6 Reasons to Move Away from Demand Forecasting in Spreadsheets

Even under the best market conditions, companies must manage fluctuating demand for their products. Failing to keep up can result in lost revenue as frustrated customers instead buy from competitors, while too much inventory can drive up overhead costs.

It’s all but impossible to manage stock levels if you can’t accurately forecast demand. And using spreadsheets for demand forecasting will only lead to more errors and wasted time for your finance and operations organizations.

The Problems with Using Spreadsheets to Forecast Demand

If your organization is in start-up mode, spreadsheets can be an efficient, low-cost tool for demand forecasting. But once your number of SKUs creep past 1,000, spreadsheet limitations become problematic. Spreadsheets can’t handle that scale and still provide a holistic picture of the business. They don’t integrate well with ERP and sales source systems, and collaboration and security are critical weaknesses. In other words, spreadsheets prevent efficient supply chain management and demand planning.

Here are six reasons why you should move away from spreadsheets for demand forecasting:

1. Data Integrity Issues. Demand forecasting requires collecting historical sales data from ERP source systems, which is an incredibly time-consuming process. Creating a comprehensive picture from that disparate data is an even bigger challenge. If your spreadsheets exist in isolation from your data sources, that means you’ll spend a lot more time on consolidation. Plus, every time someone manually manipulates the data in a spreadsheet, there is an increased chance for human error.
2. Poor Collaboration. The demand forecasting process requires finance and operations managers to collaborate with other parts of the organization in order to produce an accurate forecast. Spreadsheets are not well suited for collaboration and are not designed for multiple users with complex requirements. The more people that interact with the spreadsheet, the greater the potential for those data integrity issues mentioned above. And once mistakes invade the data, your demand forecasts are compromised.
3. Lack of Version Control. Demand forecasts in spreadsheets are manually prepared, shared and collected, which leads to version control issues. Multiple versions make it challenging to pinpoint the latest changes to a spreadsheet and put additional strain on the team responsible for forecasting demand. Spreadsheets are also vulnerable to manipulation due to inherent lack of controls when it comes down to restriction of access to data.
4. Not Scalable. Unfortunately, spreadsheets can’t scale if your company is growing rapidly. You would need ample time and patience and strict access controls to be able to rebuild demand planning and forecasting models manually every time a core assumption changes, for example a sudden uptick in sales orders. A fast-growing business environment needs you to focus your time on analysis, not manually maintaining the demand forecast model itself. While spreadsheets are good tools for forecasting demand for a limited number of SKUs, it is less effective when you need to evaluate business expansion plans, like new product launches and regional diversification. In these scenarios, the existing spreadsheet-based demand forecasting process will struggle to handle the volume of data and running what-if scenarios for quick decision-making.
5. Lack of Scenario Analysis. Another way that spreadsheets can undermine credibility is by leaving you with no clear answers to business-critical “what-if’’ questions. What if a key supplier suddenly runs into unexpected problems and has to push back delivery dates? An agile supply chain plan enables you to plan for the unexpected. But that’s simply not possible when you rely solely on spreadsheets as your primary planning tool. A spreadsheet can perform only limited analysis, and relying on it for scenario planning requires a high degree of sophistication. Any significant change to the model will likely require substantial adjustments that are time-consuming and error-prone.
6. Difficult to Incorporate Historical Data. Predictive demand forecasting matches historical data with industry statistical models, allowing you to develop an initial demand plan that can be quickly altered as needed. With spreadsheets, it is very difficult to retrieve historical data from multiple source systems and analyze it using forecasting methods to predict performance. The manual nature of copying and pasting data and dealing with a large number of spreadsheets makes it impossible to adjust the demand forecast models to reflect changing assumptions.

Accurately Forecast Demand with NetSuite

Accurate demand forecasting drives more accurate downstream demand plans that boost profitability and keep customers satisfied. With NetSuite ERP and Planning and Budgeting, companies gain real-time access to financial and operational data without needing to transfer data manually. They can use predictive demand forecasting capabilities for seasonality and intermittent demand, to evaluate multiple demand scenarios faster so they can proactively act on changing market conditions and assumptions. This results in a higher level of data integrity, improved collaboration and better controls across departments all within a single solution.

NetSuite Planning and Budgeting lets you plan at the customer level, location level, and any level within the item hierarchy such as SKU, items, product lines and more. That data is automatically available in NetSuite, and can be used to create a demand plan and generate purchase orders. Demand forecasting helps your organization make smarter buying and stocking decisions. Planning your demand based on sales data and market research will help your business stay ahead of your competition and grow.

This article was written by Rami Ali and originally posted on netsuite.com