Importance of Demand Forecasting in Retail
The retail sector is diverse and dynamic, the industry seems to continue to grow every year, independently of economic cycles or capital costs. Inventory is the largest investment a retailer makes. Competitors are outdoing each other in accessing new capabilities and using technology to optimize inventory and warehouses. Retailers typically order inventory months before a product begins selling. The inventory-related cost goes far beyond payment to suppliers. There are also costs involved in the operation of distribution centers. Once inventory arrives, the product will generate carrying costs, along with the costs associated with slack and clearing prices. Moreover, inventory ties up cash flow and consumes storage space for other possible goods, driving up cost. Therefore, it is crucial to implement best practices and actually working solutions that will optimize inventory and save money with accurate retail demand forecasting.
Demand Forecasting Constraints to Consider in the Digital Age
Traditional retail predictions are struggling to cope with such a dynamic retail market. In the past, retailers may reserve SKU forecasts for the most important goods while covering the rest of the assortment with forecasts at the category or subcategory level. This strategy doesn’t apply to our dynamic retail environment now. In order to optimize inventory investment, the buyer needs more precise knowledge about the needs of each product. Demand differs according to locations within the store, time of year, and other SKUs. Add to this the growing need to address the realities of multichannel retailing.
An accurate forecast could solve the majority of these problems and reduce huge costs. Despite the fact that the importance of accurate forecasts is undeniable, most companies accept even 20-40% miscalculations, which is a huge level of error associated with the demand forecasting process.
In the following article, you will learn the reasons behind such circumstances, and we will propose a solution that directly contributes to noticeable savings for companies in any vertical sector.
Ready-made solutions are available for Demand Forecasting
There are many ways to implement demand forecasting depending on various factors, such as business area or company size – not to mention Microsoft Excel – but without a doubt, if an ERP (Enterprise Resource Planning) system is already implemented in an organization, there is a pretty good chance that forecasting is done directly in it. Of course, not all types of demand can be included there due to the lack of specific data (for example: if I would like to predict the demand for energy consumption in my warehouse, I most likely won’t find the right information about it in the ERP). However, the typical challenges around supply chain management forecasting are still appropriate.
Major vendors such as SAP, Oracle ERP, and Infor have already enhanced their solutions with a set of preconfigured features that allow users to forecast demand with ease – typically without the need for an extensive IT background or data analysis skill set. However, these systems are not fully developed analytical platforms. Consequently, we have to reckon with the inevitable limitations that come with it. This means that when using an ERP-based forecasting mechanism, we have minimal control over mathematical operations, and we cannot interfere with improving the results obtained. We’re unlikely to find anything fancier than classic statistical methods such as ARIMA or linear regression, although this absolutely does not mean that these types of models are bad for time series forecasting.
Modern Demand Forecasting Methodology
In fact, traditional forecasting techniques cannot deal efficiently with today’s retail demands for demand prediction. With advanced analytics systems that can leverage modern techniques like artificial intelligence (AI), all the variables that influence sales can be also considered. TT PSC uses proprietary algorithms to assess the history and value of retail data. Understanding the true historical demand can help retailers identify the areas of low inventory levels, but also overstocking.
We’re talking about tangible opportunities to build a competitive advantage by minimizing out-of-stock and reducing overstock, thereby increasing sales and reducing losses. Machine learning models are able to “learn” the new reality in two weeks, which means responding to changes automatically. The in-depth statistical analysis supported by expert knowledge gives the business new insights into the current market situation.
Well, traditional retailers have developed the baseline forecast through time-series models and used historical figures as a basis for predicting future demand. This forecast was often adjusted by a causal simulation or by manual input. But today’s business has replaced these old methods of forecasting demand with machine-learning technologies. They can take advantage of the vast amount of historical data and the exponential increase in computing power, which simply leads to slightly better results, especially in dynamic environments.
Small improvements, huge savings
In the age of digital transformation, retailers are challenged with managing increasingly complex supply chains and rapidly evolving customer behavior. Demand forecasts have become critical to retailers’ success over recent years. High-end retailers can no longer rely on outdated forecasting methods for demand forecasting. The retail sector needs a detailed demand forecast every time GMROI is available, this is important for the success of retailers. Achieving these measurable forecasts of sales volumes requires the use of an analytics platform for demand prediction — designed for the dynamic digital world of retail.
How is prediction accuracy increased in large retail stores?
Still, most retail stores’ lack of awareness that existing forecasts based on ERP systems can be improved often leads to the misconception that companies have to take calculated prognostications for granted. Whereas even a couple of percents of refinement can generate significant budget savings. Just calculating how much warehouse storage space can be preserved if retailers could accurately predict demand for their products, or how profit margin could be optimized if only planners have a precise tool for simulating upcoming revenues in their possession, leads to an even more efficient supply chain planning. There are actually a lot of business areas in which correct foresight can generate additional funds and improve the cash flow of any organization. Furthermore, it is a relatively simple task to calculate ROI if a company decides to invest.
A new approach to retail demand forecasting
The most important question at the beginning of the journey with AI-based demand forecasting is: how do you achieve that few percent improvements in forecasting accuracy?
A key step for organizations at the start is to accept and be willing to step out of their comfort zone. Until now, the safe harbor has been the basic tools they know and use every day to forecast their operations. The next step is the decision to implement a fully dedicated, bespoke analytics platform, in our opinion preferably in a Cloud environment. There are many choices when it comes to specific technology (for example, Databricks or SAS), but the conclusion is that today’s cloud providers (AWS, Azure, or GCP) not only provide access to unlimited and scalable data storage options, but also introduce a range of tools and capabilities to simplify and enrich data processing.
Taking that first step opens the door to unlimited possibilities for data analysis, and companies that choose to do so are no longer limited to any kind of predefined model types, techniques or specific mathematics. In addition, and just as importantly, inputs to predictive models can come from any potential data source (ERP, CRM, HR, external factors, sentiment, weather – other demand-relevant indicators), which is not difficult to achieve thanks to the variety of integration mechanisms available in AI-based solutions.
Maximize inventory data processing
A key aspect of accurate demand forecasting is the use of relevant and valuable data. Often pure raw data, even if it comes from multiple sources, is probably not the best source for building the most accurate forecasting models. A common step beyond selecting the key data we need is to extract the most relevant features from the data with the help of domain experts (Features Engineering). The experience of the specialists preparing the data for further processing is insanely important for the final quality of the forecasts. Even a top-notch forecasting technique can generate poor results if it is not fed with good quality, reasonable information.
Accurate demand forecasting, achieved through Machine Learning and Artificial Intelligence
A new departure in improving predictive models is to leverage the advantages of artificial intelligence (AI) by implementing non-trivial types of models based on machine learning (ML). The use of techniques such as artificial neural networks in forecasting provides the opportunity for a wide range of experiments that can be conducted by Data Scientists to prepare the most accurate type of model for a given demand forecasting problem. In the mindset of the Retail industry market, AI and machine learning are becoming increasingly important in the demand forecasting process. These new solutions offer incredible opportunities and help retailers predict future demand by analyzing past trends, current market conditions, and competitive behavior.
Retailers need to forecast the demand for their products in order to save money and optimize their inventory.
Nowadays, many companies are turning to data-driven retail optimization to increase their efficiency and profitability. The cloud-based platform, ingesting information from multiple sources, properly preparing data, and using machine learning for predictive modeling can certainly allow companies to improve existing demand forecasts, generating savings for the business.
Demand forecasting is a crucial part of any retail business. Retailers can use AI to forecast demand at different inventory levels and find the most cost-effective way of managing their inventory. Retail demand forecasting solution based on Artificial Intelligence can also easily integrate with existing ERP system and leverage all of its extant capabilities.
Learn how AI will transform demand forecasting in the supply chain.