Inventory Forecasting: How Automation Can Improve Your Forecasting for Better and Cheaper Inventory Management


Through forecasting, a company can project where it’s going, and it may adjust its budget and allocate more or less funds to an activity, depending on the forecast. Joining Arkieva in 2013, Abhishek works as a Consultant and Project Manager helping various Manufacturing, Chemical, and CPG industry clients. He holds a Bachelor’s degree from IIT Roorkee and a Master’s degree from The University of Wisconsin-Madison in Industrial Engineering.

  • Now that your forecast is ready, the next step is to validate it.
  • The system automatically evaluates the significance of each divergence, analyzes influence factors, and offers adjustments to short-term plans.
  • If the first few items you see are all out of stock or size broken, it’s likely that the page is showcasing the things closer to depletion.
  • Sales forecasts will be dependent on being able to decipher each rep’s call, instead of a predictable sales cycle.
  • Some of these elements are rooted in real facts, while others are conjecture.
  • Or automate front line tasks for sales, or back office work for operations.

To take advantage of the machine learning solution, you need sufficient processing power and really large batches of high-quality data. Otherwise, the system won’t be able to learn and generate valuable predictions. Demand sensing solutions extract daily data from POS systems, warehouses, and external sources to detect an increase or decrease in sales by comparison with historical patterns.

Building a Resilient Supply Chain

Typically, the more client heartbeat with xero you have, the more impacting factors would be considered, and the more accurate your forecasts will be. But building such complex, custom analytical infrastructure requires investment and engagement of ML engineers, data scientists, and other specialists. Let’s talk about some real-world examples of successful ML-based demand forecasting. If not, you’ll have to engage IT specialists to build internal integrations. To predict the future, statistics utilizes data from the past. That’s why statistical forecasting is often called historical.

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Check our detailed article about roles in a data science team to get a picture of which specialists have to be involved. Read the ProfitWell blog to learn what forecasting software is, the different types of forecasting software, and how they can benefit your business. See how subscription revenue and one-time transactions lead to cash flow for your business. You can’t accurately forecast what money will be coming in and going out if you don’t also accurately plan your finances for the time period being considered. As you can see, this method directly uses cash inflow and outflow to generate its output. The reason this method isn’t very common is that it can become cumbersome to gather the data, especially for companies that use accrual-basis rather than cash-basis accounting.

These people can see the likelihood of a companies’ commercial success or the outcome of elections better than anyone else. They are called “super-forecasters”, and what they can teach us about how to make smarter judgements could save companies billions, or even prevent countries from going to war. The startup shrank a weather data center into its new device, an L-shaped machine slightly longer than a Ford F-150 that packs 50 kilowatts of power.

Demand Forecasting Methods: Using Machine Learning to See the Future of Sales

Of course, you can’t make all decisions based on this technique alone, as it doesn’t work for mid- or long-term planning. But it may serve as a valuable complement to traditional forecasting methods. In this blog, we are going to analyze how accurate the various forecasting methods were in predicting future demand.

When deciding the time period for a moving average technique, an analyst should consider whether the forecasts should be more reflective of reality or if they should smooth out recent fluctuations. Passive demand forecasting looks at past data to predict future sales. This type of model keeps things simple by only accounting for internal factors that your business can control. Seasonal trends that your business typically experiences are also taken into account.

Build your own cash flow model with this template

A rolling forecast is helpful for staying on top of any changes, negative or positive, that could have a serious impact on your business. Rolling forecasts also allow you to pivot as needed based on any new data presented so all decisions are based on what’s happening now and not on what happened the previous year. You can update rolling forecasts and budgets based on present results, not on what a manager thought may happen several months ago. With this process, forecasting is done for the next quarter and not the entire year. Each quarter the forecasts are broader since they too will be updated again. Rolling forecasts allow you to better align your budget with your stated plan while improving the accuracy of your projections.


The quality of historical data directly bears on the quality of the forecast. Therefore, it’s critical to identify historical data issues – such as data holes and unexplained upsides and downsides – and address those data issues. For example, we find classifying bulk orders into a separate time series for the same SKU/customer very effective in better forecasting the run-rate orders’ time series. Selection of the right outlier clean-up approach before running a statistical forecast algorithm on that time series is also key to improving data quality. What should be the time granularity of your forecasts and the proper forecast offset ? Similarly, determine the offset of your forecasts by the frozen period of the supply chain process you are trying to forecast.

Your goal is to keep morale and collaboration high with a solid If forecasts are off, the company faces challenges that affect everything from pricing to product delivery to the end user. Meanwhile, if forecasts are on point, the company can make better investments, perhaps hiring 20 new developers instead of 10, or building a much-needed new sales office in a prime new territory. When you buy something online, whether that’s from a large marketplace or a small boutique, you get a delivery estimate. If your delivery comes a day or a week after it’s promised, that’ll affect your satisfaction with the company – and decrease your willingness to want to do business with them again. In the case of a car manufacturer, cars take a long time to build.

data types

The company also has a contract with 51 Degrees, a nonprofit in Kenya, as well as a regional bloc in East Africa. In 1950 the Eniac, developed by the US Army, the earliest digital computer, spent an entire day issuing the world’s first machine forecast. We run the danger of relying on AI to give us answers without knowing why it is giving us those answers, that kind of strikes me as having negative unknown consequences. One consequence includes there being less study of other forecasting methods or recognition methods if AI can do a better job. Excel creates a line chart to plot the data points for actual and forecast sales.

Sandbagging in Sales: What It Is & Why You Shouldn’t Do It

The historical data of sales shows a 10% increase ($5000 to $5500) in sales over the year. Atmo reports that its early tests have doubled the accuracy scores of baseline forecasts in Southeast Asia, where the startup is pursuing contracts. Initial tests on the ground in Uganda correctly predicted rainfall when other systems didn’t, according to UNMA officials. It might go without saying, but your forecasts are essentially useless if you don’t use them as reference points, so be sure to refer to them on a consistent basis. They’re crucial resources for guiding a wide variety of business decisions, including budgeting and directing marketing efforts. Those contributions will also add a new degree of accountability to your forecasting efforts.

Since leaders can’t use a crystal ball to predict the future, they are left analyzing quantitative, and sometimes qualitative, data to anticipate future sales. It’s rare for forecasts to be within 5%, but it does happen. If you’re within 5% of your forecast, and you’re dealing with a big number of opportunities, you’re a sales forecasting rockstar. Sales forecasting is a muscle, not an item to check off your to-do list. While you should absolutely design a framework for your sales forecasting plan each year, you should also change up your strategies from time to time so new muscles develop. The main objective of sales forecasting is to paint an accurate picture of expected sales.

Cloud-based systems have quickly become the standard for all areas of finance, not just bookkeeping services. When implemented, they allow for more flexibility, as well as better security and cost savings, than manual options. They allow you to generate accurate predictions and budgets quickly and with minimal errors. It also predicts how much pipeline will likely be needed to hit the number you’re being asked to hit based on historical conversion rates so you can prepare and plan for out-quarter opportunities. A quantitative sales forecast uses the existing data in your CRM or other tools to reflect a potential forecast.

So, for instance, define your forecast hierarchy to support discussions about product personalisation or customer-specific promotional activities where relevant. Elsewhere, use a higher level of aggregation for your forecasts. Unpredictable events have an enormous impact on your sales forecast. Extreme weather, economic crises, global pandemics like COVID-19 – all dramatically change your forecast. What you thought you knew about expected revenue growth can be suddenly flipped on its head.


Now, he is managing director of Good Judgment Inc, the commercial spin-off of the project. Ballmer did not possess the characteristics of a super-forecaster – humility, open-mindedness, inquisitiveness, among other things. What made things worse was his lack of willingness to amend his forecast. The people who make the best predictions about the future are also happy to change their prediction when presented with new information. Ballmer did not, and Microsoft’s presence in the smartphone market suffered as a result.