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RSI - v13.8.30.16

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Transcript of RSI - v13.8.30.16

Prevent Shortages and Reduce Excess Inventory
RECAP
PROCESS FLOW
DATA DETAIL
YOUR FUTURE WITH RSI
Data Uploaded to RSI: Two Steps
Full Item Data Spec
Raw Usage History
Item Data Only
How this looks in RSI
Item Details page – displays detailed model inputs and results for any given item
Model Results
Demand Data
Item Data
Item Data:
Defines the supply side of an item’s supply chain. Variables include desired service level, lead time, lead time variation, order frequency, min order quantity and more
Demand Data:
Aggregate daily demand for an item. This is often uploaded as raw transactions: sales, shipment or consumption (issues to work orders) where an RSI utility segments into aggregate daily demand

Two Data Categories
Data Uploaded to RSI: Two Steps
If option 1: Full Item Data Spec, select Full Item Data Spec and upload the one file with both item and demand data included


If option 2: Upload item data only, then either aggregate daily demand or raw usage history in a separate file

Full Item Data Specs
Item Data (No Demand Data)
Item Data Update: Raw Usage History
Step 1: Extract item and demand data in one of the two formats discussed in this presentation. Save as .csv file(s).
Step 2: On the RSI File Upload screen, select the right upload type and upload the file(s) you saved
Col A: Item ID – straight mapping to pull part number / item number from your system
Col B: Location – Can be a mapping or just a default such as “Columbus Whse”, “Factory”, etc.
Col C: Unit Cost – straight mapping to pull standard unit cost from your system
Col D: On-Hand Inv – straight mapping to pull current on-hand inventory from your system (for this given item in this given location)
Col E: Replenishment Method – How you signal replenishment today? For example, ‘MRP’, ‘Min-Max’, ‘ROP’, ‘Kanban’, etc.
Col F: Current ERP Inv Policy 1 – Current target replenishment parameters from your system. For example, if Min-Max – this is the Min

Col G: Current ERP Inv Policy 2 – Current target replenishment parameters from your system. For example, if Min-Max – this is the Max

Col H: Family – commodity code, family name or other segmenting title from your system
Col I: User / Buyer – name or number from your system for
the buyer / planner for each item
Col J: Supplier – name or number from your system for the supplier for each item
Col K: Flex 1 – optional field – any identifying data you choose to help segment items
Col L: Flex 2 – optional field – any identifying data you choose to help segment items

Col M: Flex 3 – optional field – any identifying data you choose to help segment items
Col N: Flex 4 – optional field – any identifying data you choose to help segment items
Col O: Reserved – include in input file but leave blank (reserved by RSI for future use)
Col P: Reserved– include in input file but leave blank (reserved by RSI for future use)
Col Q: Target Service Level (%) – desired percentage of orders that can be fulfilled by stock without expediting
Col P: Probability of demand cancellation – percentage likelihood that a customer will cancel an order if you can’t fulfill from stock. High % setting says backlog is unlikely and has a slight dampening effect on buffer stock requirements. Low % says your products are unique and customers are likely to accept backlog and longer delivery commit on out-of-stock items. This has the effect of slightly increasing buffer stock requirements.
Col S - Lead Time Type: Enter Typical or Binomial
Typical means your lead time will vary randomly based on two parameters you specify
Col T - Lead Time Field 1: For ‘Typical’ lead times, this field should be the average lead time you normally see for this item
Col U - Lead Time Field 2: For ‘Typical’ lead times, this field should be a higher LT that would occur only ~2% of the time
Col V – Lead Time Field 3: Blank
Col W - Order Multiple: Incremental size for order / replenishment of this item. Generally, this is driven by package size. Recommended replenishment settings will be rounded up to the nearest multiple if a number is entered here
Col X - Re-order Quantity: Minimum order quantity. Generally driven by lot size, volume pricing or supplier mandate. If none, enter 1
Col Y - Fixed Reorder Interval: Enter a ‘1’ if reorder can occur anytime there is demand. If a number other than ‘1’ is entered, replenishment will be modeled on that interval. For example, if weekly orders, a ‘5’, ‘6’ or ‘7’ should be entered here…depending on value in Col Z
Col Z - Number of days per week: days per week when your company actively orders, receives and ships. Most common values here are ‘5’ or ‘7’
Aggregate daily demand

This method requires that you have the ability to pull aggregate daily demand from your system or assemble aggregate daily demand from raw demand data.
Demand Data: Raw Usage History
You pull raw transactional demand history: e.g. sales, shipments, RM issues to work orders, scrap, etc.
RSI aggregates demand by item by day
Example: In this sample data, on Oct 14th, Item ‘Test 5’ in FL had an aggregate demand qty of 17 (all demand within blue box in this image)

Col T - Lead Time Field 1: For ‘Binomial’ lead times, this field should equal the shorter LT typical for this item

Col U - Lead Time Field 2: For ‘Binomial’ lead times, this field should equal the longer LT typical for this item

Col V - Lead Time Field 3: For ‘Binomial’ lead times, this field should be a % equal to the probability of occurrence for the shorter lead time – LT Field 1

Demand Data
RSI builds it’s model using historical aggregate daily demand
Why historical?
History has real magnitude and volatility. Forecast only has magnitude. Forecast can be included to modify RSI models via an expected growth or decline trend. However, for the basic model, we use historical demand for the best measure of demand volatility.
Why daily?

You fulfill orders on a daily basis, so we measure demand variation by day. We believe this provides the most complete and accurate portrayal of demand variation for any given item.

Two methods
1. Full Item Data Spec:
Aggregate daily demand – one day in each column – in columns to the right of the item data (starting in col AA)
2. Raw Usage History:
Transactional ‘consumption’ of inventory through sales, shipments, issues to work orders, scrap, etc. If uploaded as raw transactional data, RSI has a utility that segments into aggregate daily demand
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