Supply Chain Model
An Overview
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Supply Chain Fundamentals
Typically a Supply Chain consists of:
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Material flows
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Supply of raw material: Lead Times, storage..
Production: scheduling, batch and continuous processes, changeover time, batch
Warehousing: dispatch, replenishment, stock policies…
Market: customer service level and expectations, storage, On Time In Full (OTIF)…
Transportation: simple or complex?, travel times, variability, small, big orders…
Third Parties: Outsourcing or Third Party Supply may be a factor at any stage…
Information flows
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Forecasts: customer demand, Supply Chain forecasting, manual, automatic…
Actual orders: Order size and frequency profiles, seasonality…
Processing: Automated or manual, ERP?, Information sharing, emergency orders…
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Supply Chain Fundamentals
Ideal Supply Chain
Real Supply Chain
Inventory
Need for Safety Stock
Stock due to large factory batch sizes
Stock due to orders arriving too early
Little or no stock held
Production
Longer than desired scheduling horizon
Schedule adherence less than 100%
Unacceptable trade-off between flexibility
and costs
Intelligent scheduling
Low changeover times
Flexibility
Lead Times
Minimum (production or transport time)
Far exceed production, transport times
Effective Lead Times fluctuate…
Customer Service Levels
Perfect JIT, On Time In Full (OTIF)
measure is 100%
Orders not always on time
Orders not always complete
Result: Customer Service Level Agreements (SLAs) are necessary
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Service Levels and metrics
Service Levels
Lead Time, OTIF…
Service Levels
Lead Time, OTIF…
Service Levels
Lead Time, OTIF…
Service Levels
Lead Time, OTIF…
Service Level Agreements through the Supply Chain
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Supply Chain Fundamentals
Why the difference between perfect and real Supply Chain? : Uncertainty
Market Demand
Consumer or customer demand may be variable
More importantly demand patterns may be difficult to predict
Demand is often forecasted poorly. Automated systems with manual interference are typical
Market demand forecasts, Supply Chain forecasts and factory forecasts are calculated in isolation from
each other, leading to duplication of effort and to the amplification of errors
Production
Often production efficiency is at the expense of overall Supply Chain goals
Production batch sizes may be larger than necessary
Long forecasting horizon may allow production scheduling to be optimised but lengthens lead time
Various unknowns combine so that production schedule adherence is not 100%
Warehousing
Information about existing stock is not shared adequately through the system
Safety stock calculation is likely to be less than optimal
A replenishment policy which “works” is likely to be in operation rather than one that is best
Other
Steady predictable demand is handled similarly to volatile demand in the Supply Chain
Transportation may be unreliable or unpredictable
Raw material supply may be unreliable or unpredictable
Arrangements with Third Parties may reduce visibility and information sharing
Order Processing may require work-arounds
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Supply Chain Fundamentals
Why the difference between perfect and real Supply Chain? : Uncertainty
Uncertainty,
Variability
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What can Simulation do?
Discrete Event Simulation is an approach aimed precisely at accounting for
uncertainty:
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Mocsim’s Supply Chain Model was built using using Extend* simulation software with
an interface created in Microsoft Excel
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Note: Similar logic could be coded into any other DE package. The choice of simulation software
is not key. The advantages of Extend are that it:
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has Runkit and Player versions and so models can easily be ported and share
amongst users
is fast
is object oriented which allows for easy configuration of different Supply Chain
networks once the core modules have been designed
has adequate animation
Links easily with spreadsheets
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Supply Chain Model output
Results can be formatted to suit client conventions or for easy translation into
value
 For each Product Type, SKU the following output information is
available instantaneously and against time:
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Stock quantities at each location
Service level measures such as OTIF for each stage of the Supply
Chain or overall
Lead Times and Lead Time variance for each stage of the Supply
Chain or overall
Production metrics
Orders in transit
 All output can be converted to units of Orders, Quantity or Value
 Output can be viewed dynamically for training or demonstration
purposes or analysed when runs are complete
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Supply Chain Model dynamic interface
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Case Study
Client applied Supply Chain Simulator to:
 Prototype and design an alternative Supply Chain
 Train the Supply Chain organisation
 Demonstrate and sell advantages of the proposed Supply Chain structure
across the business
Project Stages were:
 Configuration of the model to match existing Supply Chain conditions and
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proposed alternative structures
Collection, analysis and processing of historical data
Tuning the model to match As-Is conditions
Design of simulation scenarios
Completion of simulation runs and compilation of results
Run training courses based on the scenarios tested
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Case Study detail
Sample of scenarios tested:
All variability/uncertainty parameters switched off to demonstrate the “perfect-world” Supply Chain:
Lead Times are a minimal (equal to production or transport times only)
Service levels (On Time In Full - OTIF) are 100% at each Supply/Demand stage
Introduce demand variability (order frequency, order size, then both)
Then adjust safety stock levels to increase OTIF values
Repeat runs until OTIF is at acceptable levels
Repeat the previous experiment for different types of uncertainty and variability:
Forecast accuracy
Production schedule adherence
Supply Chain accuracy
Carry out runs to show the effect of increasing agreed Lead Times relative to the average possible
throughput times
In this case a build-up of stock occurs because orders often arrive earlier than expected. This stock would
have to be either acceptable to the and customer or held until a suitable delivery point by the
supplier.
Show impact of changes to Supply Chain production batch size
Bigger batch size improves production efficiency but means increased stock must be held downstream in
the Supply Chain
In this case it was possible to reduce the negative impact of small batch sizes on factories be designing an
optimum production sequence which minimised changeover times.
The model was able to quantify the overall benefits, even in scenarios where conditions were worse for
manufacturing
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Case Study detail
Planned scenarios include:
Carry out runs to show the impact of changing the length of
Review period (for each warehouse a review period can be set. This makes warehouse management
simpler but effectively increases Lead Time)
Scheduling horizon (Increasing this makes production scheduling easier but increases Lead Time)
Customer types split into segments with different Customer Service Levels
Model would help to quantify the benefits of splitting customers in different ways for example it might be
rational to separate stable predictable demand from more unpredictable.
Show the benefits of increased visibility and information sharing
The model was configured to account for three strategies: Make to Stock, Make to Order and Vendor
Managed Inventory
For details contact [email protected]