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Issue No:03/03/2

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:

  1. 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.
  2. 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.

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