Using Demand Forecasts for S&OP in Microsoft Dynamics AX 2012: Part 2

by Scott Hamilton, Consultant and Author

published January 10, 2013

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Demand forecasts - also termed sales forecasts - often provide a key element of the

Sales and Operations Planning (S&OP) game plans for stocked products. Effective use

of demand forecasts involves several interrelated issues, such as the relevant time

increments and due dates, forecast consumption policies, and master scheduling logic.

Many firms have struggled with these issues, and they experience problems in

coordinating supply chain activities.

As the second of a two-part article, this article builds on the basics of demand forecast

information and forecast consumption logic covered in Part 1. It provides additional

suggestions for using demand forecasts within Microsoft Dynamics AX 2012 to support

S&OP for stocked products, and describes other purposes of demand forecasts. Many of

the suggestions also apply to Microsoft Dynamics AX 2009, and the key differences are

highlighted. These topics are reflected in the following sections within the article, where

section numbering reflects the additional sections within Part 2.

4. Suggestions for Using Demand Forecasts

5. Other Purposes of Demand Forecasts

6. Key Differences between AX 2012 and AX 2009

7. Summary

4. Suggestions for Using Demand Forecasts

Many of the suggestions for using demand forecasts have been covered in previous

sections within Part 1 of the article. Examples include the typical business process to

maintain demand forecasts and S&OP game plans, the definition and use of forecast

models, using demand forecasts in the master scheduling task, and the dominant

business practices concerning forecast consumption policies and reduction keys.

This section provides several additional suggestions, starting with the time horizon, time

increments and due dates for demand forecasts. It provides ideas about demand

forecasts for a customer or a group of items, and translating weekly forecasts into daily

increments. It also covers the special case of demand forecasts in a multicompany

supply chain.

Time Horizon for Demand Forecasts

The time horizon for dates assigned to demand forecasts must exceed an item's

cumulative manufacturing lead time in order to provide any forward visibility, especially

for purchased material. The time horizon should be reflected in several master plan

policies -- termed time fences and expressed in calendar days - for considering forecasts,

explosions, and coverage planning.[1] Other time fence policies are typically shorter,

such as the time fences for consideration of capacity and action messages.

The time increments for demand forecasts can be different across the time horizon, such

as weekly increments over the time horizon reflecting the cumulative manufacturing lead

time, and monthly increments thereafter. The next point covers time increments.

Time Increments and Due Dates for Demand Forecasts

The time increments and due dates for demand forecasts depend on the situation. One

or more of the following suggestions can be considered.

Granularity of time increments and period lot sizing logic. This guideline means

that the granularity of demand forecasts can support period lot sizing logic for

planned orders. Weekly forecasts provide sufficient granularity for many

scenarios, although some scenarios can benefit from daily granularity in the near

term. As a general rule, monthly forecasts do not provide sufficient granularity.

However, monthly forecasts may apply to items produced once a month, and

staggered forecast dates should reflect the anticipated production schedule

throughout the month.

Significance of due dates for demand forecasts. It is important that production

planners can consistently interpret the significance of due dates assigned to

planned production orders stemming from demand forecasts. For example, the

forecast due dates can reflect a Monday date or Friday date in scenarios

involving weekly time increments. Other scenarios may stagger the due dates, or

use daily time increments in the near term, so that the planned order due dates

will be spread throughout the week.

Time increments for demand forecasts and the time buckets for forecast

consumption. The time increments for demand forecasts can be different than the

time buckets for forecast consumption purposes. As a dominant business

practice, you might specify weekly demand forecasts and use monthly time

buckets for forecast consumption.

Time increments and reducing the impact of past-due forecasts. As time

progresses, the demand forecasts for today's date or earlier will be ignored by

master scheduling logic. This is termed past-due forecast. Past-due forecasts can

have a dramatic impact when using an overly-simplified approach to time

increments and forecast dates. An example involves monthly forecasts with a due

date on the 1st of the month, where the demand forecasts become past due as

time progresses. Smaller time increments and staggered dates can lessen the

impact of advancing time and the resulting past-due forecasts.

Reasonable approximation of the item's master schedule. This guideline means

that planned production orders (stemming from the demand forecasts) can be

easily firmed with little need for manual adjustments, and that planned orders can

provide the basis for making sales order delivery promises based on ATP logic.

The guideline does not apply to scenarios with demand seasonality requiring a

level-loaded master schedule, as defined by approved planned orders or

scheduled production orders.

Translating Statistical Forecasts into a Workable Demand Forecast

A statistical forecasting tool may be employed to create monthly forecasts. In most

scenarios, these monthly forecasts must be translated into a workable model of demand

forecasts with the relevant time increments and due dates.

Case 4: Statistical Forecasting The sales management team at a discrete

manufacturer used a statistical forecasting tool to calculate future sales demand in

monthly increments based on historical data. In addition to shipments, the historical data

included customer returns and selected inventory adjustments. Further refinements to the

historical analysis included the requested shipment date on sales orders (to give a true

picture of demand patterns) in addition to the actual shipment date. The statistical

forecasting tool also calculated suggested safety stock quantities to cover anticipated

variations in sales order demand.

Starting with Simple Approaches to Forecast Consumption Logic

Forecast consumption logic can become complex and difficult to understand. To simplify

things, most scenarios will start with forecast consumption based on one reduction key

(such as fixed monthly forecast periods), and assign it to every item. As user

sophistication evolves, you may consider using additional reduction keys (and the

assignment of different reduction keys to different items) or using implied forecast

periods.

Demand Forecasts for a Group of Items

Some scenarios employ demand forecasts for an item group rather than an individual

item. This approach employs a user-defined template (termed an Item Allocation Key)

that spreads out a total quantity across several items based on a mix percentage per

item. The synonyms for an item allocation key include a planning bill or planning BOM.

With this approach, a forecast entry for the item group would specify the item allocation

key and the ship-from site/warehouse.

Each entry within the item allocation key can optionally define the ship-from

site/warehouse. For example, the entries could define the mix percentages for shipping

the same item from different sites/warehouses. With this approach, a forecast entry for

the item group would simply specify the item allocation key.

Case 5: Aggregate Forecasts by Item Group A manufacturing company had

thousands of stocked end-items, and wanted to minimize the effort to maintain forecasts

for individual items. Items were grouped together for forecasting purposes, with a mix

percentage assigned to each item, so that aggregate forecasts could be entered for each

group of items. This approach reduced the number of forecasts to be maintained, from

thousands of individual items to a few dozen groups.

Demand Forecasts by Customer

Some scenarios involve just a few major customers, so that a demand forecast can be

entered for a specific customer in addition to (or in place of) a general demand forecast.

This provides reference information for pegging to the source of demand. However, the

customer-specific forecasts can be consumed by sales orders from any customer, which

kind of defeats the purpose of forecasting by customer.

Demand Forecasts in a Multicompany Supply Chain

Some scenarios involve a multicompany supply chain within one AX instance, where

demand forecasts must be entered for the relevant company and ship-from location.

These requirements can then be communicated across company boundaries.

When using AX 2012, the intercompany master scheduling task communicates these

requirements as planned intercompany demand.[2] Hence, you do not enter demand

forecasts related to a sister company, and the sales orders from a customer representing

the sister company (termed intercompany sales orders) should not consume demand

forecasts. Case 6 illustrates this scenario.

Communication of planned intercompany demand only applies to companies within a

single data partition, which represents a new capability within AX 2012 R2. A typical

scenario would be a holding company that consists of multiple independent companies,

or groups of companies, where each independent group would have its own data

partition.

Case 6: Demand Forecasts in a Multicompany Supply Chain A simplified example of

a global manufacturer illustrates a multicompany supply chain. As shown in Figure 3, the

simplified example consists of two different companies representing a manufacturing

company and a distribution company. In the manufacturing company, an intermediate

item (produced at one manufacturing site) gets transferred to another site for producing

the end item. The manufacturing company sells the end-item to domestic customers. The

end-item is also transferred to a different company's distribution center for sales to

foreign customers. As shown at the top of the figure, both companies define demand

forecasts in order to anticipate sales orders for the stocked item. However, the planned

intercompany demand communicates requirements across the company boundary,

thereby eliminating the need to define demand forecasts for a sister company.

Translating a Weekly Demand Forecast into Daily Increments

Some scenarios employ daily increments in the near-term demand forecast. As one

approach, you can enter a weekly demand forecast (for the date corresponding to the

first working day of the week) along with a Period Allocation Keythat will automatically

result in daily increments. For example, you would predefine the period allocation key

indicating five daily increments and a percentage for each daily increment such as 20%.[3]

This example means that a weekly forecast of 100 would result in daily increments of 20.

The assigned percentage may vary for each day to reflect demand patterns within a

week, such as higher sales on a Friday.

5. Other Purposes of Demand Forecasts

The article has focused on using one forecast model and one set of master plan data to

support S&OP for stocked items. The demand forecasts typically involve forecast

consumption logic and continuous updates. Different forecast models and different sets

of master plan data (or forecast plan data) can serve different purposes, as summarized

below.

Original Annual Forecast

One forecast model can represent the original annual forecast, thereby supporting

comparisons against other actual results or other sets of forecast data. The original

annual forecast typically reflects one aspect of the budgeting process. For example, the

identifier (and description) for the forecast model could refer to "Original Annual Forecast

for 201X".

Long-Range Planning

An additional forecast model can support long-range planning for material or resources,

or the long-range requirements can be incorporated within the current forecast. You

specify the forecast model as part of the policies for a unique forecast plan, and run the

forecast scheduling task, so you can calculate the gross requirements associated with

the long range plan. The requirements for purchased material can be used in vendor

negotiations. The requirements for resources, or aggregate requirements for resource

groups, can be used to justify equipment investments or anticipate needed head count by

skill level.

S&OP Simulations

One or more sets of demand forecast data can be used for simulation purposes. One

example would be different sets representing the best-case and worst-case scenarios,

and the calculation of corresponding requirements. As another example, you can run the

planning calculations using infinite capacity planning to anticipate overloaded periods.

After adjusting available capacity and consideration of alternate routings, you run the

planning calculations again using finite capacity and material to highlight unrealistic

delivery dates.

Project-Oriented Operations

Project budgets are based on forecasted requirements, where a unique forecast model is

often defined for each version of the project's budget. In addition, the forecasted

requirements for items and production resources can optionally be included in master

scheduling logic. As one example using a two-level forecast model, the sub-models

identify the relevant forecast models for various projects.

Lean Manufacturing using Scheduled Kanbans

The use of scheduled kanbans within Dynamics AX typically applies to the transition

period from traditional to lean manufacturing. In this scenario, you employ the master

scheduling task to generate planned kanban orders (rather than planned production

orders), and firming the planned kanban order generates a scheduled kanban. The

planned kanban orders can stem from demand forecasts.

Lean Manufacturing using Fixed Kanbans

The calculations for the number of fixed kanbans for stocked items can be based on

projected requirements or historical usage or both. By using demand forecasts for end-

items, and running the master scheduling task (or forecast scheduling task), you can

calculate the projected requirements for all items in the product structure, which can then

be used to calculate the suggested number of fixed kanbans for each item.

6. Key Differences between AX 2012 and AX 2009

There are several differences concerning demand forecasts when using AX 2009 versus

AX 2012, as summarized below.

Change in Terminology

The AX 2012 terms for a demand forecast and a supply forecast were previously called a

sales forecast and a purchase forecast in AX 2009.

Changes in Forecast Consumption Logic

The primary differences concern the options for a reduction principle (with an additional

option for implied forecast periods), and the new "Reduce Forecast By" policy for items

(so that only sales order demands consume demand forecasts). Otherwise, the

explanation about forecast consumption of demand forecasts for saleable items applies

to AX 2009.

The new AX 2012 capabilities for demand forecasts replace the historical approach of

using supply forecasts for driving replenishment of stocked components. In particular, the

new "Reduce Forecast By" policy (of "All Transactions") enables all types of demands to

consume the demand forecasts for a stocked component.

Need for Running the Forecast Scheduling Task

In AX 2009, you had to perform the forecast scheduling task prior to the master

scheduling task so it could recognize demand forecasts. Starting with AX 2012, however,

this is no longer necessary because the master scheduling task includes the calculations

performed by the forecast scheduling task.

Multicompany Supply Chain

The ability to communicate requirements across the boundary of different companies

(within one AX instance) changed significantly with AX 2012. When using AX 2012, the

intercompany master scheduling task communicates these requirements as planned

intercompany demand. Case 6 illustrated this scenario.

When using AX 2009, the capabilities for planned intercompany demand do not exist.

You can communicate requirements across company boundaries by automatically firming

the purchase orders for the vendor representing the sister company, thereby creating

intercompany sales orders at the supplying company. This approach works for

communicating near-term demand, so that demand forecasts are frequently employed (at

the supplying company) to provide longer-term visibility of anticipated intercompany sales

orders. With this approach, the sales orders from the sister company should consume the

item's demand forecasts.

7. Summary

Effective use of demand forecasts involves several interrelated issues, such as the

relevant time increments and due dates, forecast consumption policies, and master

scheduling logic. This article focused on suggestions for using demand forecasts within

Dynamics AX 2012 to support S&OP for stocked products. It covered the basics of

demand forecast information and forecast consumption logic, the context of a typical

business process to maintain S&OP game plans, and other purposes of demand

forecasts. Many of the suggestions also apply to AX 2009, and the key differences with

AX 2012 were highlighted. The intended objective is to help improve the use of demand

forecasts for S&OP purposes, and improve coordination of supply chain activities.

[1]The use of master plan policies provides the simplest approach to these time fence

policies. As an alternative, you can define them as part of the coverage group assigned

to each item. This alternative approach applies to scenarios with differing time horizons

for different products, but it also involves higher levels of data maintenance and

complexity.

[2]A more comprehensive explanation of master scheduling across a multicompany

supply chain is provided in the book "Discrete Manufacturing using Microsoft Dynamics

AX 2012", pages 433-435.

[3]Several period allocation keys may be predefined to reflect different situations, such

as spreading a weekly forecast across a 5-day week, a 4-day week and a 3-day week.

The percentages assigned to each daily increment could be 20%, 25% and 33%

respectively. The names for these period allocation keys could be 5DayWeek,

4DayWeek and 3DayWeek.