Advanced Inventory Replenishment - Demand Review for Microsoft Dynamics GP

 

Advanced Inventory Replenishment - Demand Review for Microsoft Dynamics GP

Trinity's Advanced Inventory Replenishment from Dynavistics, or AIR for short, is add-on software to extend the reach of Microsoft Dynamics GP purchase order capabilities. AIR for Dynamics GP is designed to accurately record and analyze inventory demand on a monthly 4-weekly or weekly basis. For each item it will then predict stock demand for the coming period using one of a number of industry standard replenishment calculations.

In this video we shall see how it provides the buyer with more information when reviewing demand for inventory items using Dynamic GP’s purchase order generator.

Advanced Inventory management allows distributors to:

• Set stock policy by time rather than using fixed safety and reorder quantities
• Understand demand, including lost sales and exceptional demand
• Take advantage of ABC analysis based on bin trips and frequency of picking
• Suggest the best forecasting method for each item
• Calculate the safety stock, re-order level, and order up to level
• Provides information and decision support

The enhanced Demand Review inside of Advanced Inventory Replenishment helps you Order the Right Products, at the Right Price, at the Right Time

 

Video Transcript:


Advanced Inventory Replenishment – Demand Review for Microsoft Dynamics GP

Welcome to the second of three videos on Advanced Inventory Replenishment, or A-I-R for short. 

In this video we'll see how A-I-R analyzes inventory demand and then predicts demand for the coming period using one of a number of industry standard replenishment calculations.  This calculated figure is referred to as the Estimated Period Demand and is based on either historical demand or forecasted future sales such as collaborative customer forecasts or promotion projections.  We're going to look first at how the system would arrive at estimated period demand based on historical demand.

This is the Demand Review screen.  The top part of the screen shows the filters we've applied to get the specific list of items, selecting one site and one vendor.  In addition, we can apply an item ranking filter.  Items can be ranked ABCD or X based upon their frequency of sale.  This is done using an automated routine.

Here, the breakpoint “A”, 80%, means that we want to identify items that make up the first 80% of our sales based upon frequency itself, sometimes referred to as bin trips.  In other words, the fast movers.

Let's look at the right half of the list and focus on item Z-101.  The screen is telling us what the demand for this item has been in the last nine periods.  Businesses without sophisticated software tools will often do a simple calculation like an average of six months sales to come up with their estimated period demand.  If we did that here, it would come up with 25.7.   But if it even occured to look at the date, it will show us that demand is trending up.  A-I-R has recognized this and come up with a forecasted estimated period demand of 35.9.  

So, how’s it done that? Well, it’s trying out up to fourteen different formulae on the historical data to find which one had been the closest predicted to what had actually happened in the past, and then applied that method to this item.  In this case, it has used Three Period Rolling Average with Trend.  Let's see that in action.

Over on the right-hand panel is a calculation method analysis button.  The system has tried all the calculation methods and has given each method a score, with Three Period Rolling Average with Trend being the closest fit and Weighted Seasonal is the worst.  

Back on the Demand Review screen, the average forecasting error signifies the closest of fit of the best method for the history of the product, which in this case would have been out by nearly 17%.   Items can be grouped by tiers of average forecasting error so that you can see which items are producing the most reliable forecasts. 

If we look at item Z 103 and repeat this exercise, we will see that the best method is Weighted Standard 3.0/2.52.  However, the method being used, indicated by an asterisk, is the third in list, a Weighted Standard with Trend.  If we click the update calculation method and redisplay, we will now be using the optimum method.   These changes can be done individually or en masse.  

Let's take a closer look at demand.  In many systems, this is synonymous with sales. But of course, that may not be true.  Relying on pure sales can be unreliable.  Here we can see those summary period figures that we saw on Demand Review screen broken-down:  

  • GP transactions are sales.
  • Assembly recognizes inventory billing material activity.
  • Lost Sales represents sales that are estimated to have been lost due to stock outs. Now there's no perfect way to identify lost sales, but A-I-R locks periods of stock out and, by combining that information with actual sales, a lost sales figure can be entered manually.
  • Exceptional Demand is a known one-off which is not part of repeating pattern, such as a large single order or perhaps selling from short dated stock. These can be identified at the time of sale or retrospectively, either in summary or at transaction level.   Within A-I-R, demand is defined as Sales plus Lost Sales less Exceptional Demand, as you can see against the period December 2016.  
  • Inherited Demand refers to the capability of system to inherit demand of one item from another, particularly useful where item supersession is taking place. This does not touch or distort sales history in any way, as A-I-R demand tables are held quite separately to sales analysis.
  • External Transactions signify this facility to import data, which is particularly useful at startup time as historical data from a legacy system may be used to establish initial reorder points and order up to levels.

From this screen, the inventory controller can drill down to transaction level by clicking on the blue arrow at left of the headings.   There’s also a facility to write notes against any item/period combination.   

Let's look at the rest of the columns in the list display:

  • Current EPD, is current Estimated Period Demand, as currently set for completing this review.
  • Forecast EPD is Forecasted Estimated Period Demand. More accurately finance, a proposed EPD.  For item Z-102, 45.3 was the current value for this item, 10.5 a week.  The system now thinks through the latest data, this should be decreased to 44.6, approximately 10.4 a week.  Forecast variances represent where the system wants to increase or decrease demand.  Large changes merit closer inspection for possible data flaws such as unrecorded exceptional demand or lost sales, but may be genuine.
  • Period frequency is monthly, weekly or fort-weekly.
  • If we want to update the forecasted column to current, we can either click individual items or mark all and then update.

Bear in mind that we have looked in detail at one item here.  In practice, this routine may be wrong for many hundreds of items at a time.  The forecast we have looked at so far has been based on simple, single estimated period demand, which is used in multiplier.  In many environments that may be sufficient, but maybe businesses or specific items that require a period-by-period forecast, items may be seasonal or have a forecast based on collaborative forecasts from customers, or may be the focus of upcoming promotions. 

Item Z-106 has a graph icon to its left indicating that this item has been set up with a future forecast.  Here we are seeing not a single estimated period demand, but a period demand picture.  This item is using a trending formula and the effect of this formula is the reason for the steady increase in projected demand.  

Of course, in practice you may only be buying for the next three months and these figures will be regularly recalculated.  Figure the type of projected have been calculated by this system but it is possible to plug in additional data, either manually or via an import wizard.

The purpose of this video is to demonstrate how A-I-R utilizes demand information and statistical analysis techniques to curate estimated demand figures.  These in turn help generate meaningful and usable order point and order up to level figures, as we saw in the first video.

In the final video, we will see how the enhanced purchase order generator is used, where the objective is to provide easy to access decision support to give the buyer the tools to confirm or amend the system's recommendations. 

I hope you found this interesting and if you'd like to know more please schedule your free demonstration today.  Thank you.

 

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