Get the Most Value from Enterprise Resource Planning
As manufacturers pursue performance improvements in cost, quality and delivery, they often find that data collected from their enterprise resource planning (ERP) systems do not provide an effective level of manufacturing intelligence. Whether the use is for near-real-time optimization of manufacturing processes or analyzing historical transactions, data age and access are typically the two main issues interfering with gaining the level of intelligence needed. In most cases, existing ERPs and newer decision- making tools are working under different data rules and requirements.
Rather than migrating to a new ERP system, which can often be a long, complex and expensive task, leading manufacturers are re-evaluating existing systems with the perspective of supplementing existing data and batch processing cycles to maximize the intelligence value of what is already available.
Below are three basic steps to help you get started in getting the most intelligence value from data within your existing ERP environment.
Step 1: Verify your ERP is collecting the right data
Taking inventory of the data collected by your ERP is a legacy practice and often the first step for most manufacturers. However, to get the best results, start backwards. Let me explain. What you’re actually looking for are gaps in the information your ERP provides to your decision-making tools. Starting with the systems that leverage the ERP data—and then working back to your ERP—can help you quickly zero in on the areas that have the highest impact as well as shorten the time and resources required for the solution. For instance, starting with your collaboration tool, a quick inventory can be taken from the tool back to your ERP to make sure all of the relevant pricing, sourcing, QOH (quantity on hand), UM (unit of measure) and safety stock (min/max) data is being pulled. Sometimes the simplest way to achieve this is to log in, look around and ask about any empty data fields that may be present in the collaboration system. dhree ways. The first and simplest solution is to adjust the data feed to include information which is readily available in the ERP. Second, in the case where the information is not available—such as the need for real-time materials consumption data on high value or long lead items—a data collection point can be added to deliver greater visibility in areas such as WIP (work-in-process), or the movement of materials between warehouse operations and the manufacturing floor. Third, fields can be added to existing data collection points to deliver greater granularity in areas such as quality or production. Depending on your ERP, it may be necessary to explore tools that extend functionality to capture all additional data.
Step 2: Ensure your data is as current as it needs to be
By legacy standards, data age is measured in days and weeks. However, with the advent of newer technology it’s now calculated in hours and minutes. The primary challenge is that most ERP systems are still configured to the old cycles, making integration with this new technology, such as advanced planning and collaboration tools, more complicated. It’s not surprising that timing and data synchronization can be the hidden snag in maximizing the intelligence value from your ERP data.
If your scheduling, collaboration or analytics tools are dependent on data collected from batch processes and they are not timed to run just prior to current data pulls, your decision-making process is based on outdated information. In order to leverage a greater amount of current data, you will need to evaluate alternative sources of data within your ERP to feed your collaboration and decision-making systems. Let’s return to the collaboration tool mentioned above, for example. Rather than waiting for item QOH data from MRP (manufacturing resource planning), it can be pulled in real-time or near-real-time to drive necessary changes to purchase order quantities and need-by dates.
Step 3: Create a real-time environment for your ERP-dependent data tools
Building on Step 2, reconfiguring your scheduling, collaboration or analytics tools to be less dependent on batch processed data can produce faster access to ERP data. In the case of MRP dependencies as mentioned above, critical data can be pulled individually from the ERP at more current intervals to support faster decision cycles.
To address the issue on a larger scale, leading manufacturers are also considering modifications to their planning, collaboration and analytics tools to store and utilize a greater amount of real-time data. Others elect to build a small database that can be accessed by multiple systems for the same purpose. The breadth and depth of the solution really depends on where you need faster decision-making across the enterprise.
Getting greater intelligence from existing ERP data is not simply mining the existing feeds with different methods; this would limit the value to the information you already have. Gleaning the most intelligence value from your existing ERP data involves a broader approach that utilizes additional information within the ERP—and at different times—to better align it with your planning, collaboration and analytics tools. This allows your organization to consider a greater amount of real-time and near-real time data in its decision-making processes. Achieving results begins by abandoning a legacy view of your ERP, identifying additional data it can deliver, and reconfiguring your planning, collaboration and analytics tools to work with a greater amount of real-time data to drive business.