The Complete Guide to Demand Forecasting

The Complete Guide to Demand Forecasting

 

In today’s volatile business climate, only one thing is certain: nothing is guaranteed.

No matter how well you know your industry and its trends, no matter how in-tune you are with your customer’s needs and wants, nobody has a crystal ball. Just when you think you’ve got it all figured out, everything changes.

Accurate demand forecasting is a tough thing to nail, but it’s not something you can leave to chance. Without a qualified, quantified roadmap that answers questions like – how many units do I need to order of each SKU? What flavors will be most popular? How often do I need to replenish? And what will the situation be like a year from now?

While you might be able to get some of these answers right, you’ll never get them exactly right. Unfortunately, the less accurate you are, the more it’s going to cost you. Whether you’re dealing with overstocks or understocks, the situation is never ideal.

 

What is Demand Forecasting? And What Can It Do For You?

Demand forecasting is, essentially, using historical data to predict what your customers are going to want in the future. Historical data gives you a broad view of your seasonal ups and downs. It will highlight fluctuations that occur throughout the year, and the more consistent this data is over time, the easier it will be to plan production, inventory, distribution, pricing, marketing strategies, and growth potential.

The challenge is unless these insights are delivered in real-time, the data you are looking at is outdated. If your data is outdated, your forecasting will be off. Opportunities will be missed. Signals will be misinterpreted. Money will be left on the table.

Leveraging real-time demand forecasting will, on the other hand, put you on the leading edge of your market.

 

Why You Need Demand Forecasting

Without accurate, real-time demand forecasting, you risk making poor, uninformed business decisions that could prove costly from many standpoints. The negative impact resonates throughout the supply chain from end-to-end and straight to the customer’s front door, resulting in decreased profitability.

Some of the ways demand forecasting benefits you:

  • Increased inventory turnover rates
  • Reduced cost of holding inventory
  • Optimized cash flow
  • Improved operations and
  • Enhanced resource management

Demand forecasting helps you accomplish all this and more. When your entire operation is running smoothly, your customers, suppliers, and vendors are happy too. Everybody wins.

Demand planners are responsible for more than the statistical forecast. Demand planning is a central, collaborative role on a team, interfacing with sales, marketing, operations, logistics, executive management, etc, to drive consensus around a forecast and inform business decisions.

Using an automated platform to create the statistical forecast frees the demand planner from the hours and days of manually working in a spreadsheet and enables them to spend more time on the collaboration and strategic parts of their role

 

What Could Possibly Go Wrong?

Your demand forecasting efforts rely on data. If that data is reliable, your results will be, also. If your data is garbage, your forecasting insights will reflect that.

Since you are making a lot of strategic decisions based on these results, you need to make sure your data is not only of the highest quality but that it is also accurately communicated to the forecasting tool.

Some of the areas that could produce problem data include:

  • Data entry errors: if you’re counting on humans, this is going to happen, guaranteed.
  • Artificial demand shifts: limited-time promotional offers can generate atypical results.
  • Economic shifts: recession, politics, even the weather has an impact here.

 

Seasonal Variability

Holidays and seasons have a significant effect on demand, influencing what consumers are interested in. These factors are hugely valuable as they will help you make the most out of the situation. For example, a supplier that makes comfort food such as pot pies has generally higher demand in cold winter weather, while a supplier that makes grilling sausages typically has lower demand in the same season.

 

Seasonality vs. Trends

Systemic patterns can be attributed to one of two variables: seasonality or trends. Seasonality creates a fluctuation that repeats itself over time. A trend does not repeat, though it may show some growth over a period of time and level off to a new normal. Seasonality is prevalent in retail, where patterns repeat themselves year over year.

 

Understanding Demand Forecasting Models

There are several approaches to demand forecasting. Traditionally, the technique falls into one of three categories:

 

Qualitative

If you are a fairly new business, if you’ve just launched a new product, or if you don’t have a lot of historical data to work with, a qualitative forecasting approach is used. An example might be a tech startup whose products are so new that there is no way to gauge customer interest. Because this model lacks data, it relies on expert opinions, comparative analyses, and market research to estimate what the demand is going to look like.

 

Causal

A causal model is probably the most sophisticated approach to demand forecasting as it leverages details about the relationships between things like competition, socioeconomic factors, stressors that are affecting the economy, and historical data. An example might be an outdoor adventure retailer who would be looking at past sales, promotions, marketing campaigns, competition in the area, local demographics, and fluctuations in the weather that will affect sales.

 

Time Series Analysis

If you have a lot of historical data; if the trends are fairly easy to interpret, this is when time series analysis is most useful. It identifies seasonal and cyclical sales patterns, as well as key trends. This largely statistical approach is mostly used by well-established companies who have a lot of data to work with and who see some consistency in their trend patterns. The patterns in the data then become predictions.

Next, let’s look at the various types of demand forecasting and where they are most appropriate. Primarily, they are either based on the economy or a specific period of time.

Passive demand forecasting is the milieu of established businesses that enjoy great stability and who do not plan to grow or change dramatically. Minimal assumptions are made, and most forecasting activity is built based on their historical data.

Active demand forecasting applies to companies with plans to diversify, scale, and expand aggressively into new markets. Specific products, competitors, and socioeconomic factors play a significant role.

Short-term demand forecasting focuses on a specific time period, usually anywhere from three months to a year in advance. Considerations include seasonality and how strategic decisions affect customer demand.

Medium/Long-Term demand forecasting occurs a year, two years, and sometimes up to four years in advance. Some of the areas that influence long-term forecasting include business strategy, financial planning, sales and marketing, spending, and capacity planning.

External demand forecasting is a macro-level approach that delves into the strategic goals of a business. Some of the areas it might cover include portfolio expansion, going into new markets or customer segments, shifts in customer behavior, disruptive technology, and risk mitigation.

Internal demand forecasting looks at the internal operations of a business, such as cost-of-goods estimation, cash flow, margins, and so on. Under this heading we have sales and financial divisions, product categories, and manufacturing.

 

A Real-World Demand Forecasting Example

For an example of long-term, macro demand forecasting, let’s look at the global food chain. While almost one billion people in the world don’t have enough food to eat, 20 percent of our landfills in America are filled with wasted food.

If it were possible to connect seamlessly all elements of the food supply chain – farmers, agro-suppliers, producers, processors, manufacturers, regulatory bodies, wholesalers, retailers, logistics, and import/export companies – we would have a perfect shot at reducing this incredible waste and solving food insecurity once and for all.

Currently, there are significant barriers to making this our reality, the primary one being a widely disconnected IT environment. But let’s think with a more ideological lens for a moment. If all of the above components were connected and sharing data, that information could be leveraged to bridge gaps in supply and get food to where it’s needed most.

The alternative, which we are living right now, is that farmers have barriers in getting their crops to market. Many major food retailers deal in serious overstock situations, and so much product is wasted because it does not sell and is summarily thrown away. Leveraging data to balance supply and demand will not only channel good food to the people who need it most, but it will also help businesses to keep food out of landfills and profits in their pockets.

 

Real-Time Demand Forecasting: Getting Started

Demand forecasting can be as simple as saying, “We sold this many turkeys last Christmas, the economy is good, so we should order this many more,” but it can also be an incredibly rich tool that could help you solve costly, profit-killing problems like waste, overstock, understock, and process inefficiency.

When data is delivered in real-time, companies can respond to situations as they occur. In an ever-more competitive business environment, those who take action immediately will always have the edge.

 

Removing the Barriers

To reap the benefits that real-time forecasting delivers, you need to put your data to work.

Effective use of data has already disrupted many industries, including manufacturing, logistics, and the global supply chain. The primary barrier is digital transformation, but the cost of not making the necessary changes will, sooner or later, fail.

With its automated, AI-driven forecasting platform, Crisp offers a fast, accurate method of predicting your business needs. Crisp leverages all of your various data points, simplifying what is typically a massively labor-intensive task and potentially saving you and your team hundreds of hours. Crisp eliminates errors, reduces cost and waste, increases the bottom line, and, perhaps most importantly, lets you get back to doing what you do best.

Where do you see your company in a year, two years, ten years? If you can’t answer this question, it’s time to find out. Reach out today to learn more.