Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management Dhaka, Bangladesh, January 9 – 10, 2010

Demand Planning Methodology in Supply Chain Management

Nazia Sultana Department of Industrial and Production Engineering

Bangladesh University of Science and Technology, Dhaka-1215, Bangladesh

Sadia Rahman Shathi Department of Industrial and Production Engineering

Bangladesh University of Science and Technology, Dhaka-12, Bangladesh

Abstract A supply chain is the system of organizations, people, technology, activities, information and resources involved in moving a product or service from supplier to customer. In Supply Chain Demand planning is a critical business process that impacts Fast Moving Consumer Goods (FMCG) companies’ ability to manage their value chain business performance. Revenues, costs and asset utilization are all affected by the quality, timeliness and accuracy of demand planning. Cleaning History and Reason Code Analysis offer new solutions that can improve the demand planning process and yield business results. A demand planning methodology and few applications have been shown here. The potential of this Demand Planning Methodology is to improve the certainty of demand planning decision making of a FMCG company. This methodology helps to maintain less excess and shortage quantity over the supply chain. Hence save the value lost and improve the Supply Chain Efficiency.

Keywords Industrial Engineering, Supply Chain Management, Demand Planning Methodology, Winter Model, Forecasting.

1. Introduction 1.1. Variability of Demand: Demands for any products changes rapidly from period to period, often due to predictable influence. These influences include seasonal factors that affect products, as well as non-seasonal factors (e.g. Promotional or product adoption rates) that may cause large, predictable increases and decline in sales. Predictable variability is change in demand that can be forecasted. Products that undergo this type of change in demand cause numerous problems in the supply chain, ranging from high levels of stockouts during peak demand periods to high levels of excess inventory during periods of low demand. These problems increase the costs and decrease the responsiveness of the supply chain. Supply and demand management have the greatest impact when it is applied to predictably variable products. Supply chain can influence demand by using pricing and other forms of promotion.

2. Demand Planning Methodology 2.1. Demand Planning activities:

• Changing Base Demand Forecast • Adding Impactors.

How can we measure the variability today: Data Evolution Report: This report gives a week to week view change of the plan, also broad view of supply key figures changes. This report need to be extracted to be analysed on a more aggregated level. These may be

• SKU (Stock Keeping Unit) • Brand/Range • Factory line

Sultana, Rahman

2.2. Cleaning History: Creating Base-line Demand The Demand Planning process is based on the concept that the final Consensus Plan consists of two components:

1. Baseline or Base Demand 2. Activities and Circumstances

The baseline Demand is the expected volume of a product if it is not promoted and no exceptional circumstances influence sales. Activities are generated internally through Marketing and Sales and are related to TTS (Total Trend Spend) or PFME (Product Fixed Marketing Expenses). Circumstances are internal or external and include stock outs, cannibalization, listing – delisting, competitor activity, unusual weather conditions, etc. The possible reasons for a FMCG company to push markets to adopt this fundamental concept are:

• a better control on the efficiency of promotions and the related spends • to improve the Demand Plan Accuracy (DPA)

This concept will allow the use of statistical forecast methods to forecast the baseline. 2.2.1: General Aspects on Cleaning History The Cleaned Base History (CBH) is used for statistical forecasting: The output of the cleaning process is the Cleaned Base History. These numbers are the single input for the Statistical Forecasting process. The process of cleaning is a mandatory step before applying Statistical Forecast methods. Cleaning is linked to how we forecast: Considering that cleaning is mainly carried out for the purpose of Statistical Forecasting, the process needs to be closely linked to the Statistical Forecasting process. E.g., a statistical forecast process set up with monthly bucket requires accurate Cleaned Base history at monthly level. Obtain a Cleaned Base History: Base Demand + Activities + Circumstances = Final Demand Plan When changing the demand planning process to adopt the concept, markets need to adapt historical data. This task will be done once during the implementation of the concept. Maintain a Cleaned Base History: Once the concept is in place, maintain the Cleaned Base History by cleaning history of the new historical data. This task is to be done every month/week in the monthly and weekly Demand Planning cycles. Validation of Cleaning with Statistical Forecasting: Statistical Forecasting tools are strongly recommended to support “Obtain a Cleaned Base History” process. Indeed, Demand planners and Sales & Marketing need to validate the consistency of the Cleaned Base History with statistical simulation tool. Judgmental approach: This involves the knowledge and experience of the Demand Planners and people from Sales & Marketing. Their judgment based on the business experience is the most important input in the cleaning process. Mathematical approach: Mathematical and graphical techniques support the judgmental approach. They highlight data that, potentially, need to be cleaned and thus ease the manual work. Distinction is needed for three types of products: The strategy to clean depends on the category:

• Category 1 : Mix products Detailed cleaning is necessary

• Category 2 : Baseline products Only exceptional circumstances will be removed from history (cannibalization, out of stocks, exceptional weather)

• Category 3 : Promotional products No cleaning is necessary because no baseline is forecasted. However, cannibalization which impacts other products (from category 1 and 2) should be cleaned

Sultana, Rahman

2.2.2. Step by step approach to define the Clean History Methodology:

Fig 4.7: Clean History Methodology 2.3. Performance Measurement: 2.3.1. Demand Planning Accuracy: The reason for measuring demand planning accuracy is that, it is a building block of demand planning process. The demand planner might check whether the statistical method is appropriate for the time-series, whether additional human judgment pays back or whether it is useful to incorporate information on promotions. In all cases a criterion is needed for the evaluation of his decision. But, there are many ways to get the appropriate forecast accuracy. 2.3.2. Demand Planning Bias: A perfect plan results in value of 0%. The possible values range from minus infinity to 100%. 2.3.3. Comparing DPAs: Market A had a overall DPA of 70.2% in September 2008. Market B had a overall DPA of 79.3% in the same period. Is the CDP process of Market B better? We should not reason like this. Such estimates should not be compared as is. We recommend to judge the quality of the DPA by looking at its history. If Market A has been able to increase its DPA since January 2008 continuously, but Market B is regularly fluctuating between 70% and 80%, and was simply lucky in September, then Market A performs better. High Bias, High Variability High Bias, Low Variability

Step 1 Establish a list of the major activities and circumstances in the market

Step 2 Define what is part of the baseline

Step 3 Classify the products in 3 categories

Step 4 Define the appropriate time bucket for Statistical forecasting

Step 5 Make a selection of representative SKUs

Step 6 Prepare Historical Data

Step 7 Define the appropriate time bucket and the level of details for cleaning history

Step 8 Clean History for the selected SKUs

Step 9 Run Statistical Forecast and refine cleaning of history

Step 10 Validate the clean history methodology

Sultana, Rahman

Low Bias, High Variability Low Bias, Low Variability

Fig 4.12: Variability of DP 2.4. Forecasting by Winter Model and DPA and DPA Bias:

Table 4.2: Forecasting Through Winter Model

Period

Demand per Quarter

Deseasonalized Demand

Seasonal Factor Level Trend Forecast Error

Absolute error DPA% Bias%

1 315 2 476 421.42 7.667 3 514 417.75 0.8127 439.25 0.848 357.687 -156.31 156.31 69.589 30.41

4 349 416 0.8507 438.61 0.845 373.828 24.8276 24.828 92.886 7.114 5 349 402.625 0.881 437.29 0.873 386.004 37.0041 37.004 89.397 10.6 6 428 399.125 0.8861 440.4 0.895 391.043 -36.957 36.957 91.365 8.635 7 455 411.875 0.888 444.85 0.902 395.842 -59.158 59.158 86.998 13 8 380 423.5 0.9107 444.33 0.905 405.463 25.4635 25.463 93.299 6.701 9 420 433.625 0.8169 448.68 0.829 367.214 -52.786 52.786 87.432 12.57 10 450 444.375 0.8913 452.28 0.902 403.906 -46.094 46.094 89.757 10.24 11 514 12 407

2.4.1. Cleaned History by Judgmental Approach: ss

Table 4.1: history and clean data

year Month Sales from

History (Cases) Learning log cleaned data: Baseline

Demand (Cases) 2007 Jan 111 Trade Promotion 86

2007 Feb 132 TP + Family pack 98

2007 Mar 177 TP + Family pack 119

2007 Apr 120 91

2007 May 170 CP + TP 126

2007 Jun 160 CP+ Sales Drive + TPP+ Price Increased -

Family 114

2007 Jul 181 134

2007 Aug 200 TPP only for Volume Contributors + TPP -

family pack 148

2007 Sep 133 Price increased + Display Linked TPP

(Ramadan Basket) 103

2007 Oct 148 Display Linked TPP (Ramadan Basket) 116

2007 Nov 148 Display Linked TPP (Ramadan Basket) 107

2007 Dec 111 79

Sultana, Rahman

2.5. Performance Improvement: 2.5.1. Reason Code Analysis: The objective is to reinforce the importance of a systematic analysis of performance and encourage focusing resource on issue resolution. This guideline aims at providing recommendations on how to analyze low DPA and proposes a set of standard DPA reason codes. Why a systematic analysis of DPA: A systematic analysis of DPA and Bias is a must to improve DPA and drive continuous improvements. Major cause for low DPA must be regularly communicated to take corrective actions in the short-term (e.g., change the Demand Plan to reflect a new sales trend or adjust a trade promotional quantity) and in the mid-term term (e.g. actions to improve promotional quantity phasing). The list of standard DPA reason codes: A standard list of 8 reason codes is provided to perform the DPA reason code analysis:

1. Promotion 2. Launch/ Relaunch 3. CU listing Change 4. Seasonality or Trend 5. External Factors 6. Supply Issue 7. Orders/ Plans/ Systems 8. Price Change

These are several sub reasons. e.g. in case of 1: The methodology to identify major DPA exceptions: An exception is an SKU with a DPA below target. So we should select SKUs with a DPA below target and out of the selection, we will select SKUs with a significant error.

Table 4.6: Reason Code for DPA exception

Order Quantity

Agreed Quantity

Abs diff

%DPA DPA below Target

Cumulative abs Diff. vs Total abs Diff.

Major Exception

Comment

11312 6666 4666 30% Yes 4666 28% Supply Issue 13690 9032 4668 48% Yes 9324 52% Promotion 4410 1200 3210 -168% Yes 12534 70% Price Change 1623 1217 1406 -16% Yes 15397 88% Seasonality

Reason Code for Shortage and Excess Quantity:

Table 4.5: Reason code analysis

Product-1 Excess

Short

Absolute Diff

Invoiced

Actual SKU wise

Demand Plan(DP)

Excess S. Order Recvd. against

Balance Major Reason

No.

SKU Requested

case fill

DP DP

2 SKU-

2 3 4 7 397.16 398 98% 275 123.16 -

Price Not Updated

4 SKU-

4 3 - 3 846.06 843

100%

550 293.06 - Request From Sales

2.5.2. Learning Log: A formal learning log is maintained so that lessons learned are not lost over time. The learning log is a register of major events affecting demand, e.g. major promotions, stock-outs, strikes, natural disaster, etc.

Sultana, Rahman

Poor accuracy is often caused by the failure to anticipate accurately the impact of planned and unplanned events, or a misunderstanding of historical event. The creation of a realistic forecast, specifically using statistical methods, is based on the assumption of realistic history and in time communication between units. 2.5.2.1. Activities analysis – Building a Learning Log: Demand Planning Accuracy (DPA) needs to be measure and report on a regular and continuous time basis (say monthly), so that the Learning log related to previous month DPA analysis can support process improvements. Thus:

• Formal DPA tracking and reporting enables operating companies to look for trend and erraticity over months.

• In addition, formal DPA follow-up helps actions to be decided to sustain positive trend and to reduce sources of erraticity.

• This is a new challenge for the Demand Planner. He needs to obtain a consensus from the team member involved in the MFR meeting regarding the reasons for low DPA, in order to:

o Drive detailed root causes analysis o Highlight to management the main

• Sources of improvement. Conclusion: The problems in demand planning are significant for FMCG manufacturers today. Their consequences affect product quality, retailer economics and shopper satisfaction. The potential of this Demand Planning Methodology to improve the certainty of demand planning decision making is equally significant, varying only in the degree of accuracy and detail that is economically appropriate for the product category in question. The forecasting and performance improvement methods mentioned are very useful for any newly entered FMCG company, as they might not be able to afford the costly forecasting softwares and these methods are very easy to implement with low cost. Reference:

• Thesis on Supply Chain Efficiency Improvement through Forecasting & Inventory Accuracy. • Nestle Bangladesh Limited: Demand Planning Process.

Appendix:

1. Winter Model (Trend and Seasonality Corrected Exponential Smoothing): This method is appropriate when systematic component of demand is assumed to have a level, a trend, and a seasonal factor. In this case:

Systematic component of demand = (level+ trend) * seasonal factor (1) Assume periodicity of demand be p. To begin, we need initial estimates of level (Lo), trend (To), and seasonal factors (S1…, Sp). We obtain these estimates using the procedure for statis forecasting:

Ft+1= (Lt + Tt) St+1 (2) On observing period t+1 we revise the estimates for level, trend, and seasonal factors as follows:

Lt+1=α (Dt+1/St+1) + (1-α) (Lt+Tt) (3) Tt+1=β (Lt+1-Lt) + (1-β) Tt (4) St+p+1=γ (Dt+1/Lt+1) + (1- γ) St+1 (5)

In period t, given estimates of level Lt, trend Tt, and seasonal Factors, St, the forecast for the future period is given by the following:

2. Estimating Level and Trend: If the periodicity of demand is p, and no. of time length (say month) in each period is t, then the deseasonalized demand is formulated as follows:

Dt = [Dt-p/2+Dt+p/2+∑t-1+p/2n=i+1-p/22Di]/2p for even p or ∑Di/p for odd p (6)

And Seasonality Factor, S: S= Dt /Dt (7)