project demand in business plan

Business Plan Demand Analysis, Four Things to Consider

Small businesses and entrepreneurs use demand analysis to:

• Consider substitute products and services
• Get input from (potential) customers
• Determine what “drives” demand
• Understand what variables affect demand and to what degree

Demand analysis is about challenging your preconceived notions regarding your product/service. A stress test, if you will. A demand analysis will take your idea and start molding it into something that has even higher potential.

As an entrepreneur, you can’t be too stubborn. You have to be flexible. After going through this process, the hope is that you’ll come out the other end with an even more refined idea and a greater chance at success.

Market research and competitive analysis for a business plan

This is the second post on drafting a business plan for your startup. These posts are modeled after the SBA Business Guide .

Want to know how many people are included in your “customer avatar?” Read this post: BUSINESS PLAN DEMOGRAPHICS – DEFINING A TARGET MARKET

Business plan demand analysis of the total market

When first thinking about the market for your product/service, don’t define it too narrowly. Try to think of substitutions that you might not have otherwise considered. No, you might not compete directly with these substitute products, but the presence of substitute products will have an impact on your pricing and demand.

Pricing too high could push customers to these substitute products. Even if that pricing seems in line with your value proposition when compared to direct competitors. But, theoretically, the amount demanded changes (inversely) with the price. A higher price will push customers to consider alternatives. A lower price should result in a higher volume sold.

Further defining the market for my product

As I mentioned in my first business plan post on the topic of demographics, I am working alongside you. I have a prospective product that I would like to explore the viability of, and I am creating a business plan for this product as I write these posts. As a reminder, my potential product is an all-natural hair-thickening topical supplement.

Anyhow, in the previous post, I used “customer avatars” to roughly ascertain the size of my market. I think I was fairly liberal in that estimation. The three of my avatars that were the most detailed totaled approximately 5.2 million people. The avatar that was broader included 6.5 million people.

Want to know what a top-down and bottom-up analysis would say about your market size? Read this post: MARKET SIZE FOR A BUSINESS PLAN – 2 METHODS TO GAUGE IT

Substitute products

As mentioned above, I have to keep in mind that not all of these people will pursue hair loss treatment. Many, will just accept it as a normal part of aging. Others will choose to address the problem but will pursue an alternative treatment method to topical supplements. Some of these alternative treatment methods include:

• Biotin, vitamin D, Viviscal, Nutrafol, Finasteride (Propecia), collagen powder, nutriceuticals, Spironolactone (Aldactone)
• Toupees, hair fibers
• Laser treatments, microneedling, hair transplants, protein-rich plasma injection
• HairMax LaserComb, light treatment

In addition to substitutions, I have to consider the direct competition. The alternatives that are also topical. Those include:

• Minoxidil (Rogaine), rosemary essential oil, pyrithione zinc shampoo, scalp tonic/serum

Obviously, there’s no shortage of alternatives to my prospective product. However, many of these treatments are ongoing and the potential exists for customers to combine them.

After listing these potential substitutions, it dawned on me that there are a couple of different classes of hair loss. I would probably target individuals that are in the early stages and are merely looking for help to slow down and, hopefully, somewhat reverse the initial effects of hair loss.

Another thing that dawned on me when researching substitutions is that it might be a mistake to only consider men when ascertaining the market for this product. Most of the results I found when searching “hair loss treatments” were articles targeted at women.

As I said, I’m taking this journey right along with you. So, I’m refining my idea and picking things up as I go along.

Gathering survey information for your business plan demand analysis

The next steps are mostly statistical. That might give you pause if numbers aren’t your thing.

I really do wish I could provide you with the handiest spreadsheet imaginable to manage the information you find. There are just too many variables, though. Different surveys asking different questions. Not to mention, every industry is going to address unrelated topics. I just couldn’t figure out how to make a one-size-fits-all tool.

What we’re going to do is compile whatever relevant statistical information we can get our hands-on, and interpret what we find. You can input this information into your own spreadsheet if you like

Statistical information, hopefully, can be obtained from a simple internet search. “[your topic/industry] survey results”, or something similar should yield some useful information. If you can’t find relevant info, then you might have to reach out to industry trade magazines or organizations.

As far as how much survey information to collect – there’s no clear answer. It depends, first and foremost, on the abundance of such information. If there is plenty available, then I guess I’d recommend collecting it until you’re tired of doing so. You can always circle back around and search for more specific results if you need to in the future.

What to focus on

Right now, focus on demographics information, substitute product information, and information about motivation (drivers).

This is where having it in a spreadsheet will come in handy. With the numbers in a spreadsheet, you can combine survey information and break it down as needed. Check out my example below to see what I mean.

Survey information about my product

There was no shortage of survey results regarding hair loss. In fact, I grew tired of collecting information well before I was able to read it all.

I must admit, I learned something on this step. I learned that it probably makes more sense to do this research before creating customer avatars rather than after .

This research showed me that hair loss in women is a considerably more prevalent problem than I knew. So, I should definitely not exclude women when trying to calculate the size of my target market. Additionally, I learned a lot more about the age that hair loss starts to affect men and women. Not to mention, a lot of other interesting tidbits related to marketing and substitute products.

I simply typed the figures I found into the cells and tried to organize it in a somewhat easy-to-read format.

To make this information as useful as possible, I also included a link to the survey – in case I wanted to reference it again. Also, I thought it would be useful to make note of the year the survey was conducted. That way, I could note trends, if any existed.

Finally, to top it all off, I put in some charts. Charts can help to illustrate ideas in a way that numbers can’t, sometimes.

Now, I have a nice little foundation of data to build my business plan off of. I also know that there is plenty of other information out there if I want to delve further on a specific topic.

Divide total industry demand into its main components.

Now, you want to start to organize the information you found in a logical manner.

First, isolate the information related to demographics or that which otherwise describes your potential customers to you. You want to break this information up so that you can get an idea of what your potential customers might look like. You should, hopefully, begin to see customer “avatars” take shape.

Yes, I asked you to create avatars in the previous post. As I said above, that was probably premature. It would make more sense to create the avatars with this survey information, then use the census/demographic information to estimate the size of the market based on what you found.

Live and learn…

After you have the demographic information in good order, move on to the “solution” information – if available. This is information that specifies how customers are solving their problem(s) now.

If you’re lucky, this information will join seamlessly with the demographic information you organized above.

Start with the simplest questions (those with the fewest variables) and expound from there.

What if my survey data is inconsistent?

You might run into a situation where you have conflicting information. Or you might find yourself in the fortunate situation where different surveys seem to corroborate the same statistics.

If your information sources don’t jive, you have a couple of options. First, you can move forward with the information you deem to be the most trustworthy. Or, alternatively, you can average what you found. This works well if the differing results are relatively close together. Finally, you can choose to use the data source that is most recent – particularly if your industry is especially dynamic.

All of your numbers aren’t going to jive up perfectly. However, at this point, you are armed with a lot better information than when you started. Better information will ultimately lead to better decisions.

Industry components for my product

Demographics.

For my part, I like to start simple and divide my demographics based on the variable with the fewest options. In this case, the simplest variable only has two choices – men and women.

From there, I used information that I found regarding the percentage of men and women that have had hair loss and have tried treatments.

Next, I break things down further based on the age that men and women started experiencing hair loss. I was fortunate to find information for both genders.

That’s the extent of demographic information I was able to obtain. I would have liked to have found some information regarding income or socioeconomic status. If that information proves to be critical as I move forward with my business plan, I’ll have to circle back around to see if I can track it down.

Once I felt good about my (revised) customer avatars, I moved on to “solution” information.

Want to use data.census.gov to know how big your potential market is? Read this post: CENSUS DATA MARKET RESEARCH AT THE NEW DATA.CENSUS.GOV

Again, thanks to the abundance of information I was able to find, I found similar questions for both genders. The first question was the simplest. It asked if the person with hair loss had done anything to address the problem.

From there, I had a couple of survey questions that explored the alternatives that hair loss sufferers had tried in the past. Additionally, I found results that gave insight into how effective these alternatives were.

When all was said and done, I had the groundwork laid for the ability to know how many potential customers I might have, their demographics, what they have tried so far, and how well those alternatives had addressed the issue at hand.

Here’s what my worksheet looks like after sorting my information into industry components:

Business plan demand analysis of drivers

Hopefully, in your search for survey results, you came across some information that provided insight into the “why people buy” question.

In particular, we’re looking for drivers of sales here. Specifically, what circumstances compel a customer to buy your product/service (or a substitute)? Hint: people usually buy to solve a problem. To avoid pain, not seek pleasure. Or, so I’ve been told…

Insight into what compels your customers to buy will not only be valuable in the drafting of the remainder of the business plan but in all your marketing efforts once you are up and running.

The information about who your customers are (from the previous step), why they buy, and what steps they are currently taking to solve their problems (also from the previous step) will hopefully paint a clear picture for you. A picture that will guide you to a point where you can position your strengths in a manner that will help other people’s weaknesses.

Understanding the drivers of demand for my product

Again, I was fortunate to have an abundance of survey information to draw from. A couple of my surveys not only touched on how hair loss made people feel but also on specific actions that they had taken before the hair loss started.

This information tells me an angle I can take when marketing my product, plus where a lot of my potential customers are going before they start to experience this problem. That place…the hairdresser.

Of course, that’s for women. Though there’s no rock-solid proof that it’s hairstyling that is contributing to hair loss in women, there is enough correlation to make a compelling case. For men, on the other hand, hair loss just seems to be the hand that most are dealt.

But, before we get into that, let’s look at some of the emotional drivers that might compel customers to purchase a topical hair loss supplement…

Drivers for men

On the “men” side I got information about how “worried” men were about hair loss. This told me that most men were, at least, “somewhat” worried about hair loss.

Beyond that, there was valuable information about how hair loss had affected them negatively.

Finally, the most valuable information, to me, was a question of what they would give up to solve this problem (men & women). The answers were encouraging for someone who was hoping to build a business in this industry. Almost half would rather have more hair than more money. Three quarters would give up a prized possession for more hair.

While I acknowledge that I’m not marketing a guaranteed cure to hair loss, that tells me that people are willing to try anything to fix this problem. As I know from my market segmentation analysis, supplementation works for about 1 in 17 people. Not great odds, by any means. But good enough, I hope, to at least try a new product. Especially when the ingredients are all-natural and offer no downside.

Drivers for women

About half had stress prior to experiencing hair loss. That’s a coin flip. It doesn’t mean that the hair loss was caused by the stress (though it surely didn’t help). But it provides insight into what women are feeling prior to and while they are experiencing this problem.

I also included the “What they’d give up” question on the women’s side of the analysis because my source for that information didn’t specify either gender. Plus, it seems feasible that women would feel the same or even stronger. It’s my opinion that society values female attractiveness above male attractiveness.

Finally, we get down to the brass tacks. A potential cause-and-effect situation for the problem I’m attempting to address. The number of women that are currently experiencing hair loss are also (possibly) straightening/heat processing or getting their hair colored on a semi-frequent basis.

This tells me that hairstyling might play a part in a lot of women’s hair loss (this goes back to the pressure to be attractive thing). Therefore, I should consider marketing my product in salons and other establishments that focus on women’s hair.

There’s still a lot of analysis to be done. But, two steps into the process of drafting my business plan, I feel a lot more confident about my understanding of the environment.

Here’s a look at my spreadsheet with the driver information included:

Business plan demand analysis of sensitivity

To this point, the goal has been to make assumptions and get answers. We want to have a better understanding of the environment in which our business will operate. Hopefully, you feel that you’ve accomplished that.

But, we don’t do ourselves any favors by lying to ourselves.

Well, yes. But probably not willingly.

You start off excited about your business idea. So excited that you decide to take the first step (something that the vast majority of people won’t do). You begin to write a business plan. You can feel your idea taking shape. You’ve already refined your idea a bit and feel that by the time this whole exercise is over, there’s no way you can fail. You’ve got momentum and your confidence keeps increasing.

That is all very good. Confidence is key. But, if everything looks rosy, you might be blind to a risk that could put your baby in jeopardy.

So, I don’t want to be a killjoy. But, for the sake of our businesses, let’s take a step back and play devil’s advocate. We need to ask ourselves some tough questions and challenge our assumptions. If we can rise to these challenges, and address them with confidence, our chances of success are that much greater.

Go back through your segmentation and demand drivers and think critically about this information. Some statistics might be a given, without much wiggle room. Others might be misrepresentative of reality. In these instances, tap into your inner cynic.

Make notes of what the worst-case scenario might look like. If you’re using a spreadsheet, like me, maybe use a different colored text. Address things like survey questions that might have been misinterpreted or alternative explanations for results.

Don’t get too down-and-out here and don’t dwell on this step too long. You don’t have to necessarily plan what you would do if these worst-case scenarios came to be. You just need to imagine them so that when the time comes for serious planning, you can take these risks into consideration.

Demand sensitivity for my product

I think my categorization by demographics is pretty safe. It’s rather well established how many men and women experience hair loss. The only thing that I might tweak is the number of men and women who have had hair loss and tried treatment. I lowered those estimates by 20%. It could be that the respondents’ interpretation of “treatment” is to comb their hair a different way or to shave their heads rather than to buy a product to battle hair loss.

Furthermore, what if the number of people that have “done anything” is lower? What if I misinterpreted the question for women that asked: “Do you take medication to prevent hair loss?” Maybe it was 20% of women who actually had hair loss rather than all women? The effect of that would be dramatic.

What if the alternative treatments were more effective than I’ve been led to believe? It could be that the respondents only consider “effective” to be a restoration to a full, thick head of hair? Also, just because they consider them ineffective, it doesn’t mean that they’ll stop using them. They might think that all of their hair will fall out if they stop (which could work in my favor, though). Perhaps they were overly optimistic when it came to supplements? It could be that supplements gave them other benefits, but didn’t make their hair loss any worse – so they considered them “effective.”

Could it be that fewer men are really “(very) worried about hair loss” than I’m led to believe? Are more are “Not worried at all?” Plus, it might be that those who are only “somewhat worried” aren’t motivated to do anything about it.

As far as confidence (love life, making friends, professional life) goes, it might be that that hair loss is a contributor to low confidence, but not the primary driver. Maybe they’re overweight or socially awkward and that’s why they lack the confidence they desire?

As far as “what they’d give up” it could be that the respondents were primed by the hair loss questionnaire to be more self-conscious than they usually are. If it came down to it, perhaps not so many would be willing to part with valuables to solve this problem.

Finally, as far as hair styling being a cause of hair loss in women, it could be that I am wrong. Maybe hair styling has no effect on hair loss. Or, maybe women overestimate how often they heat process or color their hair. It only feels like every day/once every 2-3 weeks. When, in fact, they do it a lot less often.

Okay, that’s enough pessimism. It seems unlikely that every worst-case scenario would be true. But, there’s probably a mix in there between my initial interpretations and the not-so-great ones.

Want to back your business plan up with valuable data? Read this post: GOVERNMENT STATISTICS FOR MARKET RESEARCH VIA USA.GOV

This exercise should help me going forward to make realistic forecasts and assumptions. Which, in turn, should help me be proactive to some of the challenges I might face.

Here’s a final look at my spreadsheet with my worst-case notes in blue:

Business plan demand analysis

This step takes a little bit of thought and a decent amount of research. This is done to give you a deeper understanding of the market you hope to compete in and the customers you hope to sell to.

What other steps would you have taken to refine estimates of demand?

Do you think my demand sensitivity was rational? Or, was I taking it too easy on myself?

Join the conversation on Twitter!

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Demand Management: Process, Importance and Tools

The relationship between supply and demand is Economics 101. Whether a business is manufacturing or managing a warehouse, understanding demand management goes a long way to ensure that stock levels are always balanced with customer needs.

This requires first an understanding of demand management and how it benefits business. Next, we’ll outline the demand management process to learn how to implement this planning methodology and how software can facilitate that process.

What Is Demand Management?

Demand management is the process of managing customer needs for a product that a company sells. It’s a planning methodology that tries to forecast what a customer will want, when they’ll want it and the logistics of getting that product to them. By planning, companies can identify and avoid potential problems, such as bottlenecks in production or the supply chain and market volatility.

In manufacturing , demand management comes after supply chain management, such as managing procurement and suppliers, but before portfolio management. Demand management is cross-functional as it crosses many disciplines, from consumer demand, supply teams and inventory to marketing and customer service.

One way to look at demand management is as a bridge between the marketplace and a company’s internal operations. That is, demand management works to create interactions between operations and marketing with the goal of being able to develop actions that align the fluctuations of the market with a company’s strategy, production capacity and customer needs.

Managing production is key to demand management and project management software is essential to that process. ProjectManager is award-winning project and portfolio management software with resource allocation features to keep teams working at capacity. Use the team page or the color-coded workload chart to monitor your team’s allocation. If some are over- or underallocated, the team’s workload can be balanced quickly to keep them as productive as needed to meet demand. Get started with ProjectManager today for free.

What Is the Importance of Demand Management for Businesses?

Understanding customer demand will benefit any business that manufactures or sells products. Demand management is a crucial part of any business strategy so they have stock on hand to meet customer needs. Here are some other reasons that illustrate the importance of demand management for businesses.

Helps Organizations Establish their Production Budget

Demand management allows companies to analyze and predict changes and trends in market demand. This leads to a reduction in costs due to overproduction or stockouts. It also informs the production budget to make sure that there are enough funds to meet demand but not add unnecessary costs through excess inventory, labor, etc.

Allows Businesses to Meet Customer Demand

Knowing what customers want allows for better planning of delivering it to them. If there’s a spike in customer demand or if customer demand is waning, production planning must follow suit or suffer a loss in business or the cost of carrying unwanted inventory. Demand management allows for a better gauge of customer demand.

Prevents Excess Inventory and Overproduction

As stated above, excess inventory is costly. All that product must be stored, which leads to money spent on items that aren’t being sold. Ideally, a company wants a warehouse full of inventory that will move due to a balance between what’s in stock and the customer demand. Demand management is a way to achieve that balance.

Helps With Supply Chain Planning

Supply chain planning is all about optimizing the manufacturing and delivery of goods. It starts with raw materials, moves to finished products and ends with customers. A clear picture of customer demand will inform these steps, from knowing how much raw material is needed to the quantity of items produced, etc. Demand management, then, is an integral part of this process and helps a business spend only what it needs.

Informs Workforce Planning

Demand management helps managers understand the current and future workforce requirements, which allows them to plan better. The managers better understand customer demand, which leads to knowing how to allocate resources to meet that demand by having the right people with the right skills.

Demand Management Process

Demand management helps businesses to oversee and manage customer demand. To do this, though, requires a process. The demand management process includes knowing what customers want and the steps necessary to fulfill those needs. To plan for current and future demand requires following these six steps in the demand management process.

Demand Forecasting

Demand forecasting is the process of predicting customer needs for a business’ products. That demand determines what adjustments need to be made or if new offerings should be added. Estimating what customers want and how much of each item they’ll want isn’t an exact science. To get an accurate estimate, businesses use many methods, some qualitative and other quantitative. Data, software and analytics are all used in this process, but predictions should always be hedged by noting their strengths and weaknesses.

Demand Planning

Once the forecast is clear, the planning begins. Demand planning is part of the demand management process that enables a business to plan to meet the demand forecast through the production of its products. This is also part of the larger supply chain process and requires an understanding of horizon (timeline for the demand plan), frequency (how often the plan is updated) and granularity (level of detail in the plan). This allows for the creation of a demand plan that meets customer needs.

Demand Modeling

One way to make a more accurate demand forecast and, therefore, have a better demand plan is through demand modeling. Demand modeling uses predictive analysis to understand customer behavior. It looks at things such as the propensity of a customer to purchase a product and how the propensity changes based on things like the price of that product. Historical data is also used to better understand the customers’ behavior.

Demand Capacity

Demand capacity is a ratio that compares the production that a company makes with the demand coming from its customers. When manufacturing, businesses measure demand capacity to make sure they have the production capacity levels that allow them to meet the demand for their products. Calculating this uses several sources, such as sales records, customer feedback, inventory levels, production reports or service logs.

Demand Sensing

Another way to predict customer demand is with demand sensing, which uses real-time data and analytics to understand and predict what a customer will want, when they’ll want it and how much they’ll want. This is done by reviewing sales history, inventory levels and customer behavior, point-of-sale systems, online sales platforms and customer surveys. While not perfect, demand sensing can reduce forecast error by up to 50 percent and increase accuracy by up to 20 percent.

Demand Shaping

Demand shaping is a supply chain strategy that uses tactics such as price and promotion incentives, product substitutions and cost modifications to lure customers to buy specific products. Through these means, a business can influence demand for a certain item to match its planned supply.

What Does a Demand Manager Do?

The person responsible for the demand management process is a demand manager or demand planning manager a professional tasked with overseeing the daily operations of the demand planning team, who analyzes customer and vendor demand to create and refine their forecasts.

Demand managers are responsible for reviewing purchase history, sales history and the marketing strategies businesses use to promote products and stimulate growth. They also evaluate their effectiveness and respond accordingly to improve.

To do this, the demand manager will come up with effective forecast models based on industry trends and demand patterns. They’ll implement solutions to improve the accuracy of demand forecasting, as well. They are highly analytical and have a deep knowledge of advanced mathematical and forecasting policies.

How to Manage Production With ProjectManager

Demand management has a great influence on production. It tells manufacturers how much of a product customers want so they can produce just the right amount or as close to that number as possible. This saves money on labor, storage and more. However, demand management can’t help create a more effective production plan, but project management software can. ProjectManager is award-winning project and portfolio management software with multiple project views to plan production activities, schedule resources and track labor costs to ensure that manufacturing goes according to budget.

Plan Production Activities

Managers can plan their production activities on robust Gantt charts that link all four types of task dependencies to avoid delays. Once the schedule is made, resources allocated and costs determined, set a baseline to capture that plan so it can be compared to actual progress and costs during production. Real-time dashboards capture key performance indicators (KPIs) on easy-to-use graphs and charts for a high-level overview of production. There are also customizable kanban boards with columns that reflect the production cycle and cards that track costs, progress and schedule resources. Use kanban to manage order fulfillment, too.

Track Labor Costs With Timesheets

Keeping a close eye on labor costs helps manage production costs. Employees can use timesheets that automatically add their hours and are securely sealed once sent to a manager to review and pass onto payroll. While this streamlines the payroll process, it’s only part of what timesheets can do. Timesheets capture labor costs and let managers see how far each team member is in terms of completing their assigned tasks. This allows managers to calculate whether the production is progressing as planned or if resources need to be allocated to get back on track.

Stakeholders can stay up to date with production by using one of the multiple project views to track progress, such as the calendar view, which is more a high-level overview of the production cycle. But there are also customizable reports on project status, portfolio, variance and much more. All can be filtered to show only the data stakeholders want to see and shared with them across formats.

ProjectManager is online project and portfolio management software that connects teams whether they’re in the office or on the factory floor. They can share files, comment at the task level and stay updated with email and in-app notifications. Join teams from companies, such as Avis, Nestle and Siemens who are using our software to deliver success. Get started with ProjectManager today for free.

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Demand Planning and Forecasting - A Comprehensive Guide by Sigmoid

• Chapter- 1 Introduction
• Chapter- 2 Understanding Demand Planning and Forecasting
• Chapter- 3 Importance of Demand Planning and Forecasting
• Chapter- 4 Four Steps of the Demand Planning Process:
• Chapter- 5 Elements of Demand Planning
• Chapter- 6 Sigmoid's Approach to Demand Planning and Forecasting
• Chapter- 7 Conclusion
• Chapter- 8 Success stories
• Chapter- 9 FAQs

Introduction:

In today's dynamic business landscape, the ability to accurately predict and plan for future demand is critical. Demand Planning and Forecasting are strategic processes that enable businesses to anticipate market trends, understand consumer behavior, and optimize their operations accordingly. At Sigmoid, we specialize in offering comprehensive solutions that empower businesses to navigate uncertain market conditions with confidence.

Understanding Demand Planning and Forecasting

What is demand planning.

Demand planning involves analyzing historical data, market trends, and other relevant factors to estimate future demand for products or services. It's a proactive approach that assists businesses in aligning their resources, production, and inventory levels to meet anticipated market needs efficiently.

Sigmoid's expertise lies in leveraging advanced data analytics to help financial institutions interpret, manage, and comply with these multifaceted regulations effectively.

What is Forecasting?

Forecasting is the process of making predictions or estimates about future demand based on historical data, statistical models, and market insights. It helps businesses anticipate changes in consumer behavior, market trends, and demand patterns, enabling informed decision-making and strategic planning.

Importance of Demand Planning:

Demand Planning and Forecasting play a pivotal role in several aspects of business operations:

• Optimized Inventory Management: Accurate forecasts aid in maintaining optimal inventory levels, reducing excess stock, and minimizing stockouts, leading to cost savings and improved efficiency.
• Enhanced Customer Satisfaction: Meeting demand promptly enhances customer satisfaction, fosters brand loyalty, and strengthens market positioning.
• Enhanced Supply Chain Management: By aligning supply with anticipated demand, businesses can streamline their supply chain operations, minimize disruptions, and improve overall responsiveness.
• Improved Decision-Making: Informed decisions based on reliable forecasts allow businesses to allocate resources efficiently, plan marketing strategies, and identify growth opportunities.
• Cost Efficiency: Efficient demand planning minimizes wastage and reduces operational costs.

Four Steps of the Demand Planning Process:

The demand planning process typically involves several steps to accurately forecast and anticipate future demand for products or services. While variations exist based on specific methodologies or industry practices, the following are the four fundamental steps in the demand planning process:

1. Data Collection and Analysis:

• Gathering historical sales data, market trends, customer behavior patterns, and other relevant information.
• Analyzing this data to identify patterns, seasonality, trends, and any external factors affecting demand fluctuations.

2. Forecasting:

• Using statistical models, algorithms, and/or predictive analytics to create forecasts based on the collected data.
• Employing techniques like time series analysis, regression analysis, or machine learning to predict future demand levels accurately.

3. Demand Planning:

• Collaborating with various departments (such as sales, marketing, operations, and supply chain) to incorporate insights and align forecasts with business strategies.
• Adjusting the forecasts based on market intelligence, upcoming promotions, product launches, or any other factors affecting demand.

4. Review and Refinement:

• Continuously evaluating the accuracy of forecasts against actual demand.
• Refining the forecasting models by incorporating new data, market changes, and insights gained from the review process.
• Iterating and improving the forecasting methodologies to enhance accuracy and responsiveness to changes in the market environment.

These steps are crucial for businesses to develop a robust demand planning process, enabling them to make informed decisions, optimize inventory levels, allocate resources efficiently, and meet customer demands effectively.

Elements of Demand Planning: Understanding the Foundations for Effective Forecasting

Demand planning is a crucial aspect of supply chain management that involves forecasting future customer demand to optimize inventory, production, and overall business operations. An effective demand planning strategy incorporates several key elements to ensure accurate predictions and streamlined processes.

• Historical Data Analysis: One fundamental element of demand planning involves analyzing historical data. This data includes past sales records, market trends, seasonality, and any other relevant historical information. Examining this data provides insights into demand patterns, enabling businesses to make informed predictions about future demand fluctuations.
• Market Intelligence and External Factors: Demand planning doesn't solely rely on internal historical data; it also considers external factors and market intelligence. Factors like economic conditions, consumer behavior, competitor analysis, industry trends, and geopolitical events significantly impact demand. Integrating these external factors into the planning process enhances the accuracy of forecasts.
• Collaborative Input: Incorporating inputs from various departments within an organization is crucial. Cross-functional collaboration between sales, marketing, finance, and operations teams aids in gathering diverse perspectives and insights. Sales teams often possess on-the-ground market knowledge, while finance teams might offer financial forecasts. Combining these inputs refines the demand planning process.
• Statistical Models and Forecasting Techniques: Employing statistical models and forecasting techniques forms the backbone of demand planning. Time series analysis, regression, exponential smoothing, and machine learning algorithms are among the methods used to forecast demand. These techniques analyze historical data patterns and variables to predict future demand with varying degrees of complexity and accuracy.
• Continuous Review and Adjustments: Demand planning is not a static process; it requires continuous monitoring and adjustments. Regularly reviewing forecasts against actual demand and refining models based on new data helps in improving accuracy. This iterative process ensures that forecasts remain adaptable and reflective of changing market dynamics.
• Technology and Analytics: Utilizing advanced technology and analytics tools is pivotal in modern demand planning. Robust software solutions and analytics platforms aid in processing large volumes of data, performing complex analyses, and generating accurate forecasts. Machine learning and AI-driven algorithms further enhance the precision of predictions.
• Demand Sensing and Real-Time Data: The ability to sense demand in real-time using advanced analytics and IoT (Internet of Things) devices is becoming increasingly crucial. Real-time data streams from various sources such as sensors, social media, and point-of-sale systems provide immediate insights into demand shifts, enabling swift and proactive decision-making.

Sigmoid's Approach to Demand Planning and Forecasting

• Advanced Analytics and Technology: Sigmoid employs cutting-edge technologies and advanced analytical tools to extract insights from vast datasets. Our expertise in utilizing AI-driven models and predictive analytics ensures accurate and actionable forecasts tailored to your business needs.
• Customized Solutions: We understand that every business is unique. Therefore, we offer customized demand planning and forecasting solutions. Whether you're a startup, SME, or a large enterprise, our solutions are designed to align with your specific requirements and industry nuances.
• Continuous Improvement: Our commitment to excellence extends beyond delivering initial forecasts. We continuously monitor and refine our models, incorporating new data and market insights to enhance accuracy and adaptability.

Conclusion:

Demand planning and forecasting are pivotal for businesses aiming to thrive in today's dynamic markets. Sigmoid's supply chain analytics services empower organizations to enhance their demand planning processes, leveraging accurate forecasts to achieve operational efficiency and meet customer demands effectively.

Success stories

$30 MN+ savings in inventory handling cost with effective demand forecasting for leading cosmetics company

15% increase in capacity utilization with automated Master Production Schedule for a Fortune 500 biopharma manufacturer

Demand planning involves the entire process of estimating, managing, and fulfilling customer demand, while demand forecasting specifically focuses on predicting future demand based on historical and current data.

Accurate demand forecasting helps businesses optimize inventory, streamline production, reduce costs, and meet customer demands efficiently, ultimately improving their competitive edge.

Sigmoid employs advanced analytics and machine learning models to analyze historical data, market trends, and various parameters affecting demand. This enables businesses to generate precise demand forecasts, optimize inventory levels, and make informed strategic decisions.

The timeline varies based on the complexity of your business, data availability, and market dynamics. However, many of our clients witness tangible improvements in efficiency and decision-making within a few months of implementing our solutions.

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Demand Forecasting: Types, Methods, and Examples

Running a business is not a piece of cake. You have to know how every aspect of your business will turn out. There are a lot of calculations you have to get right from accounting to inventory management to financial projections.

Every business manager should have an understanding of the demand for your products. Demand forecasting is one of the toughest metrics to get right because of the tendency of demand to fluctuate.

In this guide, you will learn the meaning of demand forecasting, the importance of demand forecasting, types of demand forecasting, demand forecasting methods, factors that influence the customer demand life cycle, how to forecast demand effectively, and examples of demand forecasting.

Let’s get started.

What is Demand Forecasting?

Demand forecasting is the use of historical sales data to predict the future demand for a product or service. It provides an estimate of the number of goods or services expected to be demanded by customers within a given period in the future.

What current and future customers will want to buy is identified and purchase orders or manufacturing is optimized through this information.

Through demand forecasting, businesses also get to make informed decisions about their supply chain.

Estimates of total sales and revenue in the future are the main results of demand forecasting. With these, decisions about inventory planning, future warehouse management needs, and sales become easier to make and more accurate.

Important estimations in running a business are also dependent on demand forecasting. These include inventory turnover, cash flow, profit margins, risk management, and capacity planning, among others.

Demand forecasting is an area of predictive analytics in business and deals with the optimization of the supply chain and overall inventory management. The past records of demand for a product are compared with current market trends to come to an accurate estimation.

Every company wants to be able to predict the amount of cost it has to bear to meet the demands of its customers. Demand forecasting is one of the methods of doing this.

Importance of Demand Forecasting for Ecommerce Businesses

Demand is undoubtedly one of the most important, flexible, and fragile factors that determine the success of a business. Forecasting your demand helps you a lot with running a business. Here are some of the benefits of demand forecasting.

Easier To Make Decision

Demand forecasting facilitates important management activities within a company. Decisions are easier to make and, for instance, performance evaluations are given enough context.

Companies know how well the whole business, departments, or employees can cope with future expectations and make decisions accordingly.

Deciding how much resources are needed for future demands as well as whether a business is ready for expansion is also made easier. Companies have enough information to estimate and decide on financial and managerial needs for the future.

Helps With Short and Long-Term Planning

Proper demand forecasting helps businesses to easily take care of important strategic plans for the future.

Without knowledge of your demand, long-term business plans like budgeting, financial planning, and capacity planning, among others, are harder to create. These plans are also very much susceptible to inaccuracies and unproductivity.

Short and medium-term plans like contract creation and choosing a supplier are also difficult to make.

Demand forecasting gives businesses an idea of what to expect from customers within a period in the future. It helps managers set financial goals , create budgets, and allocate the company’s resources efficiently.

Reduces Cost

Proper knowledge of the expected future demand for goods and services enables businesses to avoid suffering massive losses or opportunity costs.

Costs of production, inventory purchase, and marketing are kept streamlined with estimated forecasts. With demand forecasting, profit margins are determined and financial resources are not overspent in a way that a profit margin is closed up.

Opportunity costs are also avoided. A company knows the opportunities for expansion or the potential for increased demand for goods in the future. Enough inventory is stocked in expectation for this demand and the amount of profit that would have been lost from a stock-out situation is saved.

The staff required to take care of demand is easily determinable through demand forecasts. You ensure that you have enough manpower to deal with demand and excess wage is not paid to staff you don't need.

Pricing Strategy Is Easily Determined

The demand for a product determines the pricing strategy or the price you put on it for profit.

Too much demand for a product without an adequate supply of it causes its price to increase. On the other hand, where the supply of a product becomes more than its demand, its price drops.

Demand forecasting takes this into account and determines the elasticity of demand as it relates to price. Prices are adequately determined according to future demands of goods.

Businesses use demand forecasting to ensure that they do not place prices that are too high for customers and too low for them to generate profits.

Types of Demand Forecasting

Demand forecasting is distinctly classified based on three different factors – the scope of the market considered (Macro and Micro-level demand forecasting), the number of details required (Passive and Active forecasting), and the length of time considered (Short-term and Long-term forecasting).

1. Micro-Level Demand Forecasting

Micro-level demand forecasting involves estimations concerning the internal operations of a business.

Demand forecasting at the microeconomic level is specific to a business and different segments of its internal operations. These segments may include particular product categories, customer groups, sales division, financial division, and other internal areas of business operations.

Micro-level demand forecasting also takes metrics like the cost of goods sold (COGS), cost of goods manufactured (COGM) , net profit, and internal cash flow into consideration, among others.

2. Macro-Level Demand Forecasting

Macro-level demand forecasting deals with the broader macro-economic environment. It deals with external economic conditions and factors that affect a company's demand.

Some of the different factors considered with macro-level forecasting include general market research, customer preference change, inventory portfolio expansion, and other external macro-economic factors.

3. Passive Demand Forecasting

Passive demand forecasting is common with more stable internal and external economic environments. It involves and requires only historical data to predict future demand for goods and services.

With stable economic environments, past demand metrics can be directly used to predict future demand. Demand is expected to be the same as previous accounting periods, so other activities like trend analysis and crude statistical calculations are averted.

Passive demand forecasting is a rare but good model for businesses that aim for stability rather than growth.

4. Active Demand Forecasting

Active demand forecasting is used by startups or companies aiming for business growth and expansion . It involves extended marketing research , the study of trends, multiple calculations, assumptions, and plans for promotional campaigns and business expansion.

External factors are the main focus of active demand forecasting. Some of the factors that are typically considered include economic outlook, general market growth projections, and supply chain studies.

Active demand forecasting is most especially important for startups that do not have historical data and are forced to rely on external factors.

5. Short-Term Demand Forecasting

Short-term demand forecasting is done with a period of 3 months to a year in mind. It considers the amount of demand that is expected within this short period. Short-term business decisions are made during this period.

6. Long-Term Demand Forecasting

Long-term demand forecasting deals with time lengths of between 12 months and possibly up to 4 years. It drives long-term business decisions regarding activities like financial planning, capital expenditure, and capacity investment planning, among a whole lot of others.

Understanding Demand Forecasting Methods

Before going on about demand forecasting, you need to know the different methods and which one is appropriate for you.

Some of the most popular and crucial methods in demand forecasting include the Delphi technique, conjoint analysis, intent survey, trend projection method, and econometric forecasting.

1. Delphi Technique

The Delphi method involves the use of a group of experts that provide their individual forecasts and justifications for their forecasts.

Each forecast and explanation is then read out to other experts on the panel, with each of them influenced by the forecast of their counterparts. A subsequent forecast is then made by each expert with the new influenced knowledge and this process repeats itself until a consensus is reached.

A consensus exists when there is no significant difference between the forecasts of the different experts.

The Delphi method is based on the idea that an individual cannot accurately or effectively predict future demands all on his or her own. When executed properly, the Delphi method is a very accurate technique of forecasting demands.

However, there are downsides to it. Apart from the need for highly knowledgeable experts on this panel to ensure accurate forecasts, the Delphi method is time-consuming.

2. Conjoint Analysis

The conjoint analysis involves the use of surveys to collect information about customer preferences as relating to a product.

Surveys are typically in the form of questionnaires that seek preference information from customers. Consumers are asked about what they think of a particular product attribute and businesses make forecasts from their answers.

Information that surveys target to get from customers falls into personal, demographic, and economic information.

Conducting surveys helps a company to realize the most important selling point of their different products and services. The reasons why consumers choose a certain product over others is identified and a company gets to know which product or service feature consumers value the most.

Conjoint Analysis is a good demand forecasting method for products with no history. When a company wants to enter into another product category or increases its inventory portfolio, information about the preferred attributes allows it to start on the right track.

Market preference and how consumers react to a product are collected and used accordingly.

3. Intent Survey

An intent survey aims to collect information about which product consumers are intending to buy in the future. This technique aims at understanding the factors that push a consumer to buy a product.

Intent surveys are usually conducted through the websites of companies and typically ask website visitors to rate their intent to buy a product on a scale of 0 – 10.

Where intent is rated high, a company then decides on whether it should proceed to stock a product it was previously considering.

One point to note is that intent surveys only predict the likelihood of a product being purchased and not the actual consumer behavior. It is also better used to predict the purchase of existing products, durable products, and short-term forecasting periods.

4. Trend Projection Method

The trend projection method is effective for companies with large historical sales data. This sales data history typically spans more than 18 – 24 months.

A time series representing the past sales and demand for a particular product is then formulated. These different graphical trends are followed closely and used to determine the expected future demand for products.

From the above, it is apparent that the trend projection method is only effective and feasible in generally stable economic environments. Uncertain environments usually do not have consistent graphical patterns over this long period and, therefore, are not effective to use.

5. Econometric Forecasting

Econometric forecasting involves the use of mathematical equations and various variables to come up with a demand forecast. It uses relationships among economic variables to forecast future developments.

Factors Influencing the Customer Demand Life Cycle

Demand forecasting is all about how the supply chain meets the demand for products. Numerous factors are influencing the customer demand life cycle such as seasonality, external competition, type of product, and geographical location.

1. Seasonality

Seasonality refers to the change in demand for products over a particular period . It involves the different periods and the volume of orders that are characteristic of them.

A company that runs a highly seasonal business typically records highly distinct demand trends throughout the year . Demands are only received in a specific period or several limited periods of the year. Due to this, graphical demand trends show a spike in this period.

An example would be a company that manufactures and sells Christmas apparel. Demand for Christmas apparel is majorly received towards the end of the year, with a peak period in December.

Seasonality requires a company to optimize inventory storage following the expected demand trends.

Inventory items and staff are kept very low during quiet periods while purchase orders, manufacturing activities, and inventory storage intensify towards periods of demand spikes.

2. External Competition

One unavoidable aspect of running a business is the competition for the attention of customers. The more competition you have in the market, the more options your potential consumers have to choose from other than you.

With a lot of activities by external competitors to get the attention of consumers, the demand for your products will remain inconsistent and continuously dwindling. The effect of this factor is most especially noticeable when a new competitor comes into the market.

Competitor strategies largely affect how demand for a product shapes out to be and companies consider this while forecasting.

3. Type of Product

The demand forecast of a product is different from the forecast for other products. Each product has its own market peculiarities and, therefore, should be given distinct attention.

Perishable goods have separate market characteristics as opposed to durable goods. Services that are paid for at the end of a monthly cycle are also different from services with spontaneous payment cycles.

Nonetheless, no matter what a product or service is, certain factors are crucial for consideration. These include the lifetime and purchase value of your customers for each product as well as the combination of products that are typically ordered.

Taking these into account helps you understand how you can group or bundle products and how the demand for one inventory item affects the demand for another.

4. Geographical Location

The location where you operate greatly determines both the demand for your products and how you meet up with demands.

A lot of consumers prefer to buy items that can be immediately shipped to where they reside. Due to this, the location where your inventory is stored is very important.

Order fulfillment centers can be placed at strategic locations that allow orders to be delivered quickly. You can also use reliable order fulfillment services to help you fulfill your products if your business does not have the resources to handle them.

With demand easily met and orders quickly fulfilled, consumers are encouraged to keep purchasing products.

Bad geographical locations greatly hinder the demand for products as well as the fulfillment of orders. Where your order fulfillment record is unsatisfactory, the number of customers and demand for products continuously decrease.

How to Forecast Demand Effectively?

Due to the flexible nature of demand, predicting future sales of a product is one of the most difficult tasks in economics. However, there are straightforward steps that businesses can follow to effectively predict future demands.

Step 1. Establish A Plan

Every company runs in its peculiar business environment and has different economic factors specific to it. Demand forecasting activities must be in sync with the peculiarities of your own company for them to be effective.

A plan needs to be made according to your business goals and objectives. The period you want to consider, as well as the product and customer category you wish to focus on need to be established beforehand.

Demand forecasting takes note of these factors to predict what your customers want, when they want it, and how much they want. It needs to fit your financial, marketing, operations, and logistics plans. It is important that these plans are established before proceeding with demand forecasting.

You get to know which demand forecasting technique is best in achieving your goals and make appropriate decisions concerning it when you establish a plan.

Step 2. Compile And Record Data

After deciding on your business goals and the appropriate type of demand forecasting technique to be used, you then need to compile your historical and external analytics data.

Demand forecasting does not work without data. Even startup companies without historical data still need to make macro-level economic analyses to have enough information to work with.

Historical sales data gives a great overview of how demand trends shape out to be in the future. Having knowledge of the usual time of demand spikes for a product, the number of stock-keeping units (SKUs) usually demanded, and the typical sales channel makes demand forecasting easier.

In stable business environments, internal historical data and trends are the only metrics required for accurate forecasts. General market data are, however, important key metrics for most companies and business types in very inconsistent business environments.

Step 3. Analyze Compiled Data

Demand forecasting does not end at just compiling internal and external economic data. Data still needs to be analyzed and converted to useful information.

Analyzing data can be done manually, using different economic equations and inferences. It can also be done with the use of automated software programs that are optimized for that exact purpose.

Your previous forecasts can be compared with the eventual demand and sales for that period under consideration to see areas for improvement. Variations between your prediction and actual occurrences help you measure the effects of miscalculations and opportunity costs suffered.

For instance, a graphical spike in demand shows a company that demand for a product increases during that period. From this, the company has an idea of what it needs for that period to avoid stock-out situations.

Of course, this spike could also be caused by other factors like the folding up of a competitor. Analyzing all the data compiled from sales history, internal operations, and the external general market environment helps you come up with the most appropriate forecast for the future.

Analyzing data helps you know how quickly products are selling and which items are slow-moving. It shows you how long your current inventory will take to run out, the profitability of each order, and where your customers from, among a whole lot of others.

Data analysis for demand forecasts is relatively much easier in stable economic environments where trends are expected to always be the same as in previous periods. No complex calculations are required unlike in business environments with inconsistent variables.

Step 4. Create Your Budgets Accordingly

After making the appropriate analyses, it is then time to come up with a demand forecast. Demand forecasts are expected to follow the various inferences made from the study of historical data and the general market metrics.

You adjust your budget and other allocations to fill loopholes in previous forecasts and also take care of estimated future needs.

Hopefully, these inferences are accurate and comprehensive enough for demand forecasts to also be accurate. Accurate demand forecasts help you reduce overall inventory costs, optimize marketing strategies and costs, and maintain the appropriate number of staff to meet demand.

Examples of Demand Forecasting

Different organizations have different business objectives and plans for their future. While some may decide to pursue stable growth, some may pursue aggressive growth, while some may choose to maintain their current economic positions.

Demand forecasting can be illustrated with the following examples.

An online store checks out its sales trends from last year’s winter to prepare adequate inventory levels for the upcoming season. Sales of seasonal products like waterproof boots, winter gloves, scarves, and winter coats are looked at. Analyses show that there was a great seasonal sale for them.

However, six months ago, a competing store opened close to it and, due to this, demand for products was expected to be skewed. However, at the same time, a lot of families continued to move into the neighborhood, and business growth remained at an average of 1% month-over-month since the competing store opened.

A plan to launch a few more promotional campaigns than last year is made and channels that have generated a good Return On Investment (ROI) are considered. Some new deals to position themselves as the go-to store are also proffered to customers.

Projected forecasts for demand can be put at a 5% increase in sales from last year and budgets can be made accurately.

A fast-growing direct-to-consumer (DTC) apparel brand starts off selling 10,000 units of inventory per month. Based on past sales data, upcoming promotional campaigns, and general market conditions in the industry, a plan to sell above 30,000 orders per month in the following year is then made.

Shortly after, a total of 30,000 inventory units were stocked up and at varying levels across their 5 different stock-keeping units (SKUs). The economic environment is considerably stable and order volume only fluctuates a bit based on their replenishment cycle. Inventory is also stocked at a rate of about every 90 days.

After reaching its 30,000 sales goal, a new plan to ship in another 50,000 units is then made based on historical sales data and the rate of demand is received.

With a long-term plan, the apparel brand plans to continuously grow at the same pace, so a longer projection of 75,000 units is made for the distant future. Other factors like the purchase of land, lease of a warehouse, or outsourcing of inventory fulfillment are also decided upon according to the projected demand.

Demand Forecasting FAQ

Demand forecasting helps the business make informed supply decisions that estimate the total sales and revenue for a future period.  A huge part of a business’s operational strategy is based on demand forecasting. Through it, they can predict inventory turnover, profit margins, cash flow, product availability, and capital expenditure.  Demand forecasting, for example, helps you to determine what products will have heavy traffic at a future date, like Christmas trees in the festive season.

Building a demand forecasting model relies on many factors including the context of the forecast, the viability of available historical data, the degree of accuracy desirable, the period to be forecast, the benefits of the forecast to the company.  Forecasting models can be generally differentiated into two groups based on whether they use qualitative or quantitative methods.   Models such as a time series model or an econometric model will use quantitative methods because they need large amounts of data to predict future demand trends.  On the other hand, qualitative research like the Delphi method or sales force composite will use human opinions where data is not available or applicable. Quantitative methods are more data-accurate but qualitative methods offer more flexibility. It can readily account for external factors like inflation and market competition, unlike the qualitative model.

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Anastasia has been a professional blogger and researcher since 2014. She loves to perform in-depth software reviews to help software buyers make informed decisions when choosing project management software, CRM tools, website builders, and everything around growing a startup business.

Anastasia worked in management consulting and tech startups, so she has lots of experience in helping professionals choosing the right business software.

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A complete guide to demand forecasting: Methods and best practices.

• Irene Llamas

Factors to consider in demand forecasting

To make an accurate demand forecast, it is necessary to take into account several factors that may alter demand. Some will depend mostly on you and the way you manage your business and others will be given to you.

Here are some of the main factors to consider:

• Internal factors: Internal factors that may affect demand include the availability of products or services, their quality, prices and promotions offered, as well as the quality of customer service.
• External factors: These include economic factors, such as market conditions, consumer income levels and unemployment rates; social factors, such as culture, fashion and trends; and political factors, such as laws and regulations.

Demand forecasting methods

Demand forecasting methods can be divided into two main categories: quantitative methods and qualitative methods .

Quantitative demand forecasting methods:

Quantitative methods allow objective and accurate analysis based on numerical and statistical data, which is useful for forecasting numerical trends and making long-term projections in various fields, such as economics and finance. However, it is important to keep in mind that these methods may be limited in their ability to consider subjective or unpredictable factors that may influence the final results. Therefore, it is necessary to complement quantitative methods with other more subjective approaches, such as qualitative research and practical experience.

For example: a restaurant uses quantitative methods to predict the number of customers it will have on a given day. However, they do not take into account subjective factors such as weather, competition in the area, or the popularity of a new dish on the menu. If there is a rainy day or a popular new restaurant opens in the same area, demand could be much lower than they predicted and they could end up with excess food that doesn’t sell. This shows the limitation of quantitative methods in not considering subjective factors that may influence demand.

The main quantitative demand planning methods are:

• Trend analysis:

This method is based on the assumption that future demand will follow a linear, exponential or logarithmic trend based on historical data. The data are fitted to a trend line and used to project future demand. For example, if a company wants to forecast demand for a product for the next 6 months, it can use historical sales data for the last 12 months and fit a trend line to predict future demand. If this method is used, it is important to take into account seasonality factors that can create upward or downward patterns within a general trend. It is also important to bear in mind that in certain sectors, “fashions” have a major impact and that certain trends can be radically interrupted by the appearance of a new “fashion”.

• Statistical models:

One of the most widely used statistical models in demand forecasting is the moving average, which uses the average of a given number of previous periods to predict future demand. For example, if a three-month moving average is used, the average of the last three months’ sales will be taken to forecast next month’s demand. This model is useful for predicting short-term demand and adjusting inventory levels accordingly.

Another common statistical model is time series analysis, which is used to analyze seasonal, cyclical and trend patterns in historical data and project them into the future. This model is useful for forecasting long-term demand and for planning production and inventory levels accordingly.

• Analysis of historical data:

This method uses historical sales data and other economic indicators to project future demand. For example, if a company wants to forecast demand for a particular product, it can use historical sales data for that product, as well as relevant economic indicators, such as GDP or the consumer price index (CPI), to forecast future demand.

• Time series analysis:

This analysis is based on historical sales data. The data is analyzed to find seasonal patterns and long-term trends, and then statistical models are used to forecast future demand. This method is very useful for forecasting demand for products that have seasonal sales patterns, such as products for the Christmas season.

• Regression models:

The regression model uses historical sales data and other external factors to forecast future demand. External factors may include changes in the economy, competition and market trends. Statistical models are used to analyze the data and forecast future demand. For example, a beverage company may use a regression model to forecast the demand it will have based on the economy, health trends and changes in competition.

• Social network data analysis:

This method involves analyzing data from social networks. Data may include product or brand mentions on social media, customer comments and market trends on social media. For example, a fashion company can use this type of analysis to forecast future demand for a new clothing line based on current trends in social networks.

One example of success is SHEIN, the fashion platform that has revolutionized the retail world by implementing a fast fashion production model based on the analysis of trends in social networks. SHEIN uses social media data analysis to forecast future trends in fashion and adapt its production in real time, making it the platform par excellence for “real time retail”. For example, if a fashion trend begins to gain popularity on social media, SHEIN can respond quickly by producing and selling garments that fit that trend.

Qualitative demand forecasting methods:

The main advantage of qualitative methods is the ability to obtain detailed and in-depth information about consumers and their needs. These methods allow greater flexibility and adaptability to changing market situations, and can also be useful in the development of new products and services. However, their main drawback is that they can be subjective and do not provide an accurate quantitative measure of demand. Some examples:

• Opinion polls:

This method involves conducting surveys to obtain information on the opinions and expectations of consumers and experts. For example, a food company may conduct a survey to find out consumer preferences for flavors or the appearance of packaging.

• Delphi analysis:

The Delphi method involves bringing together a group of experts with a mediator and structured techniques to discuss and reach consensus on expectations of future demand.

The process is conducted anonymously to avoid influencing opinion and is used as a method of prospecting and analyzing future scenarios in order to make informed decisions and adopt the best strategies for the company.

3. Scenario analysis:

This is a technique in which different possible future scenarios are considered and demand is estimated for each of them. For example, a company may consider several possible scenarios in the economy and estimate demand in each of them, and then make decisions based on the results.

Common mistakes in demand forecasting to avoid

Making mistakes in forecasting demand can lead you to accumulate unnecessary stock or suffer the dreaded stock-outs .

Here are some of the most common mistakes that are often made when trying to plan for demand:

• Planning without sufficient historical data:

Suppose a clothing store has decided to add a new line of sportswear. However, the retailer has no historical data on sportswear sales, making it difficult to forecast demand for this new line. In this case, the store could collect market information, conduct surveys and seek competitor data to get a clearer picture of potential demand.It is important not to jump to conclusions based on too small a sample of data or extrapolate based on realities that do not fit our own.

• Failure to consider seasonality

A toy store can expect a significant increase in demand during the holiday season. If the store does not take seasonality into account, it could inadequately plan its production and inventory management. To avoid this, the store should consider seasonality when planning production and inventory management to ensure that it has sufficient stock during periods of high demand.It is vital to reverse engineer the onset of these high-demand periods and the timing of production and shipments to plan your orders and stock sufficient stock.

• Making decisions based on assumption or intuition:

Suppose an electronics retailer assumes that the demand for flat screen televisions will decrease due to the introduction of new technologies. However, demand for flat-panel TVs remains high due to their relatively low cost compared to the latest additions in the industry. If the store relies on this assumption, it could inadequately plan its orders and accumulate stock of a type of product with no demand and fall short of those products that its customers do order, leading to the worst situation of all: overstocking and stock-outs at the same time.

• Lack of communication between departments:

A clothing store might plan a sale in its winter clothing section to liquidate remaining inventory, but if the buying department is not aware of this sale, they might interpret it as a peak in demand and decide to continue buying more stock of that same collection, resulting in excess inventory and a financial loss for the store. To avoid this, it is important that departments work together and share relevant information for accurate and consistent forecasting.

Download the demand planning ebook now! Learn the keys to optimize the availability of your products and provide a better service to your customers.

How to improve the accuracy of your demand forecasts?

Although forecasting demand is never an easy task, there are a number of strategies that can help companies improve the accuracy of their forecasts and reduce the risks associated with inventory management and production planning. In this section, we’re going to show you some of the most effective ways companies can improve demand forecasting accuracy and ensure their operations are always one step ahead.

• Improved data quality:

Digitized stock management is the first step to ensure that you have accurate turnover data. Inventory on a frequent basis and record inter-store transfers, theft, losses and discounts.

Having tools such as a people meter can also be useful to understand not only the sales of your stores, but also their traffic and the conversion of visitors to sales of your sales team.

• Integration of data from different sources:

For example, an electronics retailer wants to improve the accuracy of its product demand forecasting. To do so, it could combine internal data, such as sales history and production data, with external data, such as market trends and consumer behavior. By integrating this data, the business could gain a more complete view of demand and improve forecasting accuracy.

• Continuous monitoring and adjustment of demand forecasting: Suppose a sporting goods store wants to improve the accuracy of demand forecasting for seasonal products, such as swimsuits in summer and coats in winter. To do so, you could use analytical tools and techniques to monitor deviations from the actual forecast and adjust accordingly. By doing so, the store could improve forecast accuracy over time and keep market demand as accurate as possible.

Find out how Stockagile can help you forecast your demand

Stockagile uses analytics and machine learning techniques to generate accurate demand forecasts and provides real-time information on sales performance and inventory levels.

In addition, Stockagile allows you to continually adjust and refine forecasts as more data is collected and new sales are made, which can help ensure that inventory levels are optimized and products are available to customers when they need them.

Finally, having a software like Stockagile will help you to adapt your purchasing and replenishment strategy to this demand, improving the efficiency of your business .

In conclusion, demand forecasting is a crucial task for any business that wants to optimize its supply chain and maximize its profitability. With so many factors that can affect demand, it is important for companies to use a combination of quantitative and qualitative methods to more accurately forecast future trends.

At the same time, it is important for companies to be on the lookout for common forecasting mistakes and to work on continuously improving data quality and monitoring their business to adjust their forecasts accordingly.

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Demand planning is a supply chain management process that enables a company to project future demand and successfully customize company output—be it products or services—according to those projections. It is the linchpin of an effective supply chain, which makes it doubly important to business.

Demand planning seeks to achieve and maintain an effectively lean supply equilibrium, one in which store inventories contain just as many products as demand dictates, but no more. Finding that perfect balance that exists between sufficiency and surplus can prove especially tricky. And although maintaining that balance is a major concern of demand planning, so is the constant effort to help shape demand through an effective use of promotions.

Effective demand planning typically requires the use of demand forecasting techniques to accurately predict demand trends, and carries added benefits, such as heightened company efficiency and increased customer satisfaction.

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Demand planning is the linchpin of an effective supply chain, serving two essential functions — which makes it doubly important to business.

First, there always exists the fundamental drive to protect the sale and ensure that expected revenues are generated. But retailers can’t sell what they don’t have in stock. And it doesn’t take long for today’s consumers to develop a lasting impression of a company, and whether it can meet supply demand. Demand planning works to see that retailers have exactly the right amount of inventory at the right place to avoid stock-outs and remain prepared for that next sale.

But protecting sales isn’t enough anymore. It’s also about running businesses more efficiently. Demand planning assists with efficiency, by helping manage inventory space smarter. Why should companies invest in more physical space than they need? Demand planning can help businesses avoid the perils of overstocking — such as increased inventory carrying costs and financial situations that require the use of product discounts or other temporary measures to alleviate overstocking by selling inventory as quickly as possible.

Demand planning and forecasting is more crucial than ever, especially since so many outside forces — such as weather events, economic trends and global emergencies — can end up shaping and reshaping demand.

Effective demand management requires a comprehensive understanding of products and their respective lifecycles. Product portfolio management offers this, detailing a product’s complete lifecycle, from its origins until its eventual phase-out. And since many product lines are interdependent, product portfolio management shows you how shifting demand can affect “neighboring” products.

Working from the traditional concept that past history is usually the best predictor of future performance, statistical forecasting uses complex algorithms to analyze historical data and develop supply chain forecasts. The mathematics of statistical forecasting methods is advanced and the exacting process demands accurate data (including from outliers, exclusions or assumptions).

Demand sensing uses a combination of new sources of data, such as weather, infectious disease trends, government data and more, with historical trend data and applies AI to detect disruptions and demand influences in near real-time.

Survival in the retail jungle depends on sparking the interest of potential customers. Trade promotions and other marketing strategies use special events (such as discount prices or in-store giveaways) to spike consumer demand. Trade promotion management works to ensure that such opportunities are properly executed and deliver all expected benefits.

Organizations vary widely in how they approach the demand planning process, but there is a general set of steps that businesses typically follow. Those steps include:

• Organizing and preparing data.
• Making a preliminary forecast.
• Integrating market data.
• Reconciling bottom-up and top-down forecasts.
• Developing a final forecast.
• Using analytics to monitor project performance.

In addition to establishing a precise set of implementation steps, successful companies usually engage in the following best practices for demand planning:

In order to process complex projections, effective demand planning requires ample amounts of data. Smart companies rely on metrics reports that help them prepare their data through increasingly sophisticated data mining and aggregation techniques.

There are numerous options when choosing demand planning software, but companies should try to be selective, based on their unique needs. Goal: Find a solution refined enough to reflect the subtleties of demand forecasting methods yet robust enough to handle reporting tasks.

Experienced demand planners typically begin their process by using descriptive analytics data to develop a testing baseline. Next, they shape the actual plan, devoting personnel and resources to cultivate and refine that plan, and then work on the exact implementation steps.

To be sure, the future is digital — and so is the outlook for demand planning. As demand forecasting in supply chain management becomes increasingly sophisticated because of advances in machine learning, companies will reap substantial benefits, such as being able to receive precise, real-time inventory updates and forecasts.

These continuing advances are drawing companies closer to the ideal promoted through demand planning. If an enterprise stocks just enough inventory to satisfy customer demand and withstand temporary market fluctuations, it’s able to run more efficiently and profitably thanks to its lean inventory strategy.

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Discover how to streamline the planning process for supply chain plans that are synchronized and responsive.

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What is demand planning?

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The platform for orchestrating performance.

Learn the basics of effective demand planning to improve the accuracy of your forecasts, align inventory levels, and enhance profitability.

Demand planning  is the supply chain management process of forecasting demand so products can be reliably delivered and customers remain satisfied. Effective demand planning can improve the accuracy of revenue forecasts, align inventory levels with peaks and troughs in demand, and enhance profitability for a particular channel or product.

Demand planners keep an eye on internal and external factors that could impact demand, such as labor force issues, natural disasters, weather patterns, and news events or other influences. Gathering information from all possible sources is the best way to generate an accurate forecast and ensure integration with the supply forecast to efficiently meet customer demand.

The importance of demand planning

The market can shift on a dime and demand plans needs to move at the speed of the changing market. If demand plans can’t be adjusted with agility, companies could end up with stock-outs and unhappy customers, or warehouses full of unused inventory, unhappy finance managers, and millions of dollars in wasted capital.

In an ideal world, demand planners must stay ahead of the market instead of merely reacting to it, and make decisions based on near real-time market data, rather than solely on historical data. That’s not always possible, but with the advent of cloud-based planning platforms, it’s closer to reality than ever before.

Check out our on-demand demo to see how a cloud-based demand planning solution will enable you to sense, shape, and orchestrate demand with a holistic view of data and trends.

Elements of the demand planning processes

Let’s take a look at a few of the processes involved in demand planning.

Trade promotion management:

Trade promotions  are marketing tactics (most often in retail companies) that focus on generating in-store demand through discounts, giveaways, in-store promotions, and other similar techniques. Trade promotion management is designed to help brands stand out from their competition through highly coordinated promotion activities and builds stronger connections with retailers.

Trade promotion planners seek to plan collaboratively at detailed and aggregated levels so they can adjust products, campaigns, and promotions without long delays, aligning an optional trade promotion spending plan that incorporates end signals from distributors and customers across all time periods, products, and geography.

Top-down and bottom-up trade promotion management and analysis includes profit and loss data, creating insights around promotion spending. It also tracks and identifies which promotions don’t optimize margins due to ineffective trade promotion spending and poor brand growth from trade promotions and creates maximum ROI using a broad range of data.

Product portfolio management:

Product portfolio management  is the process of managing every facet of the product lifecycle, from new product introduction to end-of-life planning. The goal of product portfolio management is to maintain a high-level view of the entire portfolio and reveal where product lines are interconnected and interdependent.

Product portfolio management includes planning for fitting new products into the existing product portfolio, understanding how introducing those new products will affect other products (cannibalization), and the analysis of attachment rates (how the sales of one product affects the sales of another). Planners involved in product portfolio management are heavily involved in scenario planning to ensure that they’re aware of each product line’s effect on the other product lines to optimize the product mix, maximize profitability across product lines, and increase global market share.

When new products are launched, it’s important to know how the new products will affect the global planning strategy, the cost of introducing that new product, and the revenue and profits that will be generated by the new products. Using intelligent product portfolio management techniques, feasibility models are connected to ideation processes, scenario-based profitability models are generated, and the process of taking a product from idea to commercialization is accelerated.

When this process is collaborative, a real-time granular forecast model can identify how different market segments across geographies might purchase this new product and at what price. In a collaborative system, new product introduction links sales and supply chain, resulting in key connections to  sales and operations planning (S&OP)  processes, production planning, and allocation planning.

Statistical forecasting:

Statistical forecasting  in demand planning leverages historical data to generate supply chain forecasts using various advanced statistical algorithms. In demand planning, it’s essential to have data-backed forecasts to avoid stock-outs or overstocks and ensure that customers are satisfied.

There are multiple aspects to how statistical forecasting makes demand planning more effective. Demand planners can analyze many algorithms and decide which forecast is most accurate by reviewing each model’s accuracy and bias measures. Then they can choose from the best model for each product and product family.

And when a forecasting dashboard is part of the equation, it becomes easier to customize forecast algorithm assumptions and measure accuracy with techniques like mean absolute percentage error. With statistical forecasting, demand planners can quickly identify outliers and exclusions based on user-defined parameters, including standard deviation or the inter-quartile range.

Seasonality has a major impact on demand planning. Retailers have many factors to sort through to ensure that they’re prepared for various seasonal events. Will they be ready for the holiday shopping rush? What if weather patterns shift and all those winter coats they’ve stocked aren’t purchased? With statistical forecasting in demand planning, these questions are easy to answer because multiple statistical simulations can be run, including models to forecast the impact of intermittent demand, multi-linear regression forecast quantity, price, attach rates, and discounts.

The skills demand planners need

Demand planning is undergoing large-scale radical change with an emphasis on digital transformation. Artificial intelligence (AI) and machine learning are already beginning to make an impact on how demand planners operate.

Algorithmic “touchless” supply chains that weave in the power of big data, blockchain, robotics, and 3D printing may soon be the rule rather than the exception. Demand planning of the future will be always on, dynamic, and non-linear. The power to react quickly and make value-based decisions is essential to staying ahead of the market.

To lead the way into a transformative future, demand planners need to combine technical and business knowledge with collaboration and communication skills. The ability to influence department leaders that partner with demand planning is key, as well as the skills to interact intelligently with leaders across the organization because supply chain initiatives often reach across business units. Strong business acumen is a must-have — you’ll be more effective working with your counterparts in finance, sales, and marketing if you can speak their language.

The effective demand planning leader of tomorrow is tech-savvy and comfortable working alongside the world of machines. Some have said AI won’t replace managers, but managers who work with AI will replace managers who don’t. This highlights the transformation taking place in supply chain management: Humanity is essential but so is technology. It’s not a paradox — it’s the new normal. The new demand planning leader is digitally dexterous and also skilled with people.

The already many-faceted role of a demand planning leader is changing. To thrive in this new world, demand planning professionals must grow their capacities in collaboration, communication, and leadership, and pair those skills with in-depth technical knowledge if they want to become and stay a powerful force for the future of demand planning.

Digital demand management 

The future of demand planning is what Supply Chain Brain calls  “digital demand management”  (DDM). It’s centered around implementing demand-driven structures, frameworks, and digital enterprise architectures. Multiple groups connect to facilitate a seamless exchange of information, ideas, and solutions that are synchronized with the omnichannel buying habits of consumers.

Even in our personal buying habits, the competitive demand landscape has changed radically in the past few years. There are rapidly emerging digital markets, new competitors, and faster market changes that can threaten the extinction of any enterprise that doesn’t adapt. DDM makes complex data comprehensible, actionable, predictive, and prescriptive because it provides a real-time synchronized knowledge base that permits improved customer focus.

The development of DDM starts by challenging traditional linear thinking across supply chains. The old linear thinking accepted the inevitability of forecast cycles that are weeks or months old, poor visibility into SKU locations, and the inability to address ongoing variability that disturbs traditional network and inventory optimization systems. Linear decision-making adds unnecessary time and causes potentially false demand signal amplification.

The essential change is to replace traditional functional metrics with DDM, fostering collaborative execution across the entire supply chain. The resulting demand management organization enables a business to be simultaneously planned dynamically in real time, both horizontally and vertically, enabling true digital collaboration.

DDM requires a new planning model that enables causal and external factor analysis, along with a process-control approach to smart digital network management. The beauty of DDM is that it works within a range of pre-defined acceptable variability. Using real-time DDM forecasts — based on standard deviations along with the range of consumer behaviors that are likely to occur — participants can agree to ranges of performance, commitment horizons (periods of risk), and exception conditions. The result is a beneficial digital collaboration that enables next-generation demand planning.

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Using workflow for demand management in Project Online

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This article describes how demand management is implemented in Project Web App.

You can use the demand management tools in Project Web App to capture all project ideas in one place, and then guide them through a decision-making process catered to your business's needs. Using these tools can help your users make decisions about which proposals to approve, and track progress on a project until the work is completed.

Demand management in Project Online uses:

Project detail pages - Project Web App pages where users can view and update project information.

Stages - sets of project detail pages specific to one area of the project lifecycle.

Phases - a way to organize multiple stages.

Workflows - a way to enforce your business processes as projects move through the various phases and stages.

Enterprise project types - a way to bring phases, stages, and project detail pages together with a workflow into a standardized way of doing a project.

We'll go over each of these in the sections that follow.

Project detail pages

Project Web App users view or update project data on project detail pages. You can customize a project detail page by choosing web parts that use the fields you want displayed. In demand management, project detail pages are displayed to the user at various stages of a project whenever you need to gather information from or display information to a user.

A stage includes one or more project detail pages, grouped to gather information about a project. This information can be used or updated by a workflow.

In Project Web App, you can define which project detail pages are displayed in a given stage, which fields are required and which are read/write or read-only, and which phase (we'll talk about phases in the next section) the stage is part of.

For each stage of a project, we recommend that you define what actions need to take place and what information needs to be gathered based on your business requirements for the project. This information will help you define the list of fields that you need to display in the project detail pages and what actions you need the workflow to take. The following steps list the general procedure:

Define what needs to happen with the project in each stage

Define the required information that you want to capture using project detail pages

Define the state of the fields in each stage (Required, read/write, or read-only)

A demand management phase is used to organize multiple stages that make up a common set of activities in the project life cycle. Examples of phases are project creation, project selection, and project management (represented in the default Project Web App phases as Create, Select, and Manage). The phases themselves are just a way of organizing your stages and do not determine the order in which the stages are executed. (The order of the stages is determined by the associated workflow.)

See Best practices for creating phases and stages for more information.

Workflows enforce your business processes and provide a structured way for projects to move through phases and stages. You can set up a workflow to do a variety of actions based on the user input for each stage, including sending emails, assigning tasks, and waiting for specific project actions.

For example, you might have an "Initial Proposal" stage where you include a project detail page with a custom field for project cost. You could configure the workflow to automatically accept or reject the project based on whether the project cost exceeds a certain limit.

The image below shows the five phases of demand management that are included in Project Web App and how they fit together. Within each phase are example stages such as "Propose idea" and "Initial review." Each stage can have one or more associated project detail pages. The entire collection of stages represents a single workflow that can be associated with an enterprise project type.

There are four general steps to perform to create your workflow in Project Web App:

Design the workflow based on your business requirements.

Create the needed custom fields, project detail pages, phases, and stages in Project Web App.

Create the workflow in SharePoint Designer 2013 and deploy it to Project Web App.

Create an enterprise project type that uses the workflow.

Enterprise project types

An enterprise project type brings together all the elements of demand management by combining a single workflow with its phases and stages with department and project site template definitions. Normally, enterprise project types are aligned with individual departments, for example, marketing projects, IT projects, or HR projects. Using enterprise project types helps to categorize projects within the same organization that have a similar project life cycle. For a user, the enterprise project types appear in a drop-down list when the user clicks New Project in the Project Center.

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