Demand management is an overriding factor in understanding what customers precisely value and delivering the result on point. It bestows a soothsayer’s ability to businesses. With demand management, you can make the right future prediction about your customers’ preferences and anticipate orders.
Over the last decade, demand management has become an anchor point in demand-driven supply chains, especially for eCommerce companies. In this article, we discuss everything you will need to know about demand management and how you can forecast demand.
Demand Management: Meaning and Features
Customer demand is an ever-changing phenomenon. It's as dynamic as changing weather- what trends in summer can fall away in winter?
This is why you need a methodology that predicts the volume and variations in customer demand. It enables you to plan ahead and utilize your resources effectively to meet oncoming demand.
This is what demand management is all about. It gives companies the much-needed competitive edge to overcome volatility. You can expand customer expectations, shorten fulfillment cycles, and up your logistics game.
Demand management considers multiple areas: business operations, marketing, production, inventory planning, etc. Businesses can analyze external demand by assessing market factors.
They can also perform an internal study to evaluate their production capacity and the raw materials needed to meet customer demand.
There are multiple dimensions to demand management. Here are its most salient features:
- It analyzes current demand in the short term by focusing on understanding customer needs.
- It uses predictive analytics to estimate future sales, revenue, and project growth. This is termed demand forecasting.
- It seeks to create a balanced ratio between inventory levels and anticipated order amount reducing surplus products and waste.
The Role of Demand Management and Forecast in eCommerce
1) Manage capacity and inventory levels
Demand management helps businesses plan and run production on schedule, allowing them to operate expertly on any manufacturing model. Take, for example, the manufacturing-to-stock and just-in-time manufacturing models.
Both production techniques rely heavily on understanding future demand to produce the optimal inventory level. The number of products manufactured and sold balances out.
Demand management thus makes it possible for enterprises to coordinate production time, resource amount, and stock levels. And, by extension, get a hold of their cost centers.
2) Plan for seasonal variations and economic fluctuations
The market conditions are prone to price fluctuations, raw material shortages, manufacturing halts, and even global tensions. Seasonal variations are another ever-persistent trend in business.
Take the case of this oft-quoted example. In 2015, the demand for salted caramel suddenly rose 33% in Britain after its use in the British Bake Off competition. This trend alludes to how media hype can generate sudden demand hikes that, with proper forecasting, can be anticipated. In general, demand almost always doubles when Black Friday or Holidays are around the corner.
Demand management, particularly demand planning, can create an equilibrium between inventory in stock and surging customer demand. Companies can also shape demand using incentives like price drops when they anticipate changing customer preferences.
3) Foretell finance and budget needs
Having the data on demand in near real-time can positively influence companies in allocating budgets intelligently. One facet of demand management is to identify products that have a high chance of selling. Therefore, it enables companies to prioritize projects and re-organize operational capacity and finances in the right direction.
A byproduct of demand management is managing risks and examining potentially risky factors. When everything is laid out in a concrete plan, businesses can then simplify decisions such as expanding production capacity and hiring new talents. Or getting new equipment and planning their inventory replenishment cycles. Budgeting and forecasting can go hand-in-hand.
4) Formulate competitive pricing models
Demand management, especially demand forecasting, can pave the way for a good pricing strategy. When businesses have a firm grasp on anticipated customer demand, they can leverage the situation to price their products accordingly.
For example, a highly competitive product can be priced lower when demand surges to beat off competitors while maintaining a healthy bottom line.
At the same time, the price of an item can be raised by a healthy margin when there is demand elasticity. Say exponentially popular customer trends can allow you to hike prices without losing customers.
How to Forecast and Manage Demand: 5-Step Guide?
1) Map your market situation and business goals
Demand management and forecasting strategy planning largely depend on aligning business objectives with market status. Business goals compile all expectations that you have from demand management and forecasting.
Goals determine the specificity of the forecast, the time horizon, the type of forecast admissible to pace production, and the expenses involved. More importantly, goals can frame the direction and intention of the forecast.
For example, will forecasting demand help you introduce a new product? Or expand into a new geography? Does it help you make changes to prevailing customer experiences?
Companies have to critically understand and map market situations before conducting demand management. Most of the time, markets are saturated with seasonality, like demand peaks and slumps all around the year. Likewise, there are chances of product substitutions or new radical products introduced that can affect demand calculations for your brand.
2) Collect data from different sources
Once business objectives and market conditions are decided, the next course of action is to start collecting data to begin the forecasting process. Needless to say, demand management and forecasts require a significant amount of data and information to estimate demand.
There are different types of data. You have the choice to decide on the type of data you want your forecast to be based on. It can be quantitative data, often taking the form of huge number sets, spreadsheets, and ERP data. Or, it can be qualitative data where market experts and researchers provide their inputs.
At the same time, it can be a diverse set, taking into account weather conditions, customers’ social media posts, and consumer behavior patterns. This form of data is often used in predictive and cognitive models designed with AI or ML capabilities.
For company-level forecasting, you can analyze historical sales and inventory data coupled with the datasets available from research firms. You can also conduct surveys or focus group interviews for primary data sources. For macro-level and long-term management goals, you can rely on the data that government agencies issue and publish, mostly free of charge.
3) Select the suitable forecasting type and tools
Once the data collection methodology is set in stone, you can then focus on determining the suitable demand forecasting type and tools needed for it. There are several types of demand management and forecasting models. In the following sections, we mention the specific tools you can employ to forecast demand.
In this sector, we briefly describe some of the most prevalent demand forecasting types that can aid online businesses:
- Passive forecasting: This relies on studying past sales records to predict near-future demand trends. It helps in understanding seasonal fluctuations and stable growth rates.
- Active forecasting: This model uses specialized statistical models and a diverse data set like customer surveys. It analyzes overall economic factors to predict sectoral growth. It’s apt for fast-scaling brands.
- Short-term forecasting: This projection method looks at near-future demand, say a quarter to six months, by trailing past sales data. It can understand demand surges for holidays or seasonal fluctuations.
- Long-term forecasting: This model examines the demand for a year to four years and divulges information about potential business growth trajectories. You can plan your supply chain operations in the long run.
- Macro-level forecasting: Also called external forecasting, this method studies industry-wide demand trends. This can help you situate your brand within the larger macroeconomic context of your industry.
4) Interpret the Results to plan business operations
Interpretation is the key to understanding demand forecasting results. The way you interpret and set your expectations can influence how you use the results. Though demand forecasting uses mathematical data and tools and expert opinions, they are, at best, intelligent guesswork. This is why you will need to analyze the result in your business content to derive its value.
The plan of action can depend on how you perceive the result and what you make out of it. If needed, bring in an expert to help you derive concrete policies or measures you can then implement in your business operations. While interpreting the outcome, ask yourself these couple of questions-
What are your assumptions and experience telling you when you see the result? Does your experience correlate with the outcome? What could likely go wrong if you follow along with the results or don’t?
If this is your first forecast, you may want to state the exceptional scenarios that you are willing to make to your business operations, irrespective of the derived outcome. Again, having a bottom line can help you keep your priorities in check while avoiding drastic changes in your business.
5) Formulate a Supply Chain Plan to Implement Forecast Result
Once you have your forecast results and your interpretations, the last step is to create your supply chain strategy to gear it up for the predictions. Here you brainstorm with your team, production, marketing, sales, and all other stakeholders to formulate a plan. This is what you will put into action to gain the desired results.
You can start by creating new schedules for production cycles so that you have the products ready when demand rolls in. At the same time, you can strategically place them in logistics centers near high or moderate-demand zones. Doing so will expedite your order fulfillment times.
Once you have the forecasts, you can create new product portfolios or plan new projects. You can set a timeframe for your marketing efforts and create content to arouse customers’ awareness. You can create a complete demand management lifecycle.
An extension of this step is also to keep monitoring your progress and tracking the changes you make according to the predictions. This can help you learn from errors, oversight, or unexpected occurrences. You can reiterate the process with greater accuracy the next time.
Techniques for Demand Management and Forecasting
As promised, here are some of the popular demand management and forecasting techniques you can use:
1) Demand Management
Product portfolio management studies the demand scenario for individual products and their life cycles. Since many product lines are symbiotic as collections, this method can be highly effective in understanding shifting demand effects.
Demand sensing focuses on predicting near-future demand for short-term use. And it’s truly in the short range. The data prevails over a few days or even a few hours. Demand sensing uses a mix of statistical forecasting and AI/ML models and wide-ranging signals like retail sales or order patterns. It mostly predicts demand in volatile situations.
2) Demand Forecasting
This is the elementary form of forecasting that uses past sales data to estimate future sales numbers. It is mostly concerned with the flow of variables over a period of time to account for the variations in market demand.
With a data time-series, trend projections can give insights to product developers. It can help them design a product that suits market demand and customer preferences.
This is an advanced method of forecasting that tests demand hypotheses. It creates models to understand the relationship between different demand variables. Econometric models usually help with macro-level projections and lend data to policy making.
Machine Learning Models
AI forecasting using Machine Learning is a new but widely circulating model of demand forecasting. Specific ML models like gradient boosting can handle large sets of data to predict customer behavior patterns. Moreover, ML when paired with demand planning tools can generate useful results about optimal product pricing and inventory stock levels.
Managing demand is a skill that needs to be honed. Thanks to technological methodologies like demand management and forecasting, retailers now have the chance to predict accurate demand results. The accuracy of forecasted demand actually helps a company implement changes to its production and supply chain to meet demand hikes and prepare for dips. This is now the era to benefit most from demand management.
1) What is the need for demand management?
Demand management is needed to eliminate potential bottlenecks in the supply chain. It helps with on-time production enabling brands to have adequate stock to meet customer demand. It also plays a part in adjusting prices and streamlining operations to meet customer needs.
2) What are the challenges of managing demand?
The biggest challenge in managing demand is to have adequate real-time and historical data and information. Having accurate records of market demand and analyzing ever-changing customer behaviors are difficult. Another challenge is correctly interpreting data and drawing insights to transform them into actions.