Today’s businesses are bringing data regarding decision-making processes to the table. They use historical data and predictive analytics to determine the best moves for their company. The information helps them make informed decisions that lead to better outcomes.

With technological advancements, you can only ask yourself, which business departments aren’t using data in decision-making? It is becoming more prevalent in operations, helping guide decisions about logistics, inventory, pricing, quality, and R&D. Organizations that rely on data can benefit from increased efficiency and improved customer service.

How Data-Driven Decisions are Promoting Operational Excellence

Several aspects of operations can benefit from data. Here are some ways it can improve your company’s systems.

Pricing

Data helps companies determine the best pricing for their products. They can gather information on competitor pricing, demand, and customer buying habits to determine a price system that provides competitive rates while maximizing profitability.

R&D

Companies can gather data that provides insights that are useful in product development. They can analyze metrics that reveal common customer pain points, preferences, and competitor strategies and find the best ways to improve their products, increasing customer satisfaction and conversions.

Improving Efficiency

Technology can analyze the company’s operational processes and identify obstacles that may slow down systems, such as poor resource utilization and redundant tasks. The organization can use this information to eliminate roadblocks and bottlenecks and create better processes, saving time and money.

Optimizing Logistics

Logistics is a huge part of operations, and data can lead to more efficient systems. Technology can determine the best delivery routes, helping save time and reducing gas expenses. It can also track packages out for delivery, providing real-time updates for customers and reducing losses.

Inventory Management

Overstocking and understocking are common issues for companies, leading to significant losses. Data provides analytics on customer demand, allowing organizations to order stock accordingly, improving customer service and reducing waste.

Maximizing Quality Control

In the past, humans had been responsible for quality control, a daunting task considering the volume of products that required oversight. Today, data takes over, eliminating errors and updating to the latest standards. It also provides reporting and analytics that can be useful in determining more efficient processes for future manufacturing.

What Types of Data are Used in Operations?

COOs and their teams generally deal with three types of data as follows:

• Operational Data: Includes data relevant to daily transactions, such as customer information, inventory data, sales, HR, and transactions. This information is often used for real-time decision-making. However, it can also be gathered over time to determine the most efficient systems moving forward.
• Analytical Data: Includes business intelligence, customer service, and productivity data. It uncovers trends to support strategic decision-making. Predictive analytics fall under this umbrella, helping companies determine the best outcomes.
• Historical Data: Provides historic information so companies can make decisions based on past trends.
• Data by Nature: Refers less to the type of data and more to how data is collected. Data can be:
  • Qualitative: Describing characteristics not easily quantified, like texture, color, or smell.
  • Quantitative: Numerical data that can be easily measured
  • Graphic Data: Represented as graphs or charts.

Tips for Making Data-Driven Decisions

The following tips are helpful for organizations new to incorporating data in decision-making.

• Choose the Right Infrastructure: The first step involves finding the best data-gathering technology for your systems. Consider which systems are ideal for uncovering operational insights and work best with your industry. Other factors include cost and the technology’s potential for improving efficiency.
• Security Issues: Like any technology, data software and hardware can increase the risk of cybersecurity issues. Organizations must develop robust security strategies to keep information safe.
• Incorporate Human Oversight: Once data becomes the norm for decision-making, it’s easy to ‘over-rely’ on it. However, human oversight is necessary. Ensure that every outcome is reviewed carefully by board members before signing off.
• Information Overload: Big data pulls from numerous resources, often leading to challenges in storage, retrieval, and analysis. With no clear solution, companies must determine the best way to handle large amounts of data in their systems. However, those who know what to expect will have an advantage.

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