Demand planning assignment help

Introduction

Demand planning is the process of forecasting demand for your product or service to make supply chain planning more efficient. It’s an important step in supply chain planning and should be considered essential.. Supply chain execution and supply chain planning are components of a company’s supply chain management. The planning side of SCM typically starts with demand planning, which involves creating a statistically based forecast that considers factors such as inventory and marketing and specifies where to distribute your products. The demand plan feeds into the next steps of SCM, including material requirements planning (MRP) and production planning. After manufacturing, the product enters the execution stages of SCM, including warehouse & transport.

Sales managers, marketers and manufacturing, finance and production managers are typical members of a demand planning team. Some companies have dedicated staff with titles like demand planner and demand planning analyst. The demand planner usually leads the team.

Importance of demand planning

Keeping up with the demand for a product is critical, because failing to do so can result in lost revenue for the product or, even worse, lost customers. One the main goals of demand planning is to have just the right amount of inventory to meet customer demand without incurring shortages or wasting money on making and storing surplus inventory.

Demand planning can help avoid the “bullwhip effect,” which refers to dips in demand in product distribution whose causes are not well understand. More accurate demand forecasts and better communication among supply chain participants can help to minimize the bullwhip effect.

Elements of demand planning

Demand planning is a complex process that typically includes the following elements:

• data collection from internal and external sources on the factors known to predict or influence demand;

• statistical analysis of sales, inventory and other data;

• modeling the data to predict future demand; and

• collaboration with suppliers, manufacturers, salespeople and other stakeholders to gather information on events that could affect demand, such as promotions and production delays.

The above processes lead to the creation of the following:

• demand forecast, a prediction of demand based on the data model and analysis, followed by a

• demand plan that outlines the amount, type and location of inventory needed to satisfy the forecasted demand.

Beyond these basic functions, some demand planning strategies incorporate the following processes, at least in part:

• product lifecycle management (PLM) to analyze factors that contribute to the demand for different products; and

• replenishment planning to ensure that inventory is in the right locations in quantities to meet the demand.

Key steps in demand planning

Demand planning starts with preparing a statistical forecast of how much inventory is needed. To do this, a planning team is usually assembled from the sales and marketing departments and from operational departments, including finance, production and procurement. There may be a dedicated demand planning analyst to provide expertise in data management and statistics and someone who can act as a liaison to the IT organization to address technical aspects of the software, such as forecasting algorithms, data reporting and integration.

The team starts by reviewing the available information, such as the sales forecast, historical sales data, market research and surveys. This typically involves collecting point-of-sale (POS) terminal data and analyzing it with tools in demand planning software. These may be standalone or part of an SCM or ERP software suite. The data warehouse called a demand signal repository stores data like customer purchases, inventory levels, and loyalty program information. This type of data collects everything you need to make better business decisions.

Inventory data can help put sales data in proper perspective by, for example, identifying sales lost due to inventory stock outs, which point to a higher demand than indicated by sales alone. Using analytics to find such relationships between sales and inventory can ultimately improve forecast accuracy.

The demand planning team then agrees on a forecasting model it believes will be the most effective at predicting demand. Data-driven modeling & forecasting is a powerful strategy that can be used to predict the future value of anything. The team can take in new data as it comes in, update variables/findings, and revise their model if necessary.

Demand planning is conducted early on in S&OP, which aligns supply (sales) with demand (supply) through coordination with production planning..

PLM influences demand planning by serving as a source of detailed product information. Conversely, demand planning plays a role in PLM. For example, companies can use demand planning to predict the demand for a proposed product by comparing it to similar products and seeing how certain features might affect demand.

Demand planning vs. demand forecasting

Demand planning has much in common with demand forecasting, though most experts consider demand planning to go beyond statistical forecasting to include planning the scope of the inventory.

Demand shaping is more about changing the perception of purchase through various arguments that are normally neglected. There are many others just like it but demand shaping is one that sets market right on its feet.

Making matters more complicated, it could be argued that demand planning is a bit of a misnomer, as most of the critical planning occurs in later processes, such as S&OP, MRP and production planning.

Future of demand planning

Artificial intelligence is helping to improve forecast accuracy and make information available much faster and more accurately. Software development and analytics companies like IBM, Salesforce and others are using this technology to better anticipate consumer demand, leading to quick response times.

The era of big data and artificial intelligence (AI) has reached a new era. AI applications are becoming more and more powerful. They can help organizations to solve problems better and faster, to be more effective in their work, and to deliver value for customers.

The most interesting trends in the field of Artificial Intelligence (AI) are driving a revolution in the world of copywriting. These writing assistants can understand the structure of language and create original, creative content in no time. They can produce text in a variety of formats such as blog posts, social media posts, articles, emails & more at a fraction of the cost. The best part is that you don’t Meanwhile, cloud-based collaboration tools and mobile devices are enhancing the ability of demand planners to share information and to react more quickly to changes in supply and demand.

The more a business can do with data and IP, the better off they’ll be. That’s the premise of H-Simplicity, a software platform focused on simplifying complex These broader trends lend new support to a longstanding effort to build what some vendors and industry analysts call the demand-driven supply chain, in which supply is more responsive to actual consumer demand and not primarily the product of an educated guess.

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