This document discusses quantitative methods and metrics that can be used for value chain analysis (VCA). It notes that while qualitative VCA provides useful descriptions, it is limited in its ability to measure chain performance or evaluate the impact of investments. The document then introduces some quantitative performance metrics and tools that can be used to assess value chain performance and compare different investment options for a value chain. These include metrics to measure material and financial flows, tools like system dynamics modeling to capture sector-wide dynamics and impacts of interventions over time, and other optimization models from operations research.
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Gaps in qualitative VCA
• A particular gap concerns understanding the impact of
VC investments
– The general performance of a chain
– The ability to evaluate ex-ante between different options
• Value chain analysis does a very nice job of telling
stories, of describing the chain and things that influence
it. But it is less good on measurement.
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Gaps in qualitative VCA
• A second example: let’s say you have successful started
a new value chain (maybe one of the ones from
yesterday).
• Given the tools from yesterday, how would you assess
how “successful” that chain is? What measurements
would you use to look at its performance?
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Value chain performance
• The measurement of value chain performance is a
relatively new topic (last 15 years or so).
• Early performance measures focused primarily on cost,
with little focus on non-financial indicators that could
influence the supply chain (Arayman et al. 2006).
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Value chain performance
• Types of models used to assess supply chain
performance (Arayaman et al. 2006)
– Supply Chain Operations Reference Model (SCOR)
– Balanced scorecards
– Multi-criteria analysis
– Data-envelopment analysis
– Life-cycle analysis
– Activity-based costing
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• A recent article by Trienekens et al. (2008) attempts to
do this in the context of a fruit value chain in the
Netherlands.
• Their contribution is to develop a performance-
innovation matrix that links measures of performance
with upgrading-type activities.
Value chain performance
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• Trienekens et al. (2008) first map processes within the
value chain at each node, distinguishing between
primary processes and supporting processes like in
Porter.
• They illustrate this in a performance pyramid. For each
node of the VC, companies are viewed through:
– Vision and objectives of the firm (top of the pyramid)
– Supporting processes to achieve strategic performance
– Operations (day-to-day activities)
Value chain performance
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• Trienekens et al. (2008) then look at innovation,
distinguishing between four different types:
– Product innovations
– Process innovations
– Marketing innovations
– Organizational innovations
• Do these sound familiar?
• Each of these are associated with a set of CSF and
indicators
Value chain innovation
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• A similar approach is taken for performance indicators,
classifying these according to:
– Efficiency
– Responsiveness
– Quality
– Flexibility
• Similarly, each of these are associated with a set of CSF
and indicators, some of which are qualitative and others
quantitative.
Value chain performance indicators
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• These are put together in a performance-innovation
matrix, which for each node of the VC highlights:
– Processes involved in the VC
– CSFs involved in performance and innovation at each node
– Tradeoffs between performance and innovation, and in which
processes
– Instruments to measure and improve performance and
innovation
Value chain performance-innovation matrix
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Quantitative performance measures
• Material flows are measured through the following two
measures:
1. Product quality: a set of characteristics important to
satisfy customer needs
– Normalized quality (NQ) = 1 when all criteria are satisfied
– NQ = 0 when product rejected
– NQ < 1 if some characteristics below expected quality
– NQ > 1 if some characteristics above expected quality
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Quantitative performance measures
2. Delivery value: a set of characteristics related to volumes
ordered and delivery time
– Normalized quality (ND) = 1 when all criteria are satisfied
– ND = 0 when product rejected
– ND < 1 if some characteristics below expected quality
– ND > 1 if some characteristics above expected quality
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Quantitative performance measures
• Normalized material flow performance (NMP) is
calculated by the following:
– Calculate NQ*ND; if zero, then NMP=0
– Define weights for NQ and ND and calculate the
weighted sum: wQ*NQ + wD*ND, where wQ+wD =1.
– If NMP>1 but either NQ or ND < 1, set NMP to 1.
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Quantitative performance measures
• Normalized financial flow performance (NFP) is the
actual amount of payment made by the consumer,
taking into account any delays or advancements in
payments made.
𝑁𝐹𝑃 =
𝐴𝑃𝑖(1 + 𝑟)(𝐸𝑇 𝑖−𝑇 𝑖)𝑗
𝑖=1
𝐴𝑃𝑖
𝑗
𝑖=1
Here, j=#payments, AP= actual payment, ET=expected payment time,
T=actual payment time, r= discount rate
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Source: Norina and Bailey (2005)
Balanced value: Difference
between outflow of material and
inflow of finance (for supplier);
different between inflow of
material and outflow of finance for
customer
Balanced value > 0, relative
advantage of one link in the chain
Balanced value < 0, relative
disadvantage.
Note that performance of one link
will depend on others!
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• Let’s move from performance measures to tools to
assess ex-ante the benefits and costs of different types
of VC interventions
• We will mainly focus on tools that look at the value chain
(or supply chain) as an entity.
• Other more traditional tools could have application that
are not discussed in detailed here (e.g., SAMs).
Quantitative approaches to VC modeling
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• Numerous examples of agricultural supply chains in the logistics/operations
research literature:
– Van der Vorst et al. (2000, Eur. J. OR)
– Minegishi & Thiel (2000, Sim. Prac. & Theory)
– Trienekens & Hvolby (2001, Prod. & Plant Control)
– Gigler et al. (2002, Eur. J. OR)
– Georgiadis et al. (2005, J. Food Eng.)
– Fiala (2005, Omega)
– Meijer et al. (2005, working paper, Wageningen U.)
• Main focus is on micro (firm) level strategies (reducing costs, lead times,
inventories)
Quantitative approaches to VC modeling
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• System dynamics (SD) models present a means to capture sector/macro
level interventions in supply chains:
– Assess investment options
– Analyze dynamic feedbacks to determine sustainability
• Applications of SD models in agribusiness:
– Mowat et al. (1997): R&D strategies in persimmons
– Cloutier & Sonka (1998): SD model of coordination between
producers/processors in the hog supply chain
– Fisher et al. (2000): SD model of adoption/diffusion of precision
agriculture
– Ross (2005): assessment of entrepreneurial innovations in the hog
supply chain.
Quantitative approaches to VC modeling
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PLANT ING
RAT E
HARVEST
RAT E
CROPS IN FIELD
MARKET
SALES
INVENT ORIES
PRODUCT ION DELAYS
DEMAND
DEMAND
SHIFT ERS
RELAT IVE
PRODUCT
VALUE
PRICE OF
SUBSTIT UT ES
PRICE
INVENT ORY
COVERAGE
EXPECT ED
PROFIT ABILITY
CAPACIT Y
UTILIZAT ION
EXPECT ED PROFIT ABILITY
OF NEW CAPACIT Y
VARIABLE
COST S
CAPACIT Y
LOSS
CAPACIT Y
CAPACIT Y
ACQUISITION
SUPPLY LINE OF
CAPACIT Y ON ORDER
CAPACIT Y
INIT IAT ION
CAPACIT Y
COST S
AVG LIFE
OF CAPACITY
Adapted from Sterman (2000)
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PLANT ING
RAT E
HARVEST
RAT E
CROPS IN FIELD
MARKET
SALES
INVENT ORIES
PRODUCT ION DELAYS
DEMAND
DEMAND
SHIFT ERS
RELAT IVE
PRODUCT
VALUE
PRICE OF
SUBSTIT UT ES
PRICE
INVENT ORY
COVERAGE
EXPECT ED
PROFIT ABILITY
CAPACIT Y
UTILIZAT ION
EXPECT ED PROFIT ABILITY
OF NEW CAPACIT Y
VARIABLE
COST S
CAPACIT Y
LOSS
CAPACIT Y
CAPACIT Y
ACQUISITION
SUPPLY LINE OF
CAPACIT Y ON ORDER
CAPACIT Y
INIT IAT ION
CAPACIT Y
COST S
AVG LIFE
OF CAPACITY
How do different interventions
compare over time? Are they
sustainable? Roles of public
vs. private sectors? Roles of
chain-level interventions?
INTERVENTIONS IN
TECHNOLOGY/POST-
HARVEST TECHNIQUES INTERVENTION IN
NEW MARKETING
CHANNELS
INTERVENTIONS IN
MARKETING
INTERVENTIONS IN
COORDINATION LINKAGES
(CONTRACTS)
INTERVENTIONS IN NEW
SUPPLY CHAINS