Toc Approach for Supply Chain Performance Enhancement

Brijesh Ainapur, Dr.Ritesh Singh & Dr.P.R.Vittal
International Journal of Business Research and Management (IJBRM), Volume (2) : Issue (4) : 2011 163
TOC Approach for Supply Chain Performance Enhancement
Brijesh Ainapur brijeshainapur@rediffmail.com
Research Scholar,
BIT Mesra, Ranchi, India
Dr.Ritesh Singh riteshsingh@bitmesra.ac.in
Dept of Production Engineering,
BIT Mesra, Ranchi, India
Dr.P.R.Vittal vittal_ramaseshan@yahoomail.com
Dept of Statistics
University of Madras,
Chennai, India
Abstract
Many foundries are operating at lower levels than the installed capacities. Study of 56 foundries across
India has shown that only 68% of installed capacities are being utilized. This is mainly because of lack of
co-ordination among the supply chain partners. This study is an effort in identifying these gaps using TOC
approach which will help in supply chain performance enhancement.
Field of Supply Chain Management witnessed rapid growth in recent past and proved to be a successful
tool for organizations growth. Success of supply chain improvement initiative lies in selection of
appropriate Key Performance Indicators (KPIs) using best suitable supply chain framework. These
performance indicators are to be measured, monitored and controlled with proper review mechanism.
This study presents a methodology for identification of the KPI’s from the supply chain metrics suitable
for foundries. Selection of the KPI’s is done using Supply Chain Operations Reference (SCOR)
framework. Analytical Hierarchy Process (AHP) is used for decomposing the goal into micro level for
analyzing and prioritizing KPIs. In order to study the gap between as-is-state and as-to-be state,
benchmarking is carried by comparing foundry industry KPIs with global best practice industry average.
To optimize the supply chain performance, Goal Programming function is formulated using AHP ratings
and solved using WINQSB software. Theory of Constraint (TOC) management philosophy is applied for
finding the constraints, on improving these constraints supply chain performance enhancement is
achieved.
Key Words: SCOR, KPI, TOC, AHP, GP
1. INTRODUCTION
In last few decades, concept of supply chain has witnessed widespread acceptability in the industry. Prior
to that, functions like procurement, operations, logistics and distribution are being studied as independent
disciplines. A supply chain (SC) is a wide (national/global) network of suppliers, manufactures,
warehouses, distribution centres and retailers through which raw materials are acquired, transformed and
delivered to customers. Supply chain management (SCM) deals with the coordination of manufacturing
and logistic activities between autonomous decision making units (Wong et al., 2004).
Optimizing supply chain activities is critical to all industries since it saves money, increases throughput,
decreases inventory levels and increases revenues, thereby improving the organizations financial status.

Thus, Integration of various business processes is an integral part of building supply chain initiative. This
cross functional integration is needed for four basic reasons Viz Competitive Advantage, Effective use of
technology, effective planning and finally improved customer service and response time. This
necessitates the need of building proper framework and evaluation methodology for managing supply
chain, which eventually termed as supply chain management. This forms the basis for this research work
to identify, measure, benchmark and find out the constraints which prevents SC from achieving its goal.
Anything which we want to monitor or improve needs a measurement system. Supply chain performance
is measured using KPI’s, often referred as Key Success Indicators (KSI). KPI’s are quantifiable
measurements and helps an organization to define and measure progress towards achieving goals (John
Reh, 2011). Many organizations identify the KPI’s which are internal to the organization and does not
include the performance of the supply chain members. Performance measures of supply chain which are
traditional, concentrating on the individual performance are irrelevant to the maximization of the supply
chain profit (Simatupang et.al, 2002). Supply chain performance is measured in oversimplified manner
focusing mainly on cost reduction method. By this, Supply chain limits itself to local measures of
performance, which does not work together in an integrated manner. There is need for identifying the
performance metrics which measures the overall supply chain performance rather than the performance
of the individual members. Co-relating to this many foundries found measuring local metrics as their
performance measures rather than concentrating on the entire stretch of supply chain. Commonly used
performance metrics in foundries are Liquid Metal Tonnage per Day, Good Castings Tonnage per Day,
Internal Rejections %, Units per Ton of Good Castings, Customer End Rejections and Dispatch Tonnage
per Day.
KPI must be quantifiable and should reflect the organizational goals. KPI determines the success of
supply chains, it is better not to measure than measuring wrong KPI. British Telecom wanted to measure
the effectiveness of their customer service representatives who attends the customer complaints over
telephone. Initially they fixed customer satisfaction as the performance measure and each caller has to
rate in 1-10 scale, 10 being the best. Everyone tried to solve the customer query and they took maximum
time for clearing each caller queries. This made other callers to wait in the queue for longer times and
most often they fail to get their lines connected. In order to solve this British Telecom changed the
performance measurement as number of callers attended per day. Now every representative started
answering fast and tried to cut the calls without clearing the queries. This necessities the urgency in
formulating a methodology in identifying the appropriate supply chain performance (SCP) metrics
(Douglas et al. 2001). Lack of proper metrics for a SCP measurement will result in failure to meet
consumer/end user expectations & there is no evidence that meaningful performance measures that span
the supply chain actually exists. This study is intended to address all the issues in selection of the right
KPI, measurement of the KPI, Benchmarking and finding the constraint KPI in the supply chain
performance. Many management tools are available for measurement of KPI’s , Supply Chain Operation
Reference Model (SCOR), Balanced Score Card (BSC), Global Supply Chain Forum Frame work (GSCF)
, Process Classification Framework (PCF) and Three levels model are few to name.(Douglas M.Lambert
et al. 1998)
Once the KPI’s are identified, measured and compared with benchmarks, next step is identification of the
constraint KPI’s which are preventing the SC from achieving the benchmarks. The constraint KPI’s are
identified using the concepts of theory of constraint (TOC) supported with analytical hierarchy process
(AHP) and goal programming (GP). Problem is decomposed into micro levels using AHP, weights for the
individual KPI’s are derived from the survey results and GP is formulated for constraint identification. AHP
provides a framework to deal with multiple criteria situations involving intuitive, rational, quantitative and
qualitative aspects (Alberto, 2000). Goal Programming attempts to combine the logic of optimization in
mathematical programming with the decision maker’s desire to satisfy several goals, which justifies the
use of AHP-GP combination in this research work.

The area selected for the study is Indian Foundries. Foundry is the industry which manufactures castings
of different shapes and sizes. These foundries are classified as ferrous and non-ferrous foundries. This
study is restricted to ferrous foundries in India. India is the fourth largest producer of castings in the world.
But the emphasis on this industry is very less when compared to other industries like steel, coal,
petroleum, IT etc. There are endless opportunities to grow and improve the system. Lot of technological
up-gradation has taken place in the foundry industry, starting with manual moulding changed to moulding
machines and now with high pressure moulding lines capable of producing 120 moulds per hour with
flask and 360 moulds per hour without flask moulds. But there is no or little evidence that has taken place
in managerial decision making and importance to entire supply chain as a whole. Whenever, decisions
are taken for up gradation of plant facilities without looking into supply chain partners, they resulted in
either scarcity of raw materials or excess of finished goods. Survey of 56 foundries across India has
shown that capacity utilization is 68% of their total installed capacities. 18 foundries are going for
expansions, to meet their customer schedules without actually looking into the opportunity available with
the existing plant. Hau Lee ( Hau Lee et.al, 2002) states that “Technology investments alone will provide
only minimal benefits. Organization Successes confirm the power and importance of SCPM as a
cornerstone concept and practice within SCM.” So, this research work has been taken up to address this
issue of improving the supply chain performance through constraint identification and elimination. This
research work develops a framework for identification, measurement, and elimination of constraint KPI’s
to enhance the supply chain performance in Indian iron and steel foundries.
2. CONTRIBUTIONS
The main contributions of this article are:
a) Supply chain performance measurement is formulated as multiple criteria decision making problem for
constraint identification and enhancement of the supply chain performance.
b) KPI’s for 5 attributes of supply chain are proposed suitable for foundry operations
c) Benchmarking values are collected from the leading supply chain benchmarking agencies
d) TOC has been applied with the support of AHP and GP tools for identifying the constraint KPI.
e) Elimination of constraint KPI using AHP and GP is proposed first time by the authors. Other literature
survey suggested that constraints lies inside or outside the system, this is a new concept proposed
which suggests the constraint in the measurement system itself.
f) Weights are attached to each KPI and Attributes using AHP, for formulation of GP function.
The article is organized as follows:
Brief literature review on KPI’s and tools used in this research are provided in Section-3. Section-4 deals
with proposed methodology. Flow diagram of the proposed methodology is discussed in this section. Data
collection and analysis of the data are given in Section-5. The final section is dedicated to tabulate the
research findings and conclusion.
3. LITERATURE REVIEW
Literature Review of many researchers and Eliyahu Goldratt, founder of concept TOC, in his book
“GOAL” have emphasized that constraint is the limiting factor for achieving the organizations goal. The
chain is as strong as its weakest link, which he termed as constraint. It is most important to identify this
constraint, exploit constraint, sub-ordinate the constraint with all other ways, elevate the constraint and
repeat these steps for next available chain members, because there is every possibility that constraint
shifts from one point to another. These objectives are meet only if we are able to formulate a strong
measuring, monitoring and controlling system using KPI’s. Shoshanah Cohen et.al (2007) in his research
work expressed that supply chain metrics are difficult to define and even more difficult to measure.
Measurement is the only way to understand whether process performance is improving or worsening and
whether action is required. SCOR model proposes the best set of metrics for measurement of supply
chain performance. The metrics embedded in the SCOR model are consistent with the premise of the

supply chain as an end-to-end process. These difficulties in selection of metrics are addressed in the
present research.
Ge Wang et.al (2004) employed the concepts of KPI selection through SCOR model and applied AHP-GP
combination. Eon Kyung Lee et.al (2001) applied the concepts of KPI and AHP in selection of supplier.
Manoj Kumar et.al (2003) carried out his research of supply chain vendor selection using GP. Amin Amid
(2008) extended these concepts of supply chain- supplier selection and applied AHP-GP using weighted
goal programming for finding the best supplier among the given bunch of suppliers. Elif Kongar et.al
(2000) employed GP approach for remanufacturing supply chain model. Yeliz Ekinci (2007) solved the
problem of Demand Assignment in the supply chain using AHP-GP. These papers restricted only up to
part of the supply chain and KPI’s are not focused on the entire stretch of supply chain.
A number of researchers have applied combined AHP-GP approach in the various aspects of managerial
decision making and problem solving. Schniederjans and Garvin(1997) for evaluation and selecting the
best combination of cost drivers, Kwak and Lee (1998) applied for allocating higher education institutions
resources to IT- based projects, Radasch and Kwak (1998) utilized this tool for planning, list goes on and
literature review of over 150 articles by William Ho(2007) revealed that 33 articles used the concept of
integrating AHP with mathematical programming techniques, including linear programming(LP) , integer
linear programming (ILP), mixed integer linear programming (MILP) and goal programming (GP), out of
this 16 have used AHP-GP goal programming combination. Ge Wang et al.(2004) applied this
combination of AHP-GP in supplier selection, Eon Kyung Lee (2001) applied AHP for supplier selection,
Manoj Kumar et al(2004) applied goal programming for supplier selection, Amin Amid(2008) applied GP
for supplier selection, Yeliz Ekinci (2007) used the concept of goal programming in demand assignment,
Rupesh Pati et al.(2008) applied GP for paper recycling system, Chandra Mohan Reddy et al.(2007) used
the concept of GP for distributor selection, but none of the researches have used AHP-GP combination
along with TOC for supply chain performance enhancement, they have either used only for supplier
selection or scheduling or planning. They have not considered on the aspect of supply chain performance
along the entire stretch of supply chain.
Another research survey revealed that, the urgency in formulating a methodology in identifying the
appropriate SCP metrics. Douglas et al. (2001) feels that lack of proper metrics for a SCP measurement
will result in failure to meet consumer/end user expectations and his research reveals that, there is no
evidence that meaningful performance measures that span the supply chain actually exists. This research
work has addressed the issue of proper identification of measurement metrics. Metrics are selected after
comparing the best available measurement systems viz., Supply Chain Operation Reference Model
(SCOR), Balanced Score Card (BSC),
Global Supply Chain Forum Frame work (GSCF), Process Classification Framework (PCF) and Three
levels model.
Though many organizations are adopting SCM practices, what distinguish are those who succeed and
those who fail? Most of SCM practices may take the organization to the higher level of performance, if all
the SCM activities are measured and monitored. So it is evident that a better performance measurement
tool is required for the success of SCM.
Another study by David Simchi-Levi et al.(2008) infers that supply chain performance affects the ability to
provide customer value, from the most basic dimension of availability of products. Therefore, there is a
need to develop independent criteria to measure supply chain performance. The need for well defined
measures in the supply chain stems from the presence of many partners in the process and the
requirement of a common language. This is precisely the motivation behind standardisation initiatives
such as SCC reference model. In order strengthen this finding, in this research study, selection of supply
chain performance measure metrics is adopted from SCC reference model SCOR.

Elliot Bendoly et al. (2007) assert that “Performance Metrics identification and measurement are critical
elements in translating an organisations mission or strategy into reality.” Too many metrics will lead to
uncertainty, cognitive loads, local biases, misaligned decisions and ultimately poor strategic execution. So
the basic question is how can a firm develop the right portfolio of performance metrics to bolster its
strategy and thus ultimately generate financial benefits? This concept is used to select appropriate
metrics from SCOR model which are relevant to the Foundry Industry, which is scope of this research
study.
The Theory of Constraints (TOC) has been widely known as a management philosophy coined by Eliyahu
Goldratt(2006) in his book “GOAL”(1984), since then it has become so popular that most of the
organisations have successfully implemented his concept to reap the benefits of constraint elimination.
Goldratt concept has also undergone tremendous changes in the last two and half decades up to 20th
Edition in 2006, “GOAL-A PROCESS OF ONGOING IMPROVEMENT”. Goldratt applied TOC for the
manufacturing process, with a step extension, this research work applies TOC for the entire supply chain
stretch.
R.K.Singh et al. (2005) infers that TOC is a new manufacturing strategy that aims to make money in the
present as well as in future by eliminating weak (constraint) links throughout manufacturing processes.
This concept of TOC was mainly used in the manufacturing processes, in this research an attempt is
made to use TOC concept in improving SCP through constraint KPI identification and elimination.
As stated earlier, effective supply chain design calls for robust analytical models and design tools.
Previous works in this area are mostly Operation Research oriented without considering entire supply
chain aspects. Decision-making processes should be guided by a comprehensive set of performance
metrics. Ge Wang et al.(2004) states that , Adopting supply chain operations reference (SCOR) model
level I performance metrics as the decision criteria for selecting KPI’s and an integrated analytic hierarchy
process (AHP) and preemptive goal programming (PGP) based multi-criteria decision-making
methodology will take into account both qualitative and quantitative factors in supply chain management.
Eon-Kyung Lee et al. (2001), concludes his research work on supply chain performance stating that,
“Study is needed to develop a system that would suggest the detailed action plan to implement the best
practices with respect to each managerial criterion in supply chain performance measurement.”
Study by Rueben Slone et al.(2007) infers that ,”If you’re disengaged from supply chain management,
you run the risk of sabotaging partner strategy and customer relations—and leaving money on the table
now and for the long term”.
With this thorough insight into literature review justifies the need of a robust performance measurement
and analysis system to enhance the supply chain performance, which forms the basis of this research
work.
4. RESEARCH METHODOLOGY
Many researchers have applied concept of TOC in the areas of manufacturing, logistics and distribution.
In this section research methodology proposed is emphasized. Concept of TOC was first coined by
Eliyahu Goldratt as early as during 1980’s. TOC aims at improving the performance by eliminating the
weakest link in the process. This is accomplished either using five focusing step methodology or think
process depending on the constraint type, physical or non-physical respectively. Mark J.Woeppel (2001)
outlines two distinct approaches for building an organization- Craftsman approach and System approach.
Craftsman approach relies heavily on experts (craftsmen) to accomplish the organizational objectives.
Systems approach is not as dependent on the experts. Management strives to create a repeatable
process for managing the organization- a process that can be taught to others. TOC helps in creating a

successful organization using systems approach. It helps in increasing the throughput (T), reducing
inventory (I) and operating expenses (OE).
This research work is carried out to enhance the supply chain performance on these guidelines of
systems approach using five focusing step methodology. 5 steps are,
a) IDENTIFY the system constraint(s)
b) decide how to EXPLOIT the identified constraint(s)
c) SUBORDINATE everything else in the system to step 2
d) ELEVATE the system’s constraint
e) overcome INERTIA, go back to step 1.
In order to apply TOC concepts to enhance the supply chain performance, we should primarily identify
and measure the performance indicators. These performance indicators of supply chain are termed as
KPI’s and measured under 5 attribute heads, Supply Chain Reliability, Responsiveness, Flexibility, Costs
and Asset Management. 24 KPI’s applicable for foundries are identified under these 5 attributes using
SCOR model.
The entire research goal has been decomposed to the micro level in 3 layer decomposition model
proposed by Thomas Saaty (2008). This decomposition model will help in prioritizing the variables at
each levels. KPI priorities are different for different foundries. Survey response has shown wide range of
priorities across these 24 KPI’s.
Macro objective- Supply Chain Performance Enhancement forms the first layer in the decomposition
model, followed by 5 attributes in the second layer and finally all the 24 KPI’s in the third layer. This model
was proposed by Thomas saaty (1985) and coined as Analytical Hierarchy Process(AHP). The AHP tree
for the present research is shown in fig.1

FIGURE 1: AHP decomposition model for Supply Chain Performance
Guidelines taken from Ge Wang et.al International Production Economics 2004, 1-15
Level 1 form the top most layer and is the final goal – supply chain performance enhancement
Level 2 is the middle layer and forms the first layer of decomposition, where we find 5 attributes for
supply chain performance enhancement – Reliability, Responsiveness, Flexibility, Costs and Asset
Management.
Level 3 is the last layer and the micro layer of the entire AHP tree. In this layer all the 24 KPI’s of the 5
attributes are listed and overall goal achievement depends mainly on this layer. This layer is the
foundation layer for the entire process.
AHP Weights are calculated based on the ratings and these AHP weights are used in formulation of GP
equation. All the steps involved in achieving the supply chain performance enhancement are shown in
the following flow diagram shown in fig 2.
OVERALL SUPPLY CHAIN
EFFICIENCY
SUPPLY CHAIN
COST
SUPPLY CHAIN
ASSET MANAGEMENT
SUPPLY CHAIN
RESPONSE
LEVEL 1 OVERALL OBJECTIVE
LEVEL 3 KEY PERFORMANCE INDICATORS
LEVEL 2 ATTRIBUTES
SUPPLY CHAIN
RELIABILITY
REL1
REL2
REL3
REL4
REL5
RES1
RES2
RES3
RES4
COS1
COS2
COS3
COS4
COS5
SUPPLY CHAIN
FLEXIBILITY
FLE1
FLE2
FLE3
ASM1
ASM2
ASM3
ASM4
ASM5
ASM6REL6

FIGURE 2: Flow diagram of the research work
Quite often the systems constraint will be our measurement system itself. It is better not to measure than
measuring a wrong KPI. Performance levels are calculated using GP software WINQSB by removing
each KPI at a time. Constraint KPI is the one, which increases the systems performance in its
absence.(IDENTIFY AND EXPLOIT). AHP weights are calculated again for the remaining
KPI’s.(SUBORDINATE). Constraint KPI is analysed for finding the root cause (ELEVATE) and addressed
all the possible causes for making particular KPI as constraint KPI.(Overcome INERTIA).
Goal Programming equations are formulated using the following steps.
Steps involved in the formulation of GP starts with identifying the variables
Two types of variables are part of any GP equation formulation…
The decision variables, x and The deviation variables, d
There are 6 steps in formulating the GP , they are ,
STEP 1: Define Goal / Goals
STEP 2: Define Decision variables for goal programming
Using theory of constraint and goal programming principles, supply chain effectiveness will measured by
eliminating each KPI at a time, to know which KPI is limiting the supply chain performance (constraint KPI).
STEP 1: Identification of Supply Chain Framework
Identification of supply chain frame work from the available frameworks, namely SCOR, GSFC, 3 level
model,
PCF and others.
STEP 2: Define Supply Chain in Foundries and Identify KPI’S
Based on framework selected, defining supply chain environment for foundry industry, defining supply chain
process and identifying KPI’S using SCOR Model
STEP 3: Measurement and Benchmarking
Preparation of Questionnaire, Survey, Measurement of Identified KPI’s and comparing with Benchmark data
collected from APQC- Benchmarking Agency of Supply Chain Council.
STEP 4: Framing Goal Programming Function using AHP
Using Analytical Hierarchy Process, weights are added for the KPI’s and Goal Programming Function is
formulated, constraints are defined for goal programming function
STEP 5: Using Theory of Constraints and Goal Programming

STEP 3: Define Deviation variables for goal programming
STEP 4: State Constraints
STEP 5: Determine the preemptive priorities incorporating AHP weights
STEP 6: Find Optimal solution using WINQSB
Let us explore these steps in detail with specific to this research.
STEP 1: Define Goal / Goals
G1, G2, G3, G4, G5 be the five goals of supply chain performance
Where G1: REL :Reliability
G2: RES :Responsiveness
G3: FLE :Flexibility
G4: COS :Costs
G5: ASM :Asset Management
G : SCP :Overall goal –to enhance supply chain performance
Ef : Amount of overachievement of goal f ( where f=1,2,3,4,5)
Uf : Amount of underachievement of goal f ( where f=1,2,3,4,5)
Xi : AHP ratings of corresponding attribute (where i=1,2,3,4,5)
GP equation is formulated to “MAX G”
MAXIMISE=SCP=G=X1REL+X2RES+X3FLE+X4COS+X5ASM-EREL+UREL- ERES+URES-EFLE+UFLE-
ECOS+UCOS-EASM+UASM
-------------------- (1)
STEP 2: Define Decision variables for goal programming
RELx : decision variables for goal 1 where x= 1,2,3,4,5,6
RESy : decision variables for goal 2 where y= 1,2,3,4
FLEm : decision variables for goal 4 where m= 1,2,3
COSz : decision variables for goal 3 where z= 1,2,3,4,5
ASMn : decision variables for goal 5 where n= 1,2,3,4,5,6
STEP 3: Define deviation variables for goal programming
Ef : Amount of overachievement of goal f ( where f=1,2,3,4,5)
Uf : Amount of underachievement of goal f ( where f=1,2,3,4,5)
Each goal has one or more decision variables and each of these will have deviation variables (metrics) as
Efa and Ufa .
For example E11 is amount of overachievement of goal 1 and decision variable 1
U11 is amount of underachievement of goal 1 and decision variable 1
GP equation for reliability attribute will be
MAX G1 = ∑=
+−
6
1
11
i
iUiEXiRELi ----------------------(2)
where ‘i’ is the KPI number of attribute1-Reliability
‘X’ is AHP rating
MAX G1=X1REL1+X2REL2+X3REL3+X4REL4+X5REL5+X6REL6-E11+U11
-E12+U12-E13+U13-E14+U14-E15+U15-E16+U16
--------------------(3)

similarly GP equations for other 4 attributes will be formulated.
STEP 4: State Constraints
Aim is to achieve benchmark levels from the present levels.
Parity benchmark levels are added, sum of all the GP solution performance levels should not exceed
these performance levels, and this will be RHS of the constraint
For Ex: For Supply Chain Reliability, constraint is
∑∑ ==
≤
6
1
6
1 ji
RELjRELi --------------------(4)
RELj are the parity benchmark values of the corresponding REL KPI’s
REL1+REL2+REL3+REL4+REL5+REL6 ≤ 554 --------------------(5)
Present performance levels forms the lower limits and parity benchmark levels forms the upper limits in
the goal programming equation formulation.
STEP 5: Determine the preemptive priorities incorporating AHP weights
AHP weights for each of these KPI’s are calculated and used in the GP equation.
STEP 6: Find Optimal solution using WINQSB
By using commercial software tool for goal programming, such as WinQSB, Lingo etc., the
optimal solution to the goal programming can be obtained. In this present research work WinQSB is
used. The optimal solution will decide, which KPI is preventing from achieving the maximum and it will be
treated as constraint KPI.
All these steps are evaluated using research data in the next section.
5. DATA ANALYSIS
Data has been collected from the 56 leading iron and steel foundries in India for the financial year 2009-
2010. These are classified as large , medium and small scale foundries based on the volumes they
produce. Classification is based on following criteria.
a) Large scale : > 15,000 Metric Tonnes / Annum
b)Medium Scale : From 5,000 to 15,000 Metric Tonnes / Annum
c)Small Scale : < 5,000 Metric Tonnes / Annum
Total Questionnaire sent : 85
Total responses received : 56
% Response received : 65.88%
Large Scale Foundries : LS : 21
Medium Scale Foundries : MS : 26
Small Scale Foundries : SS : 09

FIGURE 3 Survey Response
SURVEY RESPONSE
38%
46%
16%
LS MS SS
38% of the responses were from large scale foundries, 46% of the responses were from medium scale
foundries and 16% of the responses were from small scale foundries.
Primary Data: 15(27%) responses were from direct personal interview, 21(37%) responses were through
telephonic interviews and 20(36%) responses were received by email.
Secondary Data:
Indian Institute of foundry men journals and websites
Supply chain council websites
Aberdeen Group Benchmark reports
Scormark Survey reports
Data collected for supply chain reliability attribute is tabulated in the below table.
TABLE 1 Reliability Attribute Measured Values
ATTRIBUTE KPI DIRECTION UNIT
%
BENCH MEASURED VALUES
%
PERFORMANCEMARK KPI PRIORITY
SUPPLY
CHAIN
RELIABILITY
PERFECT ORDER
FULFILLMENT
MAX % 90 77.79 0.17
85.3
ORDERS DELIVERED IN
FULL
MAX % 80.5 82.58 0.17
ORDER FILL RATE MAX % 95 79.23 0.17
DELIVERY
PERFORMANCE TO
CUSTOMER COMMIT
DATE
MAX % 90 72.29 0.18
DELIVERED IN PERFECT
CONDITION
MAX % 99 98.23 0.15
DOCUMENT ACCURACY MAX % 99 98.95 0.17

From the above table 1, research survey shows the supply chain reliability attribute performance as
85.3%. From the benchmarks given by supply chain council and Aberdeen group shows that supply
chain reliability attribute is 92% for parity, 95% for advantage, and 98% for superior.
In order to improve the system performance to reach these benchmarks from the present level, we have
to identify the constraint KPI from the system, we have to remove each KPI at a time and calculate the
corresponding AHP weights and GP output. These are shown in the following table 2.
TABLE 2: AHP Ratings
ATTRIBUTE KPI RATING
AHP WEIGHTS W/O KPI
1 2 3 4 5 6
SUPPLY
CHAIN
RELIABILITY
PERFECT ORDER
FULFILLMENT
6.73 0 0.205 0.204 0.207 0.202 0.204
ORDERS DELIVERED IN
FULL
6.66 0.204 0 0.202 0.205 0.2 0.202
ORDER FILL RATE 6.55 0.2 0.199 0 0.201 0.196 0.198
DELIVERY PERFORMANCE
TO CUSTOMER COMMIT
DATE
6.95 0.212 0.212 0.211 0 0.208 0.211
DELIVERED IN PERFECT
CONDITION
6.05 0.185 0.184 0.184 0.186 0 0.184
DOCUMENT ACCURACY 6.55 0.2 0.199 0.198 0.201 0.196 0
Using AHP weights for finding the REL without KPI REL1, GP equation will be
MAX G1=0 x REL1+ 0.204 x REL2+ 0.2 x REL3+ 0.212 x REL4+ 0.185 x REL5+ 0.2 x REL6 -E12+U12-
E13+U13-E14+U14-E15+U15-E16+U16
--------------------(6)
Constraints will be REL2+REL3+REL4+REL5+REL6≤ 464 --------------------(7)
When equation (7) is solved using WINQSB, supply chain performance level has increased to 92.62%
from 85.3%. Similar analysis has been done by removing one KPI at a time and performance levels are
calculated. Summary of these results are shown in table 3.
TABLE 3: Goal Programming results
ATTRIBUTE
GOAL PROGRAM RESULTS W/O KPI CONSTRAINT
KPI1 2 3 4 5 6
SUPPLY
CHAIN
RELIABILITY
92.62 93.95 91.84 92.96 91.18 90.67
KPI 2
ORDERS
DELIVERED
IN FULL

Table 3 signifies that , in the absence of second KPI, Supply Chain Reliability Performance is maximum
and nearer to advantage position in the benchmarks. Results of GP infer that KPI 2- Orders Delivered in
Full is the constraint KPI and needs to be improved. Without the constraint KPI 2 , Supply Chain
Reliability improved from 85.3 % to 93.95%.
Next step is to study were the constraint lies, inside the system or outside the system or in both. This
can be done using any of the statistical tools such as Ishikawa diagram (fish bone diagram), 5W1H format
or 8D formats, Drum Buffer Rope, Think Process, Evapourating Clouds, Current Reality Tree, Future
Reality Tree etc., in this research work systems constraint is exploited using Ishikawa diagram, so that it
will be useful for the shop floor people to understand. This is shown in fig 4.
FIGURE 4: Root Causes for constraint KPI2
Root Causes for the constraint KPI2-Orders Delivered in Full , are shown in the Ishikawa Diagram in fig 4.
If this constraint KPI2 improves, overall Supply Chain Reliability of the system improves. Similar analysis
has been carried out for improving the other attributes of Supply Chain and overall supply chain
performance has improved from 63.17% to 71.03%.
6. CONCLUSION
We would have not seen or rarely seen in any foundry , who are using fish bone diagram for supply chain
KPI’s. It is the requirement and demand of the day for giving importance to the supply chain partners to
enhance their effectiveness.
Technological inputs and Capital infusion will help in capacity expansions but cannot improve capacity
utilizations. Supply Chain Performance monitoring and controlling helps in increasing the capacity
utilizations and increase the market share of the company by properly identifying, measuring and

controlling the KPI’s. A concrete effort has been made in this research work to address these issues.
Once the constraint KPI is removed from the system, system has shown significant improvement and
performance levels of reliability attribute increased from 85.3% to 93.95% which is nearer to the
benchmark values. Further going one step forward, we can study the system for more than one
constraint, but as per the TOC principles, we have addressed constraints one by one instead of
addressing more than one constraint at a time. Once the present constraint is addressed , constraint may
shift to the other point, this can be taken up as further scope and future prospectus of this research work.
So further scope for research is kept open by eliminating two or more KPI’s and measuring the
performance levels.
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