Unprecedented supply chain interruptions have caused havoc over the past two years. Demand planning and logistics chain experts have had to rethink their models and take new factors into account due to COVID-19, port backlogs, or trade disputes. Planning and predicting demand, already demanding processes, has become more challenging as a result of this.

Many people interchange the phrases "demand forecasting" and "demand planning." However, knowing the differences and using them to develop solid management of supply chains and S&OP strategies are necessary to successfully meet today's problems.

Demand Forecasting

A prediction is what demand forecasting is. Demand forecasting can now be based on historical and current data that represent consumer behavior and market trends. Demand planners can create a forecast that accurately predicts demand by combining data from sales, marketing, and consumer feedback.

In order to generate a trustworthy consensus forecast that end users can rely on, demand forecasting can also use other real-time resources like weather, load factor, and other variables. These projections can be made for the next week, monthly, quarter, or even entire year, and each range has a known margin of error.

• It is carried out using both historical and current data.
• It involves determining what is most likely to occur.
• It makes a consensus forecast that users may use a variety of variables.

 

Demand Planning

Demand Planning is a process for estimating what is probable, whereas planning is the implementation process for making it happen. Demand planning uses the forecast to ensure that there is capacity, raw material purchases are placed, levels of inventory are optimized for the anticipated production rates, and deliveries of materials & finished goods may be made in the appropriate order.

• It is carried out depending on the results of the prediction
• It entails organizing the steps necessary to carry out the forecasting's prediction.
• It uses the forecast to make sure that capacity, levels of inventory, and logistics are all optimum.

 

Demand Planning inside the Distribution Chain of the Future

 

Supply chain & demand planning is going digital, just like many company requirements. The supply chain can now adjust and update estimates in real-time, enabling inventories to run more efficiently without underestimating demand, thanks to advancements in machine learning technologies.

 

Recognizing how to develop new architectures and putting machine learning and artificial intelligence programs into practice that can help optimize a lean, agile, and information approach will open up new opportunities for supply chain executives to reduce operating costs, increase revenue, and provide a stronger competitive edge.

 

Demand planning may be done more quickly with a supply chain that is more integrated

 

Demand planning vs. Demand forecasting

The technique of estimating demand based on past data and patterns is known as demand planning as opposed to demand forecasting. What makes forecasting so crucial? Demand planning software serves as the foundation for forecasting, but it also goes beyond that to examine a wide range of additional factors that are crucial to producing a reliable projection. Distribution, seasonality, the location of storage and sale of the product, as well as outside variables like a global epidemic, are just a few of the aspects to consider.

Various Demands

Demand planning and forecasting are not straightforward, linear tasks. Demand planning has to take a variety of demands into account. These consist of:

• Individual Demand

Customer demand for finished items constitutes independent demand. This is triggered by a customer's single or several orders, which in the modern world can be Business - to - business, Business to customer, or a combination of both.

• Demand Dependent

The components and raw materials needed to make the completed goods make up dependent demand. Planning for demand may be impacted in this situation by additional factors such as the availability of goods and in transit.

• Service and Parts Demand

Equipment for manufacturing needs replacement parts, supplies, and consumables. Additionally, as capacity is allocated according to a demand prediction and plan, the need for these commodities must also be taken into account.

Benefits of Demand Forecasting and Planning

There are a number of advantages that businesses will experience when demand forecasting & planning are coordinated. These consist of:

• Improved financial management
  Companies may enhance cash flow, optimize inventories, and satisfy consumer expectations on something like a just-in-time (JIT) basis with precise predictions and data-driven demand planning.

• Improved operational planning
  Companies can better manage their operations with the aid of accurate demand forecasts and well-executed demand planning. A strong demand plan helps with labor utilization, maintenance planning, and capacity planning.

• Improved Marketing Techniques
  Demand planners give marketing and selling teams better knowledge and power to target sales that fit the demand curve by forecasting precisely and organizing with data-driven insights.

Supply and Demand Planning tool from Plex Demand Caster provides the most competitive and economical way to handle this.

 

Demand Caster provides agile software that enables complete supply chain visibility so that planners may effectively carry out their duties. They can then modify their inventory plans to handle any demand challenges thanks to trend analysis and other data.

 

Users can use "what-if" scenario planning to be ready for any variation by using real-time data to predict any level of aggregation. Strategic plans can be created with tactical fallbacks for interruption or modification that can be included in the new prediction.

 

AI's advantages for demand planning

 

Supply chain demand planning's primary objective is to maintain the appropriate level of inventory to satisfy customer forecasted demand without experiencing shortages or wasting money on manufacturing and holding surplus inventory. Data gathering is a crucial component in making sure of this. Real-time data access is becoming more widespread thanks to advanced machine learning, which greatly increases prediction accuracy and, in turn, the efficiency of demand planning. Additionally, it offers collaboration capabilities that let planners communicate more effectively and respond more swiftly to changes in demand and supply.

 

Advantages of Demand Planning and Forecasting

 

The company can gain various advantages when forecast and planning are integrated. Here, we go through a few advantages of planning and anticipating demand.

• Improved financial management

Companies can increase cash flow, optimize inventory, and improve customer experience with the aid of accurate forecasting & data-driven planning.

• Better operational planning

Businesses should base their operations planning on precise projections and well-executed planning. It is simpler to accurately schedule maintenance and optimize manpower, capacity, and efficiency.

• Improved Marketing Techniques

It is simpler to create superior marketing plans when data-driven insights are available. Marketing departments can use the data to match the quantity demanded and achieve target sales.