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  • Demand Forecasting

    This article explores the significance of demand forecasting in predictive analytics, emphasizing its crucial role in maintaining optimal inventory levels, preventing stock shortages or surpluses, and enhancing competitiveness through the application of data mining methods.


    Demand forecasting is the area of predictive analytics dedicated to understanding and forecasting customer demand for goods or services. Knowledge of how demand will fluctuate enables the supplier to keep the right amount of stock on hand. If demand is underestimated, sales can be lost due to out-of-stock in store and shortages in the supply chain. If demand is overestimated, the supplier is left with a surplus that can also be a financial drain. The ability to accurately predict demand is imperative for manufacturers, suppliers, and retailers to meet customer demand and will improve competitiveness in the marketplace.  Although no forecasting model is flawless, unnecessary costs stemming from too much or too little supply can often be avoided using data mining methods. Using these techniques, a business is better prepared to meet the actual demands of its customers.

    Understanding Customer Demand

    Demand Anomalies

    In demand forecasting, as with most analysis endeavors, data preparation efforts are critical. Data is the main resource in data mining; therefore it should be adequately prepared before applying data mining and forecasting tools. Without proper data preparation, the old adage of "garbage in, garbage out" may apply useless data resulting in meaningless forecast models. Major strategic decisions are made based on the demand forecast results. Errors and anomalies in the data used to create forecast models may impact the model's ability to forecast. These errors give rise to the potential for bad forecasts, resulting in losses. With adequately prepared data, the best possible decisions can be made.

    There are several sources for problems with data. Data entry errors are one possible source of error that can adversely affect demand forecasting efforts. Basic statistical summaries and graphing procedures can often make these types of errors apparent. Artificial demand shifts are another error source. For example, a customer response to a promotional offer may temporarily boost sales of an item. Without a similar promotion, the same increase cannot be expected. Some uncontrollable factors have the ability to influence customer demand as well. A factor such as economic conditions may tend to impact demand. An unusually mild winter will likely cause lower energy demand. Accounting for these influences of demand can help fine-tune forecast modeling.

    For more information on Anomaly Detection, click here.

    Seasonal Fluctuations

    Every business sees seasonal fluctuations. Holidays and weather changes influence the products and services that customers want. While it is extremely important to account for how seasonal changes affect demand, it may be possible to benefit further from this. Understanding how seasonal factors affect customers helps businesses position themselves to take advantage.

    Systematic Patterns vs. Trends

    Generally, demand patterns consist of some basic classes of components, seasonality, and trend. Seasonality refers to the portion of demand fluctuation accounted for by a recurring pattern. The pattern repeats systematically over time. The trend is the portion of behavior that does not repeat. For example, a trend may show a period of growth followed by a leveling off. In retail sales, seasonality will likely find patterns that repeat every year. With sufficient data, other seasonality trends may manifest across multiple years.



    Forecasting Customer Demand

    Forecasting Techniques & Analysis Tools 

    A wide variety of analysis tools can be used to model customer demand - from traditional statistical approaches to neural networks and data mining. Using these demand models enables the estimation of future demand: forecasting. Possibly, a combination of multiple types of modeling tools may lead to the best forecasts.

    Time series analysis is a statistical approach applicable to demand forecasts. This technique aims to detect patterns in the data and extend those patterns as predictions. The ARIMA model or autoregressive integrated moving average, in particular, is used both to gain an understanding of the patterns in data and to predict the series. Different parameters are used to detect linear, quadratic, and constant trends.

    Other approaches for building forecast models are Neural Networks and Data Mining, which can model even very complex relationships in data. Demand forecasting is a very complex issue for which these methods are well suited. 



    Forecasting Customer Stories & Use Cases

    Forecasting Customer Demand in Retail Supply Chains with Nature's Pride

    Predicting customer demand is a crucial step in many industries. It can help efficiently manage the supply chain, assess future financial and logistical impacts, provide a better customer experience, and make a business more resilient. Learn how Nature's Pride, a large food retailer, uses TIBCO technologies to predict demand for a variety of products to ensure availability when customers need it most. Watch this webinar for a live demonstration and discussion with Nature's Pride's innovation manager on the use and importance of demand forecasting in the retail and wholesale market.

    Using Time Series to Understand Customer Engagement 

    When customers engage with an organization, whether it be browsing a retailer's website to buy products, or using electricity from a utility provider ? over time people tend to exhibit standard patterns of behavior. Understanding these patterns can help better serve customer needs or identify interesting anomalies. In this blog, we explain how TIBCO uses a number of different statistical and machine-learning techniques to understand patterns of electricity consumption. We'll discuss how time series analysis can be applied in a variety of scenarios across industries to understand customer behavior.

    Real-Time Predictions in the Spotfire F1TM Simulator - A Data Science Story

    TIBCO has been in a (very successful!) partnership with the Mercedes-AMG Petronas Motorsport team, going on to win each season. While the secrets of success in Formula 1 are closely guarded, one data science story we can tell is how we brought data science into our very own Spotfire F1TM Simulator. You can read the full story here. The simulator acts as TIBCO's digital twin to learn from as well as simulate and predict future performances in the simulator. 

    Nearcasting: Comparison of COVID-19 Projection Methods

    As COVID-19 continues to impact people's lives, we are interested in predicting case trends in the near future. Trying to predict an epidemic is certainly no easy task. While challenging, we explore a variety of modeling approaches and compare their relative performance in predicting case trends. In our methodology, we focus on using data from the past few weeks to predict the data for next week. In this blog, we first talk about the data, how it is formatted and managed, and then describe the various models that we investigated

    Estimating COVID-19 Trends using GAMLSS

    In this blog, we describe our experience with a method called GAMLSS, Generalized Additive Models for Location, Scale, and Shape, available in R as package gamlss. Our intention is to provide insight into the overall history, variation, and current trend in COVID data; prediction is not necessarily our focus. This method is described by Stasinopoulos et. al. as a distributional approach, that is, we make an informed guess as to the appropriate distribution to use and then fit a linear or smooth model via a link function to the data. We use penalized b-splines to fit the time series of new cases with a smooth curve (this method creates a smooth curve adapted to the data). The gamlss package additionally provides easy-to-use diagnostics to help validate our hypothesis and the fit.

    Decanting Wine Reviews into Insights with Spotfire

    Based on the wine dataset, we present a use case that can be imagined for marketing and merchandising where different wine attributes like flavors, tannin levels, etc. can be analyzed, and then combined with additional external data for price and demand forecasting. In this blog, we show how easy it is to generate insights from review data and trends all the way to the end of the financial value chain.

    Spotfire Continuous Supply Chain Accelerator 

    The Continuous Supply Chain Accelerator allows users to evaluate historical sales to generate inventory ordering models based on Economic Order Quantity and Safety Stock principles. It includes forecasting models using ARIMA and LSTM. It also provides tools to optimize the allocation of stores to distribution centers based on constraints, as well as generating real-time routing for deliveries using integration with TIBCO Geoanalytics.

    Spotcoffee Demand Forecasting and Route Optimization demo on the Spotfire Interactive Demo Gallery

    The theme of the Spotcoffee demo is Increasing revenue from product sales and reducing the costs of operations.  It includes the following content: Demand planner & trade marketing analysis; Forecasting & trade promotion analysis; What-if marketeer analysis; and Supply chain distribution planning.

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