A Model Based Decision Support

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45A Model-Based Decision Support to Manage Outbound Logistics

A Model-Based Decision Supportto Manage Outbound LogisticsM Raja*

IntroductionNew models of managing and conducting businesses demand the organizations

to manage spatially spread organizational units and sub-units through

networking which in turn presumes to implement an integrated logistics planning

as a strategic option (Alan, 2001). Integration has been the focus in the

development of logistics which endeavors to integrate the supply and distribution

network that may comprise different tiers of suppliers and distributors and uses

different modes and means of transporting the goods. This integration has

enhanced the importance of logistic function and made the top management to

give a strategic importance to it. The strategic models as envisaged by Porter

(1985) have identified a central role to the integrated logistic function to make

a major contribution to the competitiveness and growth of a business.

2009 IUP. All Rights Reserved.

* Director, IIST, Hyderabad 500055, India. E-Mail: [email protected]

The logistics involved as a part of Supply Chain Management (SCM)

demand the development of appropriate models to support decision

making. In real life large business environment, the situation demands

handling large-scale data and rapid development of models to process

the same. These data may emerge from online transaction systems and

the same need to be prepared or consolidated to input to the

optimization models. The widespread implementation of Enterprise

Resource Planning (ERP) systems provides ample opportunity to access

transactional databases for the integration of supply chain activities

(Jeremy, 2008). In this paper, a prototype was developed to implementa decision support system for outbound logistics by a large cement

manufacturing organization with multiple plants and distribution channels.

While the dataflow from the transaction system to the optimization model

was developed smoothly and transparently, the output of the model is

integrated to the decision support system to manage outbound logistics.

The approach adopted to integrate the transaction processing application

with the decision support system is aimed at strengthening the existing

scalable Supply Chain Infrastructure (SCI).


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The IUP Journal of Systems Management, Vol. VII, No. 4, 200946

This integration involves the primary activities, namely the inboundand outbound

logistics, operations, marketing and sales and services across the organizational

units of several firms, involved in the supply chain network. An effective

management of logistics network can maximize contribution, reduce turnaround

times, and make product distribution cycle shorter and more efficient. While

planning such logistics, the inbound logistics which spans sourcing and

procurement, poses problems related to where to acquire materials andcomponents, where to store, how much to store and how to retrieve from stores,

etc. On the other hand, the outbound logistics which spans post-manufacturing

delivery into distribution channels and sales outlets poses problems related to

what markets to serve, what modes of transport to engage and when to ship, etc.

In the case of market-driven manufacturing, the logistics of manufacturing such

as what to produce, how much to produce, when to produce, etc., is bound to

depend on the outbound logistics. The manufacturing strategy and decisions

combined together with inboundand outbound logistics strategy and decisions

enable the supply chain to become more efficient and responsive, and helps the

organization to respond to the changing demand-supply scenarios at minimum

cost and time. Some of the issues related to the outbound logistics for those

organizations, which manufacture and distribute bulk materials such as cement

and fertilizers, revolve around many questions that remain answered using

management tools and techniques. What form of transportationby road, by rail

or by seais ideal to deliver the material? What size of the fleet and which routes

to optimize the distances and reduce costs without compromising on the delivery

schedules? How to reduce losses due to damage and pilferage? How to streamline

the number of stock points or depots reducing inventory based on demand and

rationalizing delivery based on demand, consumer segments and price realization.

To get the answers, the logistics management systems run algorithms simulating

the output under varying inputs. Taking the plants and market locations fixed

the software-enabled models run various permutations and combinations to

decide modes of transport, fleet sizes, route-planning to locations of depots or

supply points to work out optimum solutions.

An important factor in the implementation of mathematical programming is

the automatic generation of the mathematical formula in a format compatible to

the optimizational software. Modeling languages such as AMPL to formulate

formats in matrix forms to interface with optimizing softwares like CPLEX, MINTO

and XPRESS (Nitin, 2003). Since real life problems have to handle hundreds or

thousands of variables and constraints which also aid the multiple decision

making process, there is a growing interest in the development of modeling

languages and packages. In the present case study, the focus was to establish

information flow between transaction processing which is handled by the

Enterprise Resource Planning (ERP) software SAP R/3 and the optimization models


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developed using inbuilt modeling language of an application development

package called SAS.

Transportation Model StructureThe transportation constitutes the highest single cost of the supply chain

logistics accounting more than 60% of the total logistics cost (IBM, 2005). The

transportation model in Supply Chain Management (SCM) in general andoutbound logistics in particular, focuses on optimization of a number of criteria,

like maximizing the utilization of all the available products, maximizing the

realization or contribution, meeting the minimum demand, etc. These objectives

need to be satisfied subject to various constraints posed by loading and

unloading facilities, transport modes and capacities, sizes of the fleet deployed,

etc. Optimization techniques such as liner programing, inter programing and

goal programing were used to obtain solutions for these problems. The block

diagram given in Figure 1 may depict a generic transportation model structure.

Availability Constraints

Loading/Unloading Constraints

Packing Constraints

Minimum and Maximum DemandConstraints

Minimum and Maximum GoodsAvailable

Mode and Route Capacity Constraints

Market Segmentation Constraints

Constraints on Dispatch Volumes, etc.

Figure 1: A Generic Structure of a Logistics Model

Maximize Product Utilization

Ensure Minimum Demand

Maximize Net Realization

Maximize Contribution

Minimize Inventory Level

Avoid Waiting Time at Loading and

Unloading Points, etc.

Destination-Wise Movement PlanSource-Wise Dispatch Plan

Product-Wise Movement Plan

Estimated Costs-Freights, Taxes, Handling Charges, etc.

Total ContributionMarket Segmentation-Wise, DistributionChannel-Wise


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Though the implementations of optimization of problems have been attempted

by many organizations over a period of time, the success stories of integrating

the same as part of logistics management are yet to be demonstrated by many.

The implementation issues revolve around some of the following impediments:

The model conceptualization and its synchronization with the business

objectives and handling of conflict objectives are major obstacles.

The mathematical formulation of models and constraints needs to be

done dynamically.

The number of variables to be handled by the software and the computer

time required to solve the same, pose problems to the computing

resources.

The volume of data to be gathered and organized for the input to the

model is high.

The absence of incorporating comprehensive data checking and

business rules to avoid infeasible solutions poses severe threat while

running the models.

The routine translation of the model output into an operational plan

needs to be programmed.

Building decision support systems around the model and solvers and

integrating the same with the existing information infrastructure need to

be synchronized with the overall information system and technology plan.

Software Systems for Supply Chain and EnterpriseManagementERP and SCM are two categories of software which are widely used in many

manufacturing and distribution organizations as enterprise systems. While the

ERP systems support integrated transaction processing, backbone of an

organization, the SCM systems provide capabilities to coordinate and execute

organization-wide manufacturing and distribution processes and functions. While

ERP systems enhance organizational performance by reducing or eliminating

inconsistent information and increasing transaction processing efficiency, the

SCM systems are aimed to provide decision support and business planning

capabilities (Ball et al., 2002). Software developed to integrate enterprise

applications specifically for the supply chain is designed to achieve a number

of purposes, such as to connect trading partners with the users enterprise

systems, allow companies to use internet to reduce communication cost (John,

2008). These products differ widely in scope and breadth. Some focus on

transportation and the others on order management. SCM and ERP software


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packages are available in the market for quite some time and are now resulting

in integrated ERP/SCM solutions (Linthicum, 2000). As the industries have gained

experience in implementing these packages, the software vendors have enhanced

the features and capabilities which overlap in many aspects. The most prominent

resource planning software packages, like SAP R/3, J D Edwards, Baan, Avalon,

and PeopleSoft, etc., have included various modules to meet the requirements of

the customers. However, the Delphi study (Henk et al., 2003) conducted with 23Dutch supply chain executives shows that only a modest role for ERP in improving

future supply chain effectiveness and a clear risk of ERP actually limiting progress

in SCM. As a consequence, a number of SCM software packages such as i2,

Manugistics and Logility seem to attract the market. The Automation Research

Corporation depicts the classification of industries and the fitness of some of

these standard supply chain packages as shown in Figure 2.

The potential benefits to be derived by implementing these packages are, of

course, enormous provided that a number of decision support systems are

Figure 2: A Classification of the Software Packagesfor the SCMPROCESS

DISCRETE

INDUSTRY CONSUMERManufacturing

Intensive

Distribution Intensive

Sourcing Intensive

Semi Conductors

ConsumerDurables

i2BaanSynQuestManugisticsHK Systems

i2ParagonPeopleSoftManugisticsIMI

Drugs

Personal Care

Pulp

Bulk Chemicals

Textiles

AutoSimulationNumetrixi2

ManugisticsLogilityEXEIMI

Garments

Oil and Gas

SpecialtyChemicals

TelecomEquipment

Automobile


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identified and developed to exploit the amount of data these packages collect

and organize. The advantages often come at a high price. However, when it comes

to a stage, where we need to do aggregate planning, which is generally what the

decision-makers need to do, the solution requires various kinds of tools and

techniques. For example, the organization referred in this case study has invested

to implement various modules such as material management, sales and

distribution modules and production planning module for process industry of SAPR/3. At the end of implementation, it was realized that the implemented modules

do not support optimization techniques such as linear/goal programming

techniques. In such situations, the decision support models developed around

these techniques need to be integrated with the business processes configured

with the standard packages such as ERP packages. If similar models identified

as part of SCM are already developed using standard packages such as SAS and

put into operational use, the integration with transactional databases becomes

easier. In addition to this, if decision support models are built using packages

such as SAS system, which has in-built modules such as data warehousing and

mining tools, it will significantly improve the managements ability to exploit

information. The hardware and software generally used to handle SCM is

collectively referred as Supply Chain Infrastructure (SCI). Some of the major issues

related to this infrastructure are reported in Ball et al., 2000.

Enterprise Applications IntegrationThe integration of enterprise applications has the aim of facilitating the seamless

information exchange between various business applications to achieve

organizational goals and objectives. With the development of web and middleware

technologies (Umar, 2003), the problems related to the integration of application

are being increasingly addressed. The convergence of this hardware with the

internet technology transforms the information-based applications into service-

based enterprise-wide applications (Kostas, 2002). The integration of legacy

applications with other critical software system provides various advantages.

While it minimizes or eliminates the costs involved in re-developing the existing

applications (Umar, 1997), it extends the utilization of such applications and

thereby increases the life-cycle of legacy systems. When the transaction

processing systems were built using service-oriented architecture where services

and applications were viewed as objects with well-defined interfaces, objectwrappers play a vital role in integrating the legacy applications. Web-based

application development allows integrating enterprise-wide applications through

web gateways such as Common Gateway Interface (CGI) and Server Side Includes

(SSI). However, when the existing legacy systems were developed using integrated

system of software modules such as SAS, the models already developed and

implemented need to be integrated only thru data integration and not the control

integration. However, SAS has announced, recently, its new service-oriented


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architecture capability within the SAS enterprise intelligence platform across its

range of solutions.

A Case StudyOne of the largest cement manufacturing organizations in India having multiple

plants and multiple brands and distribution channels across a large number of

depots has to handle a complex situation while planning its supply chain aimingat reducing the distribution costs, incurred across the plants and maximizing the

contributions realized thru its various products such as various grades of cement

with various brands and cross-brands. Though the sales were done through

carry-forward agencies, the distribution management was coordinated by its

central marketing division. The block diagram given in Figure 3 depicts the value

chain for this organization at a macro level. Plants produce the main product

namely various grades of cement and dispatch the same to customers both

directly as ex-factory sales and distribute to other customers thru dealers and

carry-forward agencies. The plants may also supply semi-produced and unpacked

materials to other plants, such as grinding units and package units. Hence, the

involved logistics has to handle dispatches across its own plants and the receiving

units have to plan their own outbound logistics. In Figure 3, while Plant 1 produces

the main products and dispatches to the customers and carry-forward agencies,

it also dispatches its semi-processed material known as clinker to another plant

which is located at a geographically dispersed location for grinding. Some of the

plants producing the finished products may also dispatch the material in bulk

to other packing plants.

After a debate across senior management, the objectives and constraints to

be incorporated into the model were narrowed down and a decision support

system was evolved. The objective was narrowed down to maximize contribution

taking into account various constraints related to the utilization of the clinker

and cement, manufactured across a number of plants. Various alternative modes

of transport and their capacities need to be evaluated by monitoring and

measuring their impact on the maximum contribution to be achieved. Minimum

demands for the customers to be ensured while ensuring that the maximum

dispatches from a particular plant to a particular sub-location do not exceed the

limits agreed upon by the manufacturing association. The material availability

constraints need to be derived by taking into account various production plansdrawn by a number of manufacturing plants. The model needs to handle scenarios

related to some of the integrated plants having geographically dispersed grinding

and packing plants. The designed and developed optimization model can be

depicted using the following objective function and constraints:

Maximize (Total Contribution) = Z = Sum-Up Over (ijkl) [X(ijkl) * C(ijkl)]

where i = Plant


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j = Customer location/destination

k = Transportation mode-route

l = Product (brand/grade/packing combinations)

X(ijkl) denotes the material quantity distributed by plant ito location

j by transport mode-route k for supplying the productlwhich is a

combination of brand, grade and packing combination.

C (ijkl ) denotes the cost of material distributed by plant i to location

j by transport mode-route k for supplying the product l which is a

combination of brand, grade and packing combination.

Figure 3: Logistics Across the Involved Multiple Plants

Manage Plant 1 Manage Plant 2

InboundLogistics

Produce

Products

In-Bound

LogisticsProduceProducts

Suppliers SuppliersManageDispatches

ManageDispatches

Transport Transport

MaintainGrinding Plant

MaintainGrinding

Plant

In-BoundLogistics

Manage Sales andOutbound Logistics

ProduceProducts

ProduceProducts

ManageDispatches

ManageDispatches

CommissionAgents

Dealers CustomersCustomers

C & F Agents

Transporters


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Standard Reports from the Model OutputThe designed software can accept input from the spreadsheet and other database

systems which will be automatically transferred into data sets of SAS that may

be further altered, if necessary. The software formulates the model with

appropriate objective function and constraints and stores the same into datasets.

A number of business rules and data consistency checks were done on the input

datasets before passing it on to an OR module of the software. A typical output

of the model is as shown in Figure 5.

Figure 4: The Interface of the Outbound LogisticsPlanning System with the ERP

Min. and Max. Demand

Product Availability

Delivery Schemes

Logistics

Planning System

SAP R/3 Plants

DepotsCustomers

Figure 5: A Model Output from the LP Module


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The output generated by the OR module is again stored in a dataset from which

a number of MIS reports both tabular and graphically are being generated. These

reports allow the managers to evaluate alternative scenarios as part of decision

making. Typical graphical reports are shown in Figures 6 and 7.

Figure 6: Plant-Wise Dispatch for a Particular Product

Figure 7: Plant-Wise Contribution in Rupees per Metric Tonfor a Particular Product


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ConclusionThe implementation of ERP system offers opportunities for an organization to

access transactional databases and integrate optimization models developed to

manage supply chain activities. A logistics model was developed to handle

transportation of bulk material from a number of plants to depots, customers

and other carry-forward agencies. The same is implemented with the aid of

an operations research module of the SAS software system. The developed system

was integrated with the existing ERP software and put into operational use. The

system helped the management to significantly reduce the cost involved in

transporting the material across the country.

References1. Alan McKinnon (2001), Integrated Logistics Strategies, Handbook of

Logistics and Supply-Chain Management, Part 3, Chapter 10, Emerald Group

Publishing.

2. Ball Michael O, Boyson S, Raschid L and Sambamurthy V (2000), Scalable

Supply Chain Infrastructure: Models and Analysis, Proceedings of the 2000

NSF Design & Manufacturing Research Conference, Vancouver.

3. Ball Michael O, Meng Ma, Raschid L and Zhengying Zhao (2002), Supply Chain

Infrastructures: System Integration and Information Sharing, SIGMOD

Record, Vol. 31, No. 1.

4. Henk A Akkermans, Paul Bogerd, Enver Ycesan and Luk N van Wassenhove

(2003), The Impact of ERP on Supply Chain Management: Exploratory

Findings from a European Delphi Study, European Journal of Operational

Research, Vol. 146, No. 2, pp. 284-301.

5. Jeremy F Shapiro (2008), Modeling the Supply Chain, 2nd Edition, Thomson

Learning Press.

6. John Fontanella (2008), E-Business and Supply Chain: Is It Simply

Supply-chain.com?, AMR Research, available at http://fontanella.ASCET.com

7. Kostas Kontogiannis, Dennis Smith and Liam OBrien (2002), On the Role

of Services in Enterprise Application Integration, Proceeding of the 10 th

International Workshop on Software Technologies and Engineering Practices,

IEEE Computer Society.

8. Linthicum D (2000), Enterprise Application Integration, Addison-Wesley.


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9. Nitin Singh (2003), Emerging Technologies to Support Supply Chain

Management, Communications of the ACM, Vol. 46. No. 9.

10. Porter M E (1985), Competitive Advantage, Free Press, New York.

11. Umar Amjad (1997), Application (Re) Engineering: Building Web-Based

Applications and Dealing with Legacies, Prentice Hall PTR.

12. Umar Amjad (2003), E-Business and Distributed Systems Handbook:

Middleware Module, Nge Solutions, US.

Reference # 08J-2009-11-04-01


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