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Next
issue: Variability
and Supply Chain
Previous: Managing
Complexity
Some of the most complex systems in the present
web-centric world are supply chains spanning across enterprises, linking
suppliers and manufacturers to customers all over the world.
Supply chain performance is a resultant of interaction
among the supply chain players like manufacturers, distributors,
retailers, and so on. The behavior of these players affects, and is
affected by structure, policy, material and information flow. Analyzing
supply chains becomes complex given the dynamic interactions among these
entities and the uncertainty associated with demand and supply.
Simulation is ideal for mapping
these complex interactions and incorporating
uncertainty through What-If Analyses. It can help evaluate supply
chains in terms of performance parameters, without perturbing the reality.
Criteria
for classifying Supply Chain Simulation:
| 1. |
Granularity: Decision on right layout for loading
bay inside a warehouse requires far more detail than decision on
warehouse location or capacity. |
| 2. |
Abstraction: While conventional simulation
can simulate material and information flow, agent-based
simulations can simulate
the actual behavior of supply chain players, which are impacted
by and in turn affect material and information flow. |
| 3. |
Time treatment: Information / material flow
in most situations occurs at intervals and hence, discrete event
simulations are common. Simulating a network of oil pipelines to
arrive at loads at different locations requires consideration of
continuous time. |
| 4. |
Architecture: A decision, which affects geographically
dispersed stakeholders across a supply chain, is best addressed
through an Internet enabled simulation, while a local decision on
the right stocking policy for a particular depot is addressed through
a desktop simulation tool. |
Decisions
addressed by Supply Chain Simulation:
Supply chain simulation can address decisions where the number
of choices is limited, but the evaluation
is analytically infeasible on account of significant uncertainty
associated with decision variables. A few examples of such decisions
are:
- Structural
decisions:
- Facility
addition:
Should one add a new warehouse, given that demand is likely
to go up by 20% next year, for given warehousing capacity, lead
times and desired customer service level?
- Facility
location: Where should such a warehouse be located, given
lead times, reliability of available modes of transportation, geographical
distribution of customers, and desired customer service levels?
- Choice of
transportation mode:
Should one go for a costlier mode of transport, which promises
more reliable deliveries? The cost of lost orders / delayed orders
has to be compared with higher transportation cost.
- Policy
decisions:
- Replenishment
policy: Should one go for periodic or continuous review
for given monitoring and inventory carrying costs, and uncertainty
in demand? Periodic review systems typically mean higher inventory
carrying and lower monitoring costs.
- Stocking
policy: What should be the desired stocking level for
different classes of products, given uncertainty in demand, lead
times, review periods and desired customer service level?
- Shipment:
If one were to allow partial shipments, what will be its effect
on response time and response time of the connected destination
locations?
Simulating
alternative options and evaluating them using an objective output
parameter such as customer service level typically address all these
decisions.
Business
benefits of Supply Chain Simulation:
Determining stocking levels so as
to balance customer service levels and inventory holding costs has been
a complex task. This is mainly because of uncertain and dynamic demand
/ supply quantities and uncertain supply lead times. Simulation can
help evaluate alternative
stock levels based on impact of these on customer service
levels (order fill rates) and responsiveness (waiting times). Right
stocking policy can:
- Drive down holding costs
- Reduce cash to cash cycle time and improve ROI
- Avoid cost of lost sales
- Improve revenue through better product placement
To conclude, a well-designed supply chain simulation
holds the potential for evaluation of important decisions in a risk-free
environment with minimal costs. If used consistently, it can
help organizations anticipate changes
in supply chain structure and policy, and manage
supply chains proactively.
Next issue: Variability
and Supply Chain
Previous issue: Managing
Complexity
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scSimulator
 Supply
Chain Simulator, a DecisionCraft product, provides a powerful
user configurable engine for supply chain simulation. It equips
the supply chain planner to determine near optimum inventory policy
in terms of re-order point and maximum stock for all the locations
of supply chain. It offers a graphical comparison of this inventory
policy with the user's present stock policy and in this way, drives
users to reduce costs, improve service levels and enhance profitability.
More
Resources on Simulation
Simulation
Chain
Reaction: Economist
Supply
Chain Management at DecisionCraft
Supply Chain Management
(SCM) is one of the focus areas of DecisionCraft. Besides developing
and offering its proprietary SC products, DecisionCraft offers
consulting services in SCM and has customer engagements in Pharmaceutical,
Textile, Banking, Advertising, and Energy industries.
Dr.
Pankaj Chandra, a Ph. D in Production and Quantitative techniques
from the Wharton School, University of Pennsylvania, USA, guides
the SC team at DecisionCraft. He has been teaching, researching and
providing consulting services in SCM and related areas for about two
decades. The SC team is supported by strong IT and modeling teams.
DecisionCraft Products
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dataOrganizer™
Supply Chain Simulator
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