When Netflix’s launched the design process for its series House of Cards, the company analyzed the most replayed parts of shows and films featuring Kevin Spacey, the show’s main star. Photo courtesy of deepsense.ai

Palo Alto, CA, USA and Warsaw, Poland - May 28, 2019

By Konrad Budek and Piotr Tarasiewicz


Everything you need to know about demand forecasting – from the purpose and techniques to the goals and pitfalls to avoid.

Essential since the dawn of commerce and business, demand forecasting enters a new era of big-data rocket fuel.


What is demand forecasting?

The term couldn’t be clearer: demand forecasting forecasts demand.

The process of predicting the future involves processing historical data to estimate the demand for a product.

An accurate forecast can bring significant improvements to supply chain management, profit margins, cash flow and risk assessment.

Powering up demand forecasting with machine learning. Photo courtesy of deepsense.ai

What is the purpose of demand forecasting?

Demand forecasting is done to optimize processes, reduce costs and avoid losses caused by freezing up cash in stock or being unable to process orders due to being out of stock.

In an ideal world, the company would be able to satisfy demand without overstocking.


Demand forecasting techniques

Demand forecasting is an essential component of every form of commerce, be it retail, wholesale, online, offline or multichannel.

It has been present since the very dawn of civilization when intuition and experience were used to forecast demand.

Sybilla – deepsense.ai’s demand forecasting tool. Photo courtesy of deepsense.ai

More recent techniques combine intuition with historical data.

Modern merchants can dig into their data in a search for trends and patterns.

At the pinnacle of these techniques, are demand forecasting machine learning models, including gradient boosting and neural networks, which are currently the most popular ones and outperform classic statistics-based methods.

https://deepsense.ai/powering-up-demand-forecasting-with-machine-learning/  

The basis of more recent demand forecasting techniques is historical data from transactions.

These are data that sellers collect and store for fiscal and legal reasons. Because they are also searchable, these data are the easiest to use.

Sybilla – deepsense.ai’s demand forecasting tool. Photo courtesy of deepsense.ai

How to choose the right demand forecasting method – indicators

As always, selecting the right technique depends on various factors, including:

• The scale of operations – the larger the scale, the more challenging processing the data becomes.

• The organization’s readiness – even the large companies can operate (efficiency aside) on fragmented and messy databases, so the technological and organizational readiness to apply more sophisticated demand forecasting techniques is another challenge.

• The product – it is easier to forecast demand for an existing product than for a newly introduced one. When considering the latter, it is crucial to forming a set of assumptions to work on. Owning as much information about the product as possible is the first step, as it allows the company to spot the similarities between particular goods and search for correlations in the buying patterns. Spotting an accessory that is frequently bought along with the main product is one example.

Photo courtesy of deepsense.ai

How demand forecasting can help a business

Demand forecasting and following sales forecasting is crucial to shaping a company’s logistics policy and preparing it for the immediate future.

Among the main advantages of demand forecasting are:

• Loss reduction – any demand that was not fulfilled should be considered a loss. Moreover, the company freezes its cash in stock, thus reducing liquidity.

• Supply chain optimization – behind every shop there is an elaborate logistics chain that generates costs and needs to be managed. The bigger the organization, the more sophisticated and complicated its inventory management must be. When demand is forecast precisely, managing and estimating costs is easier.

• Increased customer satisfaction – there is no bigger disappointment for consumers than going to the store to buy something only to return empty-handed. For a business, the worst-case scenario is for said consumers to swing over to the competition to make their purchase there. Companies reduce the risk of running out of stock–and losing customers–by making more accurate predictions.

• Smarter workforce management – hiring temporary staff to support a demand peak is a smart way for a business to ensure it is delivering a proper level of service.

• Better marketing and sales management – depending on the upcoming demand for particular goods, sales and marketing teams can shift their efforts to support cross- and upselling of complementary products.

• Supporting expert knowledge – models can be designed to build predictions for every single product, regardless of how many there are. In small businesses, humans handle all predictions, but when the scale of the business and the number of goods rises, this becomes impossible. Machine learning models extend are proficient at big data processing.


How to start demand forecasting – a short guide

Building a demand forecasting tool or solution requires, first and foremost, data to be gathered.

While the data will eventually need to be organized, simply procuring it is a good first step.
It is easier to structure and organize data and make them actionable than to collect enough data fast.

The situation is much easier when the company employs an ERP or CRM system, or some other form of automation, in their daily work.

Such systems can significantly ease the data gathering process and automate the structuring.

Sybilla – deepsense.ai’s demand forecasting tool. Photo courtesy of deepsense.ai

The next step is building testing scenarios that allow the company to test various approaches and their impact on business efficiency.

The first solution is usually a simple one, and is a good benchmark for solutions to come.

Every next iteration should be tested to see if it is performing better than the previous one.

Historical data is usually everything one needs to launch a demand forecasting project, and obviously, there are significantly less data on the future.

But sometimes it is available, for example:

• Short-term weather forecasts – the information about upcoming shifts in weather can be crucial in many businesses, including HoReCa and retail. It is quite intuitive to cross-sell sunglasses or ice cream on sunny days.

• The calendar – Black Friday is a day like no other. The same goes for the upcoming holiday season or other events that are tied to a given date.

Sources of data that originate from outside the company make predictions even more accurate and provide better support for making business decisions.


Common pitfalls to avoid when building a demand forecasting solution

There are numerous pitfalls to avoid when building a demand forecasting solution.

The most common of them include:

• The data should be connected with the marketing and ads history – a successful promotion results in a significant change in data, so having information about why it was a success makes predictions more accurate. If machine learning was used to make the predictions, the model could have misattributed the changes and made false predictions based on wrong assumptions.

• New products with no history – when new products are introduced, demand must still be estimated, but without the help of historical data. The good news here is that great strides have been made in this area, and techniques such as product DNA can help a company uncover similar products its past/current portfolio. Having data on similar products can boost the accuracy of prediction for new products.

• The inability to predict the weather – weather drives demand in numerous contexts and product areas and can sometimes be even more important than the price of a product itself! (yes, classical economists would be very upset). The good news is that even if you are unable to predict the weather, you can still use it in your model to explain historical variations in demand.

• Lacking information about changes – In an effort to support both short- and long-term goals, companies constantly change their offering and websites. When the information about changes is not annotated in the data, the model encounters sudden dwindles and shifts in demand with apparently no reason. In the reality, it is usually a minor issue like changing the inventory or removing a section from website.

• Inconsistent portfolio information – predictions can be done only if the data set is consistent. If any of the goods in a portfolio have undergone a name or ID change, it must be noted in order not to confuse the system or miss out on a valuable insight.

• Overfitting the model – a vicious problem in data science. A model is so good at working on the training dataset that it becomes inflexible and produces worse predictions when new data is delivered. Avoiding overfitting is down to the data scientists.

• Inflexible logistics chain – the more flexible the logistics process is, the better and more accurate the predictions will be. Even the best demand forecasting model is useless when the company’s logistics is a fixed process that allows no space for changes.

Sybilla – deepsense.ai’s demand forecasting tool. Photo courtesy of deepsense.ai

Summary

Demand and sales forecasting is a crucial part of any business.

Traditionally it has been done by experts, based on know-how honed through experience.

With the power of machine learning it is now possible to combine the astonishing scale of big data with the precision and cunning of a machine-learning model.

While the business community must remain aware of the multiple pitfalls it will face when employing machine learning to predict demand, there is no doubt that it will endow demand forecasting with awesome power and flexibility.


About deepsense.ai:

deepsense.ai helps companies gain competitive advantage by providing them with AI-based end-to-end solutions, with the main focus on computer vision, predictive analytics and NLP.


We also deliver machine learning and deep learning training programs to support companies in building AI capabilities in-house.

Photo courtesy of deepsense.ai

We use methodology developed during research and commercial projects we have done for clients including NTT, Nielsen, L’Oreal, Google and Intel.

https://deepsense.ai/  

Photo courtesy of deepsense.ai

Photo courtesy of deepsense.ai

Find us

deepsense.ai, Inc.
2100 Geng Road, Suite 210
Palo Alto, CA 94303
United States of America

deepsense.ai Sp. z o.o.
Krańcowa 5
02-493 Warsaw
Poland


Media Contact:

Konrad Budek
konrad.budek@deepsense.ai  


Source: deepsense.ai

https://deepsense.ai/  



ASTROMAN Magazine - 2019.03.22

deepsense.ai and Google Brain design artificial imagination for reinforcement learning

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2706  


ASTROMAN Magazine - 2018.11.15

Fourth industrial revolution and machine learning

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2621  


ASTROMAN Magazine - 2018.09.01

How machine learning improves business efficiency - five practical examples

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2576  


ASTROMAN Magazine - 2018.09.01

Four ways to use a Kaggle competition to test artificial intelligence in business

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2572  


ASTROMAN Magazine - 2018.04.10

Financial Times: CodiLime among 50 fastest growing companies in Europe

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2480  


ASTROMAN Magazine - 2018.03.23

deepsense.ai becomes NVIDIA Deep Learning Partner

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2469  


ASTROMAN Magazine - 2018.03.14

Research on reinforcement learning advancements powered by deepsense.ai granted with 500,000 USD

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2458  


ASTROMAN Magazine - 2018.03.01

CodiLime's Training & Development Hub to help companies develop unique skills in-house

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2450  


ASTROMAN Magazine - 2018.01.05

United Nations enlists deepsense.ai to build out its deep learning expertise

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2406  


ASTROMAN Magazine - 2017.10.25

deepsense.ai popularize machine learning at European universities as part of the Intel Nervana AI Academy

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2367  


ASTROMAN Magazine - 2017.09.28

Seahorse goes open source! Data analysts can get more from the free BI tool powered by Apache Spark

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2345  


ASTROMAN Magazine - 2017.09.26

CodiLime's cybersecurity team among five best in the world

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2342  


ASTROMAN Magazine - 2017.09.14

deepsense.ai's research on robotics at prestigious 1st Annual Conference on Robot Learning

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2337  


ASTROMAN Magazine - 2017.09.06

The new Neptune. Data scientists on a cloud with the new Machine Learning Lab from deepsense.ai

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2331  


ASTROMAN Magazine - 2017.08.25

deepsense.ai has become the AI World's Strategic Deep Learning Partner

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2321  


ASTROMAN Magazine - 2017.08.19

deepsense.ai: A new global player on the AI solution provider market

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2316  


ASTROMAN Magazine - 2017.06.06

CodiLime's CodiSec CTF team beats out more than 200 competitors from across the globe in Göteborg, Sweden

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2270  


ASTROMAN Magazine - 2017.05.23

Intel brings data science to Polish universities with an event series powered by deepsense.io

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2254  


ASTROMAN Magazine - 2017.05.19

GPU Technology Conference 2017 - deepsense.io shares first-look on image recognition for retail analytics using deep learning

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2249  


ASTROMAN Magazine - 2017.05.05

CyCon 2017 Starts in Four Weeks. Czech Team Wins Cyber Defence Exercise Locked Shields 2017

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2239  


ASTROMAN Magazine - 2017.03.24

deepsense.io helps improve the UK's satellite defence and security intelligence

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2218  


ASTROMAN Magazine - 2016.10.21

CodiLime is second fastest-growing tech firm in Central Europe - 2016 Deloitte Central Europe Technology Fast 50 list announced

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2140  


ASTROMAN Magazine - 2016.10.20

The Deloitte Technology Fast 50 in Central Europe: First place in the 2016 ranking goes to the Polish business Codewise, the second-placed CodiLime also from Poland

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2139  


ASTROMAN Magazine - 2016.05.17

Tomasz Kulakowski, CodiLime co-founder and CEO, wins Poland's top business award for Vision and Innovation

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2066  


ASTROMAN Magazine - 2016.04.07

deepsense.io experts to present Big Data and deep learning accomplishments at Silicon Valley and Dublin conferences

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=2051  


ASTROMAN Magazine - 2015.10.31

CodiLime has won the Rising Stars Award and was ranked 2nd in Central Europe at the 2015 Deloitte Technology Fast 50 in Central Europe

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=1987  


ASTROMAN Magazine - 2015.10.23

deepsense.io Data Scientist Wins AAIA'15 Data Mining Competition

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=1983  


ASTROMAN Magazine - 2015.09.30

deepsense.io to feature Seahorse Community Edition on the Trusted Analytics Platform developed by Intel

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=1971  


ASTROMAN Magazine - 2014.12.12

CodiLime CEO, Tomasz Kulakowski became the EY Entrepreneur Of The Year 2014 in the category New Business

https://www.astroman.com.pl/index.php?mod=magazine&a=read&id=1843