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Teaching, Research, Consulting and Training : Experience sharing
Dr S G Deshmukh Director ABV-Indian Institute of Information Technology & Management Gwalior 10 Jan 2009
National Conference on “Modeling & Simulation “ (9-11 Jan 2009)
Observation..
“Mathematical ideas originate in empirics…but [then] the subject begins to live a peculiar life of its own and … after much ‘abstract’ inbreeding, is in danger of degeneration … whenever this stage is reached, the only remedy seems to me to be the … reinjection of more or less directly empirical ideas”
John von Neumann
Remarks..
Economics, Finance, Accounting and Marketing all started up as theoretical disciplines, then enjoyed an explosion of empirical studies and came to a balance between empirics and modeling.
How much of our (modeling) research can we utilize in teaching?
How much of our (modeling) research interests top managers?
Applied research can make us more relevant as researchers, teachers, consultants as well as stimulate better models!
Teaching-Research–Consulting-Training Cycle
Teaching
Training
Consulting
Research
REAL - WORLD
SYSTEM
MODEL
• Definition of the Problem
* Construction of the Model
• Solution of the Model
* Validation of the Model
• Implementation of the Final Result
Academic Environment
Lot of opportunities to enhance teaching because of demanding students !
Research
Consulting
Training
Sprit ..
Consulting assignments
Involvement of students
Exposure to model real life
Opportunity to “play” with models Applying what has been “taught” Opportunity to enhance “soft skills” (team
work, getting along with the client)
Bottom line: Upgrade “resume” !
Assignment 1 Case study in SCM of a Tyre manufacturing company
Tyre Industry in India
16 Major Players
34 Plants
Top 5 companies hold 70% market share
Rs 9000 Crore Industry
Buyers
OEM’s 30 %
Replacement 60 %
Exports 10%
XYZ Ltd.
A leading truck tyre manufacturer in India
An ISO 9000 organization
Product range : Truck Tyres, LCV Tyres Radial Tyres, Tubes/Flaps etc.
Four Plants, 8 Regional Distribution Centres (RDC) and 98 District Warehouses
Extensive & exclusive dealer network
Sales Turnover : Rs 1500+ Crores.
30 % Business from Northern region
25 % From Rajasthan, MP , and Gujarat
20 % From Bihar and Eastern Region
Remark..
"Broadly, the cost-cutting initiatives are across two areas — manufacturing and supply chain. Our facilities are functioning at 100 per cent capacity and wastages are being minimized at every step in the production process. Our target this year is to achieve two per cent operating margins and we are well on course to achieve it.,“
CEO of XYZ (Source: Hindu Business Line, March 20, 2003)
Problems with existing Supply Chain(SC)
Not having quick response layers in the distribution system
Lack of proper information flow between the echelon and head office
Dispatch schedule based on Transportation model
Various business functions such as Forecasting, Production and, Distribution acting in isolation
Use of models ..1
Instead of existing TRANSPORTATION Model use of TRANSSHIPMENT Model
LP based model conceptualized
Data intensive model developed
Existing SC
F01 ……. F04
1 2 3 4 5 6 7 8
1 2 30 47 60 80 98
Fig 1(a) : Existing Distribution network model of XYZ
Plants
RDC’s
DO’s
Proposed SC based on TRANSHIPMENT model
F01 ……. F04
1 2 3 4 5 6
1 2 30 47 60 80 98
Plants
RDC’s
DO’s
Fig 1(b) : Proposed distribution network model of XYZ
Typical Transshipment Model
Min cijxij i j s.t. xij < si for each origin i j xik - xkj = 0 for each
intermediate i j node k xij > dj for each
destination j i
xij > 0 for all i and j
xij represents the shipment from node i to node j
Shipping based on Transshipment Model
Existing Method of Allocation based on Transportation Model
Proposed method : Based on Transshipment Model
Why Transshipment
Goods Transport between any node to any node
Reduction in Inventory
Reduction in Transportation cost.
Advantages of the proposed approach
Any node (plant,RDC,DO) can act as a supply point or demand point
More sharing of information
Supply chain becomes responsive
Overall cost reduces
Modeling and Simulation ..
Decision Support System
Model base
Data Base
User interface
Partial Spreadsheet
A B C D E F G H
1
2 Constraint X11 X12 X13 X21 X22 X23 RHS
3 #1 1 1 1 50
4 #2 1 1 1 50
5 #3 1 1 25
6 #4 1 1 45
7 #5 1 1 10
8 Obj.Coefficients 24 30 40 30 40 42 30
LHS Coefficients
Partial Spreadsheet
A B C D E F G
10 X11 X12 X13 X21 X22 X23
11 Dec.Var.Values 5 45 0 20 0 10
12 Minimized Total Shipping Cost 2490
13
14 LHS RHS
15 50 <= 50
16 30 <= 50
17 25 = 25
18 45 = 45
19 10 = 10E.Dem.
W.Dem.
N.Dem.
Constraints
P1.Cap.
P2.Cap.
Partial Sensitivity Report
Constraints
Final Shadow Constraint Allowable Allowable
Cell Name Value Price R.H. Side Increase Decrease
$E$17 P2.Cap 30.0 0.0 50 1E+30 20
$E$18 N.Dem 25.0 30.0 25 20 20
$E$19 W.Dem 45.0 36.0 45 5 20
$E$20 E.Dem 10.0 42.0 10 20 10
$E$16 P1.Cap 50.0 -6.0 50 20 5
IT Enablers…2... DSS
User Dialogue
Module
Database
Module
Model base
Forecast
Logistics
Master production schedule
dispatches
inventory
relocation
Fig 2 : A Decision Support System for integrated supply chain
Model base
LP model to optimize the costs
Forecasting model for aggregate planning
Inventory model to manage inventory
Model Purpose of the Model
Forecast( Trend Analysis) Using Trend lines, Moving average to
forecast the demand for Do’sLogistic (Transportation Algorithm) Prepare appropriate product mix- Plant
wise, item wise taking consideration of
total Logistic cost.
MPS (Linear programming) Prepare the MPS taking in to consideration
the contribution and capacities of each item
at each plant.
Dispatches(Transshipment
Algorithm)
Unifying the inventory at various nodes
and minimize the transportation cost.
Inventory Using MPS to find the Material
requirement at each plant for a month and
to decide whether to place order.
Reallocation To minimize the total distribution cost.
Various models in the DSS
Model Databases
Forecast (Trend Analysis) AVG9697,AVG9798,
COST_MAS,FORE_OUT,FORE_DO
Logistic (Transportation Algorithm) TRA_COST,DEMAND,CAP,MFG_Y_N,C
OST,LOG_MIN,MPS,CAPACITY
MPS (Linear programming) VX_TEMP,VX_ICODE,PM_EQ1,PM_TEM
P
Dispatches(Transshipment Algorithm) TRANSP,TOT_DEM,TOT_SUP,T_COST,T
_COST2,TOT_OUT
Inventory BOM,RM_DEM,LOG_MIN,MPS,REVIEW,
RM_DE_HS, VENDOR,ORDER_P1
Reallocation RELOCOUT,INPUT1,INPUTR-1
Databases for Various Models
Experimentation with model..
Sensitivity analysis
“What if” scenarios related to
Cost ,Capacity
Demand, Budget, Manpower
Quantification of impacts of
Delayring the supply chain
Adding an additional node
Cost parameters
Snapshot of a typical solution
OBJECTIVE COEFFICIENT RANGES
Variable Lower Limit Current Value Upper Limit
X13 3.000 5.000 7.000 X14 6.000 8.000 No Limit X23 3.000 7.000 No Limit X24 No Limit 4.000 6.000 X35 No Limit 1.000 4.000 X36 3.000 5.000 7.000 X37 5.000 8.000 No Limit X45 0.000 3.000 No Limit X46 2.000 4.000 6.000 X47 No Limit 4.000 7.000
Benefits to the Client …
Enhancement of effective Information flow
Better monitoring of Inventory
Reduction in Transportation costs
Reduction in lead time
Reduction in inventory
Reduction in Hidden costs
Benefits to students
Enhanced soft skills
Exposure to real life
Change in perception:
Theory can be applied !
Modeling has real life use !!
Benefits to the teacher
Real life case wherein efficacy of
model building is actually
demonstrated
Use of Sensitivity analysis to gain
insights
Teaching gets strengthened
Outcomes …
Four master’s thesis
Publication of Paper
Case material developed
Use in training module
Student got good jobs !
More consulting assignments from similar domain areas
Insights …
SCM involves integration and interfacing of various business functions
Shared perception is necessary
Critical Issues- Management of Inventory, information, and infrastructure
IT plays an important role in SCM
Insight..
Building a model is an iterative, trial-and-error process. A model is usually built up in steps of increasing complexity until it is capable of replicating the observed [or anticipated] behavior of the system [in sufficient but not excessive detail]. Then it is used to learn whether the simulated behavior can be improved by changes in … variables….
What students gained ?
Value addition through professional experience in handling a live project
Importance of modeling
Interactions with the leading group
Sharpened analytical skills: enhanced communication skills
Insights into model building
Integration with teaching : MEL756: Supply Chain Management Topics Example Through
Exposure to Supply Chain
Maruti Udyog, Training Programme
Perishable Products Amul Training Programme
Supply Chain Coordination
Semi-conductor Manufacturing
Research
Cost and Inventory Tyre Manufacturing
Consultancy
Transportation Indian Railways Sponsored Research
Information Systems
India Today Consultancy
Performance Measurement
Indian Army Research, Consultancy
Closing remarks..
If we want to be relevant as a discipline, we must strike a balance between the empirical data and models. This is the way to differentiate ourselves !
Applied research appeals to much wider audiences, leads to more consulting, better teaching and training opportunities !
Every other area of business is doing it so there is little hope that we can get away with just models.
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