The importance of demand forecasting is clear. Robust forecasting improves critical KPIs like customer service levels, inventory turns, and cash. 

However, demand planning is only as effective as the data informing it. Demand forecasters may find the results less trustworthy and reliable if the content fed into a forecasting system has errors or duplicate records. Creating and adhering to a thorough information collection and processing strategy prevents such outcomes.

Decide Which Data to Use

The first step is determining which data the company will use for its demand planning. The information collected in a point-of-sale system could be valuable for highlighting sales patterns, such as which times of the year specific products are most popular and what other things people typically buy at the same time. A practical approach for companies with numerous retail outlets is to gather data showing which stores have the most robust or slowest sales.

“Inventory tools give a broader picture by showing how stock levels change over time”

Alternatively, inventory tools give a broader picture by showing how stock levels change over time. Seeing that historical context can help decision-makers determine how long upswings and downturns might last, whether these events previously occurred and what caused them.

A related question is whether those working on data quality within the organization know the location of the information identified as worth using. Many companies still maintain rigid silos that create challenges for collecting and using information across departments or teams.

Establish a Data Quality Baseline

What’s the current state of the company’s data for demand planning? A data quality baseline answers that question. People must start by identifying the critical data elements (CDE). These collectively represent the information that will shape leaders’ future decisions.

Examples of CDEs in demand planning include:

  • Supplier and customer names
  • Order quantities and dates
  • Restock frequency
  • Merchandise prices
  • Average order fulfillment timelines
  • Dates associated with short-term promotions
  • Most and least-popular product names and descriptions
  • Inventory management system reports
  • Distributor names and locations

The next step is to establish data quality indicators with input from those who understand and value the importance of demand forecasting in modern businesses. We should typically measure the following:

  • Timeliness
  • Uniqueness
  • Accuracy
  • Consistency
  • Completeness
  • Validity

They often rely on specialized tools to show data quality gaps and begin developing improvement plans. However, it’s also important to discuss challenges experienced by the people who collect and use it daily in their roles. They’ll likely have valuable input for changes that might have been overlooked.

Understand Data Governance Needs

Data governance encompasses keeping information usable, secure and available while retaining its high quality. Maintaining it is a team effort of ongoing collaboration to create and uphold standards. People on the data governance team will also help establish organizational norms by training employees to handle them and reduce the chance of errors.

Data governance policies will differ in an organization depending on the type of information used for demand planning. Anything containing payment details or personal info must be treated with more care.

“Many companies use third-party service providers to meet their data-handling needs”

Many companies use third-party service providers to meet some of their data-handling needs. In such cases, data governance plans must include steps to take so those outside businesses don’t compromise quality.

Documentation is also a major part of data governance. Keeping an ongoing record of the data source, location and associated security protections helps organizations use the information and address oversights.

It’s becoming more common for companies to collect data with Internet of Things (IoT) sensors. This gives a more detailed view of what’s happening with the information. Although confirming data sources can initially be time-intensive, the increased analysis opportunities are worthwhile. Estimates indicate the IoT sensor market will experience 24.9% growth in 2027, suggesting decision-makers are interested in using them.

Create and Maintain Data Preparation and Use Processes

Those overseeing data quality and usage within the organization must develop a preparation process everyone can use before feeding the information into platforms for further analysis.

For example, people must check the data for anything that could skew the results. Under- or overestimating demand can add to the organization’s costs, and mistakes often cause these outcomes. Thorough preparation requires looking for duplicate records, misspelled product or customer names, and any information in the wrong format. All those things could result in miscalculations or data not being included in an analysis.

The resultant process must be well-documented and easy for others to follow. Those qualities will be instrumental in getting usable, consistent results within the organization.

Next, people must make a framework for how people within the organization can and should use the data for demand planning. Which tools will they use? Must leaders invest in automated solutions or other products to support the process? Which employees will be directly involved in collecting or using the information? Getting feedback from those parties before and after making the data usage framework should optimize outcomes.

Teach the Importance of Demand Forecasting to Employees

Once the responsible parties design the processes for preparing and using data, they must communicate and teach it to all others handling the information. When all relevant employees understand the importance of demand forecasting, they’ll play important roles in upholding the requirements.

Allow plenty of time for people to get used to new tools or processes. Encourage them to give feedback about everything new and provide insights about further improvements.

Some organizations still use spreadsheets to track activities across the global supply chain. Lasting change will take longer to enact in such companies, and people may feel overwhelmed initially. However, most can adapt to new processes if their managers are patient.

“Employees who understand how to maintain high data quality will feel empowered”

Discuss how seriously the organization takes the importance of demand forecasting and explain why. Employees who understand how to maintain high data quality will feel more motivated and empowered.

Leaders should also be open to hearing about any problems, concerns or challenges that arise as employees work to keep data quality high within the organization. People are more likely to be honest about the highs and lows of this transition if they know managers will hear and respect them.

Treat Data Quality Standards as Works in Progress

High data quality allows leaders to make effective and confident demand planning decisions, no matter what a company sells or how many customers it serves. However, even those who will never act on what the information says are instrumental in gathering and preparing it.

Although these steps will assist company representatives in creating data quality processes, people must periodically revisit the current procedures and assess whether they’re still working as intended. It’s not a sign of total failure if they aren’t. However, it’s a strong indicator it’s time to get to the bottom of what’s going wrong and work to improve the shortcomings.

Data quality standards may also change as a company grows, begins offering new products or must follow updated regulatory requirements. People who understand this and know data quality is never a static measure will collectively help their organizations reach new demand planning goals.

To read more of Emily’s work across business, science and technology, head over to her online magazine, Revolutionized.


To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.