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Supply Chain Simulation - Mocksim


									Supply Chain Model

 An Overview

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Supply Chain Fundamentals

Typically a Supply Chain consists of:
   Material flows
        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
        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
     Little or no stock held                                   Need for Safety Stock
                                                               Stock due to large factory batch sizes
                                                               Stock due to orders arriving too early

     Intelligent scheduling                                    Longer than desired scheduling horizon
     Low changeover times                                      Schedule adherence less than 100%
     Flexibility                                               Unacceptable trade-off between flexibility
                                                               and costs
 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)                       Orders not always on time
     measure is 100%                                           Orders not always complete

          Result: Customer Service Level Agreements (SLAs) are necessary

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Service Levels and metrics

   Service Levels     Service Levels             Service Levels      Service Levels
 Lead Time, OTIF…   Lead Time, OTIF…           Lead Time, OTIF…    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
    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%
    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
    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


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What can Simulation do?
Discrete Event Simulation is an approach aimed precisely at accounting for

   Mocsim’s Supply Chain Model was built using using Extend* simulation software with
    an interface created in Microsoft Excel

*   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:
        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

    For each Product Type, SKU the following output information is
      available instantaneously and against time:
           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
    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

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
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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

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