Why Data Latency is a Real Problem in Decision-Centric Planning
In today’s hyper-competitive market, where supply chains need to be agile and responsive, even a minor delay in data can have significant repercussions. Companies are finding that data latency isn’t just a technical hiccup but a major obstacle to effective decision-making.
As we continue our journey through decision-centric planning, having explored its potential to unlock supply chain success, addressed real-world challenges, and discussed the internal shifts necessary for its implementation, we now turn to a critical aspect that can undermine all these efforts: data latency. Understanding why data latency is a real problem is essential for making informed, timely decisions that can make or break your supply chain efficiency. We’ll also take a look at why traditional tools like Excel are no longer sufficient for managing modern supply chain complexities.
What is Data Latency?
Data latency refers to the delay between the moment data is created and the moment it is used to inform a decision. Data latency starts early, and supply chain leaders are stuck endlessly trying to catch up. Often this latency is significant starting from the moment an inventory event occurs. For example, stock may arrive in a warehouse and get scanned into the warehouse management software. Any delay in registering those goods in inventory management systems means that decisions are already being made without critical data (an accurate count of inventory in each location). When data is pulled from the system to analyze and run scenarios, latency worsens—and continues to worsen each time that same analysis is viewed by stakeholders across a number of S&OP meetings.
In the context of decision-centric planning, where decisions are only as good as the data they’re based on, latency can be the difference between success and failure. By the time a decision is made, the underlying data may no longer be relevant.
Impacts of Data Latency on Decision-Centric Planning
Data latency has several adverse effects on decision-centric planning. It can significantly delay decision-making, preventing managers from responding quickly to changes in a dynamic supply chain environment, which leads to missed opportunities or aggravated issues. For instance, a sudden spike in demand for a product requires immediate response, such as increasing production or redistributing stock. If sales data reflecting this spike is delayed, the response will also be delayed, potentially resulting in stockouts and lost sales.
Additionally, reduced accuracy of predictions is another consequence, as accurate forecasting relies on the latest data. Latency can skew the data set, leading to forecasts that do not reflect current realities, causing overstocking or understocking. Suboptimal resource allocation is also a result of data latency, where decisions on resource allocation—whether it’s labor, materials or transportation—require up-to-date information. Latency can lead to inefficient use of resources, such as when a manufacturer operates under the assumption that materials will arrive on time, only to face production bottlenecks due to delayed updates about a supplier’s late delivery.
Lastly, increased costs are a significant impact, as inaccurate or delayed data can lead to cost overruns. Emergency measures to correct course, such as expedited shipping or overtime labor, can inflate operational costs, forcing a company to resort to expensive rush shipping to meet customer SLAs.
The Causes and Impact of Data Latency
Several factors contribute to data latency. Technical and architectural limitations, such as the speed of the network, the complexity of data systems and the processes they must go through, can introduce delays.
Operational inefficiencies, such as bottlenecks in data processing or retrieval, can also slow down the flow of information. One of the most frustratingly common causes of operational inefficiency is a lack of a single source of truth for operational data. Different parts of the business are operating on different datasets, which means that a lot of time is wasted on reconciling different version of the data. Before they can act on what the data is telling them, stakeholders have to dedicate valuable time and energy deciding which dataset is true. Without a digital supply chain twin, companies often find themselves creating additional data latency simply reconciling data.
Perhaps most common of all, however, are procedural inefficiencies. The traditional S&OP process requires scenarios to be run far in advance of the decisions that will be made based on the relevant analyses. By the time stakeholders are presented the scenarios, they are making critical decisions on outdated data. Even worse, new scenarios requested by executives in these meeting create further latencies. Either new scenarios are run on the same increasingly outdated data or all scenarios must be re-run and brought to another meeting, complicating and delaying decision-making further. Each of these factors can compound the delay, resulting in significant latency that impacts decision-making processes.
High data latency can lead to outdated information guiding critical decisions. In industries where conditions change rapidly, a delay can render critical data irrelevant, leading to missed opportunities and suboptimal decisions.
Why Excel is No Longer Sufficient
In many organizations, Excel has long been the go-to tool for managing data. However, Excel’s limitations contribute significantly to data latency issues, making it unsuitable for modern supply chain needs. Excel struggles with large data sets and complex calculations, which are common today. Managing and analyzing vast amounts of data in Excel can be cumbersome and error-prone, leading to delays in data processing. For instance, a global supply chain with thousands of SKUs and multiple data points per SKU can quickly exceed Excel’s capabilities, resulting in slow processing times and potential data loss.
Excel is also not designed for real-time data processing, a crucial requirement for reducing data latency. It lacks the ability to handle live data feeds efficiently, leading to significant delays. Real-time inventory management, for example, requires instant updates to maintain accurate stock levels and provide timely customer service, which Excel cannot deliver. This delay can result in outdated data guiding decisions, exacerbating latency issues.
Additionally, Excel files are often siloed and lack seamless integration with other systems, leading to fragmented data and inconsistencies. When multiple departments use separate Excel sheets, consolidating data becomes a manual and error-prone process, further delaying critical decision-making. The lack of integration capabilities means that data must often be manually transferred between systems, introducing additional latency and the potential for errors. Even agreeing on which data is correct takes time, adding more and more delays.
Mitigating Data Latency through Decision-Centric Planning
To effectively mitigate data latency, organizations need to adopt software solutions specifically designed for decision-centric planning. Advanced supply chain management software can handle large data volumes and complex computations with ease, reducing processing delays. These platforms offer real-time data processing capabilities, ensuring that decision-makers always have access to the most current information.
Streamlining data processing with efficient algorithms and employing more powerful computing models also accelerates data handling. Leveraging technology such as caching, data replication and parallel processing can help manage and mitigate latency effectively. These technological moves forward are important—but often costly and insufficient. The easiest way to reduce data latency? Get the right information in front of the right stakeholders in real time. This requires change management, yes, but it is often the simplest way to gain the greatest impact—and decision-centric planning centers this change.
Data latency is more than just a technical issue; it’s a critical factor that can significantly impact the profitability of supply chain decisions. Traditional tools like Excel are no longer adequate to meet the demands of today’s supply chain complexities. By understanding and addressing data latency, organizations can enhance their agility, improve accuracy and ultimately achieve greater success in their supply chain operations. Stay tuned as we continue to explore more facets of decision-centric planning and how to overcome the challenges it presents.
