Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization doesnt get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniqueswhichareusedtopredictthefuturevaluesofdemand. The input that goes in and the modeling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required.Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.

1. An Intelligent Approach to
Demand Forecasting
Authors:
Nimai Chand Das Adhikari
Nishanth D
Chinmaya Chandan
Rajat Garg
S Teja
Gaurav Gupta
Lalit Das
Dr. A Misra
Company:
SS Supply Chain Solutions Pvt. Ltd.
EIM Advanced Analytics, Philips Lighting Bangalore

2.  Project Overview
 Proposed Model and Solution
 Description of Existing Technology
 Description of the Proposed Solution
 Results
 Conclusion
Agenda

3. Key Challenges:
 Large variation in
sales patterns in
different regions
across the globe
 Limited history
data available for
forecasting
 Predecessor-
Successor linkages
not available
 Huge number of
SKUs with different
behavior
Strategy:
 Data used-
 Sales History (Sale-in data)
 Predecessor-Successor linkages
wherever available
 80:20 segmentation of SKUs based on
volume to focus more on top
contributors
 Robust forecasting models build on
platform of R and Python to give
accurate results
 Best model was chosen to minimize
MSE
Value:
 Improved Forecast Accuracy:
average by 3% to 20% for
different markets
 Robust Models which choose
the parameters accordingly
based on the History Data
Project Overview
“ The objective of the project is to provide baseline forecast for 20,000+ SKUs of 7 different
markets with good accuracy. Advanced statistical modelling techniques were used to
generate the Forecast ”

4. Analysing both quantitative and
qualitative variables
Automatic multi-dimensional
clustering
Automatic Model Construction
Creating a relevance ranking both of rules and
single variables
Creating boolean, human
intelligible rules
Run very fast with low hardware
requirements
Historical Demand
New Products
Market Model
Hierarchy
Price
Promotions
Market, Product & Demand Datasets
Promo Variables
Price Variables
Hierarchy Indicator
Consumer Attributes
NPI Variables
Statistical
Outlier Treat
Similarity Effect
Cluster formation
NPI Launch Profiles
Seasonality
Level Lift
Base-Line
Processing
Uplift
Projection
Demand Modeling
A Machine Learning Engine (MLE) models using the statistical time series algorithms and other
machine learning algorithms uses the History Data, pre-processes the data and forecasts to
generate a better forecast.
FORECAST
ENGINE
FORECAST
ENGINE
Forecast Engine
Machine Learning

5. Process: Existing Scenario
Incumbent Business Model (IBM)
Data Processing
Forecast
Generation
Final Output
Forecast generation using basic
statistical models like:
• Moving Average
• SES Model
• Croston Model
• Seasonal Linear Regression
• Double Exponential
Smoothening
Fixed methodology to impute
seasonality externally on
forecast generated in the model
Fixed methodology to impute
trend externally on forecast
generated in the model
Detect and correct outliers
using statistical techniques
SKU wise 18 months forward looking forecast

6. Process: New Scenario
Data Processing
Forecast
Generation
Final Output
Mapping of predecessor-
successors
Forecast output from time
series algorithms run on R
platform
Assign weightages to forecast
of time series and regression
based on stability test of
forecast accurarcy
Identify top SKUs based on
volume to run through the
model
Forecast output from
regression based algorithms
run on python platform
Generate final output by
ensemble of time series and
regression outputs
Detect and correct outliers
using statistical techniques
SKU wise 18 months forward looking forecast

7. Forecasting Methodology
Algorithms and Techniques
Regression Models Time Series
Ensemble
Ridge Regression ARIMA
 Weighted Average
 Best of Models
 Boosting
Lasso Regression
Random Forest
Models
Developed
Techniques
Used
Linear Regression
Support Vector Regression
Decision Tree
NN (ANN & RNN)
Holts Winter
Weighted Moving Average
ETS
Boosting Ensemble

8. Forecasting Methodology
Algorithms and Techniques
Models
Developed
Techniques
Used
Techniques
Stability Check
Greedy Selection of Models
Hyper-parameters Tuning
Feature Selection
Flexibility of Models

9. Results
Forecast Accuracy Comparison
Market 1
Market 5 Market 7
Market 3
Market
Model FACC I-B-M FACC Model vs I-B-M FACC Comparison
Apr'17 May'17 Jun'17 Apr'17 May'17 Jun'17 Apr'17 May'17 Jun'17
Market-1 53% 54% 55% 43% 45% 38% 10% 9% 17%
Market-2 36% 49% 39% 28% 49% 33% 8% 0% 6%
Market-3 29% 49% 48% 31% 39% 41% -2% 10% 7%
Market-4 64% 56% 58% 62% 58% 68% 2% -2% -10%
Market-5 30% 58% 58% 51% 56% 59% -21% 2% -1%
Market-6 48% 47% 49% 48% 43% 50% 0% 4% -1%
Market-7 49% 51% 54% 50% 53% 56% -1% -2% -2%
Overall 1% 4% 5%

10. • All the state-of-art statistical methods used in the demand forecasting
• Ensemble of the results of time-series model and regression based
model gives a better result due to the fact of nullifying the over-
forecasting and under-forecasting and bringing the forecast values near to
the actual.
• The important factor to be considered is the stability of the model and
removing the game-playing.
Conclusion

11. • Vic Barnett, Toby Lewis, et al. Outliers in statistical data, volume 3. Wiley New York, 1994.
• Ke-Lin Du and MNS Swamy. Fundamentals of machine learning. In Neural Networks and Statistical Learning, pages 15–65. Springer, 2014.
• Peter Filzmoser, Bettina Liebmann, and Kurt Varmuza. Repeated double cross validation. Journal of Chemometrics, 23(4):160–171, 2009.
• Anthony J Fox. Outliers in time series. Journal of the Royal Statistical Society. Series B (Methodological), pages 350–363, 1972.
• Adel A Ghobbar and Chris H Friend. Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Computers &
Operations Research, 30(14):2097–2114, 2003.
• Gene H Golub, Michael Heath, and Grace Wahba. Generalized crossvalidation as a method for choosing a good ridge parameter. Technometrics, 21(2):215–223, 1979.
• Clive WJ Granger and Roselyne Joyeux. An introduction to longmemory time series models and fractional differencing. Journal of time series analysis, 1(1):15–29, 1980.
• Wenwu He, Zhizhong Wang, and Hui Jiang. Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing, 72(1):600–
611, 2008.
• Michael Hibon and Spyros Makridakis. Arma models and the box– jenkins methodology. 1997.
• Robert K Lai, Chin-Yuan Fan, Wei-Hsiu Huang, and Pei-Chann Chang. Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems
with Applications, 36(2):3761–3773, 2009.
• Chi-Jie Lu, Tian-Shyug Lee, and Chih-Chou Chiu. Financial time series forecasting using independent component analysis and support vector regression. Decision Support
Systems, 47(2):115–125, 2009.
• Bjoern H Menze, B Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, and Fred A Hamprecht. A comparison of random forest and its gini
importance with standard chemometric methods for the feature selection and classification of spectraldata. BMC bioinformatics, 10(1):213, 2009.
• K-R M¨uller, Alexander J Smola, Gunnar R¨atsch, Bernhard Sch¨olkopf, Jens Kohlmorgen, and Vladimir Vapnik. Predicting time series with support vector machines. In
International Conference on Artificial Neural Networks, pages 999–1004. Springer, 1997.
• Gregory Daniel Noble. Application of modern principles to demand forecasting for electronics, domestic appliances and accessories. 2009.
• David W Opitz and Richard Maclin. Popular ensemble methods: An empirical study. J. Artif. Intell. Res.(JAIR), 11:169–198, 1999.
• Carl Edward Rasmussen and Christopher KI Williams. Gaussian processes for machine learning, volume 1. MIT press Cambridge, 2006.
• Lydia Shenstone and Rob J Hyndman. Stochastic models underlying croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6):389–402, 2005.
• Richard J Tersine and Richard J Tersine. Principles of inventory and materials management. 1994.
• Mohammad Valipour, Mohammad Ebrahim Banihabib, and Seyyed Mahmood Reza Behbahani. Comparison of the arma, arima, and the autoregressive artificial neural
network models in forecasting the monthly inflow of dez dam reservoir. Journal of hydrology, 476:433– 441, 2013.
• Vladimira Vlckova and Michal Patak. Role of demand planning in business process management. In The 6th International Scientific Conference” Business and
Management 2010”. Selected paper, pages 1119–1126, 2010.
• Vladimira Vlckova and Michal Patak. Barriers of demand planning implementation. Economics & Management, 16:1000–1005, 2011.
• Neal Wagner, Zbigniew Michalewicz, Sven Schellenberg, Constantin Chiriac, and Arvind Mohais. Intelligent techniques for forecasting multiple time series in real-world
systems. International Journal of Intelligent Computing and Cybernetics, 4(3):284–310, 2011.
• SM Watson, M Tight, S Clark, and E Redfern. Detection of outliers in time series. 1991.
• Mengdi Wei and Yang Liu. Key factors and key obstacles in global supply chain management: A study in demand planning process, 2013.
• G Peter Zhang. Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50:159–175, 2003.
References:

12. Appendix
Market Wise Comparison for Models
Accuracy Comparison