning & Control
Vol. 20, No. 2, March 2009, 158–167
Centralised supply chain master planning employing advanced planning systems
Martin Rudberga* and Jim Thulinb
a
Department of Management and Engineering, Division of Production Economics, Linköping University,
Linköping, Sweden; bOptilon, Malmö, Sweden
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(Final version received 20 November 2008)
Higher expectations on supply chain performance force organisations to reinvent themselves in order to cut
costs and increase customer service, all to gain competitive advantage. Pursuing the best network of
manufacturing, supply and distribution facilities relative to the marketplace is therefore on top of many
managers’ ‘most wanted’ list concerning supply chain management. Supply chain planners are thus in need of
decision support to be able to establish feasible and sufficient plans. This article discusses how decision
support through advanced planning systems (APS) can assist tactical supply chain planning. A case study
is presented showing how APS can act as an enabler in adapting logistics and supply chain principles, as
well as reducing costs through streamlining the supply chain. The purpose of this article is primarily
to present findings from a case study regarding supply chain planning with the aid of a master planning
APS module. The case study emphasises that APS in the scope of logistics management have several positive
effects on supply chain performance.
Keywords: supply chain management; supply chain planning; process industries; master scheduling; case study
1. Introduction
Logistics and supply chain management (SCM) as
business management concepts have gained increased
attention during the last decades. A vision of the
supply chain as a holistic construct with close
cooperation between the different organisational
units has replaced the traditional picture of the
supply chain as a collection of vertically organised
functional units (Christopher 2005, Stadtler and
Kilger 2005). Many companies that act on both
domestic and global markets experience a growing
international competition and recognise the need for
supply chain efficiency. This need stems from increasing customer demands for high-quality products at
a low price plus higher expectations of accurate
deliveries and customer service (Christopher 2005).
Advanced planning systems (APS) can be used as a
tool to meet the ever-increasing demands for effectiveness that put new pressures on swift and efficient
planning and control of the supply chain. APS as a
decision support system (DSS) for production and
distribution planning is still a new and fairly unexplored tool (Wu et al. 2000, Stadtler and Kilger 2005).
During the last few years, companies that sell
enterprise resource planning (ERP) systems have
started developing and implementing APS modules,
*Corresponding author. Email: [email protected]
ISSN 0953–7287 print/ISSN 1366–5871 online
ß 2009 Taylor & Francis
DOI: 10.1080/09537280802705047
http://www.informaworld.com
which with the aid of sophisticated mathematical
algorithms and optimisation functionality support
planning of complex systems such as supply chains
(Wu et al. 2000, Stadtler 2005).
The purpose of this article is primarily to present
findings from a case study regarding supply chain
redesign and supply chain planning with the aid of a
master planning (MP) APS module. More specifically,
the case shows how a centralised supply chain MP
process can be set up in a multi-site environment,
the supply chain planning methodology used by the
company, and the results in terms of financial and
logistics performance that the company has reached
through the project. Unlike traditional ERP systems,
APS try to find feasible, near optimal plans across the
supply chain as a whole, while potential bottlenecks
are considered explicitly (Stadtler and Kilger 2005). In
terms of software, APS means a broad group of
software applications developed by various software
vendors, such as i2, Manugistics (JDA), Oracle, SAP
and Lawson. During the last decade, the use of APS
for design, integration and control of supply chain
processes has increased. Especially the interest among
industrial companies has increased, some have invested
in the software, but only few use it in practice on
strategic and tactical planning levels. There are also
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Production Planning & Control
few documented cases showing how standardised APS
are used, especially concerning the tactical planning
level which is the focus of this article.
From a methodological perspective, this research is
based on a literature review and a case study. In
addition to an analytical approach, the research in
this article is based on a conceptual and descriptive
research method. The literature review is founded in
the fields of SCM, operations strategy and manufacturing planning and control. The case study is
descriptive and illustrative in nature, and data have
been gathered through surveys, semi-structured interviews and on-site visits at plants and distribution
centres in the case company’s supply chain, as well as
data from the company’s information system database.
The authors also had access to primary data in terms
of company reports describing processes, performance
data, etc. Typically, interviews were carried out with
project managers, master planners and members of
the implementation team. The case analysis is a
comparison between before and after the supply
chain redesign. This longitudinal approach is selected
in order to capture the results of the supply chain
restructuring which included implementing APS
(Yin 2003).
In the following, the article provides a literature
review centred on SCM and supply chain planning.
Thereafter the case study is introduced describing
the supply chain redesign, the APS implementation
and its results in particular. After an account concerning managerial implications some concluding remarks
are provided.
2. Supply chain planning
Studies on SCM often centre on what have been
identified as the two main issues concerning the
management of value-adding networks: configuration
and coordination (Rudberg and West 2008). This article
addresses both issues, i.e. the configuration of the
network and the coordination of the newly configured
network. The ever-present questions of, for example,
where to allocate production so as to be most
responsive and how to maintain low production and
transportation costs become more important when
competition increases. Still, the issue of coordination
cannot be left aside and must be included when supply
chain redesign is considered. In this article, most focus
will be put on coordination of the newly redesigned
supply chain with the aid of an MP APS module.
Supply chain planning has in recent years developed to be supported by optimisation and simulation
tools, especially concerning ‘higher’ planning levels.
159
Complex trade-off analysis can be calculated with the
aid of optimisation models and solution heuristics in
relatively short computing time (De Kok and Graves
2003, Chopra and Meindl 2004). The general trade-off
in planning is between service, costs, capital expenditure and working capital (Shapiro 2001). Cost minimisation and profit maximisation are the two most
common ways to control the solution (Stadtler and
Kilger 2005). Many planning related problems in
the supply chain are caused by poor communication
and coordination throughout the supply chain.
APS has been put forward as one solution to these
problems.
Advanced planning systems can be defined and
explained through different perspectives but commonly
APS is viewed as an extension of ERP (Wiers 2002).
On the other hand, standard APS modules stem from
the many in-house developed DSS that aid planners at
various levels in the decision hierarchy (De Kok and
Graves 2003). The literature reports on some successful
implementations of DSS in either special supply chain
planning situations or optimisation models regarding
the entire chain. Gupta et al. (2002), for example,
describe a DSS that helps Pfizer to plan their
distribution network. The model is useful in both
strategic and operational planning situations. Brown
et al. (2001) presents a large-scale linear programming
optimisation model used at Kellogg Company to
support production and distribution decision-making
on both strategic and tactical levels. Arntzen et al.
(1995) comprehensively describe supply chain optimisation at digital equipment corporation. However,
many of the cases reported in the literature are
in-house developed software based on optimisation
solver-engines, and not standard APS software which
is the focus of this study.
2.1. APS
APS aim at taking into account the finite nature of
resource capacity and other constraints in order to
provide reliable planning. Unlike traditional ERPsystems, APS do not assume that capacities are infinite,
that all customers, products and materials are of equal
importance, and that certain parameters (such as lead
times) can be fixed (David et al. 2006). Furthermore,
APS are not limited to planning and scheduling of a
single factory; rather they address supply chains with
multiple sites and transportation links. As such,
Stadtler and Kilger (2005) define the goal of APS
as ‘. . . to find feasible, near-optimal plans across the
supply chain as a whole, while potential bottlenecks
are considered explicitly’.
M. Rudberg and J. Thulin
To be able to plan and control complex supply
chain structures, powerful management decision support is needed. Planning has, therefore, found a
renaissance in the use of optimisation and simulation
tools. APS use such optimisation and simulation tools.
They consider the supply chain constraints and
produce near optimal plans and are, therefore, sometimes called supply chain optimisation software.
During the last few years not only APS niche vendors,
but also ERP vendors have started developing and
implementing advanced planning modules, with the
aim to support complex planning problems.
Nowadays, APS modules are often a part of larger
software suites and work as add-ons to existing ERPsystems. APS do not replace ERP; they extract data
from the ERP database and send the resulting plans
back for distribution and execution. Often, solver
engines based on linear programming and mixed
integer programming are used to unravel the large
amount of data. To cut computing time, heuristics are
used built on operations research (OR) knowledge
(De Kok and Graves 2003). APS, consequently, try
to automate and computerise the planning through
simulation and optimisation. Still the decision-making
is done by planners, who have insight into the
particular supply chain, know about the system
constraints and also have a feeling about feasibility in
the plans that are created. Planners also do the
modelling and decisions regarding use of input to
the model. APS are, thus, based on a systems and
Supply
management
Strategic/
long-term
Production
management
Distribution
management
Demand
management
Multi-site master plan ning
Supplier
management
Purchasing
and
materials
planning
Source
Production
planning
Distribution
planning
Production
scheduling
Transport
planning
Make
Deliver
Demand planning
Strategic network design
Tactical/
mid-term
Operational/
short-term
process approach and try to bridge the gap between the
supply chain complexity and the day-to-day operative
decisions.
Considering the complex environment that most
companies have to cope with, most decision support
systems advocate a hierarchical distribution of the
decision-making processes, where the next upper level
coordinates each lower level (Wortmann et al. 1997).
Strategic decisions (long horizon and long periods)
cannot be based on the same level of detail in the
information as is the case for operational decisions
(short horizon and short periods). Hence, decisions
made at a high hierarchical level are normally based
on aggregated information (in terms of product
families, factories, etc.) and aggregated time periods.
Thereafter these high-level decisions form the context
for the decision-making processes at lower-level decision centres, where decisions are disaggregated into
more detailed information and time periods, but the
considered horizon is made shorter (Wiers 2002).
Decisions are thus exploded through the hierarchical
structure until the lowest level is reached and detailed
decisions are executed (cf Figure 1). One way to classify
standard APS is by categorising different modules
depending on the length of the planning horizon on the
one hand, and the supply chain process that the module
supports on the other. Figure 1 categorises the most
common standard APS modules according to these two
dimensions (Meyr et al. 2005, Stadtler 2005). This
module segmentation is commonly used among
Demand fulfilment (ATP/CTP)
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Execution
Order release, shop floor control, vehicle dispatch, order management, etc.
Procurement
Production
Distribution
Figure 1. APS planning structure and categorisation of typical APS modules.
Sales
Customer
management
Production Planning & Control
software vendors. This study focuses on the tactical
level, i.e. multi-site master planning (MMP) in Figure 1.
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2.2. Supply chain MP
MP looks for the most efficient way to fulfil demand
forecasts and/or customer orders over a mid-term
planning interval (Figure 1), which often covers a full
seasonal cycle. MP not only balances demand forecasts
with available capacities, but also assigns demands
(production and distribution amounts) to sites in order
to avoid bottlenecks (Rohde and Wagner 2005). MP is
an important supply chain decision level because to be
effective, inputs from throughout the supply chain are
required and its results have great impact on the supply
chain (Chopra and Meindl 2004).
Mid-term planning concerns rough quantities of
material supplied, workforce requirements, production
quantities, seasonal stock and use of distribution
channels. Before OR tools like optimisation and
simulation entered the enterprise planning arena, MP
was often done by traditional infinite MRPII systems,
or by simple calculations using spreadsheets without
considering capacity limitations (Fleishmann and
Meyr 2003). New OR-related software that conducts
MP using linear programming tries to maximise profit
(or minimise costs) while meeting demand (Chopra and
Meindl 2004) and taking constraints (e.g. capacity)
into consideration as an integrated part of the planning
process. To be able to optimise the mid-term supply
chain model, production, inventory and distribution
must be regarded concurrently. MP uses data on
products and material in aggregated product groups.
Inputs are demand data and network constraints in
terms of a model that defines capacity and dependencies between different processes. The MP results in a
common supply chain plan regarding production,
distribution, inventory, procurement and materials
requirements (Rohde and Wagner 2005). This tactical
supply chain plan is thereafter on the one hand
exploded down the product structure to be used in
the detailed production planning and scheduling, and
on the other hand used as basis for demand fulfilment
and order promises. In the demand fulfilment process,
the positioning of the customer order decoupling point
determines how orders are booked against the master
plan (Rudberg and Wikner 2004, Wikner et al. 2007).
In terms of APS, MMP aims at synchronising the
flow of materials along the supply chain, and thereby
balancing demand and capacity. It supports the
mid-term decisions concerning efficient utilisation of
production, distribution and supply capacities
(Stadtler and Kilger 2005). MMP not only balances
161
demand with available capacities but also assigns
demands (production and distribution amounts) to
sites in order to avoid bottlenecks, wherefore it has to
cover one full seasonal cycle, or at least 12 months
in terms of weekly or monthly time buckets. Due to the
complexity and detail required in the model only
constrained (or near-constrained) resources are modelled in detail. To increase the solvability of the model,
most vendors distinguish between hard and soft
constraints in the LP or MILP model that is used
(Entrup 2005). While hard constraints have to be
fulfilled, the violation of soft constraints only renders
a penalty in the objective function. The MMP model
uses aggregation to establish reasonable solution times
and the solvers included in the standard APS modules
are often a combination between internally developed
and third party solvers.
The cases found in literature reporting on the use
of standard APS software are often focused on either
the system setup or the mathematical model behind
the user interface (see e.g. Neumann et al. 2002,
Günther and van Beek 2003, Stadtler and Kilger
2005). It can be concluded that companies using the
MMP module often have an established sales and
operations planning process or a centralised MP
function. One important feature of the MMP module
is the ability for companies to coordinate sourcing,
production, distribution and seasonal stock decisions
on a multi-site basis. Besides the positive planning
effects the MMP is also intended to enhance
visibility and coordination throughout the supply
chain. Yet, the MMP module is seldom found at
companies today, although it has gained more
interest in recent years. David et al. (2006) report
on the use of APS to solve the specifics of scheduling
problems in the aluminium conversion industry, and
also highlights some problems with using APS in
process industries due to the divergent nature of the
material flows. Wiers (2002) reports on how ERP
and APS are integrated in a steel processing plant,
mainly focusing on the system setup. Jonsson et al.
(2007) provide a comparative case study where the
use of APS is analysed in three companies in an
explorative manner. Zoryk-Schalla (2001) focuses on
the implementation issues in a longitudinal study of
APS implementation in an aluminium manufacturing
company. In general, none of these cases analyse
APS from a supply chain planning perspective and
none of them reports on the financial impact and
logistics performance resulting from the use of APS,
which is the focus of this case study. The case in
the following section will describe how supply
chain MP coordinates procurement, production and
162
M. Rudberg and J. Thulin
distribution on the mid-term planning level, and also
how it affects the supply chain performance.
during the spring season. The product (seed) is,
according to Fisher’s (1997) definition, functional in
its characteristics: low profit margins, low product
variety and long lead times for requiring customised
products (5–10 years). Due to the functional characteristics of the product, physical efficiency is the
main objective for supply chain design, hence a low
cost focus through the entire chain of processes. The
demand for seed is highly seasonal, and about 70% of
the volume is sold during a period between December
and March (Figure 2). The large volumes and the many
suppliers and customers make the business dependent
on efficient inventory and distribution management.
The planning process is difficult due to the high
seasonal fluctuations and the fact that seasonal stock
can only be built up in restricted amount.
Lantmännen (the Swedish Farmers Supply and Crop
Marketing Association) is one of the largest farming
and food industry groups in Europe, and the leading
group within the grocery and agriculture industry
in Sweden. It is a producer cooperative that works
together in marketing, distribution, sales, processing
and supply. Large profit margins are not the goal so
much as cost reduction through the entire chain. This
is due to the fact that the owners, some 44,000 Swedish
farmers, are both suppliers and customers to the
central production and distribution function. The
group has some 13,000 employees, markets its products in 19 countries and has a yearly turnover of SEK
32 billion (SEK 100 EUR 10). In Sweden, the group
is organised in 13 geographically separate areas and
supplies its customers with seed, fertilisers and feed
among other things, and of course processes and sells
what the farmers produce. Prior to 2001 the farmers
acted in local and regional cooperatives but in 2001,
Lantmännen was founded out of merging these
cooperatives. Since then the group has suffered from
inefficiency and surplus capacity. Several structural
changes and reorganisations have been carried out in
order to streamline the business.
The study presented in this article centres around
the seed supply chain which belongs to the division
with company-wide logistics responsibility. Lantmännen Seed handles an entire supply chain, from
cleansing raw seed to packaging and distribution.
The company has 270 articles, including different
packaging sizes, and on average delivers about 4000
tons of seed a week in season and a total of 65,000 tons
3.1. Supply chain redesign
In 2004, a major restructuring of the seed supply chain
was undertaken. Two out of six production plants were
shut down and two out of four central warehouses
were closed. Restructuring the seed supply chain
resulted in less capacity in both production and
warehouses, and also put higher requirements on
distribution activities. Lower capacity and equal
requirements on throughput make the planning
process harder, wherefore Lantmännen also needed
to establish a new centralised MP function. The seed
supply chain, as depicted in Figure 3, contains 30,000
farmers who act as both suppliers and customers to
the four production plants and the two distribution
centres.
Every plant supplies a restricted number of
customers within the nearest geographical regions.
Only in a few cases the production is differentiated
between the plants. The main distribution strategy is to
July 03–June 04
Customer orders per week 03/04 and 04/05
July 04–June 05
6000
Quantity (Tonne)
5000
4000
3000
2000
1000
Week
Figure 2. Customer orders per week 03/04 and 04/05 (Andersson 2006).
21
18
15
12
9
6
3
53
50
47
44
41
38
35
32
29
26
0
23
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3. Case: the Farmers Group
163
Production Planning & Control
ship finished products directly from the finished goods
inventory at the plants. This leads to a large number of
distribution relations that put pressure on transportation efficiency. To be able to meet the high seasonal
demand peaks, surplus capacity is needed. Sales
forecasts are conducted by the marketing and sales
function and are mostly dependent on historical
sales data. The access to raw material is to a great
extent dependent on weather and other factors that are
hard to predict.
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3.2. Implementing an APS
The restructuring of the supply chain was complemented by changes in the mid-term supply chain MP in the
form of a new centralised planning function. Earlier
the mid-term planning (including production and
distribution) had been carried out locally/regionally
Suppliers
30,000 farmers
Production
4 plants
Distribution
2 centres
Customers
30,000 farmers
Figure 3. The seed supply chain structure with 13 geographical areas representing customers and suppliers,
four production plants and two central warehouses
(Andersson 2006).
with simple spreadsheets. Hence, the new centralised
planning function was in need of software decision
support in order to find feasible plans for the entire
supply chain. The logistics division investigated the use
of decision support through APS in a feasibility study
in mid-2004. Two months later the APS module
Lawson M3 Supply Chain Planning (M3 SCP) was
implemented and in use. The tactical planning process
and related APS modules are visualised in Figure 4.
Planning with M3 SCP balances supply and
demand for each weekly time bucket during the
planning horizon (the remaining season). Input data
are forecasts, customer orders, access to raw material
and available capacity in terms of production, warehouses and transportation. Forecasts are based on the
yearly budgeting process and only updated twice a
year. The forecast is consumed by the actual orders
entered in the system and the first two weeks of the
planning horizon is fixed and includes only customer
orders. Capacity and raw material data are extracted
from the company’s ERP system and used by the M3
SCP module. Production and inventory levels are
matched with capacity for each period, with regard to
the four production and two distribution sites and the
available transportation capacity. The objective function is set to minimise total costs (production, holding
and transportation), given that all demand (customer
orders and the unconsumed portion of the forecast)
is satisfied. The solver engine uses linear and mixedinteger programming to solve the planning problem
with respect to total cost minimisation. Short-term
production scheduling as well as planning supply is
carried out locally at the plants according to the
directions given by the aggregated master plan. The
plants plan their production and release production
Forecasts per:
• Week
• Final product
Strategic/
long-term
Tactical
mid-term
Access to
raw material
Lawson M3 Supply Chain Planning
Customer orders
Operational/
short-term
Yearly
budget
Availability:
• Raw material
• Capacity
Delivery plan :
• Week
• Final product
Production plan:
• Week
• Final product
Figure 4. Lantmännen Seed’s tactical planning process and related APS modules.
Distribution plan:
• Week
• Final product
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164
M. Rudberg and J. Thulin
orders in congruence with the two-week fixed planning
period. Detailed transport planning (routing, loading,
etc.) is outsourced to a third party that executes the
deliveries within the frames of the distribution plan.
The planning process with M3 SCP as such is done
in an iterative manner where bottlenecks are identified
and handled by the central master planner. The
planner uses a four-step methodology when the central
master plan is established. At first an unconstrained
master plan is established in order to get an understanding of the level of overloaded resources and
inventory as a baseline model. Thereafter a new run
with constrained production facilities is carried out.
Two types of resources in each plant are considered
to be bottlenecks (seed cleansing and packaging) and
hence included in the model. For production facilities
mainly soft constraints are used since the planner can
change the number of shifts that are used in the plants.
Constraints on the run sizes are also included in terms
of minimum batch sizes due to the fact that there are
high start up costs for each new run in the plants.
When the number of shifts are determined, limits on
the capacity in warehouses are included in the model,
and a new set of simulations are run in order to
determine the best combination of production, distribution and stock holding. All warehouses are
modelled as hard constraints in terms of maximum
inventory levels. As a third step, also constraints on the
distribution are included. Yet, since the actual transportation is outsourced to a third party the capacity
in transportation is virtually unconstrained as long as
the transportation provider gets the information about
required transportation capacities a few weeks in
advance. As a final step, a last run in the SCP is
carried out with all constraints included and the final
plan is compared to the plans developed in an iterative
manner. The reason for using the iterative approach
instead of a ‘black box’ single run is for both learning
and model validation purposes. Also, some decisions
have to be made on other grounds than pure cost
minimisation, and the iterative approach allows the
planner to include such decisions in the model.
3.3. Results
In a study carried out in 2006 (Andersson 2006), the
effects on total costs regarding the seed production
and distribution at Lantmännen were evaluated with
respect to the supply chain redesign. At the same time,
the flow of goods and the implementation of the APS
at the centralised MP function were investigated.
The results show that the structural changes and the
implementation of the APS have streamlined
the production and distribution network regarding
the flow of goods. Total costs have decreased by some
13% on a yearly basis, while at the same time the
quantity of sold units has increased. This results in a
total reduction of cost by some 15% per tonne.
Furthermore, inventory levels in production facilities
and warehouses were reduced by almost 50%, and
inventory reductions were realised for raw material,
WIP and finished products.
The supply chain planning trade-off has had the
following consequences: in general increased production batch sizes (due to fewer production plants and
hence less capacity), slightly higher transportation
costs due to more direct distribution (leading to
lower fill-rates in the trucks), decreased production
cost and less capital tied up in inventory because of
better throughput. The reinvented supply chain planning has also reduced the total planning time, and the
central planning function has increased the control of
material flows in the chain as well as the cost structure.
A higher understanding of the supply chain trade-off
makes further development of immediate importance.
Optimising the supply chain has not been the most
important objective with the APS implementation
from an organisational perspective. Rather, the main
focus has been to gain acceptance for the central MP
process and to enable communications between various business functions. In addition to the monetary
gains through lower supply chain costs, the new way of
planning increased the communication between logistics, manufacturing, marketing and sales functions.
The master plans produced by M3 SCP are distributed
and discussed between the functions and thereby
improved for better fit with actual supply and demand.
4. Case analysis and managerial implications
At the start of the project, Lantmännen was in need
of reinventing the logistics planning function and a
DSS that could assist in planning the entire chain of
processes. They investigated the use of decision
support through APS in a feasibility study in mid2004 and all plants went live simultaneously with SCP
in the autumn of 2004. The project involved the four
seed production plants on the Swedish mainland, two
central warehouses and transportation planning units.
Implementing SCP was a quick affair. With only a few
weeks’ delay and under the projected budget,
Lantmännen was up and running two months after
the project began. Because the users were directly
involved in the project, they did not need any additional
training to start using the system. Lantmännen Seed
has only one super user plus two people as backups.
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Production Planning & Control
Since everything is now managed centrally, there is no
need for more people.
The relatively satisfactory results from implementing the APS MP module have raised the question of
whether the same restructuring can be applied to other
product group supply chains as well. For now, decision
support regarding only mid-term planning has been
implemented. Development for detailed production
and distribution planning support are under way.
This study indicates that the APS MP module together
with the centralised MP function has contributed to
the above stated changes. Nevertheless, further potential in the use of the M3 SCP has been identified. The
seed supply chain has been used as an internal
benchmark and since this study was carried out,
Lantmännen has implemented a similar solution for
its manure operations. The group is now on its way to
implement a corporate-wide centralised MP function
supported by an APS MP module.
From a managerial perspective the following
positive effects from the APS project have been noted:
. Higher throughput at a lower total cost.
. Higher service level (delivery reliability) with
reduced total capacity and lower inventory
throughout the supply chain.
. Increased supply chain visibility and coordination, thanks to the centralised MP function
and the APS decision support.
. More time-efficient planning and replanning
with fewer persons involved in the planning
process.
. More proactive planning through the possibility to swiftly run a number of scenarios at
very short computing times.
. Better and more frequent communication
between various functions within the
company.
. Better integration between production and
distribution planning, leading to more efficient use of scarce resources.
The project management has also learnt some lessons
for the projects to come, of which the most important
was that the project goal and strategy need to be
clearly communicated at an early stage of the projects.
Furthermore, the resistance from the planners at the
local sites was larger than first expected even though
most of them turned positive during the implementation of the new software. Finally the project management has come to the conclusion that it most likely
would have been possible to carry through the
restructuring of the supply chain without implementing
the APS. However, centralising the MP function
required a DSS that could handle MP of a supply
165
chain with multiple sites and a complex distribution
system. Thus, combining the restructuring with the
APS implementation and organisational changes made
the division focus its resources in a relatively short time
with positive effects on both implementation times and
bottom-line results. All people involved in the project
are convinced that the positive results would not have
been reached at full without support from the APS.
5. Concluding remarks and further research
It has been argued that one of the key problems in
research associated with APS and related concepts is
the absence of the application of theory to industrial
practice (McKay and Wiers 1999) and the absence
of documented cases of APS implementation (Wiers
2002). Especially, there are very few documented cases
showing how standardised APS are used for supply
chain planning on strategic and tactical levels (Jonsson
et al. 2007). This article is one brick in filling this gap
and increasing the understanding of when and how to
use APS in supply chain planning. To be able to plan
and control complex supply chain structures, powerful
management decision support is needed.
Advanced planning systems can thereby be used
as a tool to meet the ever-increasing demands for
effectiveness that put new pressures on swift and
efficient planning and control of the supply chain. APS
as a DSS for production and distribution planning is,
however, still a new and fairly unexplored tool. This
article thereby enhances the understanding of APS
and its use in practice through a detailed case study.
The article is therefore of value to both academics and
practitioners, since it provides an illustration of how
state-of-the-art commercial software is applied in a
practical setting.
Finally, this research indicates that APS together
with a centralised MP function contributes to the
restructuring of the supply chain with more efficient
operations as a result. The conclusion that can be
drawn from this study is that positive effects on
throughput and inventory levels, and thus supply chain
performance, have been realised with the aid of an
APS. However, a prerequisite is that the organisation
employing the APS is operated effectively, in this case
through the restructuring of the supply chain and the
centralisation of the MP function. To further the
research on APS and its use in practice, more case
studies need to be carried out. The case in this study
depicts a supply chain with fairly low complexity in
terms of product structure and number of sites.
The complexity lies in the highly seasonal demand
and the vast amount of transportation links in the
166
M. Rudberg and J. Thulin
supply chain. Case studies focusing on how APS are
used in more complex settings are of utmost interest
and also cases focusing more on implementation and
information system integration issues are needed to
further extend the body of knowledge concerning APS
implementation and use.
Downloaded By: [Rudberg, Martin] At: 07:48 23 February 2009
Notes on contributors
Martin Rudberg is an associate professor at the Department of
Management and Engineering, at
Linköping University in Sweden.
He received an MS in Industrial
Engineering and Management, and
a PhD in Production Economics,
both from Linköping Institute
of Technology, Sweden. He has
published in International Journal of Operations &
Production Management, International Journal of Production
Economics, International Journal of Production Research,
Omega, Production Planning & Control, International Journal
of Manufacturing Technology and Management, and
Integrated Manufacturing Systems. Dr Rudberg’s research
interests include advanced planning systems, operations
strategy and supply chain management.
Jim Thulin received an MS from
Linköping University and spent a
few years as research assistant before
he joined Lawson Software Sweden
AB. Mr Thulin is currently a senior
business consultant at Optilon, with a
special focus on advanced planning
systems.
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