Even though markets are in good condition, businesses must manage the varying needs for their products. Failing to keep the inventory in check will result in revenue loss as disappointed customers will start to buy from competitors and also, have to pay more for overhead.
It will be difficult to control stock levels if you are unable to foresee demand correctly. Depending on spreadsheets for predicting demand will result in more time consumption and errors for your business and operations.
The Troubles with using Spreadsheet for Demand Forecasting
For organizations that are in start-up mode, the spreadsheet will work as an excellent and inexpensive tool for forecasting demand. But when your amount of Stock Keeping Units increases over 1000, the spreadsheet has many limitations which will prevent it from handling that scale and yet producing a comprehensive view of the business. They don't merge with ERP and sales source systems, and alliances and reliability are critical weaknesses. In short, spreadsheets preclude effective supply chain management and demand planning. Spreadsheets are human resource induced tool hence automation may not be its scope.
Reasons to step away from spreadsheets for demand forecasting.
Data Integrity Issues: Demand forecasting calls for gathering previous sales data from ERP source systems, which is a tedious task. Understanding data completely is a backbreaking challenge. If one’s spreadsheet stands separated from the data sources it will result in consuming more time on consolidation. In addition to that, if anyone manually distorts the data in a spreadsheet, this may heighten the chances of human errors.
Poor Collaboration: The demand forecasting procedure needs financial and functional heads to cooperate with various parts of the firm to provide an exact forecast. Spreadsheets are not convenient for collaboration and are not built for many users with problematic demands. As more people use the spreadsheet, there is a greater chance that the data integrity problems mentioned above will happen. Every time there are mistakes in the data, the demand forecasting is pushed off.
Lack of Version Control: In spreadsheets, demand forecasts are produced, transferred, and gathered manually resulting in version control issues. Numerous versions make it demanding to spot the new changes in a spreadsheet and give extra pressure on the group in charge of demand forecasting. Spreadsheets are easily prone to manipulation as it lacks control in restricting access to data.
Not Scalable: Spreadsheets are unable to scale when a company develops quickly. One would require enough, patience and proper access controls to reconstruct demand planning and forecasting models physically each time a central presumption alters. A rapidly-developing business habitat requires one to prioritize time on analysis, not manually sustaining the demand forecast model itself. While spreadsheets are better tools for forecasting demand for a minimum amount of Stock Keeping Units, it is less efficient when one requires to analyze business development plans, like the latest product launches and regional diversification. In such cases, the prevailing spreadsheet-based demand forecasting process will strive to control the amount of data and in making quick decisions.
Lack of Scenario Analysis: Another means that spreadsheets compromises to answer business-critical “what-if’’ questions. What if a core distributor rapidly runs into unanticipated troubles and has to decline delivery dates? An active supply chain plan allows one to organize for the unexpected. But that’s impossible when one depends completely on spreadsheets as your chief planning tool. A spreadsheet can operate only with minimum evaluation, and depending on it for scenario organizing needs a large degree of experience. Any major alteration to the model will need considerable rearrangements that are tedious and are highly vulnerable to error.
Difficult to Incorporate Historical Data: Predictive demand forecasting suits former data with industry statistical models, permitting one to create an initial demand plan that can rapidly change as required. Using spreadsheets, it is challenging to recover old data from various source systems and evaluate it with forecasting ways to foretell performance. The physical task of copying and pasting data and operating with a huge amount of spreadsheets makes it impossible to rearrange the demand forecast models to reflect altering presumptions.
Accurately Forecast Demand with NetSuite
Exact demand forecasting operates more precise downstream demand schemes that improve profitability and make customers gratified. Using NetSuite ERP and Planning and Budgeting, enterprises achieve instantaneous access to economical and functional data without requiring to share data manually. They are able to utilize predictive demand forecasting capabilities for periodic and irregular demand, to analyze numerous demand cases quickly so they can dynamically act on varying market scenarios and presumptions. This leads to a greater level of data unity, enhanced collaboration and improved controls over departments all inside a single solution.
Both NetSuite Planning and Budgeting allows to organize at the consumer level, regional level, and any level inside the component hierarchy such as Stock Keeping Units, items, product lines and more. That data is instinctively accessible in NetSuite and is able to make a demand scheme and initiate purchase orders. Demand forecasting assists an organization in making efficient purchasing and stocking choices. organizing demand grounded on sales data and market research enables businesses to stay efficient in competition and enlarge.
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