Supply chain cell activities for a consumer goods company

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Supply chain cell activities for aconsumer goods companyS. K. Mukhopadhyay a & Amar Kanti Barua aa National Institute of Industrial Engineering , Vihar Lake, PONitie, Mumbai, 4000 87, IndiaPublished online: 14 Nov 2010.

To cite this article: S. K. Mukhopadhyay & Amar Kanti Barua (2003) Supply chain cell activitiesfor a consumer goods company, International Journal of Production Research, 41:2, 297-314,DOI: 10.1080/00207540210164468

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int. j. prod. res., 2003, vol. 41, no. 2, 297–314

Supply chain cell activities for a consumer goods company

S. K. MUKHOPADHYAYy* and AMAR KANTI BARUAy

The supply chain cell is a new cross-functional area in many industries in India. Itinvolves the integration of production planning, dispatch order generation andlogistics planning, apart from general logistics, which primarily includes distribu-tion. This paper deals with the study conducted for a fast moving consumer goodscompany having 14 production locations and 22 branches/clearing and forward-ing agents. There were 36 packing lines, with each line packing different varietiesof stock-keeping units. In this company, supply chain cell activities were concen-trated on the middle of the classical supply chain. An attempt has been made tointegrate production logic with the logistics by considering the total cost concept.The total cost concept also includes non-quantifiable parameters, which havebeen incorporated by the use of the analytical hierarchy process. An optimizationtechnique has been proposed simultaneously to support the planning logic devel-oped. The supply chain cell helps to optimize the total supply chain cost as well asenabling it to supplement other benefits such as an improvement in inventoryturnover and a reduction in the inventory carrying cost.

1. Introduction

In the era of globalization and fierce competition with the ever-changing andnever-ending customer demands, the supply chain cell (SCC) is emerging as an activefunction with the objective of satisfying customer service goals and companies’competitive strategy. Historically, the SCC was not well defined in the industrycontext. Each individual has to carry out separately the transportation and storagefunctions based on their needs (Ballou 1999). The newness of the field resulted fromthe concept of integrated management of the related activities that add value to thecustomer. The strategic decisions made by SCC include the location and capacities ofproduction and warehousing facilities, the product to be manufactured and stored,and the mode of transportation (Chopra and Meindl 2001). As the product life cycleis reducing, manufacturing organizations are required to become flexible to respondto changing customer needs. Getting the product to the customers when, where andhow, and in the quantity required in a cost effective manner is the new challenge forthe SCC function (Handfield and Nicholas 1999).

An SCC is a cross-functional team in many organizations. The responsibilities ofthis cell include production planning, dispatch order generation and logistics plan-ning during the initial stages of an organization implementing supply chain concepts.The functioning of an SCC cannot be compared with the traditional definition ofsupply chain management depicting the art of managing the flow of materials andthe products from the acquisition of raw material to the delivery of finished products

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DOI: 10.1080/00207540210164468

Revision received June 2002.{National Institute of Industrial Engineering, Vihar Lake, PO Nitie, Mumbai 4000 87,

India.* To whom correspondence should be addressed. e-mail: [email protected]

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to the ultimate users. Several researchers (O’Neil and Bommer 1988, Bagchi 1989)discuss various issues, namely reliability and responsiveness of the carriers, delay indelivery schedules, future financial stability, anticipation of expected sales and inven-tory management on a route-by-route basis connected with the distribution ofdemand to various end-points using the unidirectional approach of AnalyticalHierarchy Process (Saaty 1980b). Deriving the method of priority vendors by pair-wise comparison in a transport system was also discussed by Fichtner (1986). Thefuture direction of logistics research encompassing the various elements of supplychain was projected by Backer and Golden (1985).

Over the past decade, globalization has driven all business systems to respond ata faster pace. This drive has also created a tremendous impact on logistics.Therefore, the conventional method of distribution and transportation was foundto be rampantly overburdened with the pressure of the integrated production–supplyconfiguration. As inventory has an important part to play in a company’s cash flowposition, as important as profit and return on investment, the late delivery of finishedgoods jeopardizes the inventory turnover substantially. Consequently, not only thecash flow suffers, but also the inventory cost rises rapidly. Thus, an integratedapproach of a production plan with logistics through a supply chain outlet pullingthe demand is considered necessary. The problem becomes more severe with multi-locations of manufacturing units and warehouses.

In this paper, a study of the supply chain activities was conducted for a fastmoving consumer goods (FMCG) company in North India. This company is an ISO9002-certified company, established in the early 20th century and is one of the oldesthealth and personal care companies in India. It also enjoys the distinction of beingthe biggest Indian FMCG company and is poised to become a true Indian multi-national. As the company enters the new millennium, the leading Indian FMCG hasset itself a new mission: to maximize the shareholder value by offering superiorquality nature-based products that offer value for money and contribute to improv-ing the quality of a consumer’s lifestyle in the areas of personal care, healthcare andfood processing. The company is a multilocation enterprise with 14 manufacturinglocations in India, Nepal and Egypt, with workforce of over 5000 people. It also hasa manufacturing license in the Middle East. The total span of business involves thefollowing strategic business units: (1) healthcare, (2) family products, (3) processedfoods, (4) pharmaceuticals, (5) Ayurvedic specialities and (6) exports division. Thereare 36 packing lines in different varieties of stock keeping units (SKUs) at thedifferent manufacturing locations. The distribution of this huge volume of produc-tion is supported by a strong network of 22 clearing and forwarding agents.

Currently, in developing the production plans, SCC considers rated capacities,i.e. capacities available when one SKU is run on a packing line throughout theplanning period. The rated capacities and number of shifts available for theselines were defined by considering both the manufacturing and packing line capaci-ties. The available capacities are good enough to absorb the demand, but when itcomes to the actual production, it is supposed to consider breakdowns and change-over times. Since the company is trying to meet the requirements of all SKUs usingthe same packing line, the production plan is not being converted into actual pro-duction and almost at all times production gaps exist between the actual and therated production.

As stated above, the company is much like a multinational company having itsmanufacturing locations in different nations with a vast network of clearing and

298 S. K. Mukhopadhyay and A. K. Barua


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forwarding agents. Therefore, integrating the production of each of these geogra-phically separated manufacturing locations and meeting the demands of differentdistribution centres are a big challenge. The company is also experiencing highinventory, long lead-time and poor service (not meeting the due date) in its supplychain. Many decisions in the supply chain activities are on an intuitive basis and donot contribute to the overall system optimization. A challenge includes the need tochange volume orientation of the organization into profit focus and institutionaliza-tion of cross-functional workforce.

A rough-cut capacity planning logic was provided by considering packing linebreakdowns, lunch breaks and packing line changeover times. Weighting factors tothe demands of the SKUs getting packed on the same packing line were calculatedconsidering past experience and an expert’s viewpoint. A decision matrix was devel-oped based on a transportation algorithm to allocate the production plans to dif-ferent manufacturing locations, which leads to minimizing the total cost. Further,the influence of non-quantifiable factors comprising geographical benefits, labourefficiencies, technological standards, warehousing, manufacturing consistency andeffectiveness, service levels, etc. were included in the cost matrix. This was doneusing an analytical hierarchy process (AHP) (Saaty 1980a, b, Weiss and Rao 1987)that was used to establish the relative importance of one factor over the other withrespect to a given branch-manufacturing location combination. The total costincludes manufacturing cost, tax rates, freight rates and inventory carrying costs.

The objectives of the paper involve designing a decision procedure to develop adispatch policy by considering the variability of cost elements in the supply chain ofa family product division and a healthcare product division to integrate productionplanning with logistics planning. The uniqueness of this study reveals the followingmajor contributions.

. Depending on the rough-cut capacity planning, the production allocation todifferent branches with respect to various SKUs is uniquely determined.

. Normally, the transportation model to optimize logistics considers only thequantitative elements. In this model, along with the quantitative elements,various qualitative factors have also been included, making the model morerealistic.

. Previous research using AHP (Saaty 1980a, b, Saaty and Kearns 1985) dealtonly with unidirectional dependencies, whereas in this model, iterativemethods were envisaged to take care of bidirectional dependencies until theconsistency is obtained.

2. Current approach

At the beginning of the month, the marketing department sends the rolling salesforecast (RSF) for the next 3 months for each division and each SKU to the SCC.The SCC develops the rolling production plan (RPP) based on this RSF for the firstmonth’s production plan and the next 2 months’ tentative plans. This productionplan is circulated within the first week of the month. Each production unit has tomanufacture according to the plan and if there are any problems about manpower,raw materials or machine breakdown, the plan is relocated to the other productionunit where available capacity exists. The SCC follows up each production unit’smanufacturing capacity on a weekly basis and solves any problems. This processof planning fails in many cases to meet the due date necessitates the rescheduling of

299Supply chain cell activities for a consumer goods company


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the planned delivery. Under the backdrop of this problem, there exists a scope ofimproving the quality of a production plan decision and, thereby, resorting to thecommitment of delivery. Over the years, such legacies are increasing, making theplanning system less productive and cost ineffective.

3. Proposed method

It was observed that the planning staff considered the rated capacities of SKUs inproduction planning. However, considering the rated capacities of each SKU, whendifferent SKUs are supposed to share the same packing line, capacity estimation isnot correct. In all 36 packing lines were identified in all the manufacturing locationsand their data were collected. Those packing lines (which packed more than oneSKU) were identified and the actual capacities calculated by assigning weights to thedemands of the respective SKUs getting packed on the same line. Since the linecapacities were defined in terms of the manufacturing bottlenecks, the packing linecapacities were also considered the same.

In the present problem, we considered the demand in a number of cases for SKUs1–5 for the planning period May–July (table 1). This is based on the 3-month RSF.In order to obtain a priority of production plan involving the demand requirementof each SKU, relative priority between the SKUs were developed by preparing areciprocal matrix whose Eigen value and the priority vector were calculated. Toknow that those priorities are consistent, the consistency index (CI) and the consis-tency ratio (CR) were also determined. Thus, the demand data as determined abovewere converted to the reciprocal matrix (table 2) to find out the weights for eachSKU. The formation of the matrix was as follows:

½Sn�n� ¼ faijg; i ¼ 1; 2; . . . ; n and j ¼ 1; 2; . . . ; n;

where n is the number of SKUs per packing line (i, rows; j, columns), aij ¼ di=dj ,aji ¼ 1=aij and di ¼ demand of SKU i.

The matrix was solved by using the AHP to obtain the relative priorities for theSKUs, which is represented by the Eigen vectors of the [Sn�n] matrix. This priority



SKU 1 2 3 4 5

Demand 42 642 63 963 23 690 21 321 28 428

Table 1. Demand estimates of different SKUs.

SKU 1 2 3 4 5

1 1 0.67 1.8 2 1.52 1.5 1 2.7 3 2.253 0.555 0.3704 1 1.111 0.8334 0.5 0.333 0.90 1 0.755 0.667 0.444 1.2 0.333 1

Principal Eigen value ð�maxÞ ¼ 5.Consistency index ðCIÞ ¼ 0:Consistency ratio ðCRÞ ¼ 0.

Table 2. Reciprocal matrix of SKUs based on demand estimates.

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vector is shown in table 3. The relative priorities were then taken as the weights for

calculation of the capacity.

After determining the weights for the different SKUs getting packed in the same

packing line, the final capacities available at each manufacturing location for the

different SKUs were estimated.

3.1. Assumptions

The total number of days available during the planning period was: 90 12 ¼ 78

days. Here, an average of 30 days per month has been assumed, and since the

planning period is 3 months, therefore 3 � 30 ¼ 90 days is the total number of

days available. However, the working days available during this planning horizon

are 90 12 days, which are the Sundays (i.e. non-working days).

The total shift duration available was: 480min, including such other allowances

such as:

. Lunch break: 45min.

. Morning and evening snacks: 2 � 10min each ¼ 20min.

. Average break down allowance at 5% of the total shift time.

Therefore, the actual shift time is: 480 ð45 þ 20 þ 0:05 � 480Þ ¼ 391min, and the

total actual time available during the planning period ðtotal number of days � actual

shift durationÞ ¼ 30 498min.

With these assumptions, the calculation for rough-cut capacity planning was

carried out at different manufacturing locations, and the calculation for manufactur-

ing location M1 is shown in table 4. The attributes shown in table 4 are as follows.

. Rated capacity: capacity available when one SKU is running continuously on a

packing line.

. Effective capacity: capacity available after considering allowances.



SKU 1 2 3 4 5

Priority 0.2368 0.3553 0.1316 0.1184 0.1579(w)

Table 3. Overall priorities/weights of SKUs.

SKU

Ratedcapacity

(r)

Effectivecapacity

(e)Weight

(w)

Weightedtime(T)

Actual no.of shifts

(s)

Totalcapacity

(t)

1 697 568 0.2368 7223 18 10 4862 929 757 0.3553 10 835 28 20 9723 1045 851 0.1316 4013 10 87384 995 811 0.1184 3612 9 74905 95 811 0.1579 4815 12 9987

Table 4. RCCP table for manufacturing location 1.

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. Weighted time available to each SKU getting packed on same packing line:T ¼ w� 30 498min, where w is the priority of each SKU, as shown in table 3.

. Actual number of shifts available to each SKU at any manufacturing location:s ¼ T=391.

. Total capacity available for each SKU: t ¼ s� e, where e is the effective capa-city and s is the actual number of shifts.

3.2. Calculation of Eigen vectors representing relative priorities of branches andmanufacturing locations

As stated above, the direct approach to determining the cost elements of thetransportation matrix by using the total cost concept would neglect the impact ofcertain non-quantifiable parameters on the cost. These factors can be as follows.

G geographical benefits,C manufacturing consistency and effectiveness,L labour efficiency,F availability of transportation facilities,S technological standards,R service levels,E warehouse management effectiveness.

These non-quantifiable parameters play a major role to prescribing the reality func-tion. While the geographical benefits include resource availability and infrastructurefacility, the availability of transportation facilities is responsible for effective logisticssupport and the warehouse management gives a positive administration to thesupply chain activities both at the input and output levels. Manufacturing consis-tency and effectiveness are important from the responsiveness and flexibility point ofview. The technological standard coupled with labour efficiency and service levels arethe part of production management. Therefore, it is important to integrate thesenon-quantifiable parameters with the total cost. An AHP is used for this purpose. Itis a useful technique under situations wherein one wants to quantify ideas, feelingsand emotions to provide a numeric scale for prioritizing decision alternatives. As afirst step, we identified levels of hierarchy individually at the manufacturing andbranch levels (figure 1). An important inference that can be drawn is the interdepen-dence of the factors at different levels. Unlike in the conventional AHP models,where the factors at any level are dependent on the factors at a superior level, andnot vice versa, for the model suggested here this limitation has been eliminated. Asshown in figure 1, the factors at level 3 are dependent on factors at level 2.Simultaneously, the factors at level 2 are dependent on factors at level 3. Thus, inthis manner the effects of the different intangible factors both at the manufacturingand branch ends have been incorporated. Level 2 consists of the intangible factorsand level 3 comprises the branch and manufacturing locations.

Under each of the factors at level 2, the reciprocal matrices for the differentbranch and manufacturing locations have been prepared. The entries in thesetables represent the relative importance of one location over the other for each ofthe factors at level 2. Note from figure 1 that four factors influence each branchlocation and five factors influence each manufacturing location. This is representedas for ½B5�5�i; i ¼ 1; . . . ; 4; where i represents the four factors at level 2 for branchlocation. These matrices have been shown in tables A1–4. For manufacturing loca-



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tions, ½M5�5�j; j ¼ 1; . . . ; 5; where j represents the five factors at level 2, and therelevant matrices are indicated in tables A5–9.

The reciprocal matrices of the different branch locations and the manufacturinglocations were solved to determine the final priority of each location under eachfactor:

½BF5�4� ¼ fXijg; i ¼ 1; . . . ; 5 and j ¼ 1; . . . ; 4;

where Xij is the priority of branch i under factor j and is shown in table A10:

½MF5�5� ¼ fYijg; i ¼ 1; . . . ; 5 and j ¼ 1; . . . ; 4;

where Yij is the priority of manufacturing location i under factor j and is shown intable A11.

Under each of the locations at level 3, the reciprocal matrices for the differentfactors at level 2 have been formed. The entries in these tables represent the relativeimportance of one factor over the other for each location at level 3. For branchfactors, ½F14�4�i; i ¼ 1; . . . ; 5; where i represents the five branch locations at level 3and for manufacturing factors, ½F25�5�j; j ¼ 1; . . . ; 5; where j represents the fivemanufacturing locations at level 3. Tables A12–16 and A17–21 represent thebranch factors and manufacturing factors, respectively.

The reciprocal matrices of the different factors at level 2 were solved to determinethe final priority of each factor under each location:

½FB4�5� ¼ fXijg; i ¼ 1; . . . ; 5 and j ¼ 1; . . . ; 4;

where Xij is the priority of factor i under branch j and:



G - Geographical benefits C - Manufacturing consistency & effectiveness

L - Labour efficiency F - Availability of transportation facilities

S - Technological standards R - Service levels

E - Warehousing

M i - Manufacturing locations B i - Branch locations

Level 1

Level 2

Level 3

G C L F S

M1 M2 Mn

Preferred

mfg. location

Preferred branch

location

R EG F

B2B1 Bm

Figure 1. Hierarchy levels in AHP modelling for branch and manufacturing locations.

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½FM5�5� ¼ fYijg; i ¼ 1; . . . ; 5 and j ¼ 1; . . . ; 4;

where Yij is the priority of factor i under manufacturing location j. These factors are

shown in tables A22 and A23, respectively.

Since the study reveals interdependency between the two levels, an iterative

approach is therefore used to solve the problem for determining the final overall

priority of the locations. The detail computation for final overall priority of branch

location has been worked out as follows:

Iteration I:

. Select the elements of column 1(B1) of [FB4�5] as the initial overall priority of

the factors, [G,R, F, E]1.

. With this initial priority of the factors, determine the initial final overall prior-

ity of the locations: [BF5�4]*[G,R, F, E]T1 ¼ [B1, B2, B3, B4, B5]1.

. With this initial final overall priority of the branch locations, determine the

improved final overall priority of the factors: [FB44�5]*[B1, B2, B3, B4, B5]T1 ¼[G,R, F, E]2.

Iteration II:

. With the final overall factors priority in Iteration I, [G,R, F, E]2, determine the

improved final overall priority of the locations [B1, B2, B3, B4, B5]2.

. With the final overall locations priority [B1, B2, B3, B4, B5]2, determine the

new final overall priority of the factors [G,R, F, E]3.

. Continuing in this manner, replace the final overall priority with the new

values, unless the values of the final overall priority of the locations in two

successive iterations become equal. The values obtained after the iterations will

give the actual overall priority of the locations: [B1, B2, B3, B4, B5] for the

branch locations and [M1,M2,M3,M4,M5] for the manufacturing locations,

and are shown in tables A24–27.

3.3. Determining the priority of supplying an SKU from one manufacturinglocation to a given branch location

From the above step, the final priority of each branch and manufacturing loca-

tion is obtained. Once these priorities are known, the priority of supplying an SKU

from one manufacturing location to a given branch location can be determined by

performing the matrix multiplication of the branch and manufacturing location

priority, resulting in table 5 as:



M1 M2 M3 M4 M5

B1 0.0174 0.0399 0.0228 0.0174 0.0087B2 0.0360 0.0825 0.0471 0.0359 0.0179B3 0.0218 0.0499 0.0285 0.0218 0.0109B4 0.0587 0.1345 0.0767 0.0586 0.0292

Table 5. [BM] overall priority matrix of supplying a SKU to abranch from a particular manufacturing location.

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½BM5�5� ¼ ½B1;B2;B3;B4;B5�T � ½M1;M2;M3;M4;M5� ¼ fXijg;

i ¼ 1; . . . ; 5 and j ¼ 1; . . . ; 5;

where Xij is the priority of supplying an SKU to branch i from manufacturinglocation j.

3.4. Development of a decision matrix to allocate production plansAs the present two activities of the supply chain are acting at a lower interaction

level, there is a necessity to develop a new logic of production planning that couldaccelerate the penetration of SCC into other functional areas. To address a produc-tion plan to different manufacturing locations, we used the concept of minimizationof the total cost of supply chain. The total cost is based on the interrelationship ofvariable manufacturing costs, transportation cost, tax rates, inventory carrying costsand warehousing cost. To avoid many tables, we prepared the transportation modeland actual cost elements for one SKU (SKU 1) only. However, the same method ofcalculation will prevail for all other SKUs as well.

The actual cost elements for each SKU for a planning period was calculated byconsidering all the above-mentioned costs. This is represented in the form of trans-portation matrix as (table 7):

½T � ¼ Xijkm;

where i ¼ 1; . . . ; 5; j ¼ 1; . . . ; 5; k ¼ 1; . . . ; 3;m ¼ 1; . . . ; 3; and Xijkm is the cost ofsupplying one unit of SKU 1 to branch i at period k from manufacturing locationj at period m. In this study, only the cost elements for SKU 1 has been shown and themodel will be solved for only SKU 1.

Estimation of X :

. Xij11 (i ¼ 1; . . . ; 5; j ¼ 1; . . . ; 5): the cost of supplying one unit of SKU 1 tobranch i at period 1 from manufacturing location j at period1 ¼ manufacturing cost þ transportation cost ðto supply one unit of SKU1from manufacturing location j to branch location iÞ þ tax rates ðvaries loca-tion-wiseÞ þ warehouse cost (varies location-wise).

. Xijkm ði ¼ 1; . . . ; 5; j ¼ 1; . . . ; 5Þ ¼ 9999 ðpenaltyÞ, for m > k. Being infeasiblefor manufacturing location j, supplying SKUs produced in a later period andmeeting the demand of a branch location i at a previous period, the penalty isintroduced.

. Xijkm ¼ Xij11 þ ðkmÞ � inventory holding cost (assumed 10 per period perunit of SKU), for k > m.



M1 M2 M3 M4 M5

B1 0.9826 0.9601 0.9772 0.9826 0.9913B2 0.9640 0.9175 0.9529 0.9641 0.9821B3 0.9782 0.9501 0.9715 0.9782 0.9891B4 0.9413 0.8655 0.9233 0.9414 0.9708B5 0.9699 0.9310 0.9606 0.9699 0.9850

Table 6. [BM 0] non-desirability matrix of supplying an SKU toa branch from a particular manufacturing location.

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M11

M21

M31

M41

M51

M12

M22

M32

M42

M52

M13

M23

M33

M43

M53

Dd*

B11

915

915

900

841

862

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

5524

B21

714

670

724

706

716

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

369

B31

756

810

813

805

812

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

4421

B41

797

806

804

796

810

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

2800

B51

839

848

830

826

852

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

138

B12

925

925

910

851

872

925

925

910

851

862

9999

9999

9999

9999

9999

4410

B22

724

681

734

716

726

724

681

734

716

716

9999

9999

9999

9999

9999

2870

B32

766

820

823

815

822

766

820

823

815

812

9999

9999

9999

9999

9999

4616

B42

807

816

815

806

820

807

816

815

806

810

9999

9999

9999

9999

9999

2100

B52

849

858

840

836

862

849

858

840

836

852

9999

9999

9999

9999

9999

207

B13

935

935

920

860

882

935

935

920

860

903

925

925

910

851

862

1575

B23

734

691

744

726

737

734

691

744

726

756

724

681

734

716

716

4000

B33

776

830

833

825

832

776

830

833

825

848

766

820

823

915

812

3704

B43

817

827

825

816

830

817

827

825

816

840

807

816

815

806

810

2250

B53

859

869

850

946

872

859

869

850

846

871

849

858

840

836

854

207

Sy

3451y

3451

3135

3531

3135

3451y

3451

2300

2701

2300

3584y

3584

1437

1686

1445

42642

*D

emands(D

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ista

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cell

1ofta

ble

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yThes

eare

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pply

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(Sy)ofSK

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cation

1atdiff

eren

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iods.

Theadditio

nofth

esequantities

equals

theto

talca

pacity

valu

efo

rSK

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table

4.

Table

7.

Tra

nsp

ortation

model

with

act

ualco

stelem

ents

(IN

Rper

unit)fo

rSK

U1.

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M11

M21

M31

M41

M51

M12

M22

M32

M42

M52

M13

M23

M33

M43

M53

Dd

B11

899

878

879

826

855

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

5524

B21

688

615

690

681

703

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

369

B31

740

770

790

787

803

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

4421

B41

750

698

742

749

786

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

2800

B51

814

789

797

801

839

9999

9999

9999

9999

9999

9999

9999

9999

9999

9999

138

B12

909

888

889

836

864

909

888

889

836

855

9999

9999

9999

9999

9999

4410

B22

698

625

699

690

713

698

625

699

690

703

9999

9999

9999

9999

9999

2870

B32

749

779

800

797

813

749

779

800

797

803

9999

9999

9999

9999

9999

4616

B42

760

706

752

759

796

760

706

752

759

786

9999

9999

9999

9999

9999

2100

B52

823

799

807

811

849

823

799

807

811

839

9999

9999

9999

9999

9999

207

B13

919

898

899

845

874

919

898

899

845

895

909

888

889

836

855

1575

B23

708

634

709

700

724

708

634

709

700

742

698

625

699

690

703

4000

B33

759

789

809

807

823

759

789

809

807

839

749

779

800

895

803

3704

B43

769

716

762

768

806

769

716

762

768

815

760

706

752

759

786

2250

B53

833

809

817

918

859

833

809

817

821

858

823

799

807

811

841

207

Sy

3451

3451

3135

3531

3135

3451

3451

2300

2701

2300

3584

3584

1437

1686

1445

42642

Table

8.

Tra

nsp

ortation

model

with

tota

lco

stelem

ents

(modifi

edco

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ents

after

adju

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tive

fact

ors

)fo

rSK

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M11

M21

M31

M41

M51

M12

M22

M32

M42

M52

M13

M23

M33

M43

M53

Dd

B11

3531

1993

5524

B21

3451

369

369

B31

3451

197

773

4421

B41

2800

2800

B51

138

138

B12

2701

1709

4410

B22

2870

2870

B32

3451

574

591

4616

B42

581

1519

2100

B52

207

207

B13

666

909

1575

B23

3584

416

4000

B33

3584

120

3704

B43

1437

813

2250

B53

207

207

Sy

3451

3451

3135

3531

3135

3451

3451

2300

2701

2300

3584

3584

1437

1686

1445

42642

Table

9.

Fin

alalloca

tion

table

forSK

U1.

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It is evident that having associated the elements of matrix [BM] with the costelements, the lower the probabilities of shipping from a manufacturing location toa branch, the higher will be the costs. Hence, we have to modify the elements of table5 as:

½BM 0� ¼ 1 ½BM�;which gives the non-desirability of shipping from a manufacturing location to agiven branch location (table 6). Thus, this matrix becomes compatible with thetransportation matrix and can be directly incorporated.

The elements of table 6 will be used to prepare the transportation matrix of table8 in conjunction with table 7. Note that the element of table 6 corresponding to aparticular branch and manufacturing location combination multiplied with the cor-responding combination of table 7 remains constant across the planning horizon of 3months. This figure gives the total cost element for the transportation matrix.

3.5. Development of the transportation matrixThe transportation algorithm was then used where the cells of the transportation

matrix were filled with the total cost elements (including the intangible factors). Bytaking weighted average capacities and demands as inputs and applying the least costmethod to allocate the plans, the minimization of the total cost of the supply chain isachieved.

The final transportation matrix (table 8) was solved using the transportationalgorithm to give the final optimum allocations in terms of quantities (table 9).The allocations when multiplied with their corresponding unit costs gives the totalcost of transportation

Total cost ðIndian RupeesÞ ¼ INR31:783907 millions:

4. Conclusions

The whole exercise is a vivid representation of the use of AHP to include theinfluences of non-quantifiable parameters in evaluating the optimum productionquantity allocations to optimize the total supply chain costs. This optimum alloca-tion will help the production planners to review the supply schedule more mean-ingfully to avoid the inventory-carrying cost in the upstream. Since the productionplan is interdependent with the cost of supply chain activities, the inventory turnoverwill also be high.

Approximately a 5% saving on the transportation cost alone was achieved.Although the percentage saving is small, the actual cost saving was INR12 million.However, since the tax rates vary in various states and are not uniform, more savingcould have been accrued with a lesser tax rate. The effect of adapting the proposedmethodology is clearly visible in the company’s balance sheet where the inventoryturnover ratio increased from 7.15 to 7.25 times. Though the increase in the ratioappears to be marginal, the actual inventory reduction achieved is INR30 million.This results in better cash flow and a less inventory carrying cost.

The impact of the proposed integrated approach to a production plan withlogistics through a supply chain outlet pulling the demand had reduced the averagedelivery lead-time from 6 to 4 days. This leads to a reduction in the stock at eachlevel in the distribution channels and nodes by 2 days, which amounts to INR20million.



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The method suggested projects a complete picture of a production planning

system in connection with manufacturing and branch level distribution, which

should be able to systematize and ease out the delivery legacies prevailing in the

company.

Appendix



B1 B2 B3 B4 B5

B1 1 1/2 3 1/3 3/2B2 2 1 3 1/2 2B3 1/3 1/3 1 1/4 1/2B4 3 2 4 1 3/2B5 2/3 1/2 2 2/3 1

�max ¼ 5:16, CI ¼ 0:04, CR ¼ 0:04.

Table A1. Reciprocal matrix for the rela-tive importance of branch locationswith respect to branch factors G.

B1 B2 B3 B4 B5

B1 1 1/3 1 1/4 1/2B2 3 1 2 1/2 2B3 4 2 1 1/4 1/2B4 4 2 4 1 3/2B5 2 1/2 2 2/3 1

�max ¼ 5:08, CI ¼ 0:02, CR ¼ 0:02.

Table A2. Reciprocal matrix for the rela-tive importance of branch locationswith respect to branch factors R.

B1 B2 B3 B4 B5

B1 1 1/2 1/2 1/4 1/2B2 2 1 3/2 1/2 1B3 2 2/3 1 1/3 1/2B4 4 2 3 1 2B5 2 1 2 1/2 1

�max ¼ 5:04, CI ¼ 0:01, CR ¼ 0:01.

Table A3. Reciprocal matrix for the rela-tive importance of branch locationswith respect to branch factors F.

B1 B2 B3 B4 B5

B1 1 1/2 1 1/3 1/2B2 2 1 3/2 1/2 1B3 1 2/3 1 1/3 1/2B4 3 2 3 1 2B5 2 1 2 1/2 1

�max ¼ 5:02, CI ¼ 0:004, CR ¼ 0:004.

Table A4. Reciprocal matrix for therelative importance of branch locationswith respect to branch factors E.

M1 M2 M3 M4 M5

M1 1 1/3 1 1/2 3M2 3 1 2 2 4M3 1 1/2 1 3/2 3M4 2 1/2 2/3 1 2M5 1/3 1/4 1/3 1/2 1

�max ¼ 5:15, CI ¼ 0:04, CR ¼ 0:03.

Table A5. Reciprocal matrix for the rela-tive importance of manufacturing loca-tions with respect to manufacturingfactors G.

M1 M2 M3 M4 M5

M1 1 1/2 1 1 3/2M2 2 1 3/2 3/2 3M3 1 2/3 1 1 3/2M4 1 2/3 1 1 3/2M5 2/3 1/3 2/3 2/3 1

�max ¼ 5:02, CI ¼ 0:004, CR ¼ 0:004.

Table A6. Reciprocal matrix for the rela-tive importance of manufacturing loca-tions with respect to manufacturingfactors C.

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M1 M2 M3 M4 M5

M1 1 1/4 1/3 1/2 2M2 4 1 3 2 7M3 3 1/3 1 1/3 2M4 2 1/2 3 1 4M5 1/2 1/7 1/2 1/4 1

�max ¼ 5:2, CI ¼ 0:05, CR ¼ 0:05.

Table A7. Reciprocal matrix for the rela-tive importance of manufacturing loca-tions with respect to manufacturingfactors L.

M1 M2 M3 M4 M5

M1 1 1/3 1/2 2 3M2 3 1 2 3 5M3 2 1/2 1 2 4M4 1/2 1/3 1/2 1 2M5 1/3 1/5 1/4 1/2 1

�max ¼ 5:08, CI ¼ 0:02, CR ¼ 0:02.

Table A8. Reciprocal matrix for therelative importance of manufacturinglocations with respect tomanufacturing factors F.

M1 M2 M3 M4 M5

M1 1 1/2 1 1 2M2 2 1 2 3/2 2M3 1 1/2 1 1 3/2M4 1 2/3 1 1 1M5 1/2 1/2 2/3 1 1

�max ¼ 5:07, CI ¼ 0:02, CR ¼ 0:01.

Table A9. Reciprocal matrix for the rela-tive importance of manufacturing loca-tions with respect to manufacturingfactors S.

G R F E

B1 0.1682 0.0905 0.0894 0.1101B2 0.2474 0.2509 0.1913 0.1959B3 0.0738 0.0982 0.1330 0.1166B4 0.3584 0.3746 0.3826 0.3697B5 0.1521 0.1858 0.2038 0.2077

Table A10. [BF5�4] factor-wise prioritiesof the branch locations.

G C L F S

M1 0.1574 0.1796 0.0995 0.1680 0.1929M2 0.3735 0.3205 0.4281 0.4037 0.3144M3 0.2067 0.1901 0.1539 0.2482 0.1796M4 0.1871 0.1901 0.2586 0.1165 0.1768M5 0.0753 0.1117 0.0599 0.0635 0.1363

Table A11. [MF5�5] factor-wise priorities ofthe manufacturing locations.

G R F E

G 1 1/2 1/2 3R 2 1 1/2 5F 2 2 1 6E 1/3 1/5 1/6 1

�max ¼ 4:05, CI ¼ 0:02, CR ¼ 0:02.

Table A12. Reciprocal matrix for the rela-tive importance of branch locationfactors with respect to branch B1.

G R F E

G 1 1/2 1/2 2R 2 1 1 4F 2 1 1 2E 1/2 1/4 1/2 1

�max ¼ 4:08, CI ¼ 0:03, CR ¼ 0:03.


G R F E

G 1 1/2 1/3 2R 2 1 1 4F 3 1 1 4E 1/2 1/4 1/4 1

�max ¼ 4:02, CI ¼ 0:01, CR ¼ 0:01.


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G R F E

G 1 1/3 1/2 3R 3 1 1/2 4F 2 2 1 4E 1/3 1/4 1/4 1

�max ¼ 4:16, CI ¼ 0:05, CR ¼ 0:06.

Table A15. Reciprocal matrix for therelative importance of branch locationfactors with respect to branch B4.

G R F E

G 1 1/2 1 2R 2 1 1/2 5F 1 2 1 4E 1/2 1/5 1/4 1

�max ¼ 4:23, CI ¼ 0:08, CR ¼ 0:09.

Table A16. Reciprocal matrix for therelative importance of branch locationfactors with respect to branch B5.

G C L F S

G 1 2 3 1/2 5C 1/2 1 4 1/3 2L 1/3 1/4 1 1/5 1/2F 2 3 5 1 7S 1/5 1/2 2 1/7 1

�max ¼ 5:2, CI ¼ 0:05, CR ¼ 0:04.

Table A17. Reciprocal matrix for therelative importance of manufacturinglocation factors with respect to manu-facturing location M1.

G C L F S

G 1 2 2 1/2 5C 1/2 1 4 1/3 2L 1/2 1/4 1 1/5 1F 2 3 5 1 7S 1/5 1/2 1 1/7 1

�max ¼ 5:2, CI ¼ 0:05, CR ¼ 0:04.


G C L F S

G 1 2 3 1 4C 1/2 1 4 1/3 3L 1/3 1/4 1 1/4 1F 1 3 4 1 5S 1/4 1/3 1 1/5 1

�max ¼ 5:12, CI ¼ 0:03, CR ¼ 0:03.


G C L F S

G 1 3 4 1 5C 1/3 1 4 1/2 2L 1/4 1/4 1 1/4 1/2F 1 2 4 1 5S 1/5 1/2 2 1/5 1

�max ¼ 5:15, CI ¼ 0:04, CR ¼ 0:04.


G C L F S

G 1 2 4 1 5C 1/2 1 3 1/2 2L 1/4 1/3 1 1/5 1/2F 1 2 5 1 7S 1/5 1/2 2 1/7 1

�max ¼ 5:11, CI ¼ 0:03, CR ¼ 0:03.


B1 B2 B3 B4 B5

G 0.1884 0.2089 0.1616 0.1808 0.2016R 0.3023 0.3679 0.3555 0.3271 0.3567F 0.4443 0.3179 0.3939 0.4129 0.3612E 0.0651 0.1051 0.0889 0.0792 0.0804

Table A22. [FB4�5] branch-wise prioritiesof the branch factors.

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References

Backer, E. K. and Golden, B. J., 1985, Future directions of logistics research.Transportation Research, 19A, 405–409.

Bagchi, P. K., 1989, Carrier selection: the analytic hierarchy process. Logistics andTransportation Review, 25, 63–73.

Ballou, R. H., 1999, Business Logistics Management—Planning, Organizing and Controllingthe Supply Chain (Englewood Cliffs, NJ: Prentice-Hall).

Chopra, S. and Meindl, P., 2001, Supply Chain Management: Strategy Planning, andOperation (Englewood Cliffs, NJ: Prentice-Hall).



M1 M2 M3 M4 M5

G 0.2593 0.2461 0.3008 0.3559 0.3246C 0.1642 0.1706 0.1912 0.1704 0.1692L 0.0621 0.0775 0.0769 0.0631 0.0622F 0.4369 0.4405 0.3608 0.3263 0.3637S 0.0774 0.0654 0.0704 0.0842 0.0803

Table A23. [FM5�5] manufacturing location-wise priorities of the manufacturingfactors.

ITERATION I II III

G 0.1884 0.1889 0.1891R 0.3023 0.3426 0.3427F 0.4443 0.3836 0.3834E 0.0651 0.0848 0.0848

Table A24. Final overall priorities ofbranch factors.

ITERATION I II III

B1 0.1057 0.1062 0.1062B2 0.2168 0.2194 0.2194B3 0.1346 0.1329 0.1329B4 0.3585 0.3579 0.3579B5 0.1844 0.1837 0.1837

Table A25. Final overall priorities ofbranch locations.

ITERATION I II III IV

G 0.2593 0.2841 0.2844 0.2844C 0.1642 0.1739 0.1738 0.1738L 0.0621 0.0713 0.0712 0.0712F 0.4369 0.3981 0.3978 0.3978S 0.0774 0.0727 0.0727 0.0727

Table A26. Final overall priorities of manufacturing factors.

ITERATION I II III IV

M1 0.1648 0.1639 0.1639 0.1639M2 0.3768 0.3759 0.3758 0.3758M3 0.2167 0.2146 0.2145 0.2145M4 0.1604 0.1639 0.1638 0.1638M5 0.0812 0.0816 0.0817 0.0817

Table A27. Final overall priorities of manufacturing locations.

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Fichtner, J., 1986, On deriving priority vectors from matrices of pairwise comparisons.Socio-Economic Planning Science, 20, 341–345.

Handfield, R. B. and Nicholas, E. L., Jr, 1999, Introduction to Supply Chain Management(Englewood Cliffs, NJ: Prentice-Hall).

O’Neil, B. F. and Bommer,M.R.W., 1988, Direct store delivery distribution—an evaluation.Logistics and Transportation Review, 24, 237–247.

Saaty, T. L., 1980a, A scaling method for priorities in hierarchical structures. Journal ofMathematical Psychology, 15, 234–281.

Saaty, T. L., 1980b, The Analytical Hierarchy Process (New York: McGraw-Hill).Saaty, T. L. and Kearns, T., 1985, Analytical Planning (New York: McGraw-Hill).Weiss, E. N. and Rao, V. R., 1987, AHP design issues for large-scale systems. Decision

Sciences, 18, 43–61.


314 Supply chain cell activities for a consumer goods company

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Supply chain cell activities for a consumer goods company

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