Most organizations today use traditional statistical methods (TSM) to predict the future of their business. Anyone who works with TSM would agree that the process is tedious and not very much accurate. The most common factor that accounts for the inefficiency of TSM is human errors.
People find it boring to parse through tons of data to arrive at meaningful insights. They often ignore, misinterpret or simply skip modules to the convenience. But, things are changing, globally, organizations are embracing machine learning powered demand forecasting techniques to amp up their business efficiency. In this article, we’ll discuss the possibilities, use the case of demand forecasting with Artificial Intelligence.
Take the COVID-19 crisis, for instance. The pandemic completely upended the markets, which caused immense difficulties for most businesses. Companies that manufactured masks and sanitizers were left grappling with a drastic increase in demand, whereas other businesses began to witness plummeting sales and a huge excess of inventory. If you’d like a less extreme example, just consider cryptocurrency. A single tweet from a celebrity like Elon Musk can have a huge impact on the trading of cryptos and consumer demand. There are multifarious factors that can impact the minds of buyers and their demand for a certain product. Although there is no quick fix to this problem, businesses can reduce the impact of market volatility through demand forecasting.
As per the survey of the Institute of business forecasting (IBF), 70% out of 200 business professionals believed that AI-powered demand forecasting is essential for business growth.
Demand forecasting can be considered as the approximation of future demand for a product or service. Put simply, demand forecasting can help you figure out the number of goods and services that your customers will need in the foreseeable future. Many businesses make use of demand forecasting methods to prevent issues that may occur from an imbalance of supply and demand in the market. These methods help them enhance their decision-making, capacity planning, and risk assessment.
For example, a car manufacturer that is interested in demand forecasting could analyze the actual sales of its cars for the past year after considering factors like engine type, color, etc. Based on this information, the manufacturer would be able to predict the short-term consumer demand for the next year. This would play a major role in the purchase, production, and inventory planning.
Before the advent of advanced technology like predictive analytics, most businesses would use traditional forecasting models to predict consumer demand. These traditional methods are usually based solely on historical data. If a company wanted to forecast demand through these methods, it would need to collect and analyze its performance in the past couple of years. Depending on previous performance, a conclusion will be reached about future performance.
However, this method only works when you assume that history will keep repeating itself, which is not the case. Traditional methods fail to consider external variables such as PMI index, inflation, social media sentiment, and competitor pricing. This makes the forecast highly inaccurate. In addition to this, most traditional models fail when there are high sales volumes or volatility involved. Hence, it is pretty evident that businesses need to turn towards more viable solutions for demand forecasting. This is where machine learning comes in.
Businesses today generate tons of data, given the advent of digitization. With so much data to analyze, is it really sustainable to use old-school techniques for demand forecasting? In order to keep up with changing times, it would be much wiser to invest in machine learning technology.
ML algorithms provide systems with the capability to learn automatically. As the system keeps collecting data, it becomes increasingly accurate. Additionally, it can also handle enormous volumes of data without breaking a sweat.
When you feed data into an ML solution, the algorithms try to identify patterns in the provided information. It uses these patterns to make data-driven decisions that are speedy and accurate. With such solutions, businesses can finally consider external factors during demand forecasting, resulting in better quality predictions. This is a major breakthrough since something as simple as weather data can have multiple different factors that can affect consumer demand. Some of the primary advantages of demand forecasting machine learning solutions over traditional methods can be seen as follows:
According to Mckinsey, AI-powered demand forecasting solutions can reduce errors by 30-50 percent. This improved accuracy can result in a whopping 65% reduction in lost sales due to inventory out-of-stock situations. Additionally, warehousing costs can also be decreased by around 10-40 percent.
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Machine learning solutions go a long way in optimizing the demand forecasting process- they enable smarter forecasts based on real-time and they also use various internal and external data sources like demographics, weather, online reviews, and social media. From this data, it is quite obvious that traditional forecasting models like ARIMA are outdated and inefficient.
Now, if your product is relatively new to the business world, you may have reservations about implementing an AI solution due to a lack of data. Unsurprisingly, machine learning has this scenario covered as well. Machine learning forecasting tools can quickly and easily identify clusters of prior products that have similar attributes and lifecycle curves. The system makes use of these datasets as a substitute to make predictions.
There is a wide array of applications for demand forecasting with artificial intelligence. Solutions that incorporate these technologies can be seen in various industries like retail, manufacturing, logistics, etc. Some of the most commonly used use cases of demand forecasting are:
Demand forecasting using machine learning can be extremely useful for retail outlets, irrespective of the type of products being sold. Take a fashion outlet for example. Clothing brands launch new products, at least every season. Fashion outlets need to make sure that the existing collection is sold before the next collection of new clothes arrives. In a situation like this, demand forecasting solutions can be used to analyze fashion trends, seasonality, and other external factors — along with historical data related to previous collections. This will help retailers understand the volume of clothes that needs to be purchased from a certain brand. Similarly, fresh food stores cannot buy too many food products as they will rot over time. By using demand forecasting, they can buy just enough inventory to satisfy customers and prevent wastage.
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ATMs are an important aspect of the banking industry as they allow customers to withdraw money quickly and easily. However, many banks face the issue of predicting how much money they need to deposit in these ATMs so that they can meet customer demand. Depositing too much money can be risky and prevent banks from using these funds for other lucrative purposes. Customer demand can change based on local events, weather, holidays, and more. Demand forecasting solutions that incorporate AI can consider these factors and produce reliable reports that tell banks how much money they need to deposit in their ATMs on a daily basis.
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Organizations conduct numerous promotional events as a part of their marketing efforts. Unfortunately, most of these events are ineffective and don’t gain much traction from consumers. In fact, several studies have indicated that the majority of these events don’t break even. One of the primary reasons behind this is the use of traditional methods like spreadsheets to plan promotions and forecast their impact. Companies that make use of these methods are unable to produce accurate forecasts for their promotional activities. This is because there are a number of factors with intricate relationships that affect these events. Machine learning solutions can analyze these factors and produce accurate predictions accordingly. This helps businesses cut down on marketing costs that don’t pay off.
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Demand forecasting solutions are often used by telecom providers for capacity planning. In telecommunication, a cell refers to the geographical area that is covered by a single base station in a cellular network. Wi-Fi networks consist of several base stations in order to make full use of the radio spectrum. Each cell is capable of handling a specific number of connections and can support a finite bandwidth. However, as the population density in a cell increases, the cell can reach maximum capacity very quickly. This is because a large number of people are using applications like Facebook, Netflix, YouTube, etc. all at once. As a result, these applications become slow and calls cannot go through. This can lead to extreme customer dissatisfaction.
In order to fix this problem, telecommunication companies will have to install new calls. This installation needs to be planned for months before it can be put into action. Hence, these companies must predict the demand for the capacity of the network for future planning. Currently, most companies use manual processes to forecast demand for cell capacity. These methods lead to inaccurate and unreliable results. By adopting AI solutions, telcos will be able to analyze historical internal usage data along with external data like housing statistics in a particular area, plans for densely-populated facilities like hospitals, schools, and office buildings. This will allow them to make informed decisions while planning for capacity demand.
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Capacity planning is a key priority for all product shipping organizations. E-commerce is becoming increasingly popular all over the world, which means that more and more packages are being delivered directly to customers. Companies like Blue Dart and FedEx need to predict the requirement for their delivery services so that they can ensure that there are enough drivers and vehicles for deliveries. Demand forecasting solutions can accurately produce demand forecasts that will allow these companies to gain comprehensive insights into future consumer demand.
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A major obstacle for most casinos is planning their manpower requirement. Staffing the game floor is a major challenge as managers will need to open and close tables according to the number of players present. If too many dealers are present, with many closed tables, many of these employees will end up sitting idle. However, if there are fewer dealers available, then customers will be dissatisfied. Hence, casinos are looking for ways to forecast demand for seats at the gaming tables in order to schedule the right number of dealers for a given shift. Demand forecasting with machine learning techniques allows these casinos to understand how many dealers to schedule, how many tables to open, and how many players are expected during different time periods of the day and week. This improves the customer experience while allowing casinos to optimize staffing levels and ensure maximum business productivity.
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As world events like COVID-19 cause havoc to the markets, business executives are becoming more astute when it comes to technological investments. This has led to a drastic increase in the popularity of AI-powered demand forecasting. If you are wondering how demand forecasting solutions can help your business.
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