Several times in the past I came across these questions. Why the forecast accuracy should be improved? What is the necessity in spending money towards improvement and implementation of some expensive demand planning software?
To know the significance of demand planning in Supply Chain management you need to know what a demand planning means. The most crucial aspect of any organization, be it the services or the manufacturing sector, is demand planning or sales forecasting. It is a business process that entails futuristic predictions of demand for products and services and calibrates production and distribution capabilities accordingly. If you are a part of manufacturing company then you will be responsible for estimating demands for the manufactured goods and work towards activities like supply of raw materials, production capacity, and distribution, etc. Being a part of service organization makes no difference, still you will be accountable for estimating the demand for its services and thereby gear up to service demand.
For any organization, demand planning plays a strategic role in planning for various products involving different business functions and requires timely data, precise processing of this data and agreement on joint business plans along the Supply Chain. Several software is available currently which helps in conducting effective demand planning. The most widely used is Microsoft Excel and several ERP products like Oracle, SAP and SCM products have demand planning functionality incorporated in their suite. So let’s figure out some of the important functionalities and features which may be useful for companies in demand planning.
1. Generation of statistical baselines
The base requirement to initiate the process is to have an accurate forecast on the products your company sells. Any profit-driven Supply Chain depends on precise forecasts to maintain customer satisfaction high and inventory low.
There are various models to choose from, each reflects different operational patterns shown by products and markets. These include linear models, seasonal models, multivariate linear and non linear models, Croston’s model, etc. It is a never ending list. This may look simple but to select the correct model for each of the product can be an intricate task without any shortcuts to take. Statistical forecasting models need to be repeatedly tested and refined. Based on the storage of data in the demand planning tool, statistical forecasting can be performed at various levels.
In some industries like semiconductor or high-tech, the product lifecycle is too short and fast changing trends impede the statistical forecast from being a direct input to the operational forecast. However, even in these industries statistical forecast do provide values by acting as a control measure in identifying exceptions in other forecast sources. It provides a scientific approach to understand how external factors impact demand. It also caters a way to forecast new products based on lifecycle profiles and characteristics, to identify prior products which are similar to the new ones
2. Driving Consensus Forecast
It is important that your demand planning tool support consensus planning features. In demand planning it is required to merge all the projections from different departments and experts, into one forecast that represents the prime revenue in the market place. The tool should be capable enough to intelligently blend the inputs from all the sources on top of statistically forecasted numbers.
Consensus forecast involves intervention by the planner based on facts and data and not bias or personality. Depending on past measures of accuracy collected from various sources and time horizons, the final judgement is done. Organizations should determine if they are on target to meet the revenue plan by focusing on the analytical views which can be broken down by any attributes. If not on target, then the divisions that are not meeting their share of the financial plan should be notified. The main advantage of the analytics is that it provides a virtual view to the management from several angels to determine trends and problems.
3. Inventory Goals
For an organization, expected customer demands are not the only source of demands. Having backup inventories ensure efficient running of Supply Chain operations and protection against uncertainty. A company with larger number of inventory has lesser risk to their revenue plan and can be more efficient in operations. However, higher inventory can result in the extension to Return-on-Assets and can reduce profit drastically on becoming discontinued or obsolete.
So, for a company apart from the value to inventory, considerable measure should be taken to predict demand and supply uncertainty in order to identify how much inventory should be in buffer. For a profit-driven company, it is necessary to understand that each market segment is different and hence the buffer stock policies vary with each situation. It is very important to have a proper inventory strategy which involves effective distribution of products, like some products may be held as components and assembled to order, later.
In the end, the consensus forecast and the project revenue plan are blended to the inventory plan to get a complete picture of the demand and inventory targets. The operation planning team is then responsible for identifying which demands and inventory targets can be met, and the cost related in doing it. Adapting to this process helps in estimating the profitability accurately.
4. An effective Demand Planning tool
An effective demand planning tool should have the ability to filter and cut down bulk data within a short interval of time, and archive old data for referral without affecting the product functionality. It should be able to translate demand of various market segments on the basis of business sales and revenue plan through the use of attributes. The selected tool should be able to determine the average selling price of the mix products in market and predict expected revenue out of it. A tool built on data warehousing platform can project data from different dimensions and makes it possible to have statistical forecasting at various levels.
To manage the front-end of a Supply Chain with a profit oriented mind frame requires a business process and system, diverse from the traditional demand planning systems. The first step to any profit-driven Supply Chain planning cycle is demand planning and forecasting. Any error during this phase can have an adverse effect which only keeps expanding. This is popularly known as the Bullwhip effect in Supply Chain.
So, the key to a profit-driven demand planning process lies in the ability to plan revenues by product lines and customers, effective management of inventory, overseeing progress to goals and fair selection of demand planning tool.