Forecasting is a fully developed business process that most organizations still struggle with today. Almost everyone’s top priority is probably to be able to consistently and accurately forecast Sales, Demand, Costs, Inventory, etc. The inability to obtain a good forecast frequently has a significant business impact. Inaccurate forecasting leads to overstocking or running out, resulting in high costs and excess, impacting the bottom line and the success of the company.
A good forecast should give you enough confidence to make sound business decisions. For a more efficient forecast, consider these best practices:
- What are the most common forecasting methods, and why do they produce inaccurate results.
- How to achieve better ROI and optimal processes through scale, granularity, and agility
- How to improve forecasting accuracy
- How to use simple machine learning and artificial intelligence tools to get accurate and scalable forecasts
The Automatic Forecasting Feature
Automatic forecasting is the most popular and most used feature of SmartForecasts and Smart Demand Planner. Creating Automatic forecasts is easy. But, the simplicity of Automatic Forecasting masks a powerful interaction of a number of highly effective methods of forecasting. In this blog, we discuss some of the theory behind this core feature. We focus on Automatic forecasting, in part because of its popularity and in part because many other forecasting methods produce similar outputs. Knowledge of Automatic forecasting immediately carries over to Simple Moving Average, Linear Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Winters’ Exponential Smoothing, and Promo forecasting.
A Gentle Introduction to Two Advanced Techniques: Statistical Bootstrapping and Monte Carlo Simulation
Smart Software’s advanced supply chain analytics exploits multiple advanced methods. Two of the most important are “statistical bootstrapping” and “Monte Carlo simulation”. Since both involve lots of random numbers flying around, folks sometimes get confused about which is which and what they are good for. Hence, this note. Bottom line up front: Statistical bootstrapping generates demand scenarios for forecasting. Monte Carlo simulation uses the scenarios for inventory optimization.
6 Observations About Successful Demand Forecasting Processes
Forecasting is both an art and a science, requiring a balance between professional judgment and objective statistical analysis. In this blog, we will explore how to generate accurate predictions by leveraging statistical methods, incorporating business knowledge, and enhancing credibility through refinement and graphical representation. Learn about aligning techniques with data nature and integrating them with other business processes, creating a comprehensive planning approach that acknowledges margin of error and forecast bias. Gain the principles and techniques for successful demand forecasting, empowering informed decision-making and optimized planning.
Don’t Blame Excess Stock on “Bad” Sales / Customer Forecasts
Sales forecasts are often inaccurate simply because the sales team is forced to give a number even though they don’t really know what their customer demand is going to be. Let the sales teams sell. Don’t bother playing the game of feigning acceptance of these forecasts when both sides (sales and supply chain) know it is often nothing more than a WAG.
What makes a probabilistic forecast?
What’s all the hoopla around the term “probabilistic forecasting?” Is it just a more recent marketing term some software vendors and consultants have coined to feign innovation? Is there any real tangible difference compared to predecessor “best fit” techniques? Aren’t all forecasts probabilistic anyway?
A Practical Guide to Growing a Professional Forecasting Process
Many companies looking to improve their forecasting process don’t know where to start. It can be confusing to contend with learning new statistical methods, making sure data is properly structured and updated, agreeing on who “owns” the forecast, defining what ownership means, and measuring accuracy. Having seen this over forty-plus years of practice, we wrote this blog to outline the core focus and to encourage you to keep it simple early on.
Recent Posts
Daily Demand ScenariosIn this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies. […]
The Cost of Spreadsheet PlanningCompanies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies. […]
Learning from Inventory ModelsIn this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses. […]
The Methods of ForecastingDemand planning and statistical forecasting software play a pivotal role in effective business management by incorporating features that significantly enhance forecasting accuracy. One key aspect involves the utilization of smoothing-based or extrapolative models, enabling businesses to quickly make predictions based solely on historical data. This foundation rooted in past performance is crucial for understanding trends and patterns, especially in variables like sales or product demand. Forecasting software goes beyond mere data analysis by allowing the blending of professional judgment with statistical forecasts, recognizing that forecasting is not a one-size-fits-all process. This flexibility enables businesses to incorporate human insights and industry knowledge into the forecasting model, ensuring a more nuanced and accurate prediction. […]
Smart Software to Present at Epicor Insights 2024Smart Software will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #13 or #501, and learn more about Epicor Smart Inventory Planning and Optimization. . […]
Inventory Optimization for Manufacturers, Distributors, and MRO
Why MRO Businesses Need Add-on Service Parts Planning & Inventory SoftwareMRO organizations exist in a wide range of industries, including public transit, electrical utilities, wastewater, hydro power, aviation, and mining. To get their work done, MRO professionals use Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) systems. These systems are designed to do a lot of jobs. Given their features, cost, and extensive implementation requirements, there is an assumption that EAM and ERP systems can do it all. In this post, we summarize the need for add-on software that addresses specialized analytics for inventory optimization, forecasting, and service parts planning. […]
The Forecast Matters, but Maybe Not the Way You ThinkTrue or false: The forecast doesn't matter to spare parts inventory management. At first glance, this statement seems obviously false. After all, forecasts are crucial for planning stock levels, right? It depends on what you mean by a “forecast”. If you mean an old-school single-number forecast (“demand for item CX218b will be 3 units next week and 6 units the week after”), then no. If you broaden the meaning of forecast to include a probability distribution taking account of uncertainties in both demand and supply, then yes. […]
Why MRO Businesses Should Care About Excess InventoryDo MRO companies genuinely prioritize reducing excess spare parts inventory? From an organizational standpoint, our experience suggests not necessarily. Boardroom discussions typically revolve around expanding fleets, acquiring new customers, meeting service level agreements (SLAs), modernizing infrastructure, and maximizing uptime. In industries where assets supported by spare parts cost hundreds of millions or generate significant revenue (e.g., mining or oil & gas), the value of the inventory just doesn’t raise any eyebrows, and organizations tend to overlook massive amounts of excessive inventory. […]
Top Differences Between Inventory Planning for Finished Goods and for MRO and Spare PartsIn today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]
Problem
Generating accurate statistical forecasts isn’t an easy task. Planners need to keep historical data continually up to date, build and manage a database of forecasting models, know which forecast methods to use, keep track of forecast overrides, and report on forecast accuracy. These steps are typically managed in a cumbersome spreadsheet that is often error-prone, slow, and difficult to share with the rest of the business. Forecasts tend to rely on one-sized fits all methods that require seasonality and trend to be added manually resulting in inaccurate predictions of what comes next
Solution
SmartForecasts ® Cloud
Accurate Demand Forecasts
Best Forecasting Methods
Imports Historical Data
What can you do with SmartForecasts?
- Run a forecasting tournament that selects the right forecasting method for each item.
- Hand-craft forecasts using several time-series forecasting methods and non-statistical methods.
- Automatically predict trends, seasonality, and cyclical patterns.
- Imports demand data from files
- Leverage ERP connectors to automatically import demand data and return forecast results
Who is SmartForecasts for?
• Demand Planners.
• Forecast Analysts.
• Material & Inventory Planners.
• Operational Research Professionals.
• Sales Analysts.
• Statistcally Minded Executives.
A Reliable and Secure Platform
