Closing the Gaps in Manufacturing Data Flows

Closed-loop manufacturing data flows

Complete and connected manufacturing data flows are the platform on which manufacturers profitably grow and expand. Yet too often, manufacturers leave gaps in their data flows that can create a cascading series of errors, delays, and missed opportunities.

The impact is wide because manufacturing is a whole-cloth operation where almost every process impacts the entire business: No sales orders, no production. No production, no shipments. No shipments, no customers.  And on it goes, with each process and its associated data flows being vital to the success of the entire business.

The best way to close these gaps is by implementing a closed-loop system that automates data flows across the organization. Let’s look at common approaches to managing data flows in manufacturing and then review examples of how a closed-loop approach can minimize data disconnects.

Understanding Manufacturing Data Flows

Manufacturing data flows largely follow the sequence in which manufacturers operate. Sales orders inform procurement, scheduling, and production. Production delivers product to quality control, quality releases product to shipment. Shipping completes the cycle and informs sales operations that the order has been fulfilled.

Today, manufacturers operate at many different levels of data flow sophistication and maturity. This ranges from word of mouth to email, spreadsheets, and accounting systems, and on to enterprise resource planning (ERP) systems and manufacturing execution systems (MES). However, approaches to managing data flows tend to fall into one of four categories.

  •  Basic accounting data flows – Here, manufacturers rely on QuickBooks or similar products to manage their general ledger, accounts payable, accounts receivable, and profit and loss reporting. Meanwhile, remaining data flows to other parts of the organization for planning, procurement and production, and shipping are handled manually via email, spreadsheets, and clipboards—creating gaps in time and information.
  •  Basic ERP-driven data flows – This approach builds upon the accounting flow to add material resource planning (MRP) driven by sales orders and production spreadsheets. While more processes are automated, there are still many disconnected data flows.
  •  Full ERP-driven data flows – In this scenario, there is a complete set of enterprise planning functionality that spans everything from finance to sales orders, customer relationship management (CRM), supply chain and inventory management, production planning, scheduling, quality control, shipping, and receiving. Automating the business side in this way helps to eliminate the gaps of a basic ERP approach, but it’s an open-loop system. Manufacturers have all the planning data, but they don’t have the actuals.
  •  Closed-loop, end-to-end ERP and manufacturing operations – This level of data flow sophistication is the goal of most businesses. And in fact, many manufacturers already operate at this level, especially if they are growing, have multiple plant locations, and service multiple product lines and supply chains.

Closed loop, end-to-end data flow not only includes all the functionality of a full ERP system; they also include real-time production and process monitoring data captured in the MES. This approach enables manufacturers to have feedback loops that automatically update and alert multiple areas of businesses in real-time as the business executes its daily operations.

Comprehensive Data Flows and Their Impacts: Four Real-World Scenarios

Now let’s review four real-world examples—based on on-site visits and discussions with manufacturers—of how closed-loop, end-to-end ERP and manufacturing operations can help improve business performance.

  1. Understanding capable to promise (CTP) A sales order initiates a front-to-back response from the entire manufacturing operation. In this case, a customer wants to order 1,000 units of product and receive them in three weeks. The order desk has to decide whether to commit by answering questions, such as: Is sufficient quantity on hand? Do we have enough raw materials; if not, can we procure them? Can we provision the necessary tooling, equipment, and labor in time? How long will it take to produce the product? And if we transfer resources from another job to this job, what is the downstream impact? Together, the ERP system and MES automate data flow from sales to inventory control to planning, scheduling, and procurement, as well as track any existing order backlog and currently running jobs. So, with just a few keystrokes, the order desk can decide whether to commit to the order.
  2. Addressing a production bottleneck – A job is scheduled to run in eight hours, with each piece taking 20 seconds to produce 1,500 pieces in a shift. However, real-time production monitoring reveals that each piece actually requires 30 seconds to produce, taking 50% more time than planned. The second shift job will not start on time because the tooling changeover must be delayed. Additionally, different operators will be required. Closed-loop data flows between departments in a manufacturing operation may not have been able to prevent the issue, but they can minimize the impact on the overall business. In this case, because of the real-time data flow between production and scheduling, and sales in a closed-loop approach, subsequent jobs can be proactively rescheduled; manpower can be reassigned, and customers can be notified of new delivery dates.
  3. Managing inventory spoilage – Alerts in combination with cross-discipline data flows are powerful tools to keep production moving forward by proactively making adjustments rather than reacting at the last minute to surprise disruptions. Consider the example of a warehouse management system alert that triggered a periodic physical cycle count of warehouse section C7. The cycle count discovered that two bags of a stabilizing agent had been exposed to a water leak and were no longer viable.  A ‘where used’ report was initiated and revealed that the stabilizer was scheduled to be consumed in two jobs the following week. The manufacturer removed the two bags of stabilizer from inventory, which led the MRP system to create a demand for replenishment of the stabilizer that was sent to procurement and triggered the rescheduling of jobs until the stabilizer was scheduled to be available.
  4. Enabling opportunistic scheduling – Forecasting looks at sales orders and historical demand to create advanced production plans. In this closed-loop data flow example, a production forecast is compared to machine schedules. It identifies that a product will be required in the next three months and that the equipment necessary to run the job will be idle over the next two weekends. It also determines that the raw material is on hand and not committed to any other jobs during the planning horizon. Based on these insights, the forecast demand is scheduled into the open production slots, and the product is built in anticipation of its need. This forward-looking data flow not only ensures that finished goods are on hand for future needs; it also consumes otherwise idle machine time, improving both operational equipment effectiveness (OEE) and inventory utilization.


Complete and connected data flows are the platform on which businesses profitably grow and expand.  Manufacturing operations are inherently highly interdependent—so much so that once a business grows beyond a small operation, it becomes very difficult to manage all the dependencies without the automated data flows and interactions provided by modern ERP and MES systems. These systems act as sentinels for disruption, provide advanced planning and scheduling, and lock the various manufacturing disciplines into a synchronized operation.

Steve Bieszczat, DELMIAworks (IQMS) Chief Marketing Officer, is responsible for all aspects of DELMIAworks' (IQMS) brand management, demand generation, and product marketing. Prior to DELMIAworks (IQMS), Steve held senior marketing roles at ERP companies Epicor, Activant and CCI-Triad. Steve holds an engineering degree from the University of Kansas and an MBA from Rockhurst University.