Using business forecasting solutions facilitates balanced financial and operational decisions. Forecasting algorithms might still look like a black box for people with a management background.
I am an AI Team Leader at MobiDev, a company with extensive experience in such projects. I’ve been solving a variety of exciting business challenges with AI for over seven years. So, in this article, I’ll focus on explaining what is needed for effective business forecasting with AI. Based on our case study, I’ll show how custom business intelligence software can be implemented to help you effectively make decisions.
How AI Improves Business Forecast Accuracy
With forecasting, companies can better serve clients and manage staff and resources, including cash, equipment, raw materials and goods. For example, knowing the demand brings an ability to manage logistics and track inventory costs or even predict ROI for a new product.
Data-driven forecasting can cover many aspects of a company’s day-to-day operations:
- demand and sales
- finances
- inventories and supply chains
- manufacturing and product quality assurance
- personnel, etc.
At the same time, it is clear that the key to unlocking the benefits of AI for forecasting is gathering relevant data within the company. Let’s even assume that you have a startup and are not yet planning to order the development of an AI-based forecasting solution because you have more priority issues at this stage. I still advise you to collect data from the very start of operations, thereby investing in the future of your project. The need to apply forecasting methods in business will appear sooner or later anyway, and the accumulated data is the game changer that will help quickly translate this issue into action.
Today, due to the ability to process large amounts of data and continuously improve already operating models, machine learning (ML) is the most promising basis for forecasting and business intelligence solutions.
With this approach, companies can replace manual techniques with ML, getting the following benefits for their business:
- Acquiring insights and detecting hidden patterns that are difficult to trace with traditional approaches. Training ML forecasting models on large amounts of data and moving computation to the cloud is becoming the de-facto industry standard.
- Reduce the number of errors in forecasting.
- Ability to infuse more data in a model. External data may be valuable here and change the outcomes in terms of predictions.
- Flexibility and rapid adaptability to changes. Compared to traditional non-AI approaches, ML forecasting algorithms can be quickly adapted in case of any significant changes.
Please note that we’re considering forecasting, not predictive modeling. We’ll explain the difference between these two models in simple terms.
Difference Between Forecasting & Predictive Modeling
Both forecasting and predictive algorithms are applied to address cumbersome challenges related to business planning, customer behavior, and decision-making. Nevertheless, these techniques differ.
Forecasting modeling implies analysis of historical data to find patterns, or trends, which allow us to estimate the probability of future events. In contrast to predicting, forecasting modeling should have traceable logic. Typical use cases include a forecast for energy consumption in the following 6–12 months, an evaluation of how many customers will reach support in the next 7 days, or how many agreements for the supply are expected to be signed. All this could be forecasted based on previous (historical) data.
Predictive modeling is the process of applying AI and data mining to assess more detailed, specific outcomes and use much more diverse data types. The difference between predictive and forecasting modeling is blurred, still, we can consider an example to understand it better. Just imagine that a credit institution plans to launch a new premium card. At this point, two questions may arise:
- The first will probably be, how many cards will be issued in the next 6 months? Forecasting modeling will help us find an answer to this question thanks to analysis of similar products launched in the past.
- But there is also a second question because we still don’t know to whom we can recommend this card. Here predictive modeling comes into play. It enables us to analyze a customer information database with such fields as age, salary, preferences, consumer habits, etc. With this approach, we will eventually understand which clients are more likely to use this card.
Having mentioned the main concepts, let’s move on to the application areas of business forecasting software.
Use Сases For Machine Learning Forecasting For Business
Business forecasting software finds its application in all spheres of entrepreneurship. After all, forecasting demand and sales, prices for raw materials and goods, supply chains, and cash flows is vital for any company. Here are the main directions where business forecasting tools will come in handy.
FINANCIAL FORECASTING
Financial forecasting allows you to plan processes and productivity. ML forecasting will shift the focus of the manager’s work from manual management of routine tasks to business drivers. Moreover, ML financial forecasting reduces the number of ineffective strategies in play and human errors and helps predict supply, demand, inventory, future revenues, expenses, and cash flow.
For example, stakeholders of the business are aiming to know the company’s turnover and key factors for growth during the next financial period to understand and analyze areas of improvement. Based on historical key company business indicators and existing turnover information during the past periods, we can develop an ML forecasting model using deep learning or regression models. It will predict future required metrics, based also on seasonal information and other influencing factors. In this case, business owners will be able to plan the next period of time accordingly.
SUPPLY CHAIN FORECASTING
Supply chain management is one of today’s most pressing business challenges. On the one hand, supply chains are becoming more and more globalized and complex. On the other hand, it is sometimes quite difficult to protect their stability and avoid disruptions in the delivery schedules. Not all industries have yet stabilized the state of their supplies after the pandemic overloads as well.
ML-based forecasting solutions enable companies to efficiently respond to issues and threats as well as avoid under and overstocking. Machine learning algorithms for forecasting can learn relationships from a training dataset and then apply these relationships to new data. Thus, ML improves the selecting and segmenting of suppliers, predicting supply chain risks, inventory management, and transportation and distribution processes.
Let’s look at an example of using machine learning for supply chain forecasting. The chain of hypermarkets operates around 100 stores in different locations and has an average of 50,000 SKUs per store. For such a big chain, it’s required that the process of replenishment of warehouses be automated. There are two main benefits in this case:
- No need to store a lot of hard-to-sell products
- Frequently sold products should be delivered on time
Based on the previous information on the replenishment of warehouses, as well as data that shows how fast certain products are selling, we can develop an ML model for predicting the number of products per SKU. The prediction could be shown with different time horizons (e.g. daily, weekly, monthly, etc.). This can help managers properly organize the system of storing products and minimize cases of product absence.
PRICE PREDICTION
Price prediction algorithms determine how much a product should cost to be attractive to consumers while meeting company expectations and generating the highest level of sales. Such forecasting must consider many factors, including product characteristics, demand, and existing trends. At the same time, business forecasting software must cope with the tendency toward fluctuations of many parameters of the external environment.
Often business owners want to have an understanding of price changes for a specific product for a future period. Having taken into consideration client data with related price changes for a past period for all of the existing products, we can catch general patterns from the previous data and extrapolate them for the next periods. The positive impact could also be applied by adding external third-party data that could influence prices as well, for instance: inflation rate, holidays, seasonal patterns, etc. Wrapping up all of this data, we can develop an ML forecasting model that will be able to predict price trends for specific products.
DEMAND & SALES FORECASTING
A fluctuation in demand is a traditionally cumbersome challenge. We advise our clients to base custom business intelligence software on forecasting ML models.
ML demand forecasting allows company management to predict buyers’ behavior and find out how many products to produce or order. With ML models, it’s possible to avoid excess inventory or stockout. Moreover, such an approach to demand forecasting enables an understanding of the target audience and competition.
Let’s say a restaurant chain business wants to plan demand. It will help the business in several ways:
- to know the number of dishes that will be sold in the restaurant to plan food stocks
- to understand and define an appropriate number of employees that are required to provide quality customer service
- to come up with the proper and timely marketing campaign
How to Develop an ML-Based Business Forecasting Software
To cooperate effectively with machine learning professionals, it is essential to learn about the process of creating ML models. So in this section, let’s take a look at the workflow MobiDev applies to building custom BI software.
We will illustrate the main stages of business forecasting software development with meaningful examples from our project.
CASE STUDY: VENUE MANAGEMENT AND POINT OF SALE FOR NIGHTCLUBS AND BARS
Client and Business Goals:
The client is a provider of premium venue management and POS software to high volume bars, restaurants, and nightclubs based in the US.
Product Description:
SmartTab is a premium hardware and software that streamlines venue management operations and POS terminals. It covers the needs of bars and nightclubs — from inventory and taxes to serving the crowd on a busy night.
Also, SmartTab provides access to customizable analytical dashboards that present sales and demand forecasting capabilities, as well as other metrics tailored for different venues.
Let’s consider a step-by-step flow to create business forecasting software.
STEP 1. BUSINESS ANALYSIS FOR FORECASTING SOFTWARE
This stage is dedicated to defining the goals and objectives of the project. It is necessary to determine which business problem the product is aimed at solving. Also, the project team should agree with the product owner on success metrics for evaluating the effectiveness of the forecasting model. An incorrectly chosen metric can nullify the ability of tracking business success, that is why metric definition is one of the key tasks for ML, BA, and business stakeholders. Based on the priorities, it is to be clarified which parameters the business forecasting tool should determine, for which period, and with what acceptable accuracy.
Key Points from the Case Study
One of the project goals was to enhance premium software for streamlining venue management operations with AI-based forecasting features. The implementation of machine learning models for forecasting was intended to provide venue managers with the ability to plan finances, inventory, and staff depending on expected attendance and sales.
STEP 2. MACHINE LEARNING PROBLEM DETERMINATION
Traditionally, to create a software product for business, you have to choose between a ready-made solution and developing a custom business intelligence software from scratch that will perfectly suit your business needs.
Good news and bad news are waiting for you when you’re looking for off-the-shelf solutions. Let’s start with a little bad news.
The likelihood of finding a pretrained machine learning model for business intelligence that one-hundred percent matches your product vision is slim to none. The point is that different businesses have different sales patterns, seasonality, cyclicity, and so on. Consequently, if a model generates a sales forecast for one company with a low error percentage, it does not mean that it can be reused for another company.
Likewise, there are limitations and caveats regarding the possibility of using cloud BI platforms for business forecasting using machine learning. Here are the main ones:
- Inability to fully take into account domain specifics, for example, as in our case, the restaurant business.
- The limited functionality of ready-made third-party tools by default does not cover all the client’s needs. Customization of ready-made solutions is always an additional expense of time and money.
- Difficulties with scaling are typical for platforms with template solutions. It is possible to imagine the creation of a solution on any BI platform for one venue. However, a product that is used by a huge number of venues, as in our case, needs a custom approach.
- Any third-party service adds not only extra cost but also additional risk because you do not control the environment in which the product operates. When you need independence and advanced security, and when it is cheaper to deploy and maintain a machine learning forecasting model on your own infrastructure rather than on a third-party platform, it makes sense to go with development from scratch.
- Creating a forecasting tool on a cloud BI platform is still a development that requires expertise and data preparation and also entails corresponding risks. Accordingly, bearing costs and risks, it is advisable to take advantage of the creation of models for each specific venue, which will be trained and retrained on its data.
At the same time, custom machine learning forecasting models provide greater flexibility and wider functionality and can be fully optimized for customer tasks. The good news is that MobiDev`s in-house AI engineers can build a custom ML forecasting model for business intelligence from scratch.
Key Points from the Case Study
The client needed the simultaneous availability of several versions of the software with different scopes of functionality for different categories of users. In addition, software should offer the possibility not only to forecast income from current sales but also to analyze various aspects of the operation of venues. These considerations tipped the scales in favor of custom business intelligence software.
STEP 3. DATA SOURCING, GATHERING, UNDERSTANDING AND PREPARATION
At this stage, you will have to overcome the challenges of custom business intelligence software development projects that relate to data. In particular, we are talking about challenges such as:
- Insufficient amount of data to train a model
- Handling of missing data
- Dealing with outliers/anomalies
Operating businesses have favorable conditions for preparing to use forecasting machine learning algorithms if they have taken care of gathering the necessary data. For such companies, it is possible to make their own datasets suitable for training AI forecasting models. However, the evaluation and processing of available data require careful attention.
The collected data alone may not be enough, or it may not cover all the needs of training a machine learning model for prediction. Companies have to consider many external factors. For example, the project we talked about in this article uses weather data to forecast venue attendance and beverage demand. In turn, most businesses have to take into account macroeconomic or industry data, etc. So, most often, to train machine learning models for forecasting, one must be ready to find ways to enrich one’s data sets, including by supplementing them from external sources.
Key Points from the Case Study
Our specialists will help you identify the sources of the necessary data. By taking a fresh look at your organization, they can even expand the range of data sources. This is how it worked successfully in the case of our client in the restaurant business.
Mobidev has vast experience and a well-tuned workflow for handling data for machine learning forecasting. Among the rules that our experts strictly follow, I will highlight just a few that will give you an idea of how the process is followed. So, when preparing data for the application of forecasting methods in business:
- We carefully work with clients on their expectations from the product for ML-based forecasting. By delving into the specifics of the business, it becomes clear which points are particularly significant for the customer’s business and must be taken into account when developing the forecasting model. The seamless interaction of domain experts and our AI engineers allows us to build models whose accuracy and reliability of forecasts match the customer’s expectations.
- Together with the client, we keep in sight all the data valuable for forecasting. If any specific data, according to the customer, can contribute to the improvement of the model, then our AI specialists immediately set up their collection and use. This also applies to external data. Weather, crowded public events nearby, holidays, etc. – data on all factors affecting visitor flow and demand we can collect, prepare, and use to form datasets.
Data suitability for machine learning forecasting models is evaluated according to the following criteria:
- Accessibility
- Accuracy
- Completeness
- Consistency
- Detalization
- Relevance
- Validity
Product owners do not always have the opportunity to evaluate the data themselves or with their staff. Quite often, our data science consultants need to help customers. The role of such specialists is significant for all actions that need to be performed at this stage of building an ML model for forecasting. We are talking about data preparation, which, in particular, may include analyzing it for gaps and anomalies, cleaning, labeling and checking for relevance, as well as data understanding.
STEP 4. MACHINE LEARNING MODELS DEVELOPMENT
Depending on the goals and specifics of the products, our ML experts will choose from proven machine learning forecasting algorithms. In this context, auto-regressive algorithms, deep learning algorithms, exponential smoothing, Gaussian algorithms, regression algorithms, tree-based algorithms, etc., can be noted.
It is often necessary to apply ensemble modeling that combines several machine learning approaches when building business forecasting software. Business goals, types, quantity and quality of data, forecasting periods, etc., influence the choice of forecasting models in machine learning to be made and trained.
In most cases, for forecasting ML models, it is appropriate to use the following:
- ARIMA/SARIMA
- K-Nearest Neighbors Regression
- Long Short-Term Memory (LSTM)
- Random Forest
- Regression models
- XGBoost, etc.
Let’s see how we determined and specified which machine learning forecasting models are needed for the project that serves as an example.
Key Points from the Case Study
To achieve the customer’s business goals, it was crucial to identify trends in the data, in particular, the impact of seasonality. Time series analysis is required for such purposes. Forecasting is based on analytical data obtained in this way. Time series analysis and forecasting based on it can be automated by machine learning. It is significant that for the analysis of time series, there are many methods, the use of which contributes to increasing the accuracy of forecasting. Trend analysis, signal processing, feature selection, analysis of time intervals, and clustering greatly contribute to the identification of regularities in time series. We also used time series decomposition to identify aspects of the data such as seasonality, trends, and noise, and to identify and remove outliers.
Our AI engineers also applied ensemble learning, combining several created models to obtain better prediction accuracy. In particular, we used such a method of ensemble learning as gradient boosting.
Combining several weak models allows you to get a strong learner with increased accuracy and performance. By applying gradient boosting, we iteratively added new models to the ensemble. Each of the new models corrects errors characteristic of previous models.
Choosing gradient boosting for modeling in the described case, we were based, among other things, on the following advantages of this method:
- ability to process data of various types, including not only numeric but also categorical and ordinal data
- resistance to outliers
- ability to identify and exploit complex relationships in data
- possibility of automatic processing of missing data, etc.
Having implemented the described approach, we created Time Series and Gradient Boosting ML models for forecasting sales at venues of users of our custom business intelligence software.
By applying Gradient Boosting ML models for already preprocessed data, we are predicting what amount of revenue will be earned for each day/week/month for the next year. This type of predictive technology provides immense value to customers and venue owners.
STEP 5. MODEL TRAINING, EVALUATION & DEPLOYMENT
Training
Data collected and prepared as described above should be enough to form three datasets for Training, Validation, and Testing. AI engineers train a machine learning model for forecasting with the training dataset.
Training any ML forecasting model requires evaluation. This stage foresees a comparison of predicted and actual results. It brings an understanding of how well the model performs. After that, it will be possible to compare different forecasting algorithms and choose the one that produces a minimal number of errors.
Evaluation
Next, AI specialists focus on optimizing the machine learning forecasting model to ensure the highest possible performance. As an example, a cross-validation tuning method serves such purposes. Dividing the training dataset into several equal parts, AI engineers train models with different sets of hyperparameters. They have to find out which models with which parameters forecast most accurately.
Improvement
AI engineers choose among several tested machine learning models for forecasting the one that most closely matches the requirements of the software product. Analytical results are optimized in the process of improvement.
Deployment
Once the choice is made and the optimal business forecasting model(s) is selected, it (or them) needs to be integrated into the production version of the software.
When deploying a machine learning model for business forecasting, it is crucial to create conditions for its further improvement. You should also remember to test your AI applications to make sure your ML model works well and integrates seamlessly with your infrastructure.
Key Points from the Case Study
For the venue management software product, we created and configured a pipeline to aggregate new data. First of all, our AI engineers automated the regular retraining of the ML model with such data. This greatly advances the accuracy of forecasting. In addition, the gathering of new data allows the formation of datasets for the development of new AI features for our client’s software.
From the moment you are confident that the AI forecasting models will provide the necessary parameters, it is worth paying due attention to the usability of business intelligence reporting. After all, AI-powered business forecasting tools should contribute not only to the improvement but also to the acceleration of managerial decision-making. In addition, convincing data interpretation, visualization, and comparison help to find consensus in the management team more easily. Customized dashboards, data visualization, online analytical processing (OLAP), automatic report generation with forecasts for stakeholders, and integration with corporate information systems strengthen the validity of management decisions and shorten the time for their adoption.
Some business intelligence reporting highlights:
- UI/UX: easy navigation and minimum actions to operate
- The AI dashboard is developed for operating speed and usability. Users can view relevant forecast data.
How MobiDev Can Help You Build Your Forecasting Software
The MobiDev AI team helps businesses address challenges and implement ML forecasting to yield new insights. Years of experience allow our experts to build effective solutions for enhancing business processes. We focus on the goals of each specific project to create a roadmap that will help our clients achieve measurable results.
Today, you, too, can strengthen your business with your own forecasting system. Feel free to contact MobiDev experts for custom business intelligence software development.
