{"id":14374,"date":"2021-06-29T15:00:41","date_gmt":"2021-06-29T13:00:41","guid":{"rendered":"https:\/\/www.tradecloud1.com\/?p=14374"},"modified":"2021-07-15T19:39:28","modified_gmt":"2021-07-15T17:39:28","slug":"ai-case-study-1-demand-forecasting-using-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/tradecloud.hdnk.nl\/en\/ai-case-study-1-demand-forecasting-using-artificial-intelligence\/","title":{"rendered":"AI case study 1: Demand Forecasting using Artificial Intelligence"},"content":{"rendered":"


\nThe Business Problem
\n<\/b><\/h2><\/div>

One of the biggest challenges for business executives today is demand volatility in relation to demand forecasting. Whereas data availability continues to increase, customer purchase patterns are becoming increasingly complex, and therefore harder to detect or predict (Symphony Retail, n.d.).<\/p>\n

There are too many factors influencing demand, ranging from weather fluctuations to posts by social media influencers, causing customers to frequently changing their minds. Even worse, things that will reshape customer intentions will mostly happen quite unexpectedly (Alexsoft, 2019). Traditional Forecasts are only as accurate as the data, models, resources and people that have to interpret them (Symphony Retail, n.d.). So, how can we respond to these challenges?<\/p>\n<\/div>

The Solution<\/b><\/h2><\/div>


\nAI-powered demand forecasting
\n<\/b><\/h3><\/div>

A response to demand volatility is demand forecasting using Artificial Intelligence. Traditionally, demand forecasting is a form of predictive analytics, where the process of estimating customer demand is analysed using historical data (Dilmegani, 2021). Using AI, organisations can make use of Machine Learning algorithms to predict changes in consumer demand as accurately as possible. These algorithms can automatically recognise patterns, identify complicated relationships in large datasets and capture signals for demand fluctuation. See figure 1 for a side-by-side comparison between traditional forecasting and ML forecasting.<\/p>\n

Typically, organisations use this form of AI to avoid inefficiencies caused by misalignment of demand and supply throughout the operational process. Honestly, this will never be 100% accurate (Alexsoft, 2019). Yet it can offer companies the opportunity to significantly reduce supply chain costs and make improvements in financial planning, workforce planning, profit margins and risk assessment decisions (Dilmegani, 2021).<\/p>\n<\/div>

<\/div>\n
\n\n\n\n\n\n\n\n\n\n\n
<\/th>\nTraditional
\nforecasting<\/th>\n		Machine Learning
\nforecasting<\/th>\n<\/tr>\n<\/thead>\n
Ability to consider
\nnumerous variables
\nand data sources<\/td>\n		Adding extra variables<\/span>
\nand sources requires<\/span>
\nsubstantial effort<\/span><\/td>\n		Multiple variables<\/span>
\nand sources can be<\/span>
\nsmoothly<\/span>
\nincorporated<\/span>
\nthanks to the high<\/span>
\nlevel of automation<\/span><\/td>\n<\/tr>\n
Volume of manual
\nwork<\/td>\n		High<\/span><\/td>\nLow<\/span><\/td>\n<\/tr>\n
Amount of data
\nrequired<\/td>\n		Small<\/span><\/td>\nLarge<\/span><\/td>\n<\/tr>\n
Maintenance
\ncomplexity<\/td>\n		Low<\/span><\/td>\nHigh<\/span><\/td>\n<\/tr>\n
Technology
\nrequirements<\/td>\n		Low<\/span><\/td>\nHigh<\/span><\/td>\n<\/tr>\n
Best fit<\/td>\nMid \/ long-term<\/strong>
\nplanning<\/strong>
\nEstablished products<\/strong>
\nStable demand<\/strong><\/td>\n		Short \/ mid-term<\/strong>
\nplanning<\/strong>
\nNew products<\/strong>
\nVolatile demand<\/strong>
\nscenarios<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Figure 1: Comparison of Traditional and Machine Learning Forecasting solutions<\/p>\n

Source: (Alexsoft, 2019)<\/em><\/h3><\/div>


\nExample
\n<\/b><\/h3><\/div>

A real-life example can be found at Danone Group<\/a>, a French manufacturer of food products. Danone implemented a machine learning system to make better demand forecasts. The company required more accurate and secure demand forecasts, due to the short shelf-life of its fresh products and volatile demand (Brosset, et al.). Danone uses many promotions and media events. More than 30% of the total volume is sold through promotional offers such as discounts and leaflets, so the demand forecasts were somewhat ad hoc (Best Practice AI, n.d.).<\/p>\n

The implemented machine learning system did not only improve forecasts, but also improved planning between different departments such as sales, supply chain, finance and marketing. This system improved efficiency and inventory balance, allowing Danone to achieve its target service levels for channel or store-level inventories (Brosset, et al.).<\/p>\n<\/div>

Benefits<\/b><\/h2><\/div>

Several advantages of AI in demand planning have already been mentioned. Below are the key benefits that demand forecasting with AI can bring to any company in the manufacturing industry:<\/p>\n