CashFlow ABC

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A reorder point is a calculated stock level that, when reached, generates replenishment either through procurment or manufacturing while leaving enough inventory to satisfy demand during the replenishment process.

Reorder points work better in situations where demand is more or less constant and predictable or with slight variation.

All reorder points contain safety stock to absorb variations in demand. The purpose of safety stock is to reduce or eliminate stock outs due to variablility in either demand or replenishment. Safety stock levels are determined by various issues such as lapses in inventory integrity, receiving delays, supplier issues, scrap, damage and demand variability. Safety stock is also dependent on order frequency, desired service levels and forecast accuracy. We’ll cover safety stock levels at a later date.

Reorder points are based on demand history and therefore reactive. Regular periodic review is necessary to maintain the proper reorder point.

When using reorder points it’s very important to maintain communication with your suppliers as reorder points have a degree of “surprise”.  This surprise is due to the order being generated by a stocking level rather than set ordering schedule as with periodic ordering. Reorder points usually use consistant order quantities which is usually beneficial for suppliers.  The surprise can be deminished through open communication of the demand and changes in the demand, safety stock and any changes in the supply chain parameters that would affect the safety stock.

As with so many issues discussed here you will need to know and understand your demand.  You will also need to know your replenishment lead time.  Your replenishment lead time must be from the moment your reorder point is reached to the time the replenishment is ready for your use. This means you will need to take into account:

• Purchasing or manufacturing planning lead time.
• Supplier or manufacturing response or production lead time.
• Transit lead time including any possible transit delays such as harbor inspections.
• Receiving process lead time.
• Any inspection or QC leadtime.

The calculation for determining a reorder point is:

(demand x replenishment lead time) + safety stock

Below is a graphic example of the reorder point with simulated usage:

As stated above the order quantity used is usually fixed but should be large enough to ensure you’re not at or below your reorder point when the replenishment is available for use.

For a reorder point system to work you will need a reorder point signal.  This can be done using several different methods such as:

• Visual reorder points – This is the infamous crack in the wall method and can be very effective providing you can move the crack when the reorder point changes.  Any visual que can work such as: an empty bin, shelving or other indicator.  At the company John and I worked together we used a rolling sign on the floor next to the product as a visual reorder point.
• Automated reorder points – These are calculated using a computer and sometimes using MRP.  When the item in question reaches the reorder point it is placed on an replenishment exception report or a planned PO or work order is created.  Then the planner or procurement specialist reviews the planned orders and releases them as they see fit.
• Kanban – This system is usually used with a card.  When the reorder point is reached a card is retrieved and taken to the person assigned to replenish that item.

Reorder points are powerful tools that you can use to ensure material availability without maintaining excessive amounts of material.  They do, however, require that you review them at regular intervals to ensure they’re accurate.

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A forecast is an estimate of future demand based on market indicators and/or past performance plus market indicators.   We forecast to reduce the amount of uncertainty in order to prepare for demand.

Who is impacted by a forecast?

• Finance
  • Investment capital
  • AP forecasting
  • Operational budgets
• Inventory planning – for make to or order to stock environments.
• Capacity planning – to ensure forecast does not exceed the ability of the company.
• Procurement – to obtain the raw materials or products to support the forecasted sales. The finished goods forecast is broken down further into the materials planning requirements through the bills of materials.
• Operations
• Investors

Issues you need to consider

• Data
  • Quantitative
    • Seasonality
    • Sales history
      • Record “actual” demand not sales history.
  • Qualitative
    • Expert opinion
    • Customer groups
    • Market estimates
• Supply availability
  • Supplier or production issues.
• Capacity
• Available Capital
• Current market conditions
• Frequency of the forecast
• Your planning horizon
  • Your longest lead time item will determine your planning horizon.
  • Aggregate forecasts are more accurate.
    Determine detail through product families and planning bills.  Below is an example of a product family planning bill:
    
  • Planning bills use individual item and family demand history to breakdown an aggregate forecast into individual item requirements.

Measure your forecast performance.

• Rate of forecast consumption.
  • Divide your forecast into smaller periods to track the consumption.  As an example, divide a weekly forecast into daily checks.  If the forecast was 100 for the week you should sell 20 per day if your demand is flat.  Take into consideration any increases or decreases based on your historical demand or known forecast indicators.
  • If possible adjust the forecast to match the new rate of consumption.
• Compare forecast to actual demand.
  • Track forecast accuracy by period.
  • APE (Absolute Percentage of Error) – The absolute of ((actual demand – forecasted demand) / actual demand) x 100.
    • Does not consider the direction of the variance.  Used to calculate the absolute variance for a single period.
  • MAPE (Mean Absolute Percentage of Error) – The average of the APE across a range of periods.
  • MAPE (Mean Absolute Percentage of Error) – The average of the APE across a range of periods.
    Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods. Below is an example of the APE and MAPE.
    
  • MAD (Mean Absolute Deviation) – The absolute of (actual demand – forecasted demand) / # of periods.
  • Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods.
  • Also is approximately the standard deviation / 1.25.
  • Standard Deviation – The square root of the absolute of the actual demand – the forecasted demand squared / the number of periods.
  • Does not consider the direction of the error.  Used to calculate the absolute variance across a range of periods.
    Also is approximately the MAD x 1.25.  Below is an example of the standard deviation equation:
    
  • Bias – Forecast bias is a consistent variation from the average (mean) in one direction.  The goal is a bias of zero.  This demonstrates that any variance is due to normal fluctuation and is not attributable to the method or data.  In the example above the variance total is 700.  That divided by the number of periods is +54.  This is a positive bias and you need to determine why the forecast is higher than the actual demand so you will understand the reasons for lowering your forecast or why demand has fallen short of forecast. 

   

   

   

• Understand the inaccuracy.
• Reasons for forecast inaccuracy
  • Lack of participation
  • Inaccurate data
    • Qualitative
      • No hedging or “sandbagging”.
    • Quantitative
  • Incorrect method
  • Inappropriate data
  • Insufficient data
  • Lack of monitoring
  • Oops
• Apply what you have learned.

 

 

 

Forecasts are nothing more than educated guesses.  And, as human beings, we do best what we do most often.  So that being said the more often you follow proper forecasting processes, understand that it won’t be “perfect”, understand your variances and apply what you have learned to your next forecast your forecast will only get easier and more accurate.  This will help you service your customers and keep costs down.

- David

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Materials management is the sumo arena the company’s other departments wrestle in.  Each of these departments; sales & marketing, production and finance will have different ideas on how the company is to prosper and make a profit.  Sales and marketing want a seemingly endless supply of finished goods and replacement parts to maintain sales without a single stock out, production wants an endless supply of raw materials and WIP so they can meet all the production requirements of sales and marketing and finance want to keep just enough material on hand to satisfy customer orders without any material left over.  Materials management must balance these often conflicting objectives while maintaining high levels of customer service.

You must remember that every dollar not spent is a dollar of profit.

To maintain the proper level of inventory you need to understand the demand for your products.  You’ll need to understand your company’s:

·     Sales trends

·     Any sales seasonality

·     Demand for replacement parts

·     Any variations in your demand whether they are random or cyclical. (Random is any fluctuation that doesn’t follow a pattern while cyclical fluctuations follow a pattern.)

When you do encounter fluctuations in the demand you will need to record the reason for the fluctuations.  This understanding will help provide more accurate inventory levels and forecasts. (More on that later…)

The focus this week is on finding some “low lying fruit” or those items that obviously have excessive levels of inventory.  You’ll need to begin by tracking your usage or demand.  The time frame will depend on your business needs and model but the most widely used is weekly average demand.  You can use daily average demand but that will require a larger commitment as you will need to track demand more frequently.

Then determine the “Mean Absolute Deviation” of the data you’ve collected.  The “MAD” is the average of the absolute value of the difference between the demand and the weekly average of the data being collected.  As an example, if the data for a fore week period is 5, 15, 15 and 5 your weekly average would be 10.  The absolute value of the difference between each data point and that average is 5.  Add the MAD to the weekly average to arrive at the proper inventory level.  In this case 10 + 5 or 15.  Below is an example:

If you’re a manufacturer you will need to blow this down through your bill of materials to ensure there is sufficient stock of your subassemblies and raw materials.

Now track your average on hand inventory.  To keep this simple just take the beginning inventory level and the ending inventory level for the period being tracked and use the average of the two.  Make sure you didn’t have a last minute receipt or reduction in inventory that would give a false average.

Also review your demand to make sure there isn’t a recurring spike in demand that isn’t captured within the MAD plus average.  That would be an example of cyclical variation.  You don’t want to reduce your inventory so this demand is backordered or otherwise unfilled.  Depending on the type and frequency of the high demand and your material lead times it is possible to lower your inventory while meeting this demand.  But, for now, let’s stay focused on the low lying fruit.  Below is an example:

You’ll need to inform and work with your suppliers and production team as they may need to reduce the flow of product to make the reduction a reality.

This all sounds overly simplistic and it is.  As I mentioned earlier we are looking for the low lying fruit.  There are more complicated methods for inventory tracking and reduction but every company I have ever worked for was able to track demand for a short time, measure the inventory levels, make immediate reductions and save cash.  I wanted to provide you with the means to achieve a quick win so you can reap early benefits and see the value of these methods.

-David