Key points:

  • Effective demand planning can help your business grow through increasing customer satisfaction, while increasing profit
  • Emerging technology can enable eCommerce entrepreneurs to create accurate demand plans, using a wider array of complex data in less time
  • Easyship can help you find a reliable shipping solution to meet demand and scale your business

Do peaks in demand lead to your store losing customers due to stock shortages? Creating a forward-thinking demand plan with the latest innovations can help your business grow by being ready for sudden demand spikes.

This article breaks down how demand planning works, how your store can benefit and how new tech can save you time, while helping you create more accurate demand plans.

Table of Contents

What Is Demand Planning?

Demand planning is the process of creating a strategy for future operations, based on a forecast of expected demand. An essential component in creating a successful supply chain management process, orders can be produced and delivered in the most efficient way possible

Benefits of Demand Planning

Effective demand planning can help increase profits and customer satisfaction, driving store growth. Checkout some key benefits from effective demand planning, so that every online store is ready to meet demand:

  • Increased on time delivery
  • Reducing stock outs
  • Lower inventory costs
  • Bring down expediated shipping costs
  • Reduce dead stock
  • Bulk buy prices from suppliers

Effective planning can turn meeting demand from a reactive process, into a proactive process. This is where supply is ready for future events, rather than adjusting to the latest demand.

Demand Forecasting vs Demand Planning

Although similar, demand forecasting is an estimate of customer demand for products. While this a key step in the overall demand planning process, it comes before the creation of a strategy to meet customer demand.

Demand planners will use this statistical forecasting to dive deep into the operational planning and inventory management required to build an effective demand planning process.

Demand plans typically start with an accurate forecast using historical demand based data from a store. Other data is layered on top of the model, analyzing internal and external trends that may impact demand.


What Are 4 Essential Elements of Demand Planning?

No matter how extensive the forecasting, actual demand will always differ, changing the execution of a plan. To remain objective when building a demand plan, we have broken down four key questions to keep in sight:

  1. Which products will sell?
  2. How many orders of each product will come in?
  3. When will these orders come in?
  4. Where do the products need to be when the orders come in?

Related post: 10 Must-Haves For eCommerce Supply Chain Management

What Is the Future of Demand Planning?

The future of demand planning will benefit from big data and advanced analytics, as well as the automation and the continuous optimization of complex processes. Checkout some key trends that can help your business develop a forward-thinking demand planning process:

Predictive Analytics

Predictive analytics uses historical data, algorithms and machine learning to work out future results. While this form of predictive modelling has been around for decades, its use and effectiveness is growing due to three key factors:

  • Rapidly growing volumes of data
  • More affordable and powerful computers
  • Easy to use software for nontechnical users

In demand planning, predictive analytics can be used to analyze potentially thousands of internal and external variables that shape demand. Combined with other approaches mentioned here, predictive analytics can help build a detailed demand plan.

Integrated Closed Loop demand and supply planning

Closed loop manufacturing resource planning is a software system companies use for inventory control and production planning. With the increasing connectivity between different planning steps, closed loop planning can integrate demand and supply planning into a single process.

For example, inventory carrying costs can be reduced by moving away from fixed safety stocks. Combining expected demand with supply chain forecasts, stock levels will be unique for each reorder. This can also be combined with pricing decisions and product portfolio management, with sales and operations able to adapt profit and inventory simultaneously.

Internet of Things (IOT)

The internet of things is a giant-interconnected network of people and devices, all sharing and collecting data. Performing analysis of historical data while using the IOT can be a powerful tool. However, one of the real strengths in this interconnectivity is the ability to input real time fluctuations in demand and inventory levels.

With the number of connected devices increasing rapidly, this data can be used to inform your planning and decision-making moving forward when combined with other technology. Adapting forecasts to include data available from real time updates in the supply chain process, can create agile and accurate plans.


Applied AI

While demand planning is time intensive when performed manually, much of the process can be automated. Not only does this save time, it can also ensure more accurate results compared to tasks done manually.

Arguably, AI can be the technology that will have the biggest impact on demand planning moving forward. It can be easy to draw the link between AI and demand planning, with repeated number crunching and data analytics, cycle after cycle.

With AI, you can also track every part of the integrated supply chain. From tracking fluctuating demand for individual product lines, to when seasonal stock will run out. This allows businesses to create increasing accurate and flexible demand plans, while supply chains are becoming increasing complex.

Machine Learning

Machine learning models can also further reduce the workload in demand planning, while improving accuracy. These use an algorithm to understand the available data, analyzing it for any underlying patterns. Usually, better accuracy can be achieved as more data is available.

Machine learning algorithms automatically generate continuously optimized models using only the data fed to it. Combining this technology with other emerging data rich tools and sources can lead to continuous improvements in the demand planning process.

What Data to Use?

The reliability and effectiveness of next generation demand planning requires effective data management. While data availability is becoming less of an issue, selecting the correct data will impact the effectiveness of your plan.

Internal Data

The right internal data is always specific to each business. It can be tempting to use data based on total orders from your online business, however this will only let you analyze how many purchases were made.

Demand based data will allow you to analyze how many potential sales there could have been in a given period. Other sources of internal data that can allow you to build an accurate model include:

  • Seasonal Sales data
  • Out of stock rates
  • Inventory turnover
  • Product lifecycle
  • Production time
  • Dead stock
  • Competitive offerings timing of price changes
  • Delivery times

External Data

External trends have the potential to impact demand for a business more significantly. Factors to explore for building accurate models include:

  • Major world events
  • Purchasing habits of key customers
  • Market shifts
  • Weather
  • Sensor data
  • Social media trends

Always Meet Demand With Easyship

Effective demand planning can help every business prepare for busy periods, reducing out of stock stress, increasing customer satisfaction and increasing margins. Choosing demand planning software can save online stores time on the demand planning process, while creating more flexible and accurate plans.

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