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Production control
Production control
Production control
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Within supply chain management and manufacturing, production control is the activity of monitoring and controlling any particular production or operation. Production control is often run from a specific control room or operations room. With inventory control and quality control, production control is one of the key functions of operations management.[1]

Overview

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Production control is the activity of monitoring and controlling a large physical facility or physically dispersed service. It is a "set of actions and decision taken during production to regulate output and obtain reasonable assurance that the specification will be met."[2] The American Production and Inventory Control Society, nowadays APICS, defined production control in 1959 as:

Production control is the task of predicting, planning and scheduling work, taking into account manpower, materials availability and other capacity restrictions, and cost so as to achieve proper quality and quantity at the time it is needed and then following up the schedule to see that the plan is carried out, using whatever systems have proven satisfactory for the purpose.[3]

Production planning and control in larger factories is often run from a production planning department run by production controllers and a production control manager. Production monitoring and control of larger operations is often run from a central space, called a control room or operations room or operations control center (OCC).

The emerging area of Project Production Management (PPM), based on viewing project activities as a production system, adopts the same notion of production control to take steps to regulate the behavior of a production system where in this case the production system is a capital project, rather than a physical facility or a physically dispersed service.

Production control is to be contrasted with project controls. As explained,[4] project controls have developed to become centralized functions to track project progress and identify deviations from plan and to forecast future progress, using metrics rooted in accounting principles.

Types

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Powerplant control room, 1983

One type of production control is the control of manufacturing operations.

Management of real-time operational in specific fields.

Communist countries had a central production control institute, where the agricultural and industrial production for the whole nation was planned and controlled.

In Customer Care environments production control is known as Workforce Management (WFM). Centralized Workforce Management teams are often called Command Center, Mission Control or WFM Shared Production Centers.

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Production control is just one of multiple types of control in organizations. Most commons other types are:

  • Management control, one of the managerial functions like planning, organizing, staffing and directing. It is an important function because it helps to check the errors and to take the corrective action so that deviation from standards are minimized and stated goals of the organization are achieved in a desired manner.
  • Inventory control, the supervision of supply, storage and accessibility of items in order to ensure an adequate supply without excessive oversupply.
  • Quality control, the process by which entities review the quality of all factors involved in production.

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Production control

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Production control is a core component of manufacturing operations management, encompassing the systematic , scheduling, dispatching, monitoring, and expediting of production processes to optimize the use of labor, materials, machinery, and facilities while ensuring on-time delivery and adherence to standards. It focuses on translating production plans into actionable workflows, continuously tracking performance against established benchmarks, and implementing corrective actions to address deviations such as delays or resource shortages. In essence, production control bridges the gap between strategic and day-to-day execution, enabling manufacturers to maintain efficiency in dynamic environments ranging from small-scale assembly to complex systems like , automotive, or food production (including meat processing). The primary functions of production control include routing, which determines the sequence of operations and the path of work through the production facility; scheduling, which assigns specific timelines and allocates resources to meet production targets; and dispatching, which authorizes the release of work orders to initiate manufacturing activities. Additional key functions encompass expediting, involving the monitoring of progress and acceleration of delayed tasks through coordination with procurement, engineering, and other departments; follow-up or progressing, which tracks ongoing work to identify bottlenecks; and inspection, ensuring compliance with quality and safety protocols throughout the process. These functions are often supported by tools such as material requirements planning (MRP) systems, enterprise resource planning (ERP) software, and real-time monitoring technologies to handle inventory management and feedback loops for adjustments. In modern , production control plays a pivotal in enhancing competitiveness by minimizing , reducing costs, and improving responsiveness to demands, particularly in industries requiring high precision and just-in-time delivery. It integrates with broader strategies, including and , to sustain continuous operations and adapt to variables like equipment failures or fluctuating demand. Effective production control not only ensures operational smoothness but also contributes to overall organizational goals, such as achieving sustainable practices and in government or industrial settings.

Fundamentals

Definition and Scope

Production control refers to the operational process of directing, monitoring, and regulating the flow of , labor, and equipment within systems to facilitate the efficient conversion of raw into finished products, while adhering to standards of , , and delivery timelines. According to the American Production and Inventory Control Society (APICS), it encompasses predicting, , and scheduling work tasks with consideration for manpower, availability, capacity constraints, and costs to deliver the appropriate and at the required time, followed by the and oversight of that through robust execution mechanisms. This function acts as the logistical backbone of production, ensuring that activities align with operational goals by coordinating resources in real-time. The primary objectives of production control include optimizing utilization to maximize , minimizing through precise , guaranteeing on-time delivery to meet customer demands, and sustaining overall production performance amid variability. These goals support the delivery of products in the right , at the right , and within specified timeframes, while balancing flexibility and controlling logistical expenses. By focusing on these aims, production control contributes to enhanced value delivery in environments, as evidenced by its role in aligning production with market forecasts, available capacities, and supplier inputs. In terms of scope, production control is confined to tactical and operational execution within settings, involving short- to medium-term decisions on job sequencing, , and schedule adjustments, distinct from long-term such as facility design or capacity expansion, as well as post-production activities like distribution. It operates at the tactical level to bridge higher-level strategies with day-to-day operations, emphasizing real-time responses to disruptions like failures or supply delays. Key concepts include feedback loops, where production progress is continuously measured against planned benchmarks, deviations are analyzed, and corrective interventions—such as rescheduling or resource reallocation—are applied to maintain system stability. Additionally, production control integrates closely with inventory management by ensuring material availability through work-in-progress buffers and stock coordination, thereby supporting responsive production without excess stockpiling.

Historical Development

The emergence of production control can be traced to the late 18th and 19th centuries during the , when mechanization and the rise of factory systems necessitated systematic oversight of manufacturing processes to manage increasing scale and complexity. As handcraft production shifted to machine-based operations in textile mills and ironworks, particularly in Britain and later the , early forms of coordination emerged to regulate workflows, inventory, and labor in centralized facilities. A pivotal milestone came in 1798 with Eli Whitney's introduction of in musket manufacturing, which enabled by standardizing components and simplifying assembly, thereby requiring rudimentary planning and quality oversight to ensure uniformity across batches. In the early , Frederick Winslow Taylor's principles, outlined in his 1911 book , revolutionized production control by advocating time studies, task optimization, and worker efficiency to eliminate waste and standardize operations in factories. Concurrently, Henry L. Gantt developed his eponymous charts in the 1910s as visual tools for scheduling tasks and tracking progress in job shops, enhancing the ability to coordinate production sequences and resources. Building on these, Henry Ford implemented the moving in 1913 at his Highland Park plant, which dramatically improved production flow, reduced assembly time for vehicles from 12 hours to about 90 minutes, and emphasized continuous sequencing and just-in-time coordination of parts. During and after , (OR) techniques, initially applied to wartime and , were adapted for postwar industrial production control, using mathematical modeling to optimize supply chains and manufacturing flows amid booming demand. In the 1920s, at Bell Telephone Laboratories pioneered statistical quality control with the first control charts in 1924, providing a data-driven method to monitor process variations and prevent defects, which became integral to ongoing production oversight. The mid-20th century saw the advent of computer-aided systems in the 1950s and 1960s, with (NC) machines automating tool paths and early (MRP) software, developed by Joseph Orlicky in 1964, enabling automated inventory and scheduling calculations to support complex assembly lines. In the 1970s, the Toyota Production System (TPS), refined by Taiichi Ohno and Eiji Toyoda from the late 1940s through the 1970s, introduced just-in-time (JIT) production to minimize inventory and respond flexibly to demand, influencing global shifts toward lean methodologies. Post-1980s, integration of digital tools such as enterprise resource planning (ERP) systems further evolved production control by linking real-time data across planning, execution, and monitoring, building on earlier computational foundations. These historical advancements laid the basis for modern push and pull systems by emphasizing efficiency, adaptability, and data integration in manufacturing.

Core Functions

Production Planning

Production planning serves as the foundational preparatory phase within production control, encompassing long-term and medium-term activities to determine overall production quantities, timelines, and resource requirements based on anticipated . This process ensures that operations align with business objectives by establishing feasible output levels while considering constraints such as available facilities and capabilities. Unlike short-term operational adjustments, production planning focuses on aggregate to optimize and minimize disruptions over extended horizons, typically ranging from months to years. The key steps in production planning begin with demand forecasting, which employs quantitative techniques such as moving averages—to smooth historical data by averaging recent periods—and , which weights recent observations more heavily. These methods help estimate future customer needs, providing a reliable basis for production targets; for instance, simple is particularly effective for stable demand patterns without strong trends or seasonality, while advanced variants like Holt-Winters can incorporate trends and seasonal effects for more complex patterns. Following , evaluates the availability of machinery, labor, and facilities to match the forecasted demand, identifying potential bottlenecks and determining whether expansions or reallocations are necessary to achieve targeted output rates. Finally, the (MPS) integrates these inputs to outline the total quantities of to produce across specific time periods, balancing levels with delivery commitments. Essential tools in production planning include the bill of materials (BOM), which provides a hierarchical breakdown of all components, subassemblies, and raw materials required for each product, enabling accurate cost estimation and planning. Complementing the BOM are routing sheets, which detail the sequence of operations, workstations, and processing times needed to transform inputs into outputs, ensuring that production flows logically through the facility. These tools facilitate the of complex products into manageable elements, supporting the translation of high-level plans into actionable resource allocations. By establishing these baselines, production planning directly feeds into subsequent scheduling processes, offering a structured framework for detailed sequencing and resource assignment during execution. This integration allows for proactive adjustments in monitoring phases to address variances from planned outputs.

Production Scheduling

Production scheduling involves the detailed allocation of production tasks to specific time slots, machines, and workers, transforming the broader production plan into executable timetables that optimize resource utilization and workflow efficiency. This core process breaks down the master production schedule into feasible daily or weekly sequences, considering task dependencies and capacities to ensure timely completion while minimizing disruptions. Key methods include forward scheduling, which begins from the earliest possible start date and progresses forward based on resource availability, and backward scheduling, which starts from customer due dates and works backward to determine required start times, allowing adjustments for delays or priorities. These approaches help in creating realistic timetables that align with operational constraints, as outlined in standard production planning frameworks. Priority sequencing rules further refine the allocation by determining task order within these timetables; for instance, the shortest time (SPT) rule prioritizes jobs with the least estimated duration to minimize average flow time and reduce inventory buildup, proving effective in both deterministic and dynamic environments. Other rules, such as earliest due date (EDD), complement this by focusing on completion deadlines to limit tardiness. Visualization techniques like Gantt charts provide graphical representations of these schedules, displaying task timelines, machine assignments, and progress against planned durations to facilitate monitoring and adjustments. In project-based production, the (CPM) identifies the longest sequence of dependent tasks—known as the critical path—that determines the overall project duration, enabling targeted efforts to resolve bottlenecks without extending total time. Scheduling techniques vary by production environment: job shops, which handle diverse, low-volume orders with flexible routing across machines, require adaptive rules to manage variability and setup changes, often leading to longer lead times due to non-linear flows. In contrast, flow shops process standardized, high-volume items in a fixed of operations, allowing for more predictable timetables but demanding tight coordination to avoid line stoppages. Critical factors influencing these schedules include setup times, which can be sequence-dependent and necessitate grouping similar tasks to reduce downtime; due dates, which drive backward to meet delivery commitments; and resource constraints, such as limited machine or labor availability, which must be balanced to prevent overloads and idle periods. By accounting for these, schedulers aim to minimize delays and enhance overall system responsiveness. Performance in production scheduling is evaluated through metrics that quantify efficiency and reliability, including schedule adherence rates, which measure the of tasks completed on or before their ned times to assess plan fidelity, and throughput time, defined as the total duration from job release to completion, often targeted for reduction via rules like SPT to improve cycle efficiency. These indicators provide insights into operational effectiveness; for example, high adherence rates above 90% indicate robust planning, while lower throughput times correlate with faster customer response in competitive markets. Such metrics guide iterative improvements in scheduling practices without delving into real-time execution adjustments.

Dispatching and Execution

Dispatching and execution represent the operational phase of production control, where scheduled plans are translated into actionable instructions on the . Dispatching specifically involves the release of work orders to personnel, authorizing the movement of materials, and initiating production operations to ensure timely commencement of tasks. This process serves as the bridge between and actual production, focusing on short-term with horizons spanning minutes to days, enabling immediate responses to conditions. Key activities in dispatching include issuing detailed dispatch lists that provide operators with specific instructions, such as sequence of operations, assignments, and expected start times, often updated frequently—every 5 to 10 minutes in medium-sized job shops with around 60 . Managing work-in-progress (WIP) is central, involving proactive measures to avoid bottlenecks through monitoring availability, handling preemptions, and addressing breakdowns that could disrupt flow. Expediting urgent orders is another critical activity, where dispatchers implement workarounds like using substitute materials or subcontracting to meet deadlines without compromising overall efficiency. Tools for dispatching and execution range from traditional dispatch boards, which offer visual overviews of job statuses and progress for quick shop floor reference, to modern digital work order systems integrated within Manufacturing Execution Systems (MES). These digital tools enable real-time tracking of start times, operator assignments, and material flows, providing data-driven visibility to prevent delays. In MES environments, dispatching functions direct work order execution through automated workflows, ensuring seamless integration with broader production systems. Control aspects emphasize structured to supervisors and dispatchers, who are empowered to make on-the-spot adjustments, such as extending shifts or reallocating resources, while adhering to predefined guidelines. Initial quality checks during execution are integrated to assess potential risks, including process deviations or material defects, right at the start of operations to mitigate issues early. This fosters and responsiveness, with post-execution feedback loops briefly informing adjustments without altering the immediate dispatch focus.

Monitoring and Follow-up

Monitoring and follow-up in production control involves the systematic surveillance of ongoing operations to detect deviations from planned standards and implement timely adjustments. This phase ensures that production activities remain aligned with objectives by tracking key performance indicators (KPIs) such as cycle time, which measures the duration required to complete one production unit, and yield rates, defined as the percentage of acceptable units produced relative to total output. Real-time monitoring of output rates and machine utilization—typically calculated as the ratio of actual production time to available time—allows supervisors to identify inefficiencies, such as bottlenecks or idle periods, enabling proactive interventions to maintain throughput. Techniques for progress reporting include regular status updates through dashboards or reports that compare actual performance against scheduled targets, facilitating quick identification of variances in production metrics. When deviations occur, root cause analysis tools like the diagram (also known as the ) are employed to categorize potential causes—such as methods, materials, machinery, measurement, manpower, and environment—and systematically trace underlying issues, rather than addressing symptoms alone. Corrective actions may then involve rework to repair defective items or rescheduling production sequences to accommodate disruptions, ensuring minimal impact on overall timelines. Feedback mechanisms in this process operate through closed-loop control systems, where from execution phases is fed back to refine ongoing and future operations, creating a dynamic adjustment cycle that compares actual outputs to desired standards. This approach supports continuous improvement by iteratively reducing variances and enhancing process reliability. In the context of disruptions like equipment failures, monitoring and follow-up promote adaptability, allowing for rapid reconfiguration of resources to restore alignment with production goals. , such as IoT sensors, can automate these feedback loops for more precise real-time surveillance.

Types of Production Control Systems

Push-Based Systems

Push-based systems in production control involve initiating manufacturing processes based on demand forecasts and schedules, rather than actual customer orders, thereby "pushing" materials and products through the production pipeline to build inventory in advance. These systems prioritize proactive planning to meet anticipated needs, often producing goods to stock for immediate availability upon demand realization. Key characteristics include centralized decision-making, where production schedules are derived from long-term forecasts, and the use of tools like (MRP) to coordinate material flows and timing. To manage effectively, these systems employ models such as the (EOQ), which determines optimal batch sizes to balance ordering and holding costs, thereby buffering against uncertainties through elevated stock levels. The EOQ approach, first formalized by Ford W. Harris in , supports the push logic by minimizing total expenses in stable environments. Advantages of push-based systems are particularly evident in settings with predictable demand, where they enable , reducing per-unit production costs and facilitating efficient and resource utilization. Pre-built also shortens customer delivery lead times, enhancing responsiveness in high-volume scenarios. However, limitations arise from reliance on forecast accuracy; inaccuracies can lead to , excess , increased holding costs, and risks of product , especially in dynamic markets. Examples of push-based systems include in the during the Fordist era, such as Henry Ford's assembly lines in the –, which forecasted demand for standardized vehicles and pushed components through sequential stages to stockpile finished cars. Similarly, pre-1980s consumer goods manufacturing, like and production, utilized push strategies to anticipate seasonal or steady demand, building stockpiles for distribution. In contrast to pull systems, push approaches emphasize forecast-driven initiation over demand-triggered execution.

Pull-Based Systems

Pull-based systems in production control initiate manufacturing activities solely in response to actual demand, using signals from downstream processes to trigger upstream production, thereby emphasizing smooth flow and just-in-time delivery to minimize excess and . This approach contrasts with forecast-driven methods by ensuring that production quantities and timing align precisely with consumption, fostering a self-regulating system that reduces waste across the . Central to pull-based systems are principles derived from the (TPS), developed by , which integrate (JIT) production with tools like and (SMED). Takt time, defined as the available production time divided by customer demand, sets the "heartbeat" of the system to synchronize output with sales pace, such as calculating 2 minutes per widget for a daily demand of 240 units in an 8-hour shift. SMED, pioneered by , focuses on reducing equipment setup times to under 10 minutes by classifying and converting internal changeover steps (performed while machines are stopped) to external ones (done during operation), achieving average reductions of 94% in setup durations at . These principles enable continuous flow, eliminating delays and ensuring that production responds dynamically to real-time needs without building unnecessary stockpiles. Implementation typically involves visual signals, such as cards or electronic equivalents, which authorize production or material replenishment only when a downstream signals depletion, creating closed-loop control circuits. Complementary layouts like arrange equipment in U-shaped cells to facilitate one-piece flow, where operators handle sequential tasks in a compact area, enhancing and reducing transportation waste within the facility. Originating from Ohno's innovations at in the , these methods were refined to support by limiting work-in-process inventory through supermarkets of pre-stocked parts that are replenished via signals. The primary benefits of pull-based systems include substantial reductions in levels—often to minimal —and shortened lead times, as production avoids batching and aligns directly with demand, lowering costs associated with holding and . However, successful deployment demands reliable suppliers for timely part delivery and stable processes to prevent disruptions that could halt the flow-oriented chain.

Hybrid and Advanced Systems

Hybrid and advanced production control systems integrate elements of push and pull strategies to address the limitations of standalone approaches, particularly in environments with variable demand and resource constraints. The (TOC), introduced by in 1984, identifies and manages bottlenecks to synchronize production flow, effectively balancing forward scheduling (push) with demand responsiveness (pull) by elevating, subordinating, and exploiting constraints. Similarly, Constant Work In Process (CONWIP), proposed by Mark Spearman, David Woodruff, and Wallace Hopp in 1990, limits total work-in-progress across a using a fixed number of authorization cards, releasing new jobs only when capacity frees up, thus hybridizing aggregate push planning with localized pull control. Advanced variants of these systems incorporate simulation modeling to test and optimize control parameters under dynamic conditions, enabling predictive adjustments to throughput and inventory levels. For instance, discrete-event simulation tools model CONWIP or TOC implementations to evaluate performance metrics like cycle time and utilization before deployment, reducing risks in complex setups. Key features of hybrid systems include mechanisms for buffering uncertainties and enabling agile responses to production variability. Demand-Driven Material Requirements Planning (DDMRP), developed by Carol Ptak and Chad Smith, enhances traditional MRP by strategically positioning buffers based on actual demand signals rather than forecasts, decoupling supply chain stages to mitigate volatility while maintaining push-based planning horizons. Agile adaptations, such as modular reconfiguration in response to order changes, further support variable production by allowing rapid shifts between product variants without full line resets. These systems find prominent applications in high-mix, low-volume environments, where customization and frequent changeovers are common. In the , hybrid controls like CONWIP optimize assembly lines for diverse components, minimizing buildup while ensuring on-time delivery for products like circuit boards. In pharmaceuticals, DDMRP and TOC hybrids manage batch variability and , buffering raw materials to handle uncertain demands without excess stockpiling. A notable trend since the has been the evolution toward Flexible Manufacturing Systems (FMS), which embed hybrid control logics within computer-integrated setups to support multi-product runs and rapid reprogramming. This shift, driven by advances in CNC machinery and software, has enabled manufacturers to achieve economies of scope alongside scale, with FMS adoption growing in sectors requiring adaptability. Tech enablers like AI further refine these systems by forecasting constraint elevations in real time.

Technologies and Tools

Material Requirements Planning (MRP)

Material Requirements Planning (MRP), often referred to as MRP I, emerged as an inventory control system in the 1960s, pioneered by IBM engineer Joseph Orlicky who formalized its principles around 1964 based on studies of production systems like Toyota's. Orlicky's seminal 1975 book, Material Requirements Planning, established MRP as a method to manage dependent demand for components in manufacturing, contrasting with independent demand forecasting for finished goods. At its core, MRP integrates the bill of materials (BOM)—a hierarchical list of parts needed for assembly—the master production schedule (MPS) outlining planned output, and current inventory records to compute precise material needs, enabling efficient ordering and production timing. The system's calculations rely on dependent demand logic, where component requirements derive directly from end-item demands specified in the MPS, avoiding overstocking by linking subassembly needs to parent items. offsetting shifts gross requirements backward by each item's or production to determine order release dates, ensuring materials arrive just in time for use. For lot-sizing, MRP employs techniques to balance ordering costs and holding costs, such as the (EOQ) model, given by the formula: EOQ=2DSH\text{EOQ} = \sqrt{\frac{2DS}{H}}
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