For details contact Supply Chain Model An Overview

For details contact Supply Chain Fundamentals 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… Typically a Supply Chain consists of:

For details contact Supply Chain Fundamentals Ideal Supply ChainReal Supply Chain Inventory Little or no stock held Need for Safety Stock Stock due to large factory batch sizes Stock due to orders arriving too early Production Intelligent scheduling Low changeover times Flexibility Longer than desired scheduling horizon Schedule adherence less than 100% 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) measure is 100% Orders not always on time Orders not always complete Result: Customer Service Level Agreements (SLAs) are necessary

For details contact 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 Service Levels and metrics

For details contact 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 Why the difference between perfect and real Supply Chain? : Uncertainty Supply Chain Fundamentals

For details contact Uncertainty, Variability Supply Chain Fundamentals Why the difference between perfect and real Supply Chain? : Uncertainty

For details contact Mocsims Supply Chain Model was built using using Extend* simulation software with an interface created in Microsoft ExcelExtend *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 What can Simulation do? Discrete Event Simulation is an approach aimed precisely at accounting for uncertainty:

For details contact 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 Supply Chain Model output Results can be formatted to suit client conventions or for easy translation into value

For details contact Supply Chain Model dynamic interface

For details contact 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 Case Study Client applied Supply Chain Simulator to: 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 Project Stages were:

For details contact 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 Case Study detail Sample of scenarios tested:

For details contact 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 Case Study detail Planned scenarios include: