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
How Are We Doing? KPI’s and KPP’s
Dealing with the day-to-day of inventory management can keep you busy. But you know you have to get your head up now and then to see where you’re heading. For that, your inventory software should show you metrics – and not just one, but a full set of metrics or KPI’s – Key Performance Indicators.
Confused about AI and Machine Learning?
Are you confused about what is AI and what is machine learning? Are you unsure why knowing more will help you with your job in inventory planning? Don’t despair. You’ll be ok, and we’ll show you how some of whatever-it-is can be useful.
How to Forecast Inventory Requirements
Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future demand. Traditional methods often rely on bell-shaped demand curves, but this isn’t always accurate. In this article, we delve into the complexities of this practice, especially when dealing with intermittent demand.
Six Demand Planning Best Practices You Should Think Twice About
Every field, including forecasting, accumulates folk wisdom that eventually starts masquerading as “best practices.” These best practices are often wise, at least in part, but they often lack context and may not be appropriate for certain customers, industries, or business situations. There is often a catch, a “Yes, but”. This note is about six usually true forecasting precepts that nevertheless do have their caveats.
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.
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