Tag: production flow

The Devastating Impact of Too Much WIP: How Excess Inventory Kills Manufacturing Flow
In manufacturing, WIP (Work in Progress or Work in Process) refers to partially finished goods that are at various stages of production but not yet completed. In accounting terms, WIP represents the value of raw materials, labor, and overhead that has been invested in unfinished product. Reducing WIP is a frequently cited goal for many manufacturers as WIP ties up capital and hinders production flow.

In this post, we explore the problems excess WIP causes, the operational issues it can reveal, and how Advanced Planning and Scheduling (APS) systems help manufacturers keep WIP levels under control by creating a stable and reliable manufacturing environment.

The Hidden Costs of Excess WIP

As noted in our introduction, excess WIP creates several issues for manufacturers, making it a prime target for continuous improvement initiatives.

Ties up capital – WIP is a normal component of manufacturing, as all products go through at least some processing. Lengthy cycle times can easily exacerbate WIP issues in complex manufacturing environments. Excess WIP ties up capital as the funds have already been invested, but the completed product cannot yet be shipped.

Increases storage and handling costs – WIP also takes up space, which can contribute to increased storage and handling costs. Excess WIP can also increase scrap costs as unfinished components that are moved around to make room for other production orders or inventory can easily be damaged or misplaced.

Longer lead times – Excess WIP is often a symptom of process issues, such as over releasing work, poor flow, and process misalignment. If production orders aren’t properly prioritized and scheduled, excess WIP in the pipeline increases wait/queue times, leading to longer lead times and missed delivery dates.

Difficult to Manage – Having too much WIP makes it much more difficult to manage the production process. Excess WIP leads to wandering bottlenecks, increased batching, increased expediting, and a higher likelihood of resources running incorrect work. The more WIP in the system, the more orders to keep track, the more difficult it is for management to ensure focus remains on the highest priority work

The Root Causes of WIP Accumulation

Excess WIP is a telltale symptom of deeper operational problems rooted in misaligned philosophies, inefficiencies, or poor planning. Here are several root causes of excess WIP in complex manufacturing environments.

Push-Based Production Philosophy – The classic example of a push-based system is one that schedules production based on forecasts and pushes production through the process regardless of downstream processing capacity and actual customer need. Products are manufactured in advance, assuming demand will consume the supply. Poor forecasting and disconnects between production and actual customer demand lead to overproduction, a key driver of excess WIP.

In addition, in both make-to-forecast and make-to-order environments, push-based production increases WIP when work is released or started upstream without sufficient regard for the pace or capacity of downstream processes. This allows semi-finished goods to pile up at intermediate stages, drastically increasing queue times, until capacity becomes available. Constant expediting, changes in customer demand, and downstream bottlenecks further amplify the problem.

Batch Planning – Batch planning involves processing materials or components together in a “batch” rather than one at a time in a continuous flow. Larger batch sizes increase WIP because products must travel in bulk, waiting until the entire batch is ready before moving to the next step. If equipment or processes are delayed downstream, the entire batch is held up, further compounding the accumulation of WIP across stages. Changeovers or scheduling mismatches can also create periods where WIP sits idle between processes, waiting for subsequent operations.

Poor Constraints Management – Even when a push-based production philosophy isn’t followed, poor constraints management creates similar issues. When work is released to production and upstream processes produce at full capacity regardless of the constraint’s capacity, WIP piles up in front of the constraint, roving bottlenecks occur at the non-constraints, and overall throughput is reduced.

Misaligned and/ or Outdated KPIs – Key Performance Indicators (KPIs) are used to measure system performance, but if they incentivize the wrong behaviors, they can be counterproductive. This is especially true of KPIs like “machine utilization” or “line efficiency” that prioritize keeping equipment running at full capacity rather than focusing on overall system. Additionally, if production teams are incentivized to produce as much as possible without regard for demand or system constraints, WIP will accumulate unnecessarily.

Batch Planning with a Focus on Local Optimization – Closely tied to the issue of misaligned KPIs, local optimization occurs when individual departments or production zones prioritize their own efficiency over overall operational efficiency. This can lead to departments producing large batches or components ahead of time to avoid setup costs or to meet their own output targets. These efforts to maximize efficiency within one department often lead to mismatches elsewhere, missing parts at downstream convergence points, inconsistent work availability at subsequent work centers causing delays, inefficiencies, and excess WIP throughout production system.

Fixing the Flow: Proven Strategies to Reduce WIP

Addressing WIP buildup typically requires implementing lean principles, such as pull-based systems (e.g., Kanban), optimizing constraints (Theory of Constraints), reducing batch sizes, and using KPIs that reflect overall system performance rather than local optimization. The goal should always be to enable a smooth, synchronized production flow that minimizes waste while meeting actual demand. Within Lean and TOC are several frameworks that directly address the issue:

Just-In-Time (JIT) – JIT is a manufacturing methodology that aims to reduce waste by producing goods only as they are needed for the next phase of production or for customer delivery. By aligning production schedules closely with actual sales or downstream usage, JIT minimizes the time WIP spends queuing between each step. While production runs are typically smaller under JIT, this means fewer items are in the system at any given time and only what is needed is being produced.

While not synonymous, JIT is often a feature of Lean manufacturing environments as pull system techniques like Kanban are leveraged to create a workflow where production is “pulled” by consumer demand rather than “pushed” based on forecasts.

Implementation Best Practice

We’ve seen many JIT initiatives go wrong when the manufacturer tries to push ahead too fast. They immediately eliminate almost all inventory, anticipating immediate improvements. The best success is achieved when JIT is implemented as an incremental process:
1. Start by reducing (not eliminating) inventory levels in one area of the business.
2. Monitor the system to identify issues caused by the reduced inventory.
3. Address the issues identified in step 2, then repeat the process starting at step 1.
Each time through the cycle will yield incremental performance benefits while minimizing business disruptions.

Theory of Constraints (TOC) – TOC states that there is generally one or a limited number of constraints (the weakest link) in any system at any given time. Production should be synchronized to these constraints. Failure to identify and manage constraints properly can lead to excessive WIP throughout the system, hindering material flow and increasing lead times.

Value Stream Mapping (VSM) – VSM is another tool used in Lean manufacturing to visualize and analyze the flow of materials and information required to bring a product or service to the customer. Identifying all the steps in a value stream makes it easier to see where WIP is piling up and why.

Waste Reduction – A fundamental principle of Lean, waste reduction involves eliminating anything that does not add value for the customer. JIT and VSM are useful tools for identifying excess waste. In addition, Kaizen events, where employees at all levels of an organization work together to proactively identify potential improvements to the manufacturing process, can also help reduce WIP.

How SyncManufacturing^® Enables Greater WIP Control

Technology plays an important role in implementing continuous improvement philosophies such as Lean Manufacturing and TOC. SyncManufacturing APS is our customers’ control tower for implementing proven, demand-driven processes that lower WIP and improve flow. Here’s how it works:

Finite capacity scheduling: Most ERP systems create production schedules based on material availability but assume infinite factory capacity. SyncManufacturing leverages finite capacity scheduling (also known as finite capacity planning) to create production schedules based on the actual availability and capacity of resources, including machines and personnel. Production schedules at upstream resources are optimized for capacity downstream. This minimizes queue times and smooths the flow of materials and WIP through the factory.

Real-time demand-driven scheduling: While SyncManufacturing can be used in a make-to-stock environment, many of our customers leverage our APS system to move towards true demand-driven manufacturing. As customer orders come in, SyncManufacturing uses real-time resource availability and capacity data to produce realistic capable to promise dates. When orders are accepted, schedules are updated and resources are allocated to the job.

Dynamic scheduling: SyncManufacturing adjusts and updates production schedules in real-time based on changes in the production environment, such as equipment downtime, material shortages, or changes in demand. This adaptability ensures that the flow of work remains efficient and prevents bottlenecks, which can lead to excess WIP.

Global flow optimization: SyncManufacturing aligns all production activities to ensure a seamless flow of materials and tasks across the entire value stream. By analyzing and optimizing the overall system rather than individual processes, it balances production loads and eliminates unnecessary buildup of inventory and WIP.

Constraints management: CONLOAD^™ is our proprietary algorithm for managing constraints effectively. The most critical resources (constraints) are identified, and the amount of work released into the system is controlled based on these constraints to minimize queue times, improve flow, and decrease WIP.

Real-time visibility and decision support: SyncManufacturing provides real-time visibility into every aspect of the production process using dashboards and integrated monitoring tools. This allows manufacturers to monitor WIP levels, identify bottlenecks, and make informed decisions instantly. Decision-support tools include predictive analytics and alerts, which help manufacturers proactively address potential issues before they escalate.

Do You Have Too Much WIP?

Excess WIP is more difficult to hide than other inventory management issues. Data trends like increasing queue and cycle times or decreasing inventory turns are good indicators of WIP that is becoming bloated. Production managers may also walk around the production environment and spot piles of work sitting in front of workstations or teams sitting idle as they wait for materials.

This excess WIP may be costing you more than you think! Contact us or schedule a personalized demo to see how you can improve and get rid of excess WIP to cut costs and improve production flow.

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October 29, 2025
Time to Revisit Your Constraints

We talk a lot about constraints management in our work with customers who are implementing Demand-Driven Manufacturing (DDM) in their facilities. That’s because constraints management is fundamental for synchronizing the pace of production and keeping the demand (orders) flowing throughout the shop floor. But, our focus is naturally on physical constraints, e.g., that piece of equipment or workstation that is preventing you from delivering on time or offering shorter, more competitive lead times to your customers.

Not Everything is About Production

Those of you who have spent time studying the Theory of Constraints (TOC) in-depth understand that it’s not always all about the production process. Constraints can fall into one of four categories:

Physical – These are the constraints we focus on with technologies like CONLOAD™ that set the pace for production based on the capacity of the physical constraint.

Policy – These constraints dictate how work is performed. Sometimes you can do something about them (e.g., an old company policy that no longer makes sense), and sometimes you can’t (e.g., a regulation that still might not make sense but needs to be followed anyway).

Paradigm – This is a way of thinking that gets in the way of meeting commitments, such as the COO’s resistance to outsourcing processes to other companies even if they can do it faster, better or cheaper than you can.

Market – Put simply, capacity exceeds demand. Remember, TOC emphasizes throughput (The rate at which the system generates “goal units,” Goldratt) and not productivity.

For some manufacturers, the real constraint over the last decade has been their market. Manufacturing production has seen its share of ups and downs in the last ten years. It wasn’t that our facilities couldn’t produce more, many manufacturers just didn’t have the orders to warrant increased production.

Shifting Your Paradigms

Early indications are that many of the market constraints on US manufacturers may be melting away in 2018 through 2020. (Along with a few policy constraints.) Manufacturing GDP is expected to slightly outpace GDP for all industries (2.5%) and grow by 2.8%. (Some analysts are predicting even higher numbers, but like our customers, we prefer to focus on more conservative estimates when doing mid-term forecasting.) The stock market is also at an all-time high, indicating strong investor confidence and more money for investment. Oil prices are expected to remain low, keeping the cost of manufacturing and transportation of goods to market in check.

In other words, it’s time to take your focus off the market constraints you can’t do much about and place it on the constraints that are within your control. If you have outdated policy or paradigm constraints, it’s time to rethink your thinking. If you have physical constraints – leverage them to set the optimal pace for uninterrupted production flow.

Time flies and so do great economies. Don’t let the best market in a decade pass you by without taking advantage of it. If your constraints are physical, here are a few resources that may help:

Video: Manage Manufacturing Constraints and Optimize Production Flow with CONLOAD

White Paper: Metrics That Drive Action

Case Study: GIW Industries

February 5, 2018
3 Ways to Put Big Data to Work in Your Factory

Is enthusiasm for Big Data wavering?

In 2015, McKinsey Global Institute claimed that the IIoT had the potential to create as much as $3.7 trillion in economic value in the global manufacturing sector by 2025. They also predicted that 80 to 100% of manufacturers will have implemented IIoT applications by then and already be reaping the benefits of data-driven insights into their operations.

When Gartner surveyed manufacturers in 2016, nearly three quarters said that their organization had invested or were planning to invest in Big Data, perhaps putting the manufacturing sector a bit ahead of schedule.

However, the Gartner survey also uncovered signs that Big Data investments may not yet be providing the anticipated returns. A full 85% of projects were still at the pilot stage. And, as further evidence that enthusiasm for Big Data may be wavering, only 11% of those who said they had invested claimed their Big Data investments were at least as important as other IT initiatives.

To drive ROI, begin with a purpose in mind

From our perspective, a large part of the reason Big Data/IIoT projects fizzle out is because team leaders and company executives don’t have a clear vision of the purpose of the initiative. They gather data as though it were a valuable raw material, but then they struggle to make anything useful out of it.

In this post, I’ll cover the three ways you can use Big Data to improve operational performance.

#1 Predictive analytics – The most common benefit espoused by Big Data enthusiasts is gaining insight into what might happen so you can prepare. Bernard Marr, a noted speaker and columnist for Forbes, describes it this way. “Big Data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insights and make predictions about what will happen in the future.”

Predictive maintenance is probably one of the best-known applications of predictive analytics and Big Data. Before the IIoT, manufacturers had to guess how long a piece of equipment would last and when it would need maintenance. Unplanned downtime was common and costly.

Intelligent machines (even if that intelligence is retro-fitted) provide alerts on when the equipment is performing outside of normal parameters, e.g., running at a higher temperature indicating excess friction. And when connected to smart manufacturing tools like SyncOperations™, automated workflows and alerts to maintenance address the issue before it becomes a problem. From a demand-driven manufacturing perspective, this turns unplanned downtime into planned downtime and gives the planner/scheduler time to adjust and optimize flow.

Related resource: How Technology Will Connect Your Enterprise and Create the Demand-Driven Factory of the Future – Today.

#2 Continuous improvement – Continuous improvement is the cornerstone of any Lean initiative and has become a best-practice throughout the industry, even in those organizations that don’t consider themselves Lean. Big Data gives you the data you need to measure what matters and the ability to work with real data as opposed to someone’s best guess about what’s happening on the factory floor.

Of course, it goes without saying that a BigData initiative is only as good as the data the manufacturer has to work with – and if the right data can be accessed by the right people at the right time. In a typical manufacturing operation, data may be stored in dozens of places. Managing issues impacting production is easier with software like SyncManufacturing™ that can leverage its own data in addition to that stored in an ERP or other external system – and use it to make real-time adjustments to ensure production is flowing and resources are synchronized throughout the factory and extended supply chain.

Related resource: Metrics that Drive Action

#3 Real-time responsiveness – Finally, most manufacturing operations can be considered something like “controlled chaos.” Rush orders come in. People get sick. Raw materials shipments are delayed. Scheduling to known constraints is a piece of cake compared to optimizing flow when the unexpected happens. Demand-driven manufacturing can take signals from the shop floor to automatically synchronize production based on what is actually happening in your operations.

Related resource: Set the Right Pace for Production

Just as you wouldn’t buy a piece of equipment without knowing what it’s for, you shouldn’t launch a Big Data initiative without knowing what you want to accomplish. Beginning with a clear idea of what you want to accomplish can help keep enthusiasm high and ensure you see a return on your investment and efforts.

October 16, 2017

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