Sales Forecasting Best Practices and Their Impact on DRP and MRP Demand Planning

Sales Forecasting Best Practices and Their

Impact on DRP and MRP Demand Planning

Best Practices in Sales Forecasting & Inventory Planning for Distributors & Manufacturers

Sales Forecasting Best Practices and Their Impact on DRP and MRP Demand Planning

e2b teknologies | 521 fifth avenue | chardon, oh 44024 | 440.352.4700 | www.anytimesupplychain.com

Table of Contents What is Sales Forecasting? ............................................................................................................................ 3 Who Manages Your Forecast? ...................................................................................................................... 3

Sales Rep Forecasts ................................................................................................................................... 3 Financial Forecasts .................................................................................................................................... 4 Supply Chain Forecasts ............................................................................................................................. 5 Statistical Forecasts................................................................................................................................... 5

Manual Forecasts .......................................................................................................................................... 5 Creating a Forecast ................................................................................................................................... 5 Working for Forecast Data ........................................................................................................................ 5 Collaborate ................................................................................................................................................ 6 Benefits of Manual Forecasting ................................................................................................................ 6 Manual Forecast Updates ......................................................................................................................... 6

Statistical Forecasts....................................................................................................................................... 6 Point Forecast ........................................................................................................................................... 7 Upper Confidence Limit ............................................................................................................................ 7 Forecasting Methodologies ...................................................................................................................... 8

Simple Moving Average Model ............................................................................................................. 8

Discrete Data Models ............................................................................................................................ 8

Crostons Intermittent Demand Model ................................................................................................ 8

Exponential Smoothing Model ............................................................................................................. 8

Box-Jenkins Model ................................................................................................................................ 8

Which Model Should You Use? ............................................................................................................. 9

Forecasting Best Practices ............................................................................................................................ 9 Accurate Data............................................................................................................................................ 9 Data Type ................................................................................................................................................ 10 Data Points .............................................................................................................................................. 10 Sample Size ............................................................................................................................................. 10 Manual Intervention ............................................................................................................................... 10

Forecasting & Demand Planning ................................................................................................................. 10 Distribution Requirements Planning (DRP) ............................................................................................. 11 Material Requirements Planning (MRP) ................................................................................................. 12 Exception Messages ................................................................................................................................ 12 Forecast Demand Consumption ............................................................................................................. 13 Customer Forecast Confidence ............................................................................................................... 13 MRP/DRP Planning Mistakes .................................................................................................................. 14

Conclusion ................................................................................................................................................... 15 About e2b teknologies ................................................................................................................................ 15



What is Sales Forecasting?

Sales forecasting means a lot of different things depending on who you talk to and the context of the

discussion. Ask a sales person about forecasting and they immediately think of a sales forecast in terms

of units and revenue in their customer relationship management or CRM business system. Ask someone

in accounting and they may think of sales forecasting in respect to budgets and how the sales forecast

will support departmental budgets and overall corporate financial planning. But unfortunately, very few

people ask supply chain planners what they need in respect to sales forecasting yet this has the greatest

impact on the financial success of the organization.

This white paper discusses sales forecasting from a supply chain perspective providing an overview of

sales forecasting strategies and best practices to help distributors and manufacturers to better manage

their internal and external supply chains, production and distribution plans, and available resources.

Who Manages Your Forecast?

Every company develops their sales forecasts differently because every company is different even

those in similar industries. With that said, there are four primary ways to develop a sales forecast for

supply chain planning. These are related directly to who is developing the forecast and include: sales

reps, accounting professionals, planners, and automated systems.

Sales Rep Forecasts

In some industries, the best people to create your sales forecasts are in fact the sales reps who are

selling the products. This is common in custom or highly engineered product environments or in cases



where internal planners and automated systems do not have insight into customer demand. For

example, a company that specializes in robotics for manufacturers may take months to sell a system and

every system may be very different than the previous system. This makes it almost impossible for an

internal material planner to predict future sales. Likewise, there is no relevant sales history to use for

automated, system-generated sales forecasts.

The best approach is to link your sales forecast in your CRM system to your ERP system for supply chain

planning. Realize that some sales reps will be very good at forecasting demand while others may not be

as accurate in respect to expected close dates or units. Below are some best practices to consider when

using sales reps to help determine sales forecasts for supply chain planning:

CRM Data Make sure that your CRM system has fields to capture information that is

meaningful for your supply chain planners. Planners wont necessarily care about the expected

close date for a sale if the demand for the item wont fall until a future period and they have

adequate lead time to procure the necessary products and resources. Also ensure that the CRM

system has a field to note the quantity and unit of measure for the forecasted demand as many

companies sell in different units of measure that those in which they buy product or in which

they manufacture the product.

Forecast Accuracy Some sales reps are exceptional at forecasting but others arent quite so

good. The good news is that you should be able to analyze previous forecast accuracy by sales

rep by item to determine how accurate their forecasts are. As a planner, you can then make the

necessary adjustments to your supply chain forecast. For example, if Sally Smith is 80% accurate

and is usually on the low-end of her forecast in respect to quantity and always a month early on

when her sales will actually occur you can easily adjust her forecasts down by 20% and move

them out a month. This isnt a perfect scenario but forecasting is never perfect and this

approach may be your best option.

Planner Autonomy Your supply chain planners and buyers need to have some autonomy in

managing demand forecasts. While sales reps may have their hand on the pulse of the market

and future sales, its the planner and buyer who often have a better understanding of demand

patterns. As such, you may want to take a collaborative approach where CRM sales forecasts are

made available to planners who utilize this information to develop their own demand forecasts.

For example, the demand forecast may be accurate but if the planner has visibility into a large

opportunity that is forecasted to close in the next quarter they can contact the sales rep to get

more information before the order comes in so they are assured to have this unexpected

demand in their material plan.

Financial Forecasts

As mentioned, accounting managers typically look at sales forecasts strictly from a financial perspective

or in respect to budgeting. As a best practice, finance, sales, and supply chain management should

collaborate on forecasts to meet each departments needs. Financial forecasts are important to the

supply chain planner and buyer as budgets do have a direct impact on their ability to staff up when

demand spikes (overtime), layoffs when demand is light, or for capital expenses for new machinery,

tooling, or other resources to meet forecasted demand.



In very few cases, however, will accounting forecasts ever be used for supply chain demand planning as

they are typically based on market trends and historical data and optimism that simply does not

translate well into a cohesive forecast for material and resource planning purposes.

Supply Chain Forecasts

Supply Chain forecasts are developed by material planners and/or buyers. These individuals have a deep

understanding of supplier relationships and internal manufacturing processes that affect their ability to

deliver products to customers on-time. Planners are in tune with recent shifts in demand and have the

experience needed to develop forecasts based on practical, real world experience. With that said, supply

chain planners too often have no visibility into the sales departments forecasts and even less visibility

into forecasts created by the accounting department. Forecasts are typically created in Microsoft Excel

or other popular spreadsheet applications. These forecasts are either entered manually or adjusted

based on prior sales or demand history and then uploaded to the planning system to determine what

items to buy, when, and how many or what to make, when, and how many.

Statistical Forecasts

In many cases, previous history is an indicator of future demand. Systems are available to analyze

demand history by item, by customer, and by period to predict future demand for those items taking

into account growing product lines, declining demand for products, and fluctuations in demand for

seasonality and marketing promotions. Statistical forecasting isnt right for every company but it may be

the best option especially for companies selling consumer products. For example, a clothing

distributor will see fluctuations in their demand for particular items as short-sleeve shirts will be more

popular in the Spring and Summer while long sleeve shirts will be more popular in the Fall and Winter.

Manual Forecasts

Manual forecasts can be created from scratch or by adjusting previous demand history. Most companies

rarely start from scratch unless they are growing and this is the first time that they have ever attempted

to create a supply chain sales forecast.

Creating a Forecast

The first step in the forecasting process is to get demand history into

Microsoft Excel. In many cases, companies are already maintaining this

information in an Excel file. In other cases, they may have this data in

their ERP business application and can export the data to Excel.

Working for Forecast Data

Once the data is in Excel, the planner can review the information for

each product. They will likely group demand into different periods such

as monthly or quarterly to try to identify predictable variability over

time such as seasonal products. They may will probably also consolidate

demand across customers so that demand is summed across all



accounts. You may want to keep the customer-specific data which could be useful for forecasting if you

anticipate increasing sales to a particular customer or group of customers or if you anticipate a decline

in demand from a particular customer. Further, retaining the customer-specific history can provide

insight such as customers who may have purchased a large quantity of an item as a one-time purchase

or customers that suddenly stopped buying a particular item.

Collaborate

Planners can then work with their product managers, accounting team, and sales to determine which

products or product lines they expect to continue growing and by what percentage. They can also work

collaboratively to determine what products or product lines may be on the decline. They can also work

together to create brand new forecasts for new items that have no sales history.

Benefits of Manual Forecasting

Excel is a fantastic tool for manual forecasts because it allows the planner to design the forecast in a

format that makes sense for them with generic formulas and tools to adjust forecasted values manually.

The results of the forecast are easily shared with others for review and can typically be uploaded to the

ERP business system to use for demand planning. In some cases, this process can be automated saving

an extra step in the forecasting process.

Manual Forecast Updates

Many companies spend countless hours creating demand forecasts but fail to maintain the information

during the year. Demand changes and plans sometimes never come to fruition. If you start the year

planning for 20% growth in a particular product but the market doesnt respond as expected, you will

soon find yourself with a warehouse full of inventory where there are fewer buyers for those items.

Its important that your forecast is not a static document, but rather, a living document that is

constantly changing based on changes in your business and constant feedback from everyone involved

in the sales and demand planning process. Your forecast should be updated when you lose a major

account or when you win a major account or if you add or retire a product line during the course or the

year. Things change often and your ability to balance supply and demand rests on your ability to

maintain an accurate forecasts.

Statistical Forecasts

The following information is provided by Business Forecast Systems, makers of Forecast Pro

(www.forecastpro.com) the leading statistical forecasting system for companies worldwide.

Everybody forecasts, whether they know it or not. Businesses have to forecast future events in order to

plan production, schedule their workforce, or prepare even the simplest business plan.

Most business forecasting is still judgmental and intuitive. Sometimes this is appropriate. People must

integrate information from a large variety of sources qualitative and quantitative and this is probably



best done by using the extraordinary pattern recognition capabilities of the human brain. Unfortunately,

many companies also use judgmental forecasting where they should not.

Not everyone understands the concept of forecasting. It tends to get mixed up with goal setting. If a

company asks its sales people to forecast sales for their territories, these forecasts often become the

yardsticks by which they are judged.

The main advantage of statistical forecasting is that it separates the process of forecasting from that of

goal setting, and makes it systematic and objective.

Objective, quantitative forecasting can help almost any business substantially. There is, in other words,

value added for business.

The future is uncertain, and this uncertainty must be represented quantitatively. Statistical forecasting

represents uncertainty as a probability distribution. Two kinds of information are needed to describe the

distribution: the point forecasts and the confidence limits.

Point Forecast

A point forecast is the mean value of the distribution of future values, and can be thought of as a best

estimate of the future value. Its upper and lower confidence limits describe the spread of the

distribution above and below the point forecast.

Forecast Pro Unlimited depicts this information graphically as well as numerically.

Upper Confidence Limit

The upper confidence limit is often calibrated to the ninety-fifth percentile. This means that the actual

value should fall at or below the upper confidence limit about 95% of the time. You can set the

percentiles of both the upper and lower confidence limits.

Lets illustrate this idea with an example. Suppose you were in charge of forecasting widget sales for

your company. If you wanted to determine expected revenues for next month, you would be most



interested in the point forecast, since it is the mean value of the distribution. The point forecast gives

you the minimum expected forecast error.

On the other hand, suppose you wanted to know how many widgets to produce. If you overproduce,

warehousing costs will be excessive. But if you under produce, you will probably lose sales. Since the

cost of lost sales is usually greater than the cost of overstocking, you will be most interested in the

upper confidence limit. The upper confidence limit tells you how many widgets to produce to limit the

chance of stocking out to less than 5%.

Forecasting Methodologies

A wide variety of statistical forecasting techniques are available, ranging from very simple to very

sophisticated. All of them try to capture the statistical distribution that we have just discussed.

Forecast Pro Unlimited offers the five methodologies that are most appropriate for automated business

forecasting: simple moving averages, discrete data models (Poisson or negative binomial), Crostons

intermittent data model, exponential smoothing, and Box-Jenkins. Extended exponential smoothing

models (Event models) are also included to accommodate promotions and weekly seasonality. All of

these models are univariate techniques. They forecast the future entirely from statistical patterns in the

past.

Thus you must have historic records preferably from several years of the variable you want to forecast.

Forecast accuracy depends upon the degree to which statistical data patterns exist, and their constancy

over time. The more regular the series, the more accurate the forecasts.

Simple Moving Average Model is widely used by business, mostly because it is so easy to implement.

However, it is really only appropriate for very short or very irregular data sets, where statistical features

like trend and seasonality cannot be meaningfully determined.

Discrete Data Models are used for data consisting of small whole numbers. These models are

characteristically used to model a slow-moving item for which most orders are for only one piece at a

time. Forecasts are nontrended and nonseasonal.

Crostons Intermittent Demand Model is not widely known or used technique but, in certain

circumstances, it is extremely useful. It is usually used to model data in which a significant number of

periods have zero demand but the non-zero orders may be substantial. This is characteristic of a slow-

moving item which is ordered to restock a downstream inventory. Forecasts are nontrended and

nonseasonal.

Exponential Smoothing Model is widely applicable. They are also widely used because of their

simplicity, accuracy, and ease of use. Their robustness makes them ideal even when the data is short

and/or volatile. Exponential smoothing works by identifying and extracting trend and seasonality, and

extrapolating them forward.

Box-Jenkins Model is a more elaborate statistical method than exponential smoothing. Box-Jenkins

works by capturing the historic correlations of the data, and extrapolating them forward. It often



outperforms exponential smoothing in cases when the data are fairly long and nonvolatile. However, it

doesnt usually perform as well when the data are statistically messy.

Which Model Should You Use?

Many companies ask themselves which forecasting model to use. The answer depends greatly on their

product portfolios and their customers. Most companies shouldnt use a single forecasting method

because different methods will work better for some products and customers while others will produce

more accurate results for other scenarios. Identifying the right forecasting methods for each product

and customer is time consuming and prone to error. The good news is that automated statistical

forecasting software like Forecast Pro includes built-in data analysis features to select the most

appropriate forecasting method based on the historical data by item and by customer.

You can use Forecast Pro Unlimiteds expert selection to automatically choose the appropriate

forecasting technique for each item forecasted. Alternatively, you can dictate that a specific method be

used. If you are already familiar with statistical forecasting, you can use Forecast Pro Unlimited to

customize your models. It provides extensive diagnostics and statistical tests to help you make informed

decisions.

If your data are driven by promotions or exhibit hard-to-capture seasonality (e.g., weekly data) you may

want to experiment with event models. These models allow you to assign each period into logical

categories and incorporate an adjustment for each category. For example, if you establish a category for

promoted months then your model would include an adjustment for promoted months. If you ran three

different types of promotions you could establish three categories and have a different adjustment for

each type of promotion. To build a monthly seasonal model for weekly data you would establish twelve

categories based upon which month the week was in.

If you are new to forecasting and these techniques seem a little intimidating, dont worry. Forecast Pro

Unlimited guides you completely through the forecasting process. Just follow the programs advice all

the way to accurate forecasts.

Forecasting Best Practices

Forecasting is not an exact science. With that said, forecasting is

more than gut instinct and statistical or manual forecasting can be

improved through best practices around the forecasting process.

Accurate Data

Statistical forecasting uses history of your

data to forecast the future. Thus it is

extremely important that the data is as

accurate and as complete as possible. Keep

in mind the rule, Garbage in, garbage out!



Data Type

You also need to consider which data to use for forecasting. If you want to forecast demand for your

product you should probably input and forecast incoming orders rather than shipments, which are

subject to production delays, warehousing effects, labor scheduling, and other factors that will impact

demand dates.

Data Points

The more data you supply the statistical forecasting system the better. The program can work with as

few as five data points but the forecasts from very short series are simplistic. Although collecting

additional data may require some effort, it is usually worth it.

Sample Size

If your data is seasonal, it is particularly important that you have adequate data length or duration. The

automatic model selection algorithms in Forecast Pro Unlimited will not consider seasonal models

unless you have at least two years of data. This is because you need at least two samples for each month

or quarter to distinguish seasonality from one-time irregular patterns. Ideally, you should use three or

more years of data to build a seasonal model.

Manual Intervention

Remember that forecasts are never perfect. Forecast Pro Unlimited bases its forecasts solely on past

history of your data. If you know something that Forecast Pro Unlimited did not, then you should adjust

the forecasts judgmentally. For instance you may know of future events like a large upcoming sale or the

introduction of a new product. You can use the quantitative forecasts as a starting point, and apply your

own insight and knowledge of future events to improve them.

Forecasting & Demand Planning

Requirements planning is a very complex process for many distributors and manufacturers. There are so

many inputs into the logic that companies frequently make mistakes when analyzing their supply and

demand to determine what they need to buy (or make), when, and how much. These seem like

relatively simple questions to answer but complex bills of material, capacity constraints, quality issues

from vendors or from manufacturing, outsourced activities, demand forecasts, and other factors can

wreak havoc on even the best laid plans.



Distribution Requirements Planning (DRP)

DRP is distribution requirements planning. It is the process of determine what items to purchase from

vendors or what items to transfer between warehouses in order to meet actual and/or forecasted

demand from customers and other warehouse locations.

DRP considers on-hand inventory quantities, safety stock levels, minimum and maximum order

quantities, mandatory order multiples, sales order demand, forecasted demand from a sales forecasting

system, open purchase orders, planned purchase orders, inbound supply from warehouse transfers of

stock, outbound demand for warehouse transfers of stock, and other criteria such as lead times to

determine what items to purchase or to transfer between stocking warehouse locations.

DRP planning is time-phased meaning that planning is organized in time periods to determine the supply

and demand within the period. For example, many distribution companies manage DRP on a weekly or

monthly basis looking at the entire week or month demand and supply to determine what actions they

need to take. A sales order (demand) entered today may not represent immediate demand if the

promise date to the customer is several months in the future. Instead, the DRP system will analyze the

lead times to procure the required quantity of the item and will back into the suggested purchase order

date based on supplier and internal lead times or lead times to transfer stock from another warehouse

location. As such, a planned purchase order (or transfer order) to provide the required supply for this

future sales order demand may be created only weeks prior to the customer promise date instead of the

current planning period.

Time-phasing DRP plans helps distributors maximize orders with suppliers to realize volume and pricing

discounts while minimizing over stock items and related carrying costs. DRP systems are a crucial part of



the distribution business as effective DRP planning dramatically improves customer satisfaction by

reducing stock-outs.

Material Requirements Planning (MRP)

MRP is material requirements planning. MRP is identical to DRP with the exception that the system also

plans for internal work orders to manufacturer products to meet demand. The MRP system also

considers the bill of material and the manufacturing labor routing to determine what manufactured or

purchased items are required within the manufacturing process and when production should commence

on lower level assemblies for use in upper-level finished goods.

MRP systems take into account all supply and demand for the finished goods, intermediate assemblies,

and purchased raw materials to help the manufacturer by avoiding stock-outs, overstock, and situations

where materials are tied up in work in process awaiting the delivery of other required items that are

currently unavailable due to poor demand planning.

Exception Messages

Most DRP and MRP systems also include action messages or exception messages. In an ideal world,

plans dont change. But in the real world vendors miss their delivery dates, products have quality issues,

shipments are stuck in customs from overseas shipments, and other problems occur that must be

effectively managed to avoid late shipments and lost customers.

DRP and MRP system action messages help the planner to identify which actions they need to take,

when, and why.

Typical action messages include suggestions for move-in or move-out of dates. For example, if a

customer places an order with a request date of June 1, the MRP or DRP system may suggest a work

order or purchase order to be created on May 1 to meet the demand. But what if the customer calls

back later and moves the request date out to July 1? You are now planning to buy or to make an item

that you wont need for a full month! The DRP or MRP system should suggest that you move out the

purchase order or work order 30 days so that you can free-up that working capital, avoid excess carrying

costs, and make the on-hand raw material stock available for other work orders if needed.

Another popular action message will prompt the material planner when demand for an item has

disappeared. For example, if the customer calls back and cancels their order (or if you process a return

from another customer) you will find yourself planning to buy or to make a product where there is no

actual demand. This is a worst case scenario for any business especially those with small product

margins. The planning system should notify you that there is no need for the purchase order or the work

order and should suggest cancelling the associated orders.

Other action messages are often created alerting planners of late orders or situations where the

planning system was unable to generate a suggested order due to errors or omissions in the system

setup.



Forecast Demand Consumption

A forecast is worthless if there is no way to integrate the forecast into your material requirements

planning (MRP) or distribution requirements planning (DRP) system to drive your purchase order, work

order, and transfer order plans.

The MRP or DRP system should support the ability to consume the forecast. This means that actual

demand will reduce the forecasted demand for the item and the MRP or DRP planning system will plan

to meet either the forecast or the actual demand for the item whichever is greater. This also means

that planners do not have to worry about double planning for demand actual + forecast. Rather, the

system takes care of this automatically and nets out the forecast from the actual demand in the

planning period.

Its important to consider the impact of your planning periods and forecast periods since forecasts will

drop-off at the end of the period. Few MRP and DRP systems like Anytime Supply Chain provide planners

with the ability to roll-over unconsumed forecasted demand to future MRP or DRP planning periods. For

example, a forecast for 100 units in January may only be consumed by 80 sales units leaving a balance of

20 units in the forecast. What do you want to do with this forecasted demand? Let it drop off and simply

look at Februarys forecast or add the 20 unconsumed forecasted units to the February forecast

assuming that the forecast was accurate the sales may have slipped into the next planning period?

Again, forecasting is not an exact science so most companies will carry over unconsumed forecasts to

the next 2-3 planning periods before the forecast drops off the plan.

Customer Forecast Confidence

Another problem facing planners and a common mistake in planning relates to customer forecasts and

confidence levels in those forecasts. For example, a distribution company may have a set contract to

ship 1000 units of a particular product to a customer over the next twelve months.

That demand is guaranteed based on the contractual sales agreement but the actual sales orders for

future periods do not exist yet only within the forecast. So now the planner sees that they have a

forecasted demand for 1000 units in the month and all is well unless an unexpected order from another

customer or another group of customers happens to come in. If these unexpected orders for the same

item exceed the 1000 units in the forecast the planner has no visibility that they actually need to

purchase or to produce much more product due to the fact that the original forecast was 100%

guaranteed for the original customer.

This will leave your company scrambling to fill orders. Instead, you should be able to set a confidence

level for a customer-specific forecast so that unexpected orders or bluebird orders that are received do

not affect your customer-specific forecasts.

Something that many planners forget is that MRP and DRP systems do not manage supply and demand

by customer, by sales rep, or any other criteria. They are simply looking at the total supply and demand

in a particular period regardless of the customer source. Contrary to that is the fact that it is very

common for businesses to forecast demand for their top accounts and to develop a general forecast for

all other sources of demand which could encompass hundreds or thousands of smaller customers. The



MRP or DRP system should provide a way to aggregate the customer and non-customer forecasts for

planning purposes but still provide the flexibility needed for the forecast manager to forecast in a way

that is familiar, comfortable, and accurate for their purposes.

MRP/DRP Planning Mistakes

Accurate forecasting is just one element of an accurate material plan. There are many other factors that

must be considered in order to develop a world class planning system. Many of these mistakes are

discussed in greater detail in the white paper: 23 Common (and Critical) DRP & MRP Mistakes and How

to Avoid Them. Below is a brief summary of some of these mistakes and why they are so integral to your

demand forecasting and material planning processes.

You may have the perfect demand forecast but what if your ERP system has inaccurate inventory

information? The forecast may include demand for 100 units of an item in a month but if your inventory

system tells you that you have enough stock on hand your planner wont know that theyre actually 50

units short because inventory counts were off dramatically. Other common mistakes around supply that

will impact your plans are planning using incorrect vendor lead times, not accounting for supplier

delivery and quality performance, and not planning for inter-warehouse stock transfers.

Dates are very important for inventory and manufacturing planning. Which dates is your system using

for sales orders? How do these dates consume forecasts which may be in different period sizes? Is your

bill of material and manufacturing labor routing accurate in respect to procurement lead times for raw

materials and manufacturing cycle times for production? Does your manufacturing system take into

account the time it takes for materials to move to different work centers or queue times where products

must cool, dry, or otherwise wait until they can move to upstream manufacturing operations? Does the

system allow you to define time to setup a machine or work center or time to reset the machine or work

center after producing a defined quantity of parts to ensure that the settings are still accurate, the

tooling is not damaged, or other factors that will impact product quality and production time.

Are your MRP and DRP planning periods setup properly for each item? You may be better off planning

for one item monthly but planning for other items weekly because there is much more demand for

those items. Do you have internal lead times established that the MRP/DRP system considers when

suggesting planned purchase order, work order, or transfer order dates? Often times a vendor receipt

must sit until it can be inspected and stocked in the warehouse.

Other mistakes in requirements planning center on quantities. Does your MRP/DRP system allow you to

define a minimum, maximum, or order multiple for items you buy, transfer, or make? This is common if

you are stocking one unit of measure (such as each) but purchasing in another unit of measure (case),

and possibly manufacturing in yet another unit of measure (carton). The system needs to know what

quantity to suggest based on these internal, customer, and supplier requirements.

Do you account for scrap, yield, and loss in your material plan and have you setup safety stock levels for

items to act as a buffer to protect you when suppliers fail to deliver quality products or on-time

deliveries or when machines break down or when labor resources arent available?



Are you using a resource planning system that can account for required resources to determine more

accurate planned order dates? What good is a forecast if you dont have the materials, labor, tooling, or

machines to produce the items to meet the forecasted demand dates?

Conclusion

Sales Forecasting is a crucial piece of supply chain planning. Most companies struggle with sales

forecasting because they lack the expertise, the data, or the tools to create an accurate forecast. Manual

forecasts are common and may be the best choice for some businesses while others may be better

suited for statistical forecasting using a system like Forecast Pro Unlimited. Either way, the forecast will

impact your business.

Accurate forecasts combined with accurate inventory and manufacturing information will balance your

supply and demand allowing you to reduce stocking levels and carrying costs, avoid stock-outs, increase

profits, and improve on-time deliveries to your customers providing a distinct competitive edge

especially if youre in low-margin businesses competing against low-cost foreign suppliers.

Distributors and manufacturers have a very hard job balancing supply and demand. Despite their best

efforts, they often make mistakes. Some mistakes cant be avoided but some can and technology has a

huge role in helping them make the best decisions to minimize costs while meeting or exceeding

customer expectations.

Even smaller companies should evaluate their sales forecasting and requirements planning processes to

identify areas where they can improve. Most general accounting and ERP business systems provide very

little in respect to sales forecasting and DRP or MRP planning. But there are other options available.

e2b teknologies developed Anytime Supply Chain to extend your accounting or ERP business system for

better supply chain planning. You define and manage sales forecasts manually or import forecasts from

statistical forecasting systems like Forecast Pro to drive MRP and DRP planning which uses inventory,

purchase order, and manufacturing information from your system to suggest planned purchase orders,

transfer orders, and work orders at the appropriate times, in the right quantities, and from the right

suppliers to avoid stock-outs, overstock items, late shipments, and excessive carrying costs.

Companies implementing systems like Anytime Supply Chain enjoy low monthly costs for the system

that are recovered in a short period of time with most customers realizing a significant return on

investment in just a few short months.

About e2b teknologies

e2b teknologies is the Chardon, Ohio-based publisher of Anytime Supply Chain, an enterprise-class sales

forecasting, distribution requirements planning (DRP) and material requirements planning (MRP) supply

chain management application designed for small and mid-size distributors and manufacturers. The

company also develops Anytime Collect accounts receivable credit and collections management



software and Anytime Commerce ecommerce storefront applications for business to business (B2B)

companies that need a better way to manage accounts receivable and to manage online sales to their

business accounts.

The Company traces its roots to Haitek Solutions, a long-time supply chain and ERP software developer

founded in the early 1990s. Haitek Solutions developed Envision ERP manufacturing and supply chain

management applications acquired by Sage Software in 2001 for its Sage 500 ERP product. Anytime

Supply Chain represents the third generation of supply chain management applications built on two

decades of experience and hundreds of supply chain implementations.

e2b teknologies is a member of the Information Technology Alliance, several manufacturing trade

associations, and the NACM the National Association of Credit Managers. The Company has received

numerous awards and accolades including the Inc. 500/5000, Case Weatherhead School of

Managements Weatherhead 100 and Lake-Geauga Fast Track 50 awards.

At e2b teknologies we strongly believe that while software and technology are critical, its the people

behind the software that truly bring success to your ERP project. Our team is made up of only senior

consultants, software engineers, project managers, and support technicians with an average of ten

years experience who have together helped hundreds of companies across industries get the most out

of their technology investments. We serve customers in a variety of industries including process

manufacturing, distribution, pharmaceuticals, chemicals, energy, oil and gas, business services, and

related industries. Learn more about our ERP consulting and development services here.

Our services include:

Sage 100 ERP consulting and development

Sage 500 ERP consulting and development

Sage ERP X3 consulting and development

Epicor ERP consulting and development

Customer testimonials:

Everyone is very responsive to all of our requests and patiently works through the open items with us. The entire e2b team deserves a pat on the back for a job well done- Kolbus America, Inc e2b is very effective and helpful; assisting us in finding creative ways to accurately capture metrics in Sage 500 ERP- Molded Fiber Glass Companies e2b's consultants are very helpful and they are exactly what we were looking for in a technology partner especially given their deep manufacturing knowledge and success working with larger, complex implementations like ours.- American Electric Technologies, Inc

Sales Forecasting Best Practices and Their Impact on DRP and MRP Demand Planning

Documents

Transcript of Sales Forecasting Best Practices and Their Impact on DRP and MRP Demand Planning