Linköping Studies in Science and Technology
Thesis No. 1654
Supply chain coordination using optimal
transfer pricing to balance co- and byproduct demand within a process industry
Martin Kylinger
2014
Division of Production Economics
Department of Management and Engineering
Linköping University, SE-581 83 Linköping
© Martin Kylinger 2014
Linköping studies in science and technology,
Thesis No. 1654
ISBN: 978-91-7519-358-8
ISSN: 0280-7971
Printed by: LiU-Tryck, Linköping
Distributed by:
Linköping University
Department of Management and Engineering
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Abstract
Process industries has for long been important for the development of Swedish industry
and society. All industries face different conditions that affect how to best run their
operations. This thesis aims to describe some of the conditions that characterize process
industries compared to other industries. Further one of these characteristics has been
studied more closely.
One of the traits of process industries is that they are positioned at the start of the
transformation process close to the raw material mixing, separating or forming it into
products often used for further transformation. Process industries hence become
dependent on the properties of these materials. One of the most prominent
characteristics inherent from the raw material properties is the divergent bill of
material. The divergent bill of material originates from the fact that a given raw material
is made up of different components that will yield several products with different
characteristics when processed. When splitting the raw material into the desired
products the yielded products from a certain raw material usually have different value
to the producer, some more desired than others. These multiple products generated
poses a challenge from a planning perspective raising questions like “How should we
balance the supply and demand of all the products produced?”, “What shall we do with
the excess products produced?”.
The first paper in this thesis describe the production planning in four Swedish process
industries with the ultimate aim to connect their planning to the supply chain
characteristics they face as process industries. The study concludes that the industry
specific conditions mainly affect planning at short time ranges when planning becomes
more detailed. In general the use of planning or decision support systems is low,
stemming from a, warranted or not, belief that general decision support systems do not
fit process industries. Another finding is that the case companies mainly operate in niche
markets. This study also highlighted that the planning complexity arising from
characteristic of co- and by-product generation in combination with the lack of decision
support systems requires further studies.
The subsequent two papers focus on supply chain planning and coordination with a
divergent bill of material. They present a mathematical model over the supply chain
planning in a real case company in the specialty oils industry. The second paper
investigates transfer pricing as a coordination tool by comparing decentralised supply
chain planning with centralized planning in an integrated model. Transfer pricing is
found to have a potential positive effect on supply chain planning while simultaneously
creating problems in terms of uneven distribution of the contribution margin among
supply chain partners.
Finally the third paper more closely investigates different ways to set transfer prices and
comparing them to the optimal transfer prices. Setting optimal transfer prices with a
divergent bill of material has proven to be less straightforward than the case with no
dependencies between products. Some optimal transfer prices could even be set lower
than the marginal cost for producing them due to the dependency between the products
in the divergent bill of material. This indicates that there is an opportunity cost for a
product that is dependent on the demand of other products.
Sammanfattning
Processindustrier har länge varit viktiga för utvecklingen av svensk industri och det
svenska samhället. Alla branscher möter olika förutsättningar som påverkar hur
verksamheten bäst skall drivas. Avhandlingen syftar till att beskriva några av de villkor
som kännetecknar processindustrin jämfört med andra branscher. Slutligen har en av
dessa kännetecknande egenskaper studerats närmare.
En av de egenskaper som utmärker processindustrier är att de är positionerade vid
början av omvandlingsprocessen nära råvaran och blandar, separerar eller formar den
till produkter som ofta används för ytterligare förädling. Processindustrin blir därmed
beroende av egenskaperna hos råmaterialet som används. En av de mest framträdande
egenskaperna härrörande från dessa råmaterialegenskaper är det divergenta
materialflödet. Det divergerande materialflödet har sitt ursprung i att ett visst
råmaterial består av olika komponenter som ger flera produkter med olika egenskaper.
När råmaterialet separeras i sina komponenter erhålls flera produkter som kan ha
väldigt olika värde för producenten. Efterfrågan på de produkter som erhålls ur
separeringen kan vara vitt skild från vad som erhålls ur produktionsprocessen. Denna
obalans mellan efterfrågan och försörjning är en utmaning ur ett planeringsperspektiv
och väcker frågor som " Hur ska efterfrågan och försörjning av de produkter som
produceras balanseras?", "Hur skall överskottet på vissa produkter hanteras? ".
Den första artikeln i denna avhandling beskriver produktionsplaneringen i fyra svenska
processindustriföretag inom områdena pappersmassa, specialkemi, specialoljor samt
papper och pappersmassa. Målet med studien var att jämföra planeringen av
försörjningskedjan med de förutsättningar som processindustrin ställs inför. I studien
dras slutsatsen att de branschspecifika förhållandena främst påverkar den kortsiktiga
planeringen med mer detaljer. I allmänhet verkar användningen av planerings- eller
beslutsstödsystem vara låg, som härrör från en, befogat eller inte, tro att generella
beslutsstödssystem inte passar processindustri. Fallföretagen förefaller också
huvudsakligen vara verksamma på nischmarknader. Denna studie betonar också vikten
av djupare studier av energiplanering samt den planeringskomplexitet som uppkommer
till följd av divergerande materialflöden.
De två efterföljande artiklarna fokuserar på planering och koordinering av
försörjningskedjan vid ett divergerande materialflöde. De presenterar en matematisk
modell över försörjningskedjan i ett fallföretag som tillverkar specialoljor. Den andra
artikeln undersöker effekter av att använda internprissättning som verktyg för
koordinering
av
försörjningskedjor
genom
att
jämföra
decentraliserad
försörjningskedjeplanering med en fullt integrerad centraliserad planering.
Internprissättning visar sig ha potentiellt positiva effekter på planeringen av
försörjningskedjan men skapar samtidigt problem i form av att täckningsbidraget kan
fördelas väldigt ojämnt mellan partner i försörjningskedjan.
Slutligen behandlar den tredje artikeln olika sätt att bestämma internpriser och
utvärderar deras koordineringseffekter på försörjningskedjan genom att jämföra med
optimal satta internpriser. Att bestämma optimala internpriser med ett divergerande
materialflöde har visat sig bli komplicerat i och med det beroende som finns mellan
produkterna som kommer från samma råmaterial. Optimala internpriser har visat sig
kunna vara både avsevärt högre och avsevärt lägre än marginalkostanden för att
producera dem. Detta indikerar att alternativkostnaden för en produkt kan påverkas
starkt av efterfrågan på andra produkter den har en koppling till via råmaterialet de
båda produceras från.
Foreword
Working on this thesis has been a bumpy ride and I have often doubted the sanity of
embarking on this journey. In spite of all obstacles, frustration, and disappointments I
have learnt some valuable lessons about research and life itself. On my journey to
finishing this thesis I have encountered many people that helped me on my way in one
way or another.
A very warm thank you goes to Mario Guajardo for the fantastic experience of a great
collaboration resulting in the two main articles of this thesis. Thank you for all your
efforts, interesting discussions, and good ideas. Additional thanks goes to the co-authors
of one of the papers Johan Johansson and Martin Waldemarsson, thank you for the good
cooperation, all the fun and all the support you have provided.
My supervisors Mikael Rönnqvist and Mathias Henningsson also deserve lots of thanks
for their support in me choosing which path to go down. You have brought me back on
track when I have made a wrong turn and have helped me find meaning in what I do.
I also thank all those people at the companies that have invited me and shared their
precious knowledge and time.
Every day my colleagues at Productions Economics have made coming to work a
pleasure. I have especially enjoyed my fellow PhD students, the ones still at Production
Economics as well as those that have moved on to other challenges. I thank you for all
the support and sharing of your experiences in research as well as in life.
My most precious thanks go to all my family, both those present and those not yet here.
During the work with this thesis my parents Laila and Max have been a great support
both mentally as well as helping out with everyday life and various emergencies. I am
especially grateful for my wife Malin and my son Jesper for showing me what life is all
about and what is important. You have suffered from my frustrations stemming from
this work but you have always been my highest priority and will always be.
Thesis outline
This thesis entitled Supply Chain Coordination Using Optimal Transfer Pricing to Balance
co- and by-product Demand Within a Process Industry is a licentiate thesis in Production
Economics at Linköping University. The research is performed within the Process
Industry Centre (PIC) supported by the Swedish Foundation for Strategic Research
(SSF).
The thesis is comprised by two parts, where the first part is an introduction to the
setting wherein the research has been conducted and is relevant while the second part
consists of three appended papers. The first part aims to give a background to the
studied area as well as an introduction to the theoretical foundation the papers are built
upon. Scope and research questions to be studied are followed by a presentation of the
case company. A summary of the appended papers and concluding remarks with
suggested further research concludes the first part. The second part consists of the three
appended papers listed below.
Paper 1
JOHANSSON, J., KYLINGER, M. & WALDEMARSSON M. 2012. Production planning in
process industries. Working Paper: LIU-IEI-WP-12/0002, Department of Management
and Engineering, Linköping University, Sweden.
An earlier version of this paper was presented at NOFOMA 2011 Conference, 9-10 June,
2011, Harstad, Norway.
Paper 2
GUAJARDO, M., KYLINGER, M. & RÖNNQVIST, M. 2013b. Speciality oils supply chain
optimization: From a decoupled to an integrated planning approach. European Journal of
Operational Research, 229, 540-551.
An earlier version of this paper was presented at INFORMS Annual Meeting 2011, November
13 – 16, 2011, Charlotte, North Carolina, USA.
Paper 3
GUAJARDO, M., KYLINGER, M. & RÖNNQVIST, M. 2013a. Joint optimization of pricing and
planning decisions in divergent supply chain. International Transactions in Operational
Research, 20, 889-916.
Content
1
Introduction .............................................................................................................................................. 1
1.1
Background and Context ............................................................................................................. 1
1.1.1
Importance of Swedish Process Industry and Relevance of Study .................... 1
1.1.3
Production and Raw Material Characteristics ........................................................... 6
1.1.2
1.1.4
1.1.5
2
Operations Research in Supply Chain Planning ........................................................ 8
Scope and Objective ...................................................................................................................... 8
2.1
Divergent Bill of Material ............................................................................................................ 9
Frame of Reference ................................................................................................................................ 9
2.1.2
2.2
Raw Material Properties and Divergent Bill of Material ........................................ 9
Fixed Proportions Between co- and by-products in Petroleum Industry .... 10
Supply Chain Management ...................................................................................................... 11
2.2.1
Strategic / Long-term Planning .................................................................................... 13
2.2.3
Operational / Short-term Planning ............................................................................. 14
2.2.2
2.2.4
2.3
Tactical / Mid-term Planning......................................................................................... 14
Managing Divergent Supply Chains ............................................................................ 15
Operations Research in Petroleum Supply Chain Management ............................... 16
2.3.1
Procurement and Production Planning Tasks ........................................................ 17
2.3.3
Sales and Optimal Pricing ............................................................................................... 19
2.3.2
Distribution .......................................................................................................................... 18
2.4
Transfer Price Coordination and Collaboration in Supply Chains ........................... 20
3.1
Scope ................................................................................................................................................ 23
Research Challenge ............................................................................................................................. 23
3.2
Research Design .......................................................................................................................... 23
3.2.1
4
Transfer Pricing and Coordination of Decoupled Supply Chains ....................... 8
1.2
2.1.1
3
Process Industry Characteristics..................................................................................... 3
3.2.2
Research Objectives and Research Questions ......................................................... 24
Choice of Methodology ..................................................................................................... 24
Description of Case Industry ........................................................................................................... 27
4.1
General Description ................................................................................................................... 27
4.3
Planning Processes, Forecasting, and Planning Horizon ............................................. 28
4.2
Case Company Supply Chain................................................................................................... 27
5
4.4
Experienced Difficulties at Case Company ........................................................................ 29
5.1
Paper 1 ............................................................................................................................................ 31
5.3
Paper 3 ............................................................................................................................................ 33
Summary of Papers ............................................................................................................................. 31
5.2
6
Paper 2 ............................................................................................................................................ 32
Concluding Remarks and Further Research.............................................................................. 35
References ....................................................................................................................................................... 37
1 Introduction
Coordinating activities along the supply chain is essential for the profitability for all
companies. Supply chains often span over several parties both within the same
organisation as well as between different organizations. When supply chain entities are
partially or completely independent from each other planning in the different entities
has to be coordinated in order to achieve as good overall performance as possible.
Coordination such a decoupled supply chain involves one or several mechanisms
aligning decisions in two or more directly interacting entities with the aim to achieve a
common goal. Transfer pricing is such a mechanism that provides an interface between
two different supply chain partners connecting decisions between them. Hierarchical
planning where long term plans set frames for shorter time periods is another way of
coordinating supply chain entities.
Challenges faced by a company when planning their supply chain vary from company to
company depending industry specific as well as company specific conditions. Process
industries are fundamentally different from assembly and manufacturing industries and
are often found in the beginning of the supply chain processing raw material and are
therefore affected by the properties inherent in the raw material. Not all process
industries are similar but share several characteristics that are found less frequently in
other industries.
1.1 Background and Context
To succeed in your industry alignment between your business environment and your
operation strategy is a key ingredient. (Safizadeh et al., 1996; Jonsson and Mattson,
2003; Platts et al.,1998). Process industries, like all other industries, face conditions that
are common or even unique for their specific industry. Process industries are
predominantly found in the beginning of the supply chain processing raw material into
products often used for further processing. The Association for Operations Management
(APICS) defines process industries as companies that: “produce products by mixing,
separating, forming and/or performing chemical reactions.”(Blackstone et al., 2005).
Process industries are often divided into batch and continuous flow industries Fransoo
and Rutten (1994) point out differences between these two groups. What separates
them from discrete manufacturing though is that the unit of measure is continuous and
not discrete. Even if the main part of the production process is continuous most process
industries however have some point where their products become discrete as
acknowledged by for example Pool et al.(2011).
1.1.1 Importance of Swedish Process Industry and Relevance of Study
Process industries have for long been important for the industrial development of
Sweden and is often said to have built Swedish welfare. Many provincial towns were
also built around process industries. (IVA, 2006; Skogen och kemin, 2006; Ministry of
Enterprise Energy and Communications, 2001; ITPS, 2006)
1
In a study of Swedish process industry the Royal Swedish Academy of Engineering
Sciences establish the importance of Swedish process industry for the Swedish society
and industry. Process industries account for 30% of Swedish exports and 60% of the net
export while directly employing 320 000 people. Including equipment suppliers, service
providers and other indirect industries the total number of people employed has been
estimated to be as high as 640 000. Process industries also accounts for 85% of
transported volumes in Sweden. (IVA, 2006)
In line with the identified importance of process industry for Swedish industrial
development and competitiveness the Swedish Foundation for Strategic Research
initiated two research centres connected to process industry. More recently Process
industry IT and automation has been identified as a strategically important area in an
extensive effort to strengthen Swedish growth and competitiveness.
Conditions for the development of competitive process industries in different regions
vary from industry to industry but many process industries have been and still are
dependent on proximity to raw material and suitable energy sources. Raw materials
used for several process industries are accessible in Sweden such as forests and many
important minerals. What Sweden lack though is a sufficient supply of crude oil but still
harbour crude oil refineries. Historically Sweden has had access to suitable and cheap
energy sources such as hydro power, which helped make Swedish process industries
competitive. Recent studies conflict in their view but indicate that the energy cost for
Swedish process industries are in line with European competitors while being higher
than for example for Canadian and U.S. competitors (Skogen och kemin, 2006, IVA,
2006). Sweden has a relatively well developed road network even in sparsely populated
areas, aiding the raw material dependent industry.
According to a report released by the Government Offices of Sweden (Communications,
2001) the situation for Swedish process industries differs between industries but some
common characteristics can be seen for the investigated process industries. For some
process industries Sweden has a higher added value per employee but it is not a general
conclusion for all process industries when comparing to the strongest competitors. The
degree of automation in Swedish process industry is though relatively high. The study
also concludes that Swedish process industries are prominent when it comes to
environmental thinking and development of technology to reduce environmental
impact.
Swedish process industries are characterised by a high degree of automation and
technologically advanced facilities with relatively high productivity compared to
worldwide competitors. Another trait of Swedish process industry is a high degree of
niche products made possible by good access to well educated staff and strong research
organizations. Larger than average proportion of niche products is also supported by a
well developed cooperation with suppliers and customers. The close relationship with
equipment suppliers is one of the key factors to the high level of technology in the
2
facilities and has contributed to building a strong base of equipment manufacturers for
Swedish industry in general.
1.1.2 Process Industry Characteristics
Most attempts in literature to describe what characterises process industries identify
what differentiates process industries from discrete manufacturing firms. These
differences will be the starting point for describing the conditions process industries
operate under and affects their planning.
The two most commonly used dimensions to differentiate process industries in
literature are marketing environment and manufacturing environment. Marketing
environment is also denoted product structure and manufacturing environment denoted
process structure. Henceforth marketing environment and manufacturing environment
will be the concepts used. Hayes and Wheelwright (1979) use the same dimensions
when suggesting a product-process matrix describing the usually preferred relationship
between product structure (market environment) and process structure (manufacturing
environment). Market environment range from low volume, one of a kind, to high
volume commodities while the dimension manufacturing environment range from job
shop with multipurpose equipment to flow shop with highly specialized equipment.
Taylor et al. (1981) have mapped different industries into this matrix as seen in Figure 1,
indicating that process industries in general tends to end up towards the flow shop and
commodity product end of the spectrum.
Product Structure / Market Environment
Custom
Process Structure /
Manufacturing Environment
Job Shop
Low Volume
Differentiated
High Volume
Differentiated
Commodity
Aerospace
Industrial Machinery
Machine Tools
Drugs
Specialty Chemicals
Electrical and Electronics
Automobile
Tire and Rubber
Steel Products
Major Chemicals
Paper
Containers
Brewers
Oil
Oil
Flow Shop
Steel
Forest Products
Figure 1. Product-process matrix influenced by Taylor et al.(1981).
Most mapped industries end up on or close to the diagonal of the matrix indicating that
there is a direct connection between market environment and the preferred choice of
process. Safizadeh et al. (1996) concludes that best performance is achieved if process
3
choice is aligned with market environment according to Hayes and Wheelwright (1979)
product-process matrix. Some profitable process industry companies found off the
diagonal are niche companies where process and equipment are similar to the
commodity producers while their market environment has many traits similar to
custom products. However stepping away from the diagonal must be a conscious choice
made with knowledge about the implications of such a shift.
Process industries are fundamentally different from fabrication and assembly industries,
operating in different marketing, manufacturing and financial environments (Taylor et
al., 1981). Seeing process industries as a homogenous group is though a doubtful
simplification that often is too simple. This can also be seen in that more resent
literature do not deal with process industry as a group but rather as separate industries,
albeit still with many common traits.
When trying to distinguish process industries from fabrication and assembly industries
Taylor et al. (1981) use the dimensions found in the product-process matrix as a base
and above that add the financial environment. Taylor et al. (1981) study these
dimensions one at a time to synthesize what characterizes process industries. Market
environment characteristics they contrast for commodity products compared to custom
made products are listed in Table 1.
Table 1. Commodity products market environment according to Taylor et al.(1981).
•
•
•
•
•
•
•
Marketing Environment for Commodity Products
Marketing Emphasis on Product availability and Price
Few Products
Few Product design changes
Derived Demand
High Sales Volume
Low Unit Value
Relatively High Transportation Costs
Manufacturing facilities are often classified according to the choice of production
process, ranging from job shops with multipurpose equipment at one end to flow shops
with highly specialized equipment in the other end. Process industries tend to be
toward the flow shop end of the scale. Taylor et al. (1981) contrasts job shops and flow
shops environments on a number of areas such as fixed or variable routings, layout,
flexibility of equipment, volume, lead time to increase capacity. Further they highlight a
number of characteristics from both job shop and flow shop that are frequently found in
process industries. They classify process industries in general as flow shops producing
commodity products and what they think are the most important manufacturing
environment characteristics for process industries are summarized in Table 2.
4
Table 2. Characteristics of process industry manufacturing environment according to
Taylor et al. (1981).
•
•
•
•
•
•
Process Industry Manufacturing Environment
Variability in Raw material Quality
Variability in Bill of Material
Product Yields May Vary Widely
May Have Large Demands for Intermediate Products
Co-product Demand Must be Balanced
Products or Raw Materials May Have a Shelf Life
Fransoo and Rutten (1994) have condensed process industry characteristics and give
examples of specific process industries where these characteristics are present. Some of
these are listed in Table 3.
Table 3. Process industry characteristics, adapted from Fransso and Rutten (1994).
Characteristic
Variable yield
Example of Industry
Chemical industry
Variable quality
Oil, forest products
Variable quantity/availability
Coffee, agricultural industry
Variable recipe
Oil, (animal) food industry
Divergent BOM/by-products
Oil, forestry
Unit of measures/batch problem
Fine chemicals, drugs
Safizadeh et al. (1996) list the extreme demand characteristics, their required
competitive priorities and process type attributes typical for job shops and continuous
flow shops. Ashayeri et al. (1996) makes a similar comparison, divided into four
categories; Relationship with the market, The production process, Quality and Planning &
Control.
Finch and Cox (1988) explore planning and control in six process industry companies
with the goal of describing how planning and control functions should be accomplished
in process industry firms. They identify factors that influence planning and control
functions and how they are related to planning and control. Among other they identify
type of bill of material as important for planning system requirements. The divergent
bill of material is usually used for basic producers and converters in which category
most process industries end up in. The divergent bill of material refers to the increasing
numbers of products produced from a single raw material, often described as a V shape.
Characteristics frequently emphasized in literature as present in process industries have
been divided into three categories; production and raw material, sales and distribution
and financial. These are summarized in Table 4.
5
Table 4. Process industry characteristics.
Category
Process Industry Characteristics
Production and raw material
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Divergent BOM/co- and by-products
Variable yield from raw material
Variable quality
Low flexibility
Product layout
Specialized equipment
Fixed routings
Variable yield from process
Variable recipe
Long setup times / High change-over cost
Sequence dependent setup times
Energy intensive
High degree of automation
Capacity constrained
Distribution and sales
•
•
•
•
•
•
•
•
•
•
•
•
•
Commodity products
Few products
High volumes
Long life cycles
Co-/by-product demand must be balanced
Low variance in demand
Relatively high transport costs
Market emphasis on product availability and price
Efficiency as competitive priority
Consistent quality as competitive priority
Low unit cost as competitive priority
Timely delivery as competitive priority
May have large demands for intermediate products
Financial environment
• Capital intensive
• High fixed cost
• Long lead times for capacity increases
Several of these characteristics are commonly, but not exclusively, found in process
industries and the characteristics themselves constitute an interesting area to study.
Even if different sets of characteristics are found in different process industries they still
set conditions that are common for many process industries.
1.1.3 Production and Raw Material Characteristics
Several of the characteristics for process industries are inherent from raw material
properties and also affect the design of the production process. Some of these will be
discussed in this chapter as well as what effect they have on planning of production and
distribution.
One of the traits of process industries is that they are positioned at the start of the
transformation process close to the raw material mixing, separating or forming it into
products often used for further transformation. Process industries hence become
dependent on the properties of these materials in varying degree. Since the raw material
is acquired from natural sources, such as mining operations, its content can vary very
6
much depending on where it originates from. For instance crude oils from different oil
fields have very different proportions of hydrocarbons and content of impurities such as
sulphur. Variations in amount of desirable product yielded from a specific production
process create uncertainties that have to be considered in the planning stage. Variable
quality of the raw material is a similar source of uncertainty that has to be taken into
account. Variations in moisture, acidity, colour, viscosity or concentration of the active
ingredient are all examples of quality variations affecting the production process and
planning. (Rutten and Bertrand, 1998; Taylor et al, 1981)
In many process industries recipes can be varied to obtain different proportions of
output or to alter the properties of the output to fit current product quality
requirements. Recipes can also be used to minimize material cost by considering
changes in raw material prices or variations of raw material availability (among others
Akkerman et al 2010; Floudas and Lin, 2005; Fumero et al, 2012 and Rutten and
Bertrand, 1998).
Another characteristic that certainly separates planning in process industries from
manufacturing or assembly industry is the divergent bill of material or the existence of
co- or by-products resulting from production processes separating raw material into
several products based on raw material content. Co-products are products
simultaneously generated from a production process with similar value to the producer
and all both be the trigger of production. By-products are products generated as a side
effect from producing the product that triggers production but can still bring a
significant value. Apart from co- and by-products waste products with very low or even
no value can be generated. These intermediate products are sometimes further mixed or
processed to create a larger variety of final products with specific properties.
A divergent supply chain could be characterised by both an increasing number of
physical locations as well as an increasing number of products downstream. Most supply
chains contain a distribution network resulting in an increasing number of locations. In
some cases slight variations of the same product also create an increasing number of
products. What will be referred to as divergent henceforth is the increasing number of
products derived from splitting a single raw material into several products and more
specifically when raw material content dictate what co- and by-products can be obtained
from a certain raw material. This will be further described in Chapter 2.1.
From a planning perspective it is important to balance the demand of the final products
with the availability of intermediate products. Variation of recipes is one way of dealing
with this problem. When the possibility of varying the recipe is limited the creation of
co- and by-products is highly dependent on the content of the raw material. Fixed
proportions between co- and by-products can create an unbalance between demand and
supply of several products (e.g. Taylor et al. 1981, Duncan, 1983, and Bertrand and
Rutten, 1998). Co-product demand can sometimes be balanced through affecting
demand by pricing products to balance supply and demand. (eg. Gallego and Ryzin, 1997
and Elmagrhraby and Keskinocak, 2003)
7
1.1.4 Transfer Pricing and Coordination of Decoupled Supply Chains
Transfer price is the price paid by one entity of a firm to another entity of the same firm
when exchanging goods or services. Transfer prices are used when separate entities of a
company are responsible for their own financial results or when operations cover
several taxation zones as well as most of the execution of their operations.
Herein a decoupled supply chain will denote a supply chain whose entities are partly
planned and operated independently from each other. Even if the entities of the supply
chain belong to the same company and have some common planning and control at
mainly longer time horizons they are themselves responsible for being profitable and
running the operations as efficient as possible given certain frames. Typically
independence increase the shorter time frame the decision considers. When exchanging
goods or services between two entities in a decoupled supply chain they to some degree
act as supplier and customer towards each other.
Between two more or less independent entities in a supply chain a transfer price can
increase transparency of costs and customer demand in a supply chain. Transfer prices
can also work as a control mechanism to induce certain behaviour from supply chain
partners. In either way the decisions of both upstream and downstream supply chain
partners will be affected by the transfer prices they face.
1.1.5 Operations Research in Supply Chain Planning
Operations research has supported supply chain planning for a long time. Advanced
Planning Systems (APS) have in the past years become a supplement to enterprise
resource planning system, coordinating flows, exploiting bottlenecks and keeping due
dates. APS benefit widely from using operations research tools to support planning in
the supply chain and are in fact vital part of an APS. (Stadtler and Kilger, 2008)
Operations research is still also used for supporting specific planning problems even if
not part of an integrated system as APS. Even though not specifically developed for it
many tools within operations research can be used to support planning with a divergent
bill of material. These tools can be used to solve real world problems as well as gain
theoretical insights about relationships within the supply chain.
1.2 Scope and Objective
The scope of this thesis covers supply chain planning at tactical or mid-term level in a
process industry with a divergent bill of material. More specifically the thesis will be
focused on supply chain coordination using transfer pricing to balance co- and byproduct demand.
This study will be performed in a set of Swedish process industry companies with the
overall objective to investigate supply chain planning in process industries in general
and more specifically transfer pricing coordination in a setting with a divergent bill of
material.
8
2 Frame of Reference
The aim of this chapter is to explain the context in which the thesis is relevant as well as
give a basic literature background for the papers it is based on. First the origin of
divergent bill of material and unbalanced co- and by-product demand is explained. The
second part will be devoted to different aspects of supply chain management. One view
of supply chain management and its planning tasks will be the base for further
distinguishing what characterizes supply chain management with a divergent bill of
material. Operations research tools frequently used in supply chain management will be
explained and last supply chain coordination and transfer pricing will be discussed.
2.1 Divergent Bill of Material
Several of the characteristics for process industries are inherent from raw material
properties and also affect the design of the production process.
2.1.1 Raw Material Properties and Divergent Bill of Material
One of the traits of process industries is that they are positioned at the start of the
transformation process close to the raw material mixing, separating or forming it into
products often used for further transformation. Process industries hence become
dependent on the properties of these materials in varying degree. Since the raw material
is acquired from natural sources, such as mining operations, its content can vary very
much depending on where it originates from or when it is acquired. For instance crude
oils from different oil fields have very different proportions of hydrocarbons and
content of impurities such as sulphur. Variations of amount of desirable product yielded
from a specific production process create uncertainties that have to be considered in the
planning stage. Variable quality of the raw material is a similar source of uncertainty
that has to be taken into account. Variations in moisture, acidity, colour, viscosity or
concentration of the active ingredient are all examples of quality variations affecting the
production process and planning. (Taylor et al., 1981; Rutten and Bertrand, 1998)
In many process industries recipes can be varied to obtain different proportions of
output or to alter the properties of the output to fit current product quality
requirements. Recipes can also be used to minimize material cost by considering
changes in raw material prices or variations of raw material availability. (e.g. Rutten and
Bertrand, 1998; Floudas and Lin, 2005; Akkerman et al. 2010; Fumero et al., 2012)
Another characteristic that certainly separates planning in process industries from
manufacturing or assembly industry is the divergent bill of material or the existence of
co- or by-products resulting from production processes separating raw material into
several products based on raw material content. Co-products are products
simultaneously generated from a production process with similar value to the producer
and can both be the trigger of production. By-products are products generated as a side
effect from producing the product that triggers production but can still bring a
significant value. Apart from co-and byproducts waste products with very low or even
9
no value can be generated. These intermediate products are sometimes further mixed or
processed to create a larger variety of final products with specific properties.
From a planning perspective it is important to balance the demand of the final products
with the availability of intermediate products. Variation of recipes is one way of dealing
with this problem. When the possibility of varying the recipe is limited the creation of
co- and by-products is highly dependent on the content of the raw material. Fixed
proportions between co- and by-products can create an unbalance between demand and
supply of several products (e.g. Taylor et al. 1981; Duncan, 1983 and Bertrand and
Rutten, 1998). This characteristic is often referred to as balancing co- and by-product
demand. Co- and by-product demand can sometimes be balanced through affecting
demand by pricing products to balance supply and demand (e.g. Gallego and Ryzin, 1997
and Elmagrhraby and Keskinocak, 2003).
This phenomenon is frequently occurring in process industries as for example steel-,
forestry-, chemical- and oil industry (Lasschuit and Thijssen, 2004, Viswanadham and
Raghavan, 2000, D'Amours et al., 2008, Paiva and Morabito, 2009). A steel slab can be
rolled into plates of different thicknesses which then can be cut into different lengths
and widths. Additional processing to give the plates a variety of characteristics further
increases the number of products. Timber can be cut into boards of different dimensions
and quality while other parts of the tree are used for pulp or energy production. Crude
oil is made up of a variety of hydrocarbons that is specific for every oil field. Distillation
of crude oil into desired fractions is therefore affected by the content of the crude oil and
several different fractions are always obtained, with wanted or unwanted properties. An
example from the specialty oils industry is given in chapter 2.1.2.
2.1.2 Fixed Proportions Between co- and by-products in Petroleum Industry
In the case of steel industry you are rather free to decide what intermediate and final
products you want to produce from a slab. Of course there are some restrictions given
by the production process, and raw material input but within physical limits you are free
to choose thickness and combination of dimensions to be obtained from a certain slab.
When distilling crude oil you are highly restricted by the content of hydrocarbons
therein. The raw material to a large extent dictates combinations of fractions and
proportions of them that are obtainable. Heavy hydrocarbons can be cracked into lighter
hydrocarbons in a subsequent hydrocracking process. The opposite is however not
reasonable to any larger extent.
Figure 2 illustrates the divergent bill of material with fixed proportions through an
example with distillation of crude oil into different fractions. There is some flexibility on
what fractions to extract from a crude oil. Altering which crude oil is used for distillation
will change the proportion between different fractions obtained. By changing the cut
between fractions it is also possible to change what fractions are obtained but for a
certain set of fractions the proportions is always the same for a given crude oil. If more
of one fraction is needed more of the other fractions will also be produced. If the
proportions of demand of the different fractions is different from the proportions
10
obtained from the distillation process it creates shortages of some fractions while
creating excess of others.
Figure 2. Divergent Bill of Material with fixed proportions.
An example of different fractions obtained from distillation of crude oils given by
Bengtsson et al. (2013) can be seen in Table 5.
Table 5. Yield of fractions from different crude oils. From Bengtsson et al. (2013).
Output
Refinery gas
LPG
Light naphtha
Heavy naphtha
Kerosene
Gas oil 1
Gas oil 2
Vacuum gas oil
Vacuum residue 1
Vacuum residue 2
Crude oil 1
0.0010
0.0401
0.1389
0.3182
0.1263
0.2683
—
0.0926
0.0146
—
Crude oil 2
0.0020
0.0056
0.0287
0.1330
0.0920
—
0.3540
0.2810
—
0.1037
2.2 Supply Chain Management
Many views exist on what a supply chain is and what supply chain management
includes. This chapter aims at describing one view of what supply chain and supply
chain management is with focus on processes and their planning. Additionally the
special situation with a divergent supply chain is described.
A supply chain consists of all parties involved in fulfilling customers’ requests as well as
all functions within those parties needed to fulfil the requests. The supply chain not only
includes the flow of physical products but also the flow of information and monetary
funds. (Chopra and Meindl, 2007)
Chopra and Meindl (2007) and Fleischmann et al. (2008) among others divide supply
chain decisions into three phases depending on within which timeframe the decisions
have impact. Different names for the phases are used but the content and time frame of
11
the phases are similar. These three phases are often referred to as strategic, tactical and
operational and are described below with respect to timeframe and type of decisions
made within that phase. The phases are often tied together in a hierarchical structure
where the planning from the longer timeframe sets the limits for the shorter timeframe.
Planning a supply chain is an extensive task on all hierarchical levels not to mention
when to make them work together. Advanced planning systems (APS) is a late addition
to the family of decision support systems that can aid in the planning of an entire supply
chain, considering and coordinating decisions on different hierarchical levels. As is the
case with supply chain management APS also do not have one clear definition but
Cederborg (2010) condenses APIC’s definition to that an APS should:
1. Use advanced mathematics to perform optimization or simulation
2. Consider finite resources
3. Include at least one of the following components
a. Demand planning
b. Production planning
c. Production scheduling
d. Distribution planning
e. Transportation planning
The components listed under point number 3 above can be condensed into three “areas”
to be managed, Production, Distribution and Demand. The Supply Chain Planning Matrix
developed by Rhode et al. (2000) and reworked by Fleishmann et al. (2008) in Figure 3
also includes procurement as a task to be managed in the supply chain. The Supply
Chain Matrix is used by Stadtler and Kilger (2008) when describing the modules in an
APS.
Procurement
Production
Distribution
• Physical distribution
structure
Sales
Long -Term
• Materials program
• Supplier selection
• Cooperations
• Plant location
• Production system
Mid - Term
• Personnel planning
• Material requirements
planning
• Contracts
• Master production
scheduling
• Capacity planning
• Distribution planning
• Mid-term sales
planning
Short-Term
• Personnel planning
• Ordering materials
• Lot-sizing
• Machine scheduling
• Shop floor control
• Warehouse
replenishment
• Transport planning
• Short-term sales
planning
Flow of gods
• Product program
• Strategic sales
planning
Information flows
Figure 3. The Supply Chain Matrix, adapted from Fleischmann et al.(2008).
12
Supply Chain tasks to be managed within a company and coordinated with external
parts of the supply chain according to Rhode et al. (2000) are similar to those presented
by Cederborg (2010). The tasks can be summarized as:
•
•
•
•
Procurement
Production
Distribution
Sales and demand management
2.2.1 Strategic / Long-term Planning
Long-term planning, often referred to as strategic planning, in supply chains typically
concern decisions on design and structure of the supply chain. The objective of supply
chain design is to configure the supply chain to enable the organization to maximize its
economic performance over a considerable period of time (Goetschalcks and
Fleischmann, 2008). The planning horizon at this level is typically three to five years or
even longer. Supply chain design includes decisions such as location of manufacturing
and distribution facilities, allocations of products to manufacturing and distribution
facilities, choice of suppliers, transport mode along different routes, type of information
system to be used etc. Related decisions affecting the supply chain design are product
program, strategic sales planning, and strategic cooperation with suppliers and other
supply chain partners. These decisions have impact on long term company performance
and need to support company strategy. Since changes in the supply chain design are
very slow and expensive companies must consider uncertainties in for example market
conditions. (Goetschalcks and Fleischmann, 2008, Chopra and Meindl, 2007)
Stadtler and Kilger (2008) use the term Strategic Network Design (SND) for the APS
module that supports planning over all tasks at this level. The strategic planning in an
APS often utilizes other modules foremost designed for lower level decisions such as
master planning modules. With aggregated data such modules provide important
information to the strategic planning. A Strategic Network Design module of an APS can
aid planning by incorporating optimization models when designing the structure of the
supply chain. SND modules include a linear programming solver to find optimal flows in
a supply chain. Some SND modules also supports mixed integer programming models,
enabling them to open and close facilities within a supply chain to find the best
configuration. The main functions of an SND module are according to Goetschalcks and
Fleischmann (2008):
•
•
•
•
Generating alternatives
Evaluating alternatives
Administrating alternatives and scenarios
Reporting, visualizing and comparing results
13
2.2.2 Tactical / Mid-term Planning
Supply chain planning at mid-term level takes place within the boundaries set by the
strategic planning. Time frame for decision at this level varies a lot depending on
industry and company but typically ranges from three months to a year, taking seasonal
variations into account. The goal of planning at this level usually is to maximize the
financial contribution over the planning horizon.
Sales and operations planning determine an outline of the operations based on forecasts.
Demand planning forecasting potential sales of product groups in specific regions is an
important input to the master production scheduling and capacity planning performed
at this stage. Inventory levels and policies are decided for manufacturing and
distribution facilities along with plans for which market is supplied from which facility.
Personnel planning is derived from the capacity plan to supply the appropriate
workforce. Material requirements are derived from the master production to secure
supply of material. Contracts can be established with key suppliers for key materials and
components. Time frame is shorter than in the strategic planning and thereby
uncertainties are lower and planning should utilize any flexibility from the strategic plan
to optimize performance.
Modules in an APS supporting mid-term planning are according to Stadtler and Kilger
(2008) mainly Demand planning and Master planning modules. One important task of
demand planning is to supply information in order to make decisions on replenishment
of product inventories and raw materials before actual customer orders are received.
Demand Planning modules can support this process by supplying tools for statistical
analysis of historical data. The APS also help keeping track of aggregation and
disaggregation for time, geographical regions and products groups. Based on the
forecasts from the Demand Planning the Master Planning has the main goal to
synchronize flows in the supply chain by creating aggregated production and
distribution plans. The Master Planning module supports decisions to efficiently utilize
production and distribution resources as well as balancing supply and demand by
considering capacities. Master Planning module can use modules designed for lover level
planning such as modules for production or distribution planning to do adequate
calculations when needed. To consider bottle necks in the supply chain Master Planning
module often operates with aggregated data.
2.2.3 Operational / Short-term Planning
At this level all activities that are to be executed are planned and require high degree of
detail. Time horizon for plans at this level is daily and up to three months and is
conditioned by structure and mid-term plans. Since the level of detail at this level is
very high actual tasks to plan varies very much depending on the conditions set in
earlier stages. Sales or demand planning must be executed and differ depending on type
of manufacturing environment but has to consider how to supply customers with their
demanded products in a timely manner. How and when to replenish warehouses and
raw material stock or deliver to customers on product and location level has to be
14
decided. Manufacturing planning and control activities such as lot sizing and machine
scheduling has to be performed with respect to capacity constraints.
Actual execution of supply chain operations has been left out here since focus is on
planning but it is sometimes included as for example in Chopra and Meindl (2007).
Typical APS modules used are Purchasing and Material Requirements Planning,
Production Planning, Scheduling, Distribution Planning, Transport Planning, Demand
Planning and Demand Fulfillment and Available To Promise. Several tools to tackle these
kinds of problems have since long been readily available within operations research.
The difference now is that they are more integrated with the APS than has been the case
with their predecessors.
2.2.4 Managing Divergent Supply Chains
As described earlier a divergent flow within a supply chain can arise from either an
increasing number of products or packaging as well as an increasing number of physical
locations. What will be referred to as divergent herein is the increasing number of
products derived from splitting a single raw material into several products and more
specifically when raw material content dictate what co-and by-products are obtained
from a certain raw material.
The planning tasks to be managed in a supply chain listed in chapter 2.2 usually
corresponds to an activity including processing, moving or packaging of products and
thereby increasing the number of products. Each of these tasks can be said to make up
points of divergence. A point of divergence is an activity that increases the number of
products from a planning perspective and can hence include products with different
properties as well as the same product located at different places or in different
packaging. Figure 4 illustrates the increasing number of products through the supply
chain, starting with one or a few raw materials processed in one or several steps, each
step increasing the number of products.
The general planning task Purchasing and the purchased raw material itself does not
constitute a point of divergence since the actual increase in number of products will not
occur until the raw material is processed. However the raw material will be considered a
point of divergence since the properties of the raw material will affect what products
can be obtained through the production process. The possibility to use different raw
materials as input also has an effect on what intermediate products are obtained.
Therefore raw material is also included as a point of divergence in Figure 4. Production
can have several stages represented by several processes each of them potentially a
point of divergence. Distribution planning task involves internal distribution among
production and storage facilities as well as distribution to customers in coordination
with the sales planning task. At these two last stages the increasing number of products
is inherent from mainly different physical locations and different packaging and sizes.
15
.....
Raw
materials
Production
process 1
Number of
products
Production
process n
Internal
distribution
Sales and
distribution of
final products
Figure 4. Increasing number of products with points of divergence with general planning
tasks.
Kallrath (2002) give an overview of the current state-of-the-art planning and scheduling
systems in process industries that have developed the last 20 years. These systems are
to a large extent based on operations research tools to support planning. Whether the
concept of material requirements planning (MRP) works in process industries with
divergent bill of material and continuous flows or not have been debated with
arguments on both sides. ERP (Enterprise Resource Planning) systems based on MRP or
MRP II (Manufacturing Resource Planning) are still used in many companies to support
planning (Stadtler and Kilger, 2008). Some literature (e.g. Nelson, 1983 and Duncan,
1983) argue that MRP structure do not fit process industries at all while for example a
study of six process industries by Finch and Cox (1987) shows that MRP cannot be
rejected as unusable for all process industries. The basic structure of MRP however does
not have a perfect match with divergent bill of material as is one of the basic arguments
Powell and Robertson (1982) bring forth when arguing for the benefits of a customised
decision support system in process industries. Moreover MRP does not really fit with
continuous flows that are present within many process industries.
2.3 Operations Research in Petroleum Supply Chain Management
Within operations research several tools with well developed solution methods already
exist that can potentially be used to deal with balancing co- and by-product demand in a
divergent supply chain. For the petroleum industry in particular there are some
commonly used problem formulations that can be of use. Figure 5 is an adaption to
petroleum industry of the more general activities presented in Figure 4. The refining
activity could further be divided into several company specific sub activities. Each of
these activities represents points of divergence but also present opportunities to
balance co-and by-product demand if the preconditions are correct.
16
Number of
products
Raw
materials
Refining
Blending
Internal
distribution
Sales and
distribution of
final products
Figure 5. Points of divergence with planning tasks in a petroleum supply chain.
Operations research tools that can be used to address these problems will be described
for each of the supply chain planning tasks. Several of these formulations are of cause
already used for supply chain planning problems in process industries as well as in
other industries. Operations research is frequently used in planning in the petroleum
industry (e.g. Bodington and Baker 1990, Cooper and Charnes, 2002 and Iachan, 2009)
and this chapter will focus on operations research tools frequently used in the
petroleum industry and describe how they can be used to balance co- and by-product
demand in divergent supply chains.
Some operations research planning formulations available range over more than one of
the supply chain planning tasks but the tasks will be the starting point to finding
operations research tools to balance co- and by-product demand with a divergent bill of
material. It is only natural that the operations research formulations overlap between
the different supply chain planning tasks since the planning of one affects the other.
Therefore the tasks illustrated in Figure 5 will be condensed to three areas; Procurement
and production, Distribution and Sales.
2.3.1 Procurement and Production Planning Tasks
Procurement and production planning tasks are considered together as they are too
closely connected to be managed separately from an operations research perspective.
Procurement as a separate activity is not considered but rather the choice of raw
material to use considering what is available.
Process Adjustments
In some cases the production process, for example chemical reactions or distillation
process, can be altered to control what intermediate products are obtained. As an
example a certain crude oil can be split in different ways depending on what
intermediate products are currently desired as is illustrated in Figure 2. Simply put it is
possible to control what hydrocarbons goes into which fraction. It is however not
17
possible to send a lighter hydrocarbon to a heavier fraction in this process but rather at
which weight of the hydrocarbons to draw the line between fractions. In other cases
different secondary raw materials can be used depending on what by-products are
desired from the process.
Li et al. (2003) and Li and Hui (2007) employ marginal value analysis when deciding
what fractions to produce. Both consider fixed demand and prices and no dynamic
demand.
Raw Material and Intermediate Products
The yield of different intermediate products is different depending on the content of the
raw material processed. The ability to use different raw materials in different
proportions can hence help balancing the proportion of desired intermediate products
obtained from the first stage in the productions process to better match their demand.
Operations research formulations exist for this type of problem with different objectives
depending on type of decision to support. Common objectives for this type of
formulations are minimizing raw material cost or maximizing value of produced
intermediate products.
This kind of problem formulations can advantageously be combined with blending
problems because of their high dependency.
Blending Problem / Recipes
One of the most classical operations research planning problems within petroleum
industries is the blending problem. Blending problems are closely connected to the
formulations in previous section with very similar formulation and objectives. From a
set of raw materials or intermediate products final products are to be blended with the
objective of minimizing material cost or maximizing contribution or revenue. A final
product can be blended using different recipes with different proportions of different
raw materials or intermediate products (Audet et al., 2004). Common constraints for
this kind of formulations are; properties of final products, demand of final products and
limited availability of raw material or intermediate products.
These problems are often referred to as blending or pooling problem and can sometimes
be solved using linear programming but sometimes require non-linear formulations.
An interesting example of a pooling in the food industry is given by Akkerman et al.
(2010). They show how a reduced set of intermediate products can be used to reduce
inventory costs when blending final products using different recipes. Using different
recipes to blend intermediate products to final products present an opportunity to
potentially use this type of formulation to balance co-product demand.
2.3.2 Distribution
Distribution is a complicated planning task faced by most industries. For most process
industries the challenges differ in the respect that transport cost often constitutes a
relatively large part of the total product cost. From a divergent point of view the issue at
18
hand is a physical supply chain with an increasing number of locations to store products
in the further downstream you come and is commonly found in most industries.
Depending on planning level typical planning problems are, where to place storage
facility, how much of each product to store at each facility and when and how to
replenish those inventories.
Operations research can supply good tools for these kinds of problems and are usually
implemented in APS modules for distribution planning.
2.3.3 Sales and Optimal Pricing
An area that has been present in service industries such as airline and hotel for a long
time (Bitran and Caldentey, 2003) and lately has rendered lots of attention in a wider
spectrum of industries is revenue management. Revenue management aims at
increasing revenues without necessarily increasing sales volumes by setting different
prices for different customer segments according to their sensitivity to price. Apart from
price differentiation between customer segments common tools in revenue
management is bundling, overbooking and dynamic pricing based on time. In short
revenue management aims at selling the right product to the right customer at the right
time. Related to and part of revenue management is pricing of products to affect sales
volumes. Important in both revenue management and pricing is the identification of
customers’ sensitivity to price or their demand functions.
There is a difference between long term strategic pricing, aiming to position your
product in a marketplace, and the short term opportunistic pricing aiming to utilize
available capacity as efficiently as possible. Common for the two is tough to maximize
profit by finding the optimal level of price and quantity. In pricing optimization the
objective of the optimization model is to maximize contribution margin, i.e. revenue
minus incremental cost.
Expressed in general terms the profit of a company is equal to its revenue minus costs,
where revenues are a function of quantity and price while costs are a function of
produced quantity. Assuming the demand function can be identified the profit function
can be expressed as a function of the produced and sold quantity according to equation
(1). (e.g. Samuelson and Marks, 2006 and Salvatore, 2007)
F(Q) = R(Q) – C(Q) (1)
Profit maximizing volume is reached when the derivative of (1) is zero expressing that
the marginal revenue minus marginal cost is equal to zero. With the marginal revenue
(MR) being the added revenue for selling one more unit at each quantity Q and marginal
cost (MC) being the added cost for producing and selling one more unit at each quantity
Q. Extending to several products with independent marginal cost, MC(Qi), the same
relation as in must be valid for all products i giving (2).
MC(Qi) = MR(Qi) (2)
19
2.4 Transfer Price Coordination and Collaboration in Supply Chains
Centralized and decentralized planning and control both have their advantages. Central
planning for example is taking advantage of the overall picture while decentralized
planning is more flexible to take advantage of local knowledge. Transfer pricing is one
way of trying to coordinate a decoupled supply chain by setting transfer prices in a way
to give all parties incentives that is aligned with the overall objective. Transfer price is
the price for products or components sold by one semiautonomous division to another
semiautonomous division of the same enterprise. Here transfer price will be used to
denote a price between two closely collaborating supply chain partners whether they
belong to the same organization or not.
In an early production-pricing model Whitin (1955) describe optimal pricing in
combination with EOQ lot-sizing. Jeuland and Shugan (1983) describe joint productionpricing decision for coordination of a decoupled supply chain involving not only the
marketing perspective but also production and inventory. They model a case consisting
of a single manufacturer supplying a single product to a single retailer that face a
deterministic and price sensitive demand. Jeuland and Shugan (1983) argue that a
quantity discount to the retailer will give incentive for the retailer to price its products
in a chain optimizing way. In a critical comment Moorthy (1987) disagree with the
interpretation of the results and argue that this is only valid when marginal cost is
declining. This concave cost function arises in cases with a quantity discount as for
example the EOQ setting in Whitin (1955). Further he argues that the same coordination
can be achieved with a simple two-part tariff consisting of a fixed payment and a
transfer price equal to the marginal cost of the manufacturer. The fixed portion
suggested by Moorthy (1987) is also referred to as franchise fee by for example Weng
(1995).
Zhao and Wang (2002) consider a joint production-pricing problem in a case where the
manufacturer has outsourced distribution and retailing using a convex cost function and
as opposed to much of the earlier literature. Weng (1995) includes multiple identical
retailers in his model while Chen (2010) extends this model with differentiated retailers.
Both of the previous mentioned papers investigate supply chain coordination using
concave cost functions and deterministic demand.
Samuels and Marks (2006) as well as Salvatore (2007) discuss transfer pricing between
divisions within the same organization in a decentralized setting where the
manufacturing and retailing divisions are not coordinated centrally. To achieve overall
coordination and give incentives to the parties to act in a profit maximizing way for the
entire company they distinguish between the cases when there is an external market for
the product and when there is not. If there is an external market profit maximizing
transfer price should be equal to current market price. If no external market exist
transfer price should be set to marginal cost for the manufacturer such that total
marginal cost for the retailing division becomes MC = MCmanfacture + MCretail and thereby
price the products to the final customers as would be done in a centrally controlled
20
supply chain. Problems that arise in such a setting is how to distribute total contribution
margin in a fair way since contribution margin received from the above setting can be
lower for one of the parties than it would have been if they acted to maximize their own
contribution margin and hence has to be compensated for that loss.
In the basic case where the marginal cost of producing one more unit of a product is
independent of other products optimal transfer prices between two supply chain
partners that maximize the joint contribution margin is the marginal cost. If each
echelon in the supply chain set the transfer price to the marginal cost it will give
incentive to the entire chain to price the final product to the final customer to maximize
total contribution margin. Identifying marginal cost might however not be very straight
forward and supply chain partners might set transfer prices differently for local gain,
sacrificing overall contribution margin.
21
3 Research Challenge
Divergent bill off material is present in many process industries and balancing coproduct demand has been identified as a common feature in process industries from
literature as well as from a case in the petroleum industry. Due to the nature of the
products produced in the specialty oils industry balancing co-product demand, as
described in chapter 2.1.2, becomes an important issue. The problem arises since
production of one product triggers production of other product as well enforcing a
decision to either over produce some products or not supply all demand of others.
Operations research offers several tools to support planning in supply chains in general
as well as in the petroleum industry in particular. Even if the original idea of these tools
were not to balance co-product demand some of them can still be applied to this
particular problem. What separates this research from other transfer pricing research is
the setting with divergent bill of material. Demand has been assumed to be deterministic
and has mainly been modelled as fixed prices and volumes as well as linear demand
functions. A minor addition with exponential demand functions has been investigated as
well.
3.1 Scope
The scope of this thesis covers supply chain planning at tactical or mid-term level in
process industries. More specifically the thesis will be limited to how operations
research can support supply chain planning to balance co- and by-product demand with
a divergent bill of material. The main focus will be on transfer pricing as a tool for
coordination of decoupled supply chain planning.
3.2 Research Design
The background studies leading up to this research consist of a joint workshop between
researchers and representatives from several process industry companies as well as an
exploratory study at four process industry companies. Not all ideas and needs addressed
at the workshop and the study at the companies are dealt with in this thesis but rather a
selection of them. From the workshop, with the title Supply Chain Optimization, the need
to investigate transfer prices as a supply chain coordination mechanism was brought
forward as well as the planning difficulties that arise when faced by a divergent bill of
material. The study at the four process industry companies also made it clear that the
divergent bill of material is an area that requires further investigation.
Both the workshop and the study at the companies lead up to the formulation of the
research objectives and research questions stated in chapter 3.2.1. The process of
obtaining the objectives and research questions for this thesis as well as how the three
papers are related to the questions and how they are used to answer them is described
in Figure 6.
23
3.2.1 Research Objectives and Research Questions
The objective of this thesis is to investigate the effects of internal pricing when used as a
planning and control tool within a decoupled supply chain. It is of interest to investigate
what effects a transfer pricing system can have on the coordination of a decoupled
supply chain. Further transfer pricing in the context of a divergent bill of material with
fixed proportions is to be investigated. In addition this thesis intends to present a
general overview of supply chain planning in process industries to highlight their
planning environment.
Based on the background studies the following research questions have been
formulated.
RQ1: What effects can be seen on vertical coordination when transfer prices are used for
coordination in a decoupled supply chain?
RQ2: What effects can be seen on horizontal coordination when transfer prices are used
for coordination in a decoupled supply chain?
RQ3: How does optimal transfer pricing in the presence of a divergent bill of material
differ from optimal transfer pricing in a case with no product dependencies?
Paper 2:
Speciality oils supply chain optimization: From a
decoupled to an integrated approach
Research Questions
Background studies
Workshop
Paper 1:
RQ1
Research
objectives
RQ2
Production planning
in process industries
RQ3
Paper 3:
Joint optimization of pricing and planning
decisions in divergent supply chain
Figure 6. Research design and process.
3.2.2 Choice of Methodology
The research presented in this thesis has been conducted using a combination of case
study and mathematical modelling. Paper 1 is a descriptive case study in four process
industry companies (Yin, 2009). The reason why the first study was conducted as a
24
descriptive case study is because it aims to describe how and supply chain planning is
performed as it is in four case companies to gain understanding and identify areas of
further study.
In paper 2 and 3 mathematical modelling has been used to support answering the
research questions based on a single case company. The case studies in paper 2 and 3
are mainly of explanatory character and the mathematical models built are based on the
case company supply chain (Yin, 2009). The data set used for analysis is though only
based on company data and is not the complete set of actual data. This research can be
classified as descriptive empirical since the model aims to describe causal relationships
that exist in reality to understand the process going on.
The mathematical models are very similar in paper 2 and 3 even though the model was
made more general for the third paper. The more general model can still be used as the
first model as long as the data and interpretation of the results are adjusted accordingly.
(Meredith et al., 1989, Karlsson, 2009)
The validity of the initial study in paper 1 has been ensured by using multiple sources of
evidence including internal documentation and semi structured interviews at the
companies. Several interviewees such as plant managers, market managers, process
owners, and supply chain planers have been the main source of information. When data
had been sorted and condensed to a description of the process confirmation about its
correctness was received from each company.
Creating the mathematical model was made in several steps. As a first step information
about the real supply chain was collected through interviews and material for internal
and external education or information. From this a conceptual model was created
describing the physical structure of the supply chain as well as the relationships
between the entities. The conceptual model rather than the mathematical model was
then shared with the company for validation, a procedure favoured by for example
Robinson (2008) to increase validity and credibility of the model.
25
4 Description of Case Industry
A company in the specialty oils industry has been used to model a divergent supply
chain. The company is very suitable for the purpose of investigating divergent flows due
to their limited flexibility in production of co- and by-products from a certain raw
material.
4.1 General Description
The case company produces specialty oils products for a wide range of uses in industrial
applications and are sold worldwide. The company produces two types of products
naphthenic oils and bitumen. Bitumen products are used for paving and industrial
applications such as different kind of binders. Naphthenic products have a wide range of
uses from transformer oils and lubricants to production of rubber. Only the supply chain
for naphthenic oils has been modelled even if the two types of products share some
production resources. The products exhibit very different characteristics from a market
perspective, bitumen products mainly being a low margin commodity product while the
naphthenic products typically are high margin customized products.
The company produces about 150 naphthenic oil products and distribute them in a
worldwide logistics network linking production, storage and sales in an extensive
network. Most of the naphthenic oils have tight quality specifications and therefore
exhibit relatively high margins compared to other petroleum products.
4.2 Case Company Supply Chain
The case company’s supply chain with focus on naphthenic oils is illustrated in Figure 7.
Refining takes place in three different refineries situated around the world. In the
refineries crude oils are distilled into a number of distillates that are later hydrotreated
to enhance product characteristics and remove impurities. Some intermediate products
can then be sold as they are or be blended to create products with desired properties.
Blending of intermediate products into final products take place at two hubs that supply
around 30 depots worldwide with final products for final distribution to customers. Due
to strict quality requirements normally only one recipe per end product is in use.
Transportation is mainly carried out with ships and truck but train transports are used
for some regions. Sales of products and planning of customer deliveries is mainly
handled by a decentralised sales organization, where each seller operates rather
independent from the company. Sales are realised by sellers supplying customer
demand from a depot of their selection and arranging transport.
27
Crude oil(2)
Refineries (3+)
Naphthenic oils(12)
Hubs (2)
Blending
Sold products(~150)
Depots (~30)
Standard cost
Sales force
Customers
(~2000)
Figure 7. Supply chain at the case company.
4.3 Planning Processes, Forecasting, and Planning Horizon
Supply chain planning in the company is divided into three hierarchical levels, longterm, medium-term and short term. Mainly three parts of the company are involved,
Supply Chain, Market and Production.
Long term planning is performed over a horizon of 12 months with time buckets of 1
month and updated every month. Production and market provide forecasts of capacity
and product demand respectively to supply chain whose task it is to match these to a
feasible plan for the coming year. Individual sellers’ forecasts are in ton per product
group and are aggregated by supply chain to create the monthly updated forecast. Sellers
enter forecasts in an Excel sheet based on experience and market knowledge, no
statistical support for forecasting has been used so far.
Since the refining process will yield more than one product depending on the content in
the raw material the main aim of planning at this stage is to create a reasonable balance
between products in short supply and products in excess, trying to fulfil as much of the
demand as possible without producing too much of the products with lower demand. A
linear programming model is used at this level to ensure this mass balance and to
reduce the need to sell intermediate products as low value fuel.
During medium term planning supply chain breaks down the coming three months into
monthly delivery plans for each product per production facility. Production facilities use
these delivery plans to plan production volumes the current month with detail level
down to days or even hours.
The last stage of the supply chain planning is decentralised to sellers that finalize sales
to customers by deciding which depot to supply the customer from based on an
incentive to supply the demand in the cheapest possible way and availability. Sellers
receive a bonus mainly based on the difference between sales price and estimated cost
28
throughout the supply chain. The estimated supply chain cost is expressed as a transfer
price set centrally by the company for each product at each location. Transfer prices are
calculated and distributed using Excel sheets.
4.4 Experienced Difficulties at Case Company
The unbalance between supplied and demanded volumes of different products is
perceived as an important difficulty to handle during supply chain planning. The
company produce products based on heavy hydrocarbons, leaving very little room to
affect the proportions of the products from the distillation process through further
processing such as cracking. The strict quality requirements limits the use of multiple or
varying recipes to blend the same product, leaving very little flexibility of affecting
proportions of demand among intermediate products.
Currently mainly two types of crude oil are used in the company’s facilities. However
they are looking for alternative crude oils that suit the processes and equipment at their
facilities while also containing the desired hydrocarbons. This would increase ability to
balance supply of intermediate products with their demand, possibly creating other
difficulties of managing more crude oils. The sellers’ part in the distribution is partly
influenced by the company through a transfer pricing system. Currently the transfer
pricing system only aims at minimizing total variable cost throughout the supply chain
and no consideration is taken to excess or shortage of products to affect demand to
match supply. Opportunity cost is thus not considered.
There is an opinion in the company that the unbalance between supply and demand
caused by the divergent flow is an important matter to manage for profitability.
29
5 Summary of Papers
In this section the included papers are summarized to give a brief overview of their
content and results. How the papers are related to each other and the research
questions is described in Figure 8.
Paper 2:
Speciality oils supply chain optimization: From a
decoupled to an integrated approach
Research Questions
RQ1: What effects can be seen on
vertical coordination when transfer
prices are used for coordination in a
decoupled supply chain?
Background studies
Workshop
Paper 1:
Research
objectives
RQ2: What effects can be seen on
horizontal coordination when transfer
prices are used for coordination in a
decoupled supply chain?
Production planning
in process industries
RQ3: How does optimal transfer
pricing in the presence of a divergent
bill of material differ from optimal
transfer pricing in a case with no
product dependencies?
Paper 3:
Joint optimization of pricing and planning
decisions in divergent supply chain
Figure 8. Relation between research questions and papers.
5.1 Paper 1
Title: Production planning in process industries.
Supply chain planning with emphasis on tactical production planning in four major
Swedish process industry companies has been mapped with the purpose of analyzing
how it is related to their specific planning conditions. The aim is further to identify
similarities and differences in planning conditions and how planning is conducted
within these companies. A descriptive multiple case study approach has been used to
compare conditions and planning within each company to literature and the other case
companies.
In general can be said that the longer the planning horizon the more similar the planning
process at all companies are. When planning at an aggregated level the impact of the
industry specific conditions have lower significance while those conditions become
more important the shorter the time horizon and the more detailed the planning is. In
general the use of planning or decision support systems is low, stemming from a,
warranted or not, belief that general decision support systems do not fit process
31
industries. Another finding is that the case companies mainly operate in niche markets
to stay competitive by gaining large enough volumes within those markets. Exploiting
their strengths ranging from process knowledge to strong centralized planning enables
the companies to stay competitive.
This study also highlighted that the planning complexity arising from characteristic of
co- and by-product generation in combination with the lack of decision support systems
requires further studies. For industries generating different forms of energy as a byproduct the planning becomes even more complex if the energy is both used internally
as well as being sold.
5.2 Paper 2
Title: Specialty oils supply chain optimization: From a decoupled to an integrated planning
approach.
In this paper the tactical supply chain planning in a company producing specialty oils
has been modelled with the purpose of investigating the effects of transfer pricing as a
control and coordination tool in a divergent supply chain. The supply chain described in
chapter 4 has been modelled both as a centrally controlled supply chain and as in the
case company a decoupled supply chain.
The overall objective at tactical level is to maximize total contribution margin. In the
case company this is attempted by dividing the supply chain into two separate problems.
The first problem concerns sales where final sales and distribution planning is
decentralized to sellers. The second problem is related to supplying the forecasted and
actual sales volumes to the depots. This decentralised planning has been modelled as a
decoupled supply chain with one linear programming model describing the sales
planning and one describing the distribution planning.
The objective of the sales model is for the sellers to maximize their contribution margin
which is proportional to the sales volume multiplied with the difference between sales
price and transfer price and thereby supplying all customer demand that is profitable for
them. Distributing the demanded volumes to the correct depots to as low cost as
possible is the objective of the distribution problem. The idea behind the transfer pricing
mechanism is that the sellers’ behaviour should also make them supply all demand that
is profitable for the company. Transfer prices are set to attempt to reflect variable cost
in the supply chain with the argument that this would motivate sellers to act in a way
that maximizes total contribution margin for the company. However some shortcomings
with this system have been identified. Actual transfer prices are set a long time ahead of
actual sales and forecasts and therefore a sales transaction can be profitable for the
seller but not necessarily for the company and vice versa. Further the selected depot to
supply a customer from does not necessarily minimize the cost of supplying it due to
transfer prices not representing current actual cost.
This decoupled model has been evaluated by comparison to an integrated linear
programming planning model where each deal is evaluated from a central perspective
32
with actual variable distribution costs. Not surprisingly the findings are that the
integrated planning performs much better than the decoupled with these objectives.
There are however things that has to be mentioned. Firstly the resulting bonus received
by the sellers in the integrated model is much lower than in the decoupled version. As
the total contribution has increased in the integrated model there is of cause room to
counteract this. We suggest different alternatives of sharing the contribution margin
among different supply chain parties. Alternative contribution sharing mechanisms have
been developed by for example Frisk et al. (2010). Another advantage the decoupled
planning has is that it leaves more flexibility to the seller in their interaction with the
customers, which can be important in the short run as long as the long run. Difference in
sales price and transfer price is not the only base for bonus but it is a driving factor for
the sellers.
5.3 Paper 3
Title: Joint optimization of pricing and planning decisions in divergent supply chain.
Imbalance between supply and demand caused by a divergent bill of material with fixed
proportions between intermediate products is a common feature in the petroleum
industry. Optimal transfer pricing can potentially be used to counteract the effects this
imbalance. The studied supply chain has a decoupled planning where sales are planned,
forecasted and executed by the sales organization while internal distribution planning is
planned and controlled centrally. Further different types of demand functions have been
used as well as different ways of calculating the transfer price based on variable supply
chain cost.
Revenue management by setting transfer prices has been investigated by anticipating
sellers’ reaction towards customers on the transfer prices. By modelling the sellers’
behaviour through the transfer pricing system customer demand has been expressed as
functions of transfer prices to include sellers’ behaviour and customer demand into the
central planning model. The overall objective of the model is to maximize total
contribution margin for the company for different ways of calculating transfer prices.
The inclusion of demand as a function of the sales price and later transfer price
increases the complexity of the model to be non-linear compared to the linear model in
paper 2. Finally optimal transfer prices are included as decision variables to use for
comparison and analysis.
A traditional approach of setting optimal transfer prices is setting it based on marginal
cost. In a setting with a divergent bill of material originating from a single raw material
this approach is however not as straight forward as in a non divergent case. Some
optimal transfer prices are considerably lower than the cost based while others are
considerably higher indicating the difficulty in setting the correct marginal cost in such a
case. Marginal cost should however still be the optimizing price but when marginal cost
includes opportunity cost for reduced prices of other products the calculation of the true
marginal cost is overwhelming.
33
6 Concluding Remarks and Further Research
This thesis set out to investigate supply chain planning in process industries in general
and more specifically transfer pricing coordination in a setting with a divergent bill of
material. Mathematical modelling and optimization has been the preferred approach to
answer the questions raised in chapter 3.2.1 and repeated below.
RQ1: What effects can be seen on vertical coordination when transfer prices are used for
coordination in a decoupled supply chain?
RQ2: What effects can be seen on horizontal coordination when transfer prices are used
for coordination in a decoupled supply chain?
RQ3: How does optimal transfer pricing in the presence of a divergent bill of material
differ from optimal transfer pricing in a case with no product dependencies?
In a mathematical model simplifications has to be made regarding for example human
behaviour and the models can never be used to exactly model the reality. However
insights in what effects certain mechanisms, such as coordinating transfer pricing, has
can still been achieved.
General conclusions drawn from supply chain planning in Swedish process industries is
that the use of decision support system still is very limited and that several of them
operate on niche markets to stay competitive. The study also highlighted that the
planning complexity arising from characteristic of co- and by-product generation in
combination with the lack of decision support systems requires further studies. Energy
planning in combination with supply chain planning was also identified as important in
the future.
Transfer prices have shown to have a use for vertical coordination in supply chain
planning. Vertical coordination of a decoupled supply chain can be achieved by giving
the right incentives to downstream partners through transfer prices and contribution
margin sharing to act in a chain optimizing way. By setting transfer prices in a good way
supply chain wide contribution margin can be increased, reducing the gap compared to a
fully integrated supply chain while still taking advantage of the benefits of decentralised
decisions bring. A problem though is how to connect these prices to easily accessed
variables in the supply chain. Some literature (e.g. Samuelson and Marks, 2006 and
Salvatore, 2007) suggests setting transfer prices equal to marginal cost for production
and distribution. In this case with product dependencies this approach does however
not seem to necessarily result in good transfer prices.
Sharing of the contribution margin among supply chain partners is a way of horizontal
coordination of the studied, decoupled, supply chain. In the fully decoupled supply chain
each supply chain partner downstream the decoupling could obtain a higher
contribution margin than when coordinated by transfer prices or fully integrated.
Distribution of total contribution margin among sellers in the coordinated setting is also
very uneven, with low incentives for some sellers to stay part of the supply chain. From
35
a perspective of horizontal supply chain coordination the studied case has thus revealed
that transfer pricing has drawbacks as a tool for coordination. Coordination to maximize
contribution margin through transfer pricing has proven to require a more complex
contribution sharing mechanism than the simple proportional incentive that has been
used in the model of the case company. A contribution sharing mechanism that is fair
and thereby sustainable in the long run but still gives incentives to sellers to act in a
chain optimizing way need to contain more factors than the modelled linear
contribution margin sharing. Alternative ways of sharing the contribution margin among
supply chain partners are discussed in paper 2 and include Frisk et al. (2010).
Optimal transfer prices in a setting with strong dependencies between products as in the
case with divergent bill of material become very much more complex than for the case
with no dependencies between products. The straightforward approach of transfer
pricing by marginal cost does not work as it has been shown that the optimal transfer
price can be lower than the marginal cost for that product as well as higher than the
marginal cost. An optimal transfer price can be lower than the marginal cost because the
loss on that product is compensated on products with dependencies through the bill of
material. In the optimal cost there is hence an alternative cost attached to the product
that is associated with the demand and contribution margin of connected products.
When studying the case company supply chain other interesting aspects that could
potentially be studied have also been found. The first issue that comes to mind is if a full
scale data model of the case company could be solvable. This would first of all require a
description of demand from data available which has been proven to be a very
complicated task. Further investigations regarding how to share contribution between
supply chain partners is needed to obtain mutually beneficial incentives. Additionally it
would be interesting to investigate other control tools within divergent supply chains
than internal pricing to balance co-product demand. Akkerman et al. (2010) show how a
reduced set of intermediate products can be used to reduce inventory costs when
blending final products using different recipes. It would be interesting to investigating if
the same approach can be used to balance co-product demand caused by a divergent bill
of material.
When introducing uncertainty in demand balancing of co-product demand becomes
even more complicated. It would be interesting to investigate if postponement of the
blending point could help counteract the unbalance caused by the uncertainty. From a
practical point of view in the case company several quality issues remain to be resolved
in such a case though. On the same note the number and placement of depots to make
final delivery to customers from could have a similar effect.
36
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39
Papers
The articles associated with this thesis have been removed for copyright
reasons. For more details about these see:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105483