business planning brief

8/8/2019 Business Planning BRIEF

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Business Planning

Conducted as an annual exercise, the business plan is the volume, cost and profit plan for, typically, the upcoming year. This isalso referred to as the annual budgeting process. Some companies conduct this as part of the long-range forecasting process,which cover anywhere from three to seven years.

Business planning is critical for every company, while long-term forecasting is indispensable for companies with longer productdevelopment cycles. The long-term plan provides the inputs for capacity planning and other long-term expansion initiatives.

It is common knowledge that longer-term forecasts are more erroneous than short-term forecasts. There are more dynamic

variables and error processes at play in determining the conditions expected to prevail in the future. So it is also necessary tosubject your forecast to a sensitivity analysis to understand the robustness of the forecast if the underlying economic and businessenvironment changes. DemandPlanning.Net has developed a unique methodology to develop long-term forecasting and analyzingthe forecast sensitivity.

Often the annual Marketing Plan is the driver for the demand information in the annual budget. The Marketing Plan is developedwith a volume forecast for the year along with the spend levels necessary to create and sustain the expected demand for theproducts. The process for Market share forecasting is explained here. Marketing-mix modeling is a key component of developingthe Marketing Plan.

The Business Plan or the annual Budget often fo llows the following outline:

1. Strategic analysis of external factorsa. Economicb. Political

c. Competitive2. Internal Factors3. Development of a Sales and Marketing Plan4. Calendar Monthly forecast5. Operating Budget

a. Manufacturing Costsb. Administrative costsc. Sales and Marketing Overheads

6. Capacity and other bottlenecks7. Organizational Consensus8. Revisions to Spend and volumes9. Management buy-in10. Outlining Key risks and opportunities to the Annual Budget

Key process driver is forecast reconciliation and a methodology to determine and analyze exceptions. Reconciliation canoften be painful. And it can be made worse by a simplistic process lacking an exceptions methodology. Identifying major variances

and diagnosing the root-causes for the variance can quickly result in plan consensus. If you would like to find out more details onfacilitating or developing an effective Budgeting process,

Promotional Planning

The major challenge in designing an effective Demand Management process is incorporating promotional lift and volumeinformation into the forecast. The first challenge is to lay the correct information channel between field sales into the forecastinggroup so every change to the promotional plan is captured immediately. In most cases, this can be thought of a 50% win in processdesign.

In practice, the following challenges are very common in the promotional planning process for a typical manufacturer:

1. Often there could be communication gaps between the manufacturer and the customer on changes at retail.This could more often be due to the lack of proper communication between the manufacturer's account teamand the head quarters forecasting team.

2. There is no easy apparatus to incorporate and communicate changes to the promotional calendar. This is themajor theme of more advanced Customer Relationship Management (CRM) software. But the key is for theinformation to be integrated into the demandplanning process/tool.

3. The Baseline Sales Volume is often difficult to estimate. This is further complicated by the fact that there is not asingle consistent definition of baseline versus lift within the organization.

CPFR and Collaborative-ABF have attempted to address the issue of promotional planning. It is important design the collaborativemeetings to start with a discussion of major changes in promotional events compared to what was used in the forecast in theprevious period. Once both parties are in the same page about the promotional grid, then attention should be paid to agreeing onthe correct lift for the promotional events.

In summary, Promotional planning involves the following two steps:

http://www.demandplanning.net/market_share_forecasting.htm

http://www.demandplanning.net/market_share_forecasting.htm



1. Review the latest Promotional Calendar to understand changes to key promotional plans.

2. Incorporate incremental volume changes in response to the changes identified from the promotional grid.

Assuming there is technology in place for Customer Relationship Management, here is one model of information flow in thecollaboration process for promotional planning:

Key process driver is the information-sharing between the customer, and sales teams with the demand planning group. Itis a big process win if event changes are communicated rapidly upto the manufacturer's supply chain. In the above model, the CRMprocess contains all key data about promotions and lifts. The collaboration process verifies the accuracy of this information,incorporates late breaking events and achieves consensus on the lift identified with the promotional plan.

In designing this process, care should be taken to build a correct and consistent baseline so promotional lift volume is trulyincremental to the business. Secondly, application of exception management is key as there will be so many run-of-the-millpromotions with little or no true volume effect. Hence, proper thresholds should be established to focus the process on keypromotions and retail events. If you would like to find out more details on facilitating or developing an effective promotional planningprocess

Collaborative Planning

In the manufacturer to retailer model, customer collaborative partnerships have been a dominant theme since the 1990s. Althoughthere was a lot of energy behind CPFR, manufacturers and retailers are adopting different versions of collaborative forecasting andreplenishment strategies now. These include Collaborative-VMI, CPFR, Account Based Forecasting, CMI, Shared Single Forecastand replenishment etc.

The Retailers have emphasized the adoption of Collaborative Planning for better forecasting promotional volume. So there is abroader adoption of CPFR and Account Based Forecasting in this space although there is no consistent standard among either manufacturers or retailers. The CPFR that is being preached by retailers also vary in flavor from one retailer to the other andbetween Mass, Food and Drug.

Some retailers focus on the Sales or the POS forecasts, while others focus on promotional forecasts alone. Again some Retailersfocus on starting the process with two different forecasts, while others emphasize the importance of a Shared Single Forecast.

More formally, collaboration is defined as the creation of a shared understanding between two participants where none hadpreviously existed or could have come to on their own.

http://www.cpfr.org/





Test of Collaboration

Is there good Communication between collaboration Partners?Honest and open communication. This should be clearly spelled out in the up-front businessagreement. If there are constraints here then there is little chance the collaboration effort willsucceed.

Frequent and timely communication. This is key. Communication forums, teleconference or in-person meeting, should be set up for periodic exchange of information. This may be weekly byphone or monthly in person or what ever the parties think is optimal depending on their industryand business considerations.

Are they communicating the right information?Forecast Content. The process design should clearly outline what should be in the forecast that isshared. What items are included? What time period is covered? A clear process design and buy-in by both parties at the start will substantially help the success of the process.Accuracy of the Forecast. As with any demand planning process, forecast accuracy is the key.Achieving forecast accuracy depends on the accuracy of the information that is shared as well astimeliness. If the demand information that is shared by the customer is inaccurate, a tightcollaborative relationship will spread this incorrect forecast through out the supply chain.

Probability of an accurate Collaborative Forecast

The probability of an accurate collaborative forecast depends on the accuracy of the forecast as well as proper and timelycommunication.

Probability of Correct Collaborative Forecast = Prob (Correct Forecast) * Prob (Proper and timely communication)

Collaboration Accuracy

Perfect communication and poor Forecast ==> Collaboration accuracy = 0

Good Forecast and untimely communication ==> Collaboration accuracy = 0

In the Manufacturer to retailer model, the collaboration process is based on the assumption that the retail customer can help createa better forecast for the manufacturer. This is because customer is close to where the retail take away occurs. They understandconsumer buying patterns better and have a vast database of store-level demand and promotional information.

Secondly point of sale (POS) information is easily forecastable since retail consumption is generally a smooth series. Volatilityoccurs only when there are unexpected economic or natural events. Case in point will be battery sales around before hurricanes.

Finally, the collaborative relationship allows you visibility into your customer’s planned programs. Key events will include promotionslike features, tabs, price rollbacks as well as changes in inventory policy.

If you would like to find out more details on facilitating or developing a collaborative planning process,

VMI and CMI

are examples of a more passive collaboration between the customer and the supplier. Although somerelationships in the past feature more active collaboration with a text-book style VMI, these are more anexception than the rule.

DemandPlanning.Net recommends its customers a modified VMI that incorporates a more rigorous focus onpromotional planning, event modeling and sales-force oriented collaboration. Our implementation of



promotions-focused VMI will help you drive better integration into your supply chain while achieving your customer's stated goals.

VMI – Vendor Managed Inventory

The Definition Tests of Collaboration

Vendor Managed Inventory is a program in which:

the supplier generates the customer’s order,

based on shared information on customer demand and inventoryand

upon mutually agreed conditions

Information Sharing – Yes!

Visibility into the future – May be!

The supplier obtains the EDI transactions from the Customer for Sales, Shipments, and Inventory on a dailyor weekly basis through the EDI 852. He may also get the sales forecasts through the EDI 830. But typicallythe supplier generates his own DC level replenishment forecast based on the historical data. Finally, thesupplier's VMI analyst may try to incorporate some promotional intelligence into the forecast throughdiscussions with the sales team and in rare cases, with the customer's replenishment analyst.

VMI Benefits

Person ordering, the VMI planner at the manufacturer, knows the products. This translates into a moreefficient replenishment plan

VMI results in efficient ordering and delivery of new products. Because the DC level pipe-fill is knownalong with a reasoned guess on the replenishment volumes, VMI provides a more accurate forecast for anew product launch.

More visibility into Customer inventory levels provide better info to manage supply constraints.

Increased sales at retail due to better in-stock levels. This also results in Inventory Optimization for thecustomer. However, note that the better in-stock levels assume that forecast volatility is known throughsome sort of collaboration process or information-gathering mechanism. (See this as an issue below.)

a. Improved DC In-Stockb. Improved Retail In-Stock

Reduced customer administrative costs, since the replenishment work is transferred to the supplier. Thisis purely a benefit to the customer, but many a times this acts as a major catalyst for the establishmentof a VMI relationship.

VMI – The IssuesLong-term forecasts are still generated through the supplier’s crystal ball. Because the customer doesnot give much forecast guidance or intelligence on promotional or market events, unexpected retail or inventory volatility will hit in-stock levels.Little collaboration on the forecast (very rarely)Customer gives you the data and the inventory policy and the supplier does the rest!The success of the program rests on the supplier’s creativity and initiative and a good internal consensus

process with sales staff on the field.

How is Co-managed Inventory (CMI) different from VMI?CMI is similar to VMI except the supplier manages the replenishment process and develops forecasts inthe customer’s system. A key example of this process will be the supplier process adopted by Wal-martas well as the JDA E3 process used by the Drug Chains like Eckerd, Rite-Aid and CVS.Customer provides system access to the supplier Supplier has visibility to POS at the store level, Store and DC inventorySupplier reviews info and generates order in the customer's systemThe key difference is that order placed by the supplier is still a recommendation and is not a firm order until approved by the customer. In a VMI process, the order generated by the supplier on the customer'sbehalf is a firm order to deliver product and bill the customer.

There is a comprehensive outline of a retailer CMI program at the Kmart web sites.

VMI and CMI - The Challenge

The traditional continuous replenishment process (CRP) activity does not truly generate a demandplan that can be integrated into the manufacturer’s Supply Chain for production planning purposes.Too tactical and short-term oriented and typically focuses on the next two to four weeks.More emphasis on order placement and replenishment based on near term activity

Although VMI and CMI are constrained by criticisms of short-term focused and being too tactical, in practicethey have been very popular because of their low cost to implement. The key is to leverage the low-costimplementation while driving consensus and collaboration and bringing the focus on promotional planning andmanagement.

If you would like to find out more details on our modified VMI model and implementation, please contact us.

Some useful Links:

1. Some useful details on EDI Sets.

2. A software perspective Demantra.

http://www.kmartcorp.com/corp/business/vendor/general/cmi.stm


http://www.demandplanning.net/ContactUs.php


http://www.vendormanagedinventory.com/edi.htm


http://www.demantra.com/content/solutions/vmi.shtml









S&OP Workshop

Manufacturers in the Consumer Packages Goods Sector were the early adopters of Supply Chain Forecasting. Catering to the firmdemands of the Retail Partners, CPG manufacturers focused on minimizing the forecast error and making better forecasting themajor driver of fill rates and lower inventory carrying costs.

Most CPG supply chains have an active Sales and Operations Planning Process, which is cross-functional with participation fromSales, Marketing, Logistics, Supply Planning, Finance, Sales Planning and Demand Planning functions. Typically, there is a one-number philosophy although the more practical processes advocate tolerances between forecasts with different objectives.

Sales and Operations Planning Process thrives on collaboration and honest communication between key organizational players.This is a unique process embodying several information sharing sessions and decision forums, with the final intent to generate anorganizational Plan and key Sales and Operations issues for the top organizational manager to decide on.

Our on-site workshops covers the five-step process in S&OP and addresses the following key steps in developing and implementinga workshop. By having your key stakeholders attend this workshop, you attain a huge competitive advantage in moving forward theidea of integrated business planning. You can download the brochure here.

The Key steps in an S&OP process re-design:

1. Assess the key objectives of the Planning Process- Identify and Involve stakeholders in Sales, Supply Planning,

Operations, Marketing, and Finance during the process definition phase. Interview key General Managers andunderstand their informational needs from the Sales and Operations Planning process.

2. Identify the key pain points- Since Sales and Operations Planning is a collaborative process, the key is in establishing

and improving internal communication and collaboration. The best approach is to start with the question, where do wehave communication roadblocks? We need to identify areas where communication is missed, or ineffective. We also needto identify where communication is too late to be acted upon. An example of such a pain point will be to learn of a servicefailure for the first time in a score-card meeting after the end of the month.

3. Identify the Key Component Meetings- The key step in the process design is to plan and establish effective

communication and decision sessions among the various functions. Our meeting design will derive from several whiteboarding sessions that revealed the various pain points in the process (step 2) and the key touch points in theorganization. Where the touch points are heavy and involves frequent information sharing, that will indicate the need for aformalized information sharing session. Typically, the key meetings include the Demand consensus meeting, SupplyCollaboration Meeting, the General Manager Review meeting, and the Operations Review meeting. In most organizations,there will be an executive Sales and Operations Planning meeting. But the type and content of the meeting depend on theneeds of each organization.

4. Design Content and Timing of Meetings- Working with functional players from the key touch points, we will establish

the type, sequence and timing of each meeting during the planning period. Through white boarding sessions, we will helpyou establish the key contents and the objective of each meeting.

5. Meeting Templates- we will help you design appropriate templates and summary reports to facilitate the meetings to be

focused on key issues and arrive at a consensus recommendation. Demandplanning.net, with a vast collection of processreports in its knowledgebase, will help you design a template that is customized to the process needs.

6. Supply Collaboration Process- Once a consensus demand forecast is finalized, Supply planners will refresh their

planning systems to arrive at their new schedule with constraints. The new demand may point to imbalances in their supply process including issues in raw materials, finished goods inventory, manufacturing schedule, and capacityconstraints. The collaboration process should consider these issues to problem solve and decide a set of supplyconstraints to be acted on in the Operations Review meeting.

7. Budget Shortfall Review- Depending on the pain points of the current organizational process, we design this meeting to

reconcile top-down financial and marketing forecasts with the operational demand plan. The GAP identification andresolution is a major part of the Sales and Operations Planning Process.

8. Exception Management- A well-defined process will thrive on exception management. All Component meetings will start

with a follow-up of issues from the previous meeting and deal with exception issues highlighted by the meeting templates.A concise design of meeting templates will help you achieve brief, sharply focused, effective meetings.

http://www.demandplanning.net/documents/SandOPworkshop_w.pdf


http://demandplanning.net/







9. Sales, Operations and Inventory Planning- This is a key part of the Operations Planning and review. The

organizational consensus team will examine the Sales, Production and Inventory Plans and discuss major issues andbottlenecks.

10.Supply constraints and Scenario Management- The budget shortfalls may trigger management decisions on additional

promotions and even key new product introductions. The process should be designed to be flexible enough toaccommodate key top management requests to verify supply availability for key sales generating events. Promotions onkey items can only be offered if adequate inventory is available or can be turned around in time to meet the promotional

demand.11.Value Chain Metrics- The Sales and Operations Planning process will be guided by the various value chain metrics that

highlight performance and pin point areas of improvement. The Metrics should be a good indicator of the state of thebusiness and should call for quantifiable corrective action. The design of the metrics should help you align incentivesholistically to help achieve the organizational objectives. The key metrics include customer service (FTFR), inventorytargets, forecast accuracy, on-time delivery, order cycle times. Demandplanning.net will help you design metricscustomized to how various functional players are aligned in your organization. With our research and analytics in thisarea, we have a unique advantage in designing proper Supply Chain Metrics and implementation.

12.MAPE and Bias - Introduction

13. MAPE stands for Mean Absolute Percent Error - Bias refers to persistent forecast error - Bias is a component of total calculated forecast error - Bias refers to consistent under-forecasting or over-forecasting - MAPE can bemisinterpreted and miscalculated, so use caution in the interpretation.

14. Accurate and timely demand plans are a vital component of a manufacturing supply chain. Inaccurate demand forecaststypically would result in supply imbalances when it comes to meeting customer demand. Forecast accuracy at the SKUlevel is critical for proper allocation of resources.

15. When we talk about forecast accuracy in the supply chain, we typically have one measure in mind namely, the MeanAbsolute Percent Error or MAPE. However, there is a lot of confusion between Academic Statisticians and corporateSupply Chain Planners in interpreting this metric. Most academics define MAPE as an average of percentage errors over a number of products. Whether it is erroneous is subject to debate. However, this interpretation of MAPE is useless from amanufacturing supply chain perspective. The following is a discussion of forecast error and an elegant method tocalculate meaningful MAPE.

16. Definition of Forecast Error 17. Forecast Error is the deviation of the Actual from the forecasted quantity.

Error = absolute value of {(Actual – Forecast) = |(A - F)|

Error (%) = |(A – F)|/A

18. We take absolute values because the magnitude of the error is more important than the direction of the error.19. The Forecast Error can be bigger than Actual or Forecast but NOT both. Error above 100% implies a zero forecast

accuracy or a very inaccurate forecast.

Error close to 0% => Increasing forecast accuracy

Forecast Accuracy is the converse of Error

Accuracy (%) = 1 – Error (%)

20. How do you define Forecast Accuracy?21. What is the impact of Large Forecast Errors? Is Negative accuracy meaningful?

Regardless of huge errors, and errors much higher than 100% of the Actuals or Forecast, we interpret accuracy a number between 0% and 100%. Either a forecast is perfect or relative accurate or inaccurate or just plain incorrect. So weconstrain Accuracy to be between 0 and 100%.

22. More formally, Forecast Accuracy is a measure of how close the actuals are to the forecasted quantity.

If actual quantity is identical to Forecast => 100% Accuracy

Error > 100% => 0% Accuracy

More Rigorously, Accuracy = maximum of (1 – Error, 0)

Sku A Sku B Sku X Sku Y

Forecast 75 0 25 75

Actual 25 50 75 74

Error 50 50 50 1

Error (%) 200% 100% 67% 1%

Accuracy (%) 0% 0% 33% 99%

24. Simple Methodology for MAPE25. This is a simple but Intuitive Method to calculate MAPE.

Add all the absolute errors across all items, call this A

Add all the actual (or forecast) quantities across all items, call this B

Divide A by B

MAPE is the Sum of all Errors divided by the sum of Actual (or forecast)

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Demand Metrics

The most commonly used Demand Metrics in the profession are:

1. Forecast Attainment - How much of the forecast we actually attained, in essence a comparison of Sales to Forecast from

a prior period.

2. Forecast Bias - Sum of signed forecast errors over either actual or forecast

3. Mean Absolute Percent Error - The Classic MAPE used to measure the SKU level forecast error in most supply chains

4. Mean Percent Error - An average of individual SKU level MAPEs, not a very useful measure

5. Root Mean Squared Error (RMSE) - Average of squared errors, a more rigorous measure since it weights higher errors

more heavily

6. Rolling out-of-sample errors - You calculate the average error of the same forecast at different lags using different hold-

out samples in each run of the forecast.

We present some commonly asked questions here and a discussion of the issue here.

One of the most commonly asked questions through this website is the denominator for MAPE. Why do we recommend using theActual Demand instead of the forecast as the denominator? This question comes in many forms.

A reader from Australia asks: "What are the merits of dividing the error by Actual Vs Forecast?"

Another reader from Philadelphia asks: "I am intrigued by your example for calculating Percent Error for a forecast. Why would your formula not be (Actual - Forecast)/Forecast? Would Forecast not be the baseline measurement?"

Traditionally Forecast used to be the baseline measurement since senior management was interested in how actual sales comparedto forecast or plan. However as a performance measure, this can create some subtle biases especially if used to measure how adeviation compares to the expectation.

Forecast will be the baseline measurement, if our only goal is to beat the forecast. For example if the Sales personnel areincentivized by how much they beat the forecast target by, then of course we want to use the forecast as the denominator. But thishardly does any good to any supply chain. Beating the forecast by a whisper is good, but not by a mile.

So we want a measure that emphasizes the magnitude of the error rather than how it compares to a baseline. In a low error business, the denominator becomes a moot point. Since Actuals will be close to forecast if error is small, the bias introduced is of second order importance.

However, if the error is of some magnitude, there is potential to play games with the forecast error measure by what is called as

denominator management. If the error is divided by forecast, this may introduce some forecast biases. One of the most importantbias will be to artificially over forecast. At the margin, over forecasting will reduce the percentage error and increase accuracy. Sowhen in doubt, the forecast will be high.

You may want to observe by constructing a case study using your current clients. Observe the clients that use Forecast in thedenominator and observe how often there is over forecasting.

So it is better to divide by actuals, since the actual demand is under no one's control. Although this may lead to some under forecasting biases, this is not as severe as the other bias. So traditionally we divide the error by actual demand to arrive at theclassic MAPE.

Here is an interesting site that describes forecast error and implications of under and over forecasting.

Please contact us, if you have more questions on quantitative implications of the bias from denominator management.

Customer Service

Customer service levels measure the ability of a value chain to satisfy customer demand. If your products are even remotelysubstitutable, and in a competitive market, good customer service is invaluable. Even if you are a near monopoly, bad customer service often irritates customers and they will look for the closest opportunity to switch suppliers with some what substitutableproducts.

Customer service is typically measured as the rate of order fill. The true measure of customer service is really the degree of success you achieve on every opportunity to interface with your customer. Only complete satisfaction is recorded as a win, NOTpartial resolutions.

For a call center, the number of calls where the customer's questions were completely resolved on the first call will measure your success rate versus the total number of calls. If the response was a call back by another rep, it should not count in your measure.

Customer service metrics are extremely important in keeping the goodwill and loyalty of customers. This ensures repeat businessand perhaps additional business through referrals from happy customers.

http://www.smthacker.co.uk/Forecast_errors.htm








Safety Stock Coverage at different service levels

Safety stock is the margin of error required based on the customer service level and the deviation of the demand during the leadtime. The safety stock coverage level is the z-value in standard statistics for calculating the confidence interval for a customer service level.

Safety Stock = Required Inventory Coverage Level * Standard Error of the Demand during the Lead time

Safety Stock = Required Inventory Coverage Level * RMSE * Square-root of (Lead time)

What error measure to use for setting safety stocks? (New article!)

http://www.demandplanning.net/safety_stock_article.htm





Safety Stock Coverage Values at various required Service Levels ©

Service Level Required Inventory CoverageIncremental Service

LevelIncremental Coverage

RequiredCoverage Level per point

in Service Level

50.00% 0.000

70.00% 0.524 0.2000 0.524 0.02622

75.00% 0.674 0.0500 0.150 0.03002

80.00% 0.842 0.0500 0.167 0.03343

80.50% 0.860 0.0050 0.018 0.03599

81.00% 0.878 0.0050 0.018 0.03656

81.50% 0.896 0.0050 0.019 0.03715

82.00% 0.915 0.0050 0.019 0.03778

82.50% 0.935 0.0050 0.019 0.03845

83.00% 0.954 0.0050 0.020 0.03915

83.50% 0.974 0.0050 0.020 0.03990

84.00% 0.994 0.0050 0.020 0.04069

84.50% 1.015 0.0050 0.021 0.04153

85.00% 1.036 0.0050 0.021 0.04242

85.50% 1.058 0.0050 0.022 0.04338

86.00% 1.080 0.0050 0.022 0.04440

86.50% 1.103 0.0050 0.023 0.04549

87.00% 1.126 0.0050 0.023 0.04666

87.50% 1.150 0.0050 0.024 0.04792

88.00% 1.175 0.0050 0.025 0.04927

88.50% 1.200 0.0050 0.025 0.05074

89.00% 1.227 0.0050 0.026 0.05234

89.50% 1.254 0.0050 0.027 0.05407

90.00% 1.282 0.0050 0.028 0.05597

90.50% 1.311 0.0050 0.029 0.05806

91.00% 1.341 0.0050 0.030 0.06035

91.50% 1.372 0.0050 0.031 0.06290

92.00% 1.405 0.0050 0.033 0.06574

92.50% 1.440 0.0050 0.034 0.06892

93.00% 1.476 0.0050 0.036 0.07252



Inventory

Inventory is one of the working capital components that can be influenced by poor demand information and can be a big drain in thecash-to-cash cycle. Although not measured and monitored from a supply chain perspective, receivables and payables are asimportant to monitor in the business model.

Optimal inventory is defined as the inventory that is just sufficient to meet your customer orders so you can hit your service leveltargets. Since this is a balancing act, demand forecast accuracy summarily determines both your inventory levels and servicelevels. You can make a trade-off between the two for a given demand forecast error:

1. Higher the required service levels, higher are the needed inventory

2. Lower the forecast accuracy, higher the needed inventory to attain a specific service level.

Yes, it is true you can achieve a 99% or more customer service level by keeping extremely high levels of inventory, especially if youare in the dark about your demand forecasts. Or, if the organization has no idea how customer demand is generated.

You may hear a number of supply chain experts say that demand forecast is tough and hence a waste of time. They may havefancy solutions that advocate an expensive re-engineering of the supply chain to accommodate any type of demand. Although thatis technically possible, it is not a useful way to spend organizational resources. A small effort on the demand side will result intremendous benefits and improved demand information to help the supply chain. Even if the demand is highly uncertain, you caninstall a demand management process that will link itself to the retail or the final customer demand to help determine the order forecast for the lead time.

How demand metrics affect your inventory levels?

Historical demand forecast metrics affect the amount of inventory cushion you need to hold to cover future forecast volatility. This isdone through the safety stock measure that in stat istical terms, is an estimation of confidence intervals using an one-tailed test.

Safety Stock = Customer Service Level * Standard Error of the Demand during the Lead time

Safety Stock = Customer Service Level * RMSE * Square-root of (Lead time)

Safety stock is the margin of error required based on the customer service level and the deviation of the demand during the leadtime. The customer service level is the z-value in standard statistics for calculating confidence intervals.

In practice, people use the daily average of this historical data as an approximation for the mean and in essence they ignore theforecast. The correction to this method is to use the daily average of the forecast. This also requires breaking out the monthlyforecast into weekly or daily forecast. This will result in two components to the deviation namely

1. the total forecast error and

2. the daily distribution error.

Safety stock calculations need to be adjusted depending on how wide the Lead Time is compared to how accurate the dailydistribution is. For example, if the monthly forecast is very accurate but the daily distribution is incorrect but the Lead Time is threeweeks then, you may end up carrying incorrect safety stock during the Lead t ime. Generally if the Lead Time is the same as theforecast bucket, most problems are solved.

Market Modeling

Market Share Forecasting involves forecasting the total market and your own consumption, and estimating the share of the market.In the retailer to consumer model, what matters most is the shelf take-away. Marketing Strategies aim to maximize shelf consumption (and usage) and increasing own share of that consumption.

Introduction

Market Share Forecasting involves forecasting the total market and your own consumption, and estimating the share of the market.In the retailer to consumer model, what matters most is the shelf take-away. Marketing Strategies aim to maximize shelf consumption (and usage) and increasing own share of that consumption.



Depending on the industry, forecasting the total market for a particular product may be as easy as obtaining it from the externalsources to estimating and validating market estimates through a series of forecasts involving syndicated data and POS data.

Determining Market Share

Share estimates of the total market can be done as an objective or a target market share estimate based on the promotional plansand budgets committed to achieving that share. The other option is to forecast consumption independently and derive the sharethrough two different estimations, namely the market and the consumption.

Sources of Consumption Data

The best source of consumption data is the channel closest to the consumer. The retailer has the cash register data but may havedifficulties organizing, analyzing and checking the veracity of the aggregated store level data. Commercially available syndicateddata are the following sources:

1. AC Nielsen's

2. IRI

Since Wal-mart and Wholesale Clubs do not provide cash register data to the Syndicated data sources, the data available fromthese sources are ex-Wal-Mart and Clubs. To compare it to the total channel shipments, you have to get Wal-Mart and Clubs POSdata directly from the retailers and add it to the Syndicated universe.

Coverage Factors

The Syndicated data covers most of the shipping universe however sales from small mom-and-pop stores and beach side storesare not tracked largely due to the difficulty in tracking down the cash register data from these outlets. The data providers usuallyestimate a factor for these using shipments versus consumption over a number of years. Hence this is really a derived estimate anda certain sense of business judgment is required to to use this factor.

POS Data

POS stands for Point of Sale. For CPG and technology manufacturers that compete in the retail consumer market, the salesthrough the cash register is an important component of the total information they need to forecast, plan and distribute their products. Most retailers make this data available to their suppliers so they can better forecast and replenish the retailer distributioncenters and stores. In practice, this is achieved through the VMI relationship. So the best place to look for your sell thru data isyour supply chain.

Sources of Consumption Data

The best source of consumption data is the channel closest to the consumer. Although the retailer has the cash register data, theymay have difficulties organizing, analyzing and checking the veracity of the aggregated store level data. Mass retailers like Walmartand Target are exceptions. They have sophisticated retail based systems that provide the data to their supplier partners through theWalmart Retail Link and the Target Partners Online system respectively.

The solution is syndicated data. Commercially available syndicated data are the following sources:

1. AC Nielsen's

http://www2.acnielsen.com/site/index.shtml

http://www.infores.com/

http://www2.acnielsen.com/

http://www2.acnielsen.com/site/index.shtml


http://www2.acnielsen.com/



2. IRI

3. NPD etc.

Since Wal-mart and Wholesale Clubs do not provide cash register data to the Syndicated data sources, the data available fromthese sources are ex-Wal-Mart and Clubs. To compare it to the total channel shipments, you have to get Wal-Mart and Clubs POSdata directly from the retailers and add it to the Syndicated universe.

Setting up a data warehouse to extract, clean, aggregate and load the POS data from different sources has become very importantfor manufacturers in the recent years given the vital importance of this data both in market planning as well as in supply chain

forecasting. We have seen some classic database systems and data models used by some companies. However, this is a verychallenging endeavor and needs expertise and knowledge of both the data as well as systems to develop this database.

Coverage Factors

The Syndicated data covers most of the shipping universe however sales from small mom-and-pop stores and beach side storesare not tracked largely due to the difficulty in tracking down the cash register data from these outlets. The data providers usuallyestimate a factor for these using shipments versus consumption over a number of years. Hence this is really a derived estimate anda certain sense of business judgment is required to to use this factor.

Share Analysis with an example using the Baby car seat market

Planning is a critical function in any business. Banks need a business plan outlining your plan for profitability before lending capital.So do venture capitalists when they seed new start ups. Business planning is not just a one-off process; it needs to be doneannually.

The business plan derives from a market plan, an understanding of the market you are in and your position in that market. Theprofitability and the feasibility of your business largely depend on the following things:

1. The size of the market and its rate of growth

2. Your share of the market

3. The effectiveness of your marketing plan to keep or grow your share

4. The efficiency of operations to generate a healthy margin on your sales

Market Planning vs. a Marketing Plan

Market Planning is the process of sizing up your market and calculating your share versus your competitors. A Marketing Plan is thetool used to increase your share of the market through marketing activities such as advertising, branding, and promotions.

A key component of Market Planning is market share forecasting. In the manufacturer to retailer to consumer model, what mattersmost is the shelf take-away or sales at retail. Marketing Strategies aim to maximize shelf consumption (and usage) and thusincrease your share of that consumption.

The first step is calculating the total market potential for your products. The second step is to estimate your retail sales and deriveyour share of the total market. The third step is to forecast your base case market share as well as target market share given your advertising budget and your marketing plan. Let us use the case of a infant car seat manufacturer to illustrate this process.

Estimating the Total Market

If you are a baby seat manufacturer, you need to understand the total market potential of infant car seats in both units and dollars.In this case, the entire population of infants is your market. If two million babies are expected in a year, the market for infant seats is2m units. However, some of the households may be two car households and decide to buy two seats. If 50% of the households buytwo seats, then the market is really 3m units.

Now what is the market potential for infant car seats in dollars? It depends on the price segments of car seats. There may bedifferent types of car seats with different features commanding varying prices. Let us assume that there are two kinds of seats onebeing a simple no-frills car seat and the other a fancier seat with additional features like a cup holder, sun shade, diaper holder, etc.Different consumer segments may demand these car seats at different price ranges.

We can apply several market analysis techniques to understand price points and calculate the average price of these two marketsegments. Let us say our studies show the average prices to be $40 for the basic seat and $50 for the fancy seat.

The total market potential in dollars is the sum of the basic seat segment and the fancy seat segment. Suppose the basic seat

segment is 2m units. At a price of $40, this potential is $80m. The fancy seat has 1m units at a price of $50 and a market potential of $50m. The total dollar market potential is then $130m.





Estimating Market Share

In simple terms, your market share equals total retail take-away of your products divided by the total market potential. This is just acalculation of your share of the total retail sales.

Your total retail sales depend on which segment you participate in and who the other players are in that segment. Retaining andgrowing your share depends on a number of marketing factors including product differentiation, advertising, brand value, etc. Thesize and the marketing budget of your competitors also are key determinants of your market share.

In the infant car seat manufacturer example, let us say, you compete in the basic seat segment of the market. If you own 50% of the

basic seat market, which is 2m units, then your total annual retail sales is 1m units compared to a total market of 3m units. Your share of the infant car seat market is then 33.3%.

We can calculate the dollar share using the following steps:

1. Your retail dollars equal your retail unit sales x your average price. In this case your dollar retail sales is given by(1m units x $40) which gives $40m.

2. The total dollar market potential is $130m (calculated above).

3. Your dollar market share is then $40m over $130m which is 31%.

Although your unit share is 33.3%, the dollar share is 31% because you play in the low-price segment of the market.

How can you retain or increase your market share?

The Marketing Plan is an outline of your strategy to increase your market share. It may cover a number of marketing and brandbuilding techniques as well as the budget dollars allocated to each activity. Generally, the following tools are used to retain/gainmarket share:

1. Advertising and Sales programs to increase unit share while keeping prices constant.

2. Building Brand Value to move to the high-end segment of the market. This will result in higher selling priceswithout hurting unit sales. (In our example, you may be able to sell the same basic seat for $45 becauseconsumers are willing to pay a premium price for your brand.)

3. Sales Promotions through discounts and coupons will increase unit share but may compromise the dollar share.

4. Aggressive Price cutting may also be used as a strategy to capture a higher market share from your competitors.

5. The last and perhaps the most often used strategy in the Consumer Packaged Goods sector is to capturemarket share through new product introductions.

Practical Steps to Forecasting your Market Share

1. Depending on the industry, forecasting the total market for a particular product may be as easy as obtaining themarket potential from externally syndicated sources. And it may be complex to the point of estimating themarket by extrapolation based on your own point of sale data.

2. We can forecast our retail sales independently and derive the market share as a ratio of own retail salesforecast over forecasted market potential. This is the base case share forecast. For example, if we observe theshare to be 25% over the last few years, we can assume a stable forecast at 25% for the next year.

3. We can then come up with an objective or a target market share estimate based on the promotional plans andbudgets. If we estimate additional advertising investment can produce a 3% gain in share, our forecast will be a28% share.

4. When the total market potential is increasing, our retail sales forecast may be growing even with constant

market shares.

What to do With Market Share Forecasts

Market share forecasts are important in order to understand the return on investment of advertising dollars and the budget neededto retain versus grow market share. But truly, market share forecasts tell us the long-term demand for our products. They are earlywarnings of what and how much to produce and distribute to be successful in the marketplace.

In practice, large CPG companies use share forecasts to guide their demand planning and supply chain operations. This gives thema competitive advantage in running a lean operation, controlling inventories, and maximizing customer service

Time Series

Time Series models are simple yet powerful techniques available to develop supply chain forecasts. No where the cliché "History repeats itself " is more true than in sales forecasting.



In Time-series modeling, we just postulate that all we need is past values of the variable we are trying to forecast. So if we aretrying to predict the demand for a specific product over the next six months, we use the monthly history of the product over the pasttwo to three years. We just ignore other factors such as price elasticity, promotional sensitivity, macro-economic activity, or Governmental policy changes or our own corporate policy decisions that we may be aware of.

Time series forecasts can be good starting points before incorporating other causal effects. Time series methodology examines thepast history for the following elements:

1. Historical Average: This is also called as the level of sales that you have achieved on average.

2. Trend: This is the growth or decline in Sales over time.

3. Seasonality: The tendency for sales to either peak in specific periods or dip in specific periods during the week, month or the quarter. You may have strong sales in the summer but weak sales in the spring and fall, for example.

4. Cyclicality (less often): Sales volume may go through and be affected by economic cycles. Typically, since supply chainforecasting is more focused on a time window less than one month, this is often ignored as a relevant factor affectingtime-series forecasts.

5. Outliers: Sales may be subject to a one-time, sporadic event that may not be expected to repeat.

Popular Time Series Techniques:

1. Moving average and growth models

2. Simple Exponential Smoothing

3. Winters Models

4. Holt Winters Methodology

5. Simple Trend Seasonal Models

6. Logarithmic Models

7. ARIMA models

Exception Analysis

“80% of your supply

chain errors derive from 20% of your products”

Design, Analysis and implementation of an exception management approach have helped companies to:

• streamline the planning process

• improve forecast accuracy significantly and

• focus on the major supply chain drivers that improve the bottom line

Exception management leverages the 80/20 rule. We evaluate the end-to-end chain to capture valuable information clusters thatdrive the underlying business process.

Through our unique methodology and solution, we help clients institutionalize an exception management mentality to improve focusand create value through the planning process. Through this exception management approach, we have achieved impressiveresults with major corporate supply chains.

Causal Modeling

Causal Modeling is the use of independent explanatory variables to predict your demand. Software packages also refer to this asan econometric modeling or advanced modeling or structural models. Most forecasting and demand planning software rely onsimple time series models that leverage the past demand observations to forecast the future demand. This is a time-tested provenmethod.

However, there may be external factors that drive the demand in a systematic fashion. If your business experience indicates thatthere are indeed factors that drive demand, then you need to explore the data availability and predictability of these external factors.

The most common mistake is to look for a very obvious external variable that explains your business demand and potential such asthe GDP, or interest rates or even the price of oil. Although the historical data for these entities are easily available, it is impossibleto forecast them. You can obtain external expert forecasts but then you are complicating a simple forecast problem and perhaps

made it less forecastable.

http://www.demandplanning.net/time_series.htm





The best situations to use causal modeling can be some of the following:

1. You expect the average price of your product to go up in Q2 and Q4 of next year. Although you made a forecast aboutprice, it is still a policy variable and under control. Perhaps very accurately forecastable.

2. The company always promotes the products at the end of the quarter.

To understand structural modeling or multiple regressions and Discrete variable modeling, the following sources will be helpful.

1. Manual for univariate and multi-variate statistics

2. Multiple Linear Regression from Statsoft

3. Discrete Variable Regression Models

4. An interesting theoretical approach to Linear Regression Models

Forecasting Reconciliation

Bottom-up and Top-Down forecasting - Introduction

In demand planning terminology, Forecast Reconciliation isalso referred to as Bottom-up and Top-down Forecasting or Proportional Forecasting. Forecast Reconciliation, however,could also stand for reconciling the demand forecast with amodeled forecast vs. a judgmental forecast or a financialforecast.

In this article, we will be reconciling a top-down forecastand Bottom-up forecast through aggregation anddisaggregation methodology. The second title is more directin referring to this process as “Bottom-up/Top-Downforecasting”, but we may also call it “Proportionalforecasting” since this method involves using proportions todevelop the disaggregated detail level forecasts into thefuture.

Adding more excitement to this menu of terminology, different software tools have different methodologies to perform forecastreconciliation between the different levels of forecast. Typically, forecast reconciliation is sought after in cases where you need toforecast multiple levels of the product hierarchy, so in some sense it ’s aligned with product grouping and product hierarchy. If youare also forecasting at the customer level, then the levels of aggregation multiply by the customer dimension. Customers may roll upinto a sales territory, sales district, and sales region to the national level. In this article, we are going to limit ourselves to illustratingforecast aggregation and disaggregation just using the product hierarchy. We would abstract away from further disaggregation atthe customer SKU level.

When you develop your forecasting process, you have a choice to make – at what level do you develop the forecast? You couldforecast at SKU level or slightly higher (at brand or sub-brand level) or, given a simpler supply chain you could get away withforecasting at the category or division level.

When forecasting for the supply chain, as a general rule, you forecast at the SKU level. When you have a process focused on thistype of detailed forecasting, you could aggregate an SKU level forecast to a higher level forecast, dollarize it, and use it for financialplanning, which is the typical methodology for Bottom-up forecasting.

http://www.uwsp.edu/psych/cw/statmanual/

http://www.statsoft.com/textbook/stmulreg.html

http://www.socialresearchmethods.net/kb/dummyvar.htm

http://www.socialresearchmethods.net/kb/genlin.htm

http://www.uwsp.edu/psych/cw/statmanual/

http://www.statsoft.com/textbook/stmulreg.html

http://www.socialresearchmethods.net/kb/dummyvar.htm

http://www.socialresearchmethods.net/kb/genlin.htm



In Top-Down forecasting, the forecasts are developed atthe brand, category or division level, and then allocateddown to the lower levels (to SKU, then to SKU/Warehouse).

There are different schools of thought, arguing that either method is superior. One approach says that forecasting atthe detail level results in a much more accurate forecastbecause information at the detail level is more precise.

Aggregating this detailed forecast with a time series of prices produces a realistic view of the financial plan.

The alternate view, which is equally plausible and fair,states that forecasting at the SKU level produces too muchnoise. At the SKU level, volumes could be very light; someof

the SKUs could be shipped very infrequently. So the lower the level, the more difficult it becomes to create any useful statisticalmodel because of sketchy and intermittent demand data. If that is the case, forecasting at the SKU/customer level will magnify theimpact of noise and data infrequency. Forecasting at the customer SKU level also means aggregating not only at the customer SKUto the SKU level, but also aggregating all the way to the top, to the division level.

Picking the right level to develop Forecast Models

This is why it is important to understand what the right levelis to develop the forecast models. Partially, this questiondepends on what the customer wants.

Customers may want a forecast at the detail level -especially the supply chain planning folks. But sometimesmay be not. So it is important to understand how the supplychain is structured in order to decide on the forecastdelivery level, a level at which the demand planning grouphas to deliver the forecast to supply planning group.

So it is the responsibility of the demand planning group to

deliver forecasts at the agreed upon level to the supplychain. At

most companies, for most supply chains, this happens to be the SKU level. And in some companies the SKU may coincide with apack level and at some companies may be not. It depends on how your supply chain is structured.

If the lead time to produce the finished product is much longer say 30 days, than the time it takes to pack out and ship say half aday, then perhaps the supply chain may decide to carry inventory at the finished product and NOT at the pack level. It’s probably agood idea to forecast and deliver on SKU level, but the pack forecast will be determined on the base of allocation, as more of anexecution strategy or this could be even on demand. So the forecast level is the level of your supply chain bottleneck or the level atwhich you carry the most inventory for on-demand customer fill.

This is where the question of what is on demand and what is in inventory plays a big role. If the product can be packed on demand,then inventories could be kept at SKU level, and forecast could be delivered at SKU level, which also means you could get more

accurate forecasts. Remember, the higher the level in the product hierarchy, the more accurate the forecast models are.

Getting back to forecast reconciliation, it is important to distinguish between forecast modeling and forecast delivery. Within demandplanning itself, it’s quite possible to model at a particular level but then disaggregate and come up with a forecast at a more detailedlevel and deliver a forecast at that level. The middle-out forecasting method is particularly useful since you develop the forecasts ata middle out level in the hierarchy but deliver a detailed forecast for the supply chain using disaggregation methods and aggregatethe forecasts for financial planning at the higher level.

The use of forecast proportions



For doing aggregation and disaggregation at the product level,using the product hierarchy, we need to have proportionsdeveloped in your forecasting software so it could apply thelogic of how to allocate the forecasts developed at a higher level to a lower level in the hierarchy. Simply, the proportion isa fraction that tells you how much of the total forecast needsto be distributed to multiple items at the level down.

Although it seems simple, there are a variety of ways in whichyou can build this proportioning logic. The methodologiesdiffer in terms of how you calculate these proportions, andusing what basis. There are also issues around how dynamicthese proportions are and what happens if you manuallyadjust or change these proportions.The latter is very importantfrom a tool perspective because you may need to manuallyadjust them often to achieve a desired result. The ability toadjust

forecasting proportions is as important as the ability to do forecast over-rides.

The three different methodologies to calculate proportions are as follows:

1. Equal proportions – Everything is disaggregated equally.2. Constant Proportions – proportions stay constant through the forecast horizon.3. Dynamic Proportions – proportions change based on trend and seasonality so they are different by the month of the year.

The last two methods can be based on either the historical data or the forecast data.

Here is an example to illustrate the different methodologies.

Let’s talk about a simple case where you have three levels in the product hierarchy: category, brand and SKU. Let us illustrate thiswith a simple example of five SKUs in a brand.

Equal Proportions Method

You could have equal proportions which means the forecast would be allocated 20% each, to each SKU underneath. This is a verysimplistic solution, but probably not a very desirable solution since it ignores the relative importance of each SKU in that brand. Theweighting may not be equal across all products. This as a method is rarely used.

Dynamic Proportions

Dynamic Proportions method is time-dependant and forward looking. So it creates proportions that are different each month so itincorporates seasonality. This method always uses some version of the forecast so it is forward looking. The moment you startusing future periods, you also incorporate trend influences in your proportioning logic.

This method is also called pro-rata logic since this is using proportions on the basis of forecasts that already exist for both SKU andthe Brand. Generally you will proceed using the following steps to create dynamic proportions:

1. Let’s assume you have the same five SKUs in each brand. You will first build models at the SKU level and create SKU levelforecasts for each month in the horizon.2. Then build a model at the brand level to derive the higher level forecasts also by month.3. Dividing 1 above by the brand level forecast by month will give you a dynamic proportion that varies by SKU by month. These

proportions will then be adjusted for the forecast at the lower level every time the forecast is changed. That proportion is then usedto disaggregate the model at the higher level.

This logic has a variety of advantages:

1. If the SKU is declining, the declining trend will be built into the SKU level forecast, affecting the higher level model as well asinfluencing the proportions.2. If there is seasonality, it will be factored into the SKU-to-Brand proportions as well.3. When the total brand level forecast is disaggregated, you should get a clean disaggregation, because the proportions nowincorporate both trend and seasonality using the latest forecast models.

However, there are some criticisms against this technique as well. The main argument is that lower level forecasts are inferior tobegin with. So why use that to develop the proportions?

There is some merit to this argument since the seasonality and some of the patterns at the lower level may not be very clean due toadditional noise in the data. However in reality, persistent trend would be reflected in the proportions fairly well. As in the case of most CPG/FMCG companies faced with phase-in/phase-out issues, this method would work well even if more complex models



cannot be created at the lower level.

Effect of Changing Forecast Models and Proportions

Since the business world is constantly facing changes, we will experience changes to the forecasts very often either induced by themodels or by management over-rides. So let us look at the effect of changes you make to the forecasts or to the proportions. Mostforecasting systems that have proportioning logic try to keep the hierarchy of forecasts internally consistent.

1. Re-model one of the five SKU’s and change the forecast.a. This will trigger a change in the higher level forecast based on that one changed forecast

b. Changes the sum of the forecasts at the Brand level.c. Creates new set of proportions for each SKU. So all SKU level proportions are now changed.d. Lower level forecasts will stay the same except for the SKU with the new model until you re-model the higher level forecast. If younow remodel the brand level forecast, that would trigger a change in the forecasts for all five SKU’s, because the system willassume the proportions should stay the same.2. Manually change the proportion of one of the SKUsa. When you change one SKUs proportions, this will lock that proportion to the total.b. The system will re-allocate the remaining forecast to the other four SKUs based on the old proportions within those four skus.3. Manually Change the forecast at the Brand levela. The System will disaggregate the forecast to the SKU level based on existing proportions.b. The System will aggregate the new forecasts to the category level and create new proportions at the brand level with other brands that roll up into that category.

Conclusion

In summary, it is important to understand the different approaches to keep the hierarchy of forecasts internally consistent. Althoughdifferent methods are available, time dependent dynamic proportions comes out as a clear winner.

Account Based Forecasting

Account Based Forecasting or ABF is a key building block in establishing a collaborative demand planning process withyour key customers. Variously called as Customer Specific forecasting or just Account and National Forecasting, the essence of the process is to break down the demand streams into key customer demand and an all other Demand Group that lumps a number

of smaller customers into one statistical series.

The idea behind this methodology is the fact that focused selling and promotions are designed around the major customers. As thepopular cliché on forecasting goes, such selling activity aggravates the demand volatility. Once these major customers andtheir volatility-enhancing events are identified and isolated, the remaining All Other Demand stream should be fairly predictable.

Account Based Forecasting is

an integrated approachto leverage customer intelligence (both inventory and retail activity)to model promotional activity into both Sales and shipment forecaststo build a deployable DC level Plan.

Using this integrated approach, the output is a Bottom-up National forecast for each SKU. The National Forecast can be planned astwo demand clusters: Key Customer Demand and an All Other Demand.

The ABF collaboration Model leverages consumption and inventory data from the customer and uses a statistical engine to come upwith the customer level demand forecasts. The major advantage the ABF process is it enables flexible supply chain planningbecause of the visibility of customer level demand.



If you would like to find out more details on facilitating or developing an account based forecasting process

You Will Learn to...

• Appreciate the importance of supply chain collaboration

• Quantify the benefits of internal & external supply chain collaboration

• Set up a collaboration process with your trading partners

• Utilize the various collaboration methods & understand the pros and cons of each

• Understand the key building blocks & challenges in creating a collaborative value chain

• Calculate, Use & Interpret collaboration metrics & the partner collaboration scorecard

Whether you are a Supplier, manufacturer, or distributor in the global Value Chain, an integrated supply chain planning process withpartner collaboration is a key requirement to...

- increase customer satisfaction,- speed up the time to market

and- Improve profitability.

Integrated supply Chain planning starts with a plan for customer demand, which then gets translated into a production andmanufacturing plan, complemented by effective inventory management, and network optimization for improved supply chainefficiencies in the long run.

Traditional supply chains are driven by a halo mentality where one function receives information from another and reacts with goodsand service transfers or with more information. However, collaborative supply chains work on the basis of collective inputs fromvarious supply chain participants including even the customer (CPFR) and the vendor (Comanaged Inventory, VMI or Supplier Managed inventory).

In this workshop, we will review and illustrate the various partner collaboration initiatives including VMI, CMI, ECR, and CPFR andtheir key benefits to an extended value chain. We will illustrate the mechanics of the different collaborative initiatives and show youhow to set up a collaboration process with your partners. We will also discuss the key building blocks of Supply chain collaborationincluding Demand Planning, New Product Development process, order fulfillment, and integrated business planning (S&OP).

Topics Covered

1. Supply chain collaboration Overview

2. What and Why of CPFR?



3. Review of Industry specific applications

4. Review of Case Exercise and Handouts

5. Customer Collaboration in Practice

6. Partner Collaboration Scorecard

7. Demand Driven Supply Networks

8. Collaboration Case Study addressing these questions:- Is there value in the committed future order stream for collaborative Forecast?- How choose among the many programs- What do you need to redesign in your forecasting to accommodate customer information?- How can you leverage the supply chain process to provide superior customer service?

Retail Forecasting Workshop

- Customized web or on-site workshop- For pricing and other details, please contact us.

In the Strategic Forecast process, the retail take-away is a key input. We incorporate the effects of changes in Market share,consumption patterns, and inventory cycles to model a shipment forecast. Hence this is a demand-driven pull forecast. Thisworkshop will explain how demand forecast is modeled as a function of point-of-sale consumption forecast and changes in retailinventory. We explain the mechanics of obtaining and using syndicated POS data from sources like Nielsen's and IRI.

You will learn:The details of Share calculations and inventory cycles

The consumption forecast model using POS data

The effect of Promotional spends on Market Share

A simple model of Marketing Mix and the share optimization process.




business planning brief

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