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The Demand Planning Guide: Integrate Workflow Automation into Excel Spreadsheets and Manual Tasks to Solve These 4 Retail Forecasting Challenges



Data is at the heart of day-to-day tasks undertaken by operations and Demand planning teams. Despite their adoption of the best ERP and SaaS tools, forecasting challenges due to low-quality data or non-availability of updated data still persist. That said, only when processes related to data capture are accurate, it can lead to timely and wise decisions on pricing, running promotions, and doing markdowns when necessary. Hence, it has become a priority for planners to ensure all data related tasks such as tracking, shifting or making changes in data are not entirely manual.

Manual Excel Spreadsheet Data Changes that Cause Errors and Slow Down Forecasting:

  • Changes in the channels of sales
  • Changes in pricing at the point of sales
  • Changes in inventory at the point of sale
  • Changes made by competitors
  • Changes in vendor pricing
  • Changes in SKUs ordered by distributors

The ability to track data accurately holds the key to better demand forecasting at the SKU level. Planners can rapidly upscale their forecasting abilities by using Bots and automating workflows built around excel spreadsheets used by different teams. Our simple yet powerful automations when integrated into your systems, will lead to better allocation of inventory across the entire business from the start. It will enable planners to significantly reduce occurrences of empty shelfs, out-of-stock, disappointed customers, and pricing loss mark-downs.

Let’s take a look at the challenges that can be solved by our Document and Data Automation Solutions


forecasting spreadsheet

Enable planners to significantly reduce occurrences of out-of-stock scenarios, overstocking, disappointed customers, and miscalculated pricing markdowns.

Let’s take a look at the challenges that can be solved by our ‘MailBox Automation, Document AI, and Spreadsheet Integration Solution.

Challenge No. 1: Use of Multiple Excel Sheets for Forecasting and Budgeting

Solution: Build a single source of the truth using automated workflows around the use of excel spreadsheets.

Typically, large manufacturers depend on a team of 5 to 6 people to dedicatedly handle data and multiple spreadsheets for budgeting and forecasting. These tasks require data management related to raw materials, resources, freight, shipping, and other components that have to be input/added into multiple spreadsheets used for financial planning. It implies relying on an arduous manual process that is essential for managing costs and match the supply with demand.

Manual teams rely on 12-20 different spreadsheets to do tasks such as the following:

  • Model stocking and markdown decisions
  • Lay out scenario planning models
  • Calculate impact of price optimization
  • Manage daily incoming sales order data
  • Calculate lead time between order to sales
  • Calculate and maintain total inventory value
  • Allocate SKUs into different categories
  • Calculate inventory turnover ratio
  • Maintain data on replenishment and reordering
  • Maintain data on inventory allocation

Hence, what manufacturing business leaders need is a solution that offers data integrations, pre-built templates and a user-friendly Excel interface.

Discover more about the benefits of integrating multiple spreadsheet dependent processes:

Go from multiple excel-based planning to automated workflows

Because the excel sheet is widely used by every planning team, an ideal Demand Planning Solution must integrate data inputs from the strategic day-to-day excel spreadsheets maintained by Financial, Sales, and Operations teams. When data input is seamless, the leaders will get an automated analysis of data related to demand planning, supply chain planning and resource and material planning.

Automate management of supply chain changes

If an excel sheet is used to manage data related to freight, shipping, inventory and other components, workflow automation can be used to scale up efficiencies. Imagine a team of employees manually receiving change requests in the form of emails, attachment documents, calls, etc. For example, any information related to a change in freight forwarding cost can be auto captured using Mailbox & Document Automation. The captured data can then be transferred downstream to the planning software used by other stakeholders.

Automate processes related to resources and bill of materials

In case the re-order point calculations are done using excel spreadsheets, planners would then have to gather data related to lead times between inventory orders to shipping arrivals manually. Workflow automation on the other hand ensures data related to production schedules, labor requirements, etc., are automatically updated in all the reports. Any change made to the production schedules and raw materials would thereby automatically update predictions on future order deliveries.

Challenge No. 2: Absence of Data that Can Foresee a Cascading Effect in SKU Portfolios

Solution: Automate spreadsheet data processes to see how changes made in one SKU category can have a downstream impact on several others.

The cascading effect in product portfolios impacting demand planning refers to how changes in demand for one product can affect the demand for other related products within a manufacturer’s portfolio. Here’s an example to illustrate this:

Let’s consider a manufacturer that produces smartphones and related accessories, such as cases and screen protectors. The manufacturer’s demand planning team needs to consider the cascading effect when planning for the different products in their portfolio.


Due to the launch of a highly anticipated smartphone model, the manufacturer forecasts a significant increase in the demand for smartphones during the upcoming quarter. However, they might also experience a cascading effect on the demand for related accessories.

Impacts on Demand Planning:

  1. Cases: With the increased demand for the new smartphone model, there will likely be a corresponding increase in the demand for protective cases specifically designed for that model. The demand planning team needs to analyze historical data and market trends to anticipate the potential increase and adjust their production plans for cases accordingly. Failing to meet the increased demands for cases could lead to stockouts, dissatisfied customers, and missed sales opportunities.
  2. Screen Protectors: Similarly, the launch of a new smartphone may generate a higher demand for screen protectors due to customers wanting to preserve the display of their new device. The demand planning team must factor in this increased demand for screen protectors, considering the sales forecasts and trends. They need to ensure an optimal level of screen protector inventory to meet the demand while avoiding excess inventory.
  3. Other Accessories: The impact of the cascading effect can extend to other related accessories as well. For example, the increased demand for smartphones may also lead to a higher demand for charging cables, wireless chargers, or Bluetooth headphones. The demand planning team needs to identify the correlations between the different products and adjust their production and inventory plans accordingly.

Here’s how our solution can help:

Our Workflow Automation Consultation can help you to identify areas that you can easily enhance decision making and performance by integrating data from all the different tools of communication and data storage that are already in use – Microsoft Team Chat, Folders, Spreadsheets, CRMs, ERP, etc.

Identify new areas of data automations in your supply chain processes to significantly improve the accurate capture of data that can alert all concerned business leaders about the cascading effect caused by any change in raw materials, resources, timelines, etc.

Ensure timely capture and reporting of the cascading effect of data so that demand planning teams can ensure that supply aligns with the overall demand within their product portfolio.

Build automated processes based on relationships and interdependencies between the different products and factor in market trends, customer preferences, and historical data to generate accurate forecasts.

Challenge No. 3: Insufficient Data to Perform Statistical Forecasting

Solution: Automate spreadsheet entries to significantly speed-up the effectivity of statistical forecasting to predict future demand.

Manufacturers undertake these statistical forecasting frameworks to predict future demand and make informed decisions.

  1. Time Series Forecasting: This method involves analyzing historical data to identify patterns and trends and make predictions based on those patterns. Time series forecasting techniques include moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models.
  2. Demand Planning Forecasting: Manufacturers use demand planning techniques to anticipate future customer demand based on historical data, market trends, and insights from sales and marketing teams. Demand planning forecasting may incorporate statistical models, market research, and collaboration with customers and suppliers to estimate future demand accurately.
  3. Sales Forecasting: Sales forecasting focuses on predicting future sales volumes based on historical sales data, market trends, customer behavior, and other relevant factors. Manufacturers can use statistical models, historical sales trends, and input from sales teams to forecast future sales accurately.
  4. New Product Forecasting: When introducing a new product to the market, manufacturers need to estimate its potential demand. New product forecasting involves analyzing market research, consumer trends, competitive analysis, and customer feedback to forecast sales and adoption rates for the new product.
  5. Market Demand Forecasting: Manufacturers monitor and analyze market trends, economic indicators, competitor actions, and customer behavior to forecast the overall market demand for their products. This type of forecasting helps manufacturers understand the broader market dynamics and adjust their production plans and strategies accordingly.
  6. Seasonal Demand Forecasting: Many manufacturing industries experience seasonal fluctuations in demand, such as for holiday or weather-related products. Seasonal demand forecasting involves identifying and analyzing historical patterns and trends related to seasonal variations in customer demand. Manufacturers can then use statistical models or other techniques to forecast future demand during specific seasons or periods.
  7. Collaborative Forecasting: Collaborative forecasting involves sharing information and insights with customers, suppliers, and other supply chain partners to generate a more accurate demand forecast. This method leverages inputs from various stakeholders to improve the accuracy and reliability of the forecast.

Manufacturers may choose to employ one or a combination of these forecasting methods, depending on their industry, available data, and specific business requirements. The goal is to generate reliable and accurate forecasts that support effective production planning, inventory management, and supply chain coordination.

Here are some easy to trigger workflows enabled by sales data integration:

Use our No-Code Consulting Practice to train business users to sync information between the company’s eCommerce or order management with inventory data.

Easily remove duplicate processes where the same data has to entered in multiple systems to create or orchestrate information that is always in sync. For example, have the pricing team, sales team and finance team be in sync about the same information created by our no-code integration.

Build customized dataflows and dashboards by connecting a wide range of apps that fit together, including hub apps (ERPs like NetSuite and SAP), CPQ tools (such as Coupa), eCommerce platforms (like Magento and Shopify), and CRMs (such as Salesforce).

Challenge No. 4: Absence of Real-Time Data for Demand Planning

Solution: Automate excel-driven demand planning with agile processes and close to real-time data inputs from different teams.

An example of real-time data used for manufacturing demand planning is the integration of real-time sales data from point-of-sale (POS) systems. By connecting the POS systems with the manufacturing demand planning system, manufacturers can capture sales data as soon as a customer makes a purchase.

For instance, let’s consider a clothing manufacturer. They receive real-time sales data from various retail stores, both owned and third-party retailers, through their connected POS systems. This data includes detailed information on which products were sold, quantities, and the location of the sale.

Planners can then leverage this real-time sales data to perform the following:

  1. Generate Accurate Demand Forecasts: By capturing real-time sales data, the manufacturer can analyze current demand patterns and identify any sudden spikes or fluctuations in sales. This allows them to generate more accurate demand forecasts for their products at a granular level, such as by SKU, store, or geographical region.
  2. Plan Production and Inventory: With real-time sales data, the manufacturer can adjust their production plans and inventory levels to meet the changing demand. For example, if a particular product is experiencing higher-than-expected sales, the manufacturer can increase its production or expedite the replenishment process to avoid stockouts.
  3. Replenishment and Supply Chain Optimization: Real-time sales data can also aid in optimizing the replenishment process and supply chain operations. The manufacturer can use this data to identify the best shipping routes and modes of transportation, adjust production schedules, and coordinate with suppliers to ensure timely delivery of raw materials or finished products.
  4. Promotional Planning: Real-time sales data can help manufacturers track the effectiveness of marketing campaigns and promotions. By analyzing the impact of promotions on sales in real-time, they can make informed decisions about the timing and duration of future promotions, as well as assess the need for additional production capacity or inventory.

By leveraging real-time sales data in manufacturing demand planning, manufacturers can make more accurate forecasts, optimize production and inventory, improve supply chain efficiency, and respond quickly to changing customer demands.

Here’s how you can get real-time data the way business leadership demands using pre-built rules, business logic and calculations:

  • Automate manual data inputting to produce income statements by product line or SKUs
  • Automate all manual data processes related to matching and gathering of all data related to transaction history related a specific SKU
  • Eliminate time spent on validating unreliable data by enabling a cloud-based enterprise application that lets field sales to provide real-time data
  • Customize reporting templates for net income, actual expenses, balance sheets, income statements

Join us for an interactive session to identify the areas that are the best fit for No-Code Automation involving Business Users.

Our experts will show you how to enable self-service analytics using existing data from all the tools and platforms used by your domain teams. 

Enable planners to significantly reduce occurrences of out-of-stock scenarios, overstocking, disappointed customers, and miscalculated pricing markdowns.

Let’s take a look at the challenges that can be solved by our ‘MailBox Automation, Document AI, and Spreadsheet Integration Solution.

Automate data entries from multiple sources, software, and APIs to automate retail demand forecasting.

  • Build automated workflows around the use of excel spreadsheets.
  • Foresee the cascading effect of one SKU category on others
  • Automate excel-driven demand planning processes for retail SKUs

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