Just as manufacturing realities are continuously changing, so is lean thinking evolving. For example, traditionally, given competitive realities, it was almost exclusively automotive companies that deployed lean techniques such as kanban and sequencing. Today, however, there are some strong indications that only one in five companies using kanban are in the automotive industry.
This is Part Six of a multi-part note.
Customarily, lean endeavors lead almost inevitably to flow manufacturing, focused factories, or cellular manufacturing. This is because a key focus of lean is to do only what is needed. It is the polar opposite of the traditional economies of scale, with their large batch approach and resulting long lead times and bloated inventory levels. Large lot sizes are a way of compensating for the fixed cost of a process, such as changeover or set-up costs, transaction-level costs (e.g., releasing orders, issuing parts, closing and reconciling orders, moving product batches into stocks, etc.), and other per order factors. With a large run, these costs can be distributed over a larger number of units, and thus become a smaller cost on a per piece basis. As long as changeover costs are high, small lot quantities are not cost efficient or justifiable. The obvious solution, then, is to lower or eliminate these fixed costs as much as possible so that smaller runs become feasible.
It is this type of thinking that results in production lines that are designed so that there is little or no cost to change from one product to another. This means that a lot size of one (or only a few) is as economical as a large lot on less efficient, non-lean premises. But to achieve this, it is often necessary to restrict the range or variety of product processed in a given cell. Thus, despite those who proclaim that flow manufacturing principles can be implemented successfully regardless of the industry, type of manufacturing environment, or product volumes, the concept has not been all things to all people so far. There are many instances where either flow manufacturing is not appropriate or it is simply not affordable for companies to rearrange their facilities to accommodate the convenient movement of work from one resource to the other.
In fact, manufacturers need to do quite a lot of preliminary work, such as adapting their plants to a flow production model, before even thinking about deploying demand-driven manufacturing software. In other words, they will have to operate in work cells that build families of products, rather than in functional work centers that produce large batches of components or products. They will also need established rules for sending replenishment signals to their internal (i.e., preceding work station) and external suppliers. By establishing time-based process families (and techniques similar to pitch) and monitoring resource loads routinely, there could be a relatively rapid and significant reduction in manufacturing cycle time and a corresponding improvement in delivery performance and productivity, even in job shop environments. Still, these changes will not happen overnight, and the process should begin with the conversion of a few appropriate products with relatively simple production processes, and then progress to other product lines. The implementation of such changes explains why many manufacturers happen to be in a hybrid production mode, with part of the plant running according to flow principles and the rest using traditional material requirements planning (MRP) methods.
For some companies, however, there is simply not enough product similarity to make even this practical. It is challenging or even unsuitable to deploy flow or cells in a job shop that makes highly configured-to-order (CTO) or engineered-to-order (ETO) products with high setup times and long lead times. These companies might still appreciate kanban replenishment and demand smoothing, but not line design and standard operation procedures (SOP) or operation method sheets (OMS), since these features would not bring much benefit, if any, to ETO manufacturers. However, such companies' product families often include products that require one or two unique and expensive components in addition of their share of common parts, which could benefit from flow methods of smoothing spikes in demand.
In fact, with appropriate changes in workflow management and the appropriate software to help manage the approach, even companies operating in particularly complex environments can realize significant benefits. For instance, smaller make-to-order (MTO) companies, those that make large or complex products in small quantities or one-at-a-time, and those unwilling or unable to rearrange the plant for flow manufacturing should still be able to reap the primary lean benefits of smaller lots, shorter lead times, reduced inventory and work-in-process (WIP), and higher quality. Infor, for example, claims that dozens of its customers, operating in a similar environment, have had significant improvements in performance and profitability within two or three months. As long as management buys in, the methods and the tools are available. There is some relativism to be considered, however, since the improvements may not be on par with those obtained by high volume, repetitive manufacturers. Nonetheless, relative to industry competition, results could be quite impressive.
In a nutshell, flow systems cannot handle demand variability, variable product mix, shared resource constraints, or complex products with long lead times. This limits flow's applicability to items where variability is only at the end item mix, and not with frequent content variations of option mixes. For this reason, as well as all the above reasons, most manufacturers implement this method gradually and use flow manufacturing to make one product family at the time. This necessitates the use of enterprise resource planning (ERP), MRP, or advanced planning and scheduling (APS) for the rest of the business (see Best Manufacturing Scheduling Systems).
While flow manufacturing may have limits in terms of the complexity it can handle, MRP is not without its drawbacks either. MRP is a set of techniques that uses bills of material (BOM) data, inventory data, and the master production schedule (MPS) to calculate requirements for materials so as to make recommendations to release replenishment orders for materials. Because MRP is time-phased, it makes recommendations to reschedule open orders even when due dates and need dates are not in phase. MRP will, by default, create orders with specific due dates for products. Consequently, to manufacture these orders, companies prioritize resources based on these calculated due dates. The unfortunate result is that other orders, perhaps more important orders, are neglected, which often leads to overtime in the factory. Therefore, slack needs to be built into the schedule through conservative, often unjustifiably pessimistic lead times.
Combined with information from actual customer orders, MRP is still the tool most widely used in manufacturing industries to track, monitor, and order the volumes of components needed to make a certain product. However, for the above reasons, many manufacturing environments have discovered that MRP has trouble controlling stock levels, which results in poor delivery performance.
Moreover, MRP is incapable of handling demand-driven, ever-changing manufacturing, since it works especially well when demand for a particular product is constant and predictable. If there is any variation in demand, however, MRP loses many of its advantages and the benefits of using alternative planning approaches increase. In fact, the main flaw with MRP is that it is too deterministic—it does not allow for the natural variation that occurs in real life (e.g., people get sick or go on strike, trucks or shipments get delayed, machines malfunction, quality issues require scrap or rework, and customers do not always, if ever, order according to forecasts). In other words, MRP is a static model of a stochastic reality. Manufacturing requirements change all the time, according to customer orders, available parts, and so on; thus, MRP attempts to apply a high degree of precision to something that is inherently imprecise.
However, Just-in-time, Lean, and Flow Are Not Universal Panaceas
The challenge in using lean and flow manufacturing as a panacea for the shortcomings of MRP is often in setting the number of kanban cards in the system and the size of the kanban. Even with a help of computerized systems, this can become complex if the demand for each product varies significantly and the production layout is not line- or cell-based.
The just-in-time (JIT) approach normally begins with limiting inventory in the system using a two-bin kanban method. This prevents the shop floor from being flooded with inventory and WIP, and the result is shorter production cycle times and improved inventory control. With JIT, production planning centers efforts on takt time, and the result is that production volumes are determined by the market rate of pull. Process improvement is achieved by gradually reducing kanban size and monitoring decreased inventory, but JIT is useful mainly where demand is relatively stable and there is single-piece flow production feasibility.
In more of a job shop environment, however, the kanban JIT approach no longer makes sense, since the product mix by product type, routings, and process times becomes widely divergent, causing the prediction of kanban sizes to be impractical and temporary, or wandering bottlenecks to appear all over the shop floor. Indeed, where the order mix changes or not all resources are dedicated to lean flow manufacturing, then kanban sizes must continuously be reevaluated. In these situations, a theory of constraints (TOC) approach is often more appropriate.
This is Part Six of a multi-part note.
Customarily, lean endeavors lead almost inevitably to flow manufacturing, focused factories, or cellular manufacturing. This is because a key focus of lean is to do only what is needed. It is the polar opposite of the traditional economies of scale, with their large batch approach and resulting long lead times and bloated inventory levels. Large lot sizes are a way of compensating for the fixed cost of a process, such as changeover or set-up costs, transaction-level costs (e.g., releasing orders, issuing parts, closing and reconciling orders, moving product batches into stocks, etc.), and other per order factors. With a large run, these costs can be distributed over a larger number of units, and thus become a smaller cost on a per piece basis. As long as changeover costs are high, small lot quantities are not cost efficient or justifiable. The obvious solution, then, is to lower or eliminate these fixed costs as much as possible so that smaller runs become feasible.
It is this type of thinking that results in production lines that are designed so that there is little or no cost to change from one product to another. This means that a lot size of one (or only a few) is as economical as a large lot on less efficient, non-lean premises. But to achieve this, it is often necessary to restrict the range or variety of product processed in a given cell. Thus, despite those who proclaim that flow manufacturing principles can be implemented successfully regardless of the industry, type of manufacturing environment, or product volumes, the concept has not been all things to all people so far. There are many instances where either flow manufacturing is not appropriate or it is simply not affordable for companies to rearrange their facilities to accommodate the convenient movement of work from one resource to the other.
In fact, manufacturers need to do quite a lot of preliminary work, such as adapting their plants to a flow production model, before even thinking about deploying demand-driven manufacturing software. In other words, they will have to operate in work cells that build families of products, rather than in functional work centers that produce large batches of components or products. They will also need established rules for sending replenishment signals to their internal (i.e., preceding work station) and external suppliers. By establishing time-based process families (and techniques similar to pitch) and monitoring resource loads routinely, there could be a relatively rapid and significant reduction in manufacturing cycle time and a corresponding improvement in delivery performance and productivity, even in job shop environments. Still, these changes will not happen overnight, and the process should begin with the conversion of a few appropriate products with relatively simple production processes, and then progress to other product lines. The implementation of such changes explains why many manufacturers happen to be in a hybrid production mode, with part of the plant running according to flow principles and the rest using traditional material requirements planning (MRP) methods.
For some companies, however, there is simply not enough product similarity to make even this practical. It is challenging or even unsuitable to deploy flow or cells in a job shop that makes highly configured-to-order (CTO) or engineered-to-order (ETO) products with high setup times and long lead times. These companies might still appreciate kanban replenishment and demand smoothing, but not line design and standard operation procedures (SOP) or operation method sheets (OMS), since these features would not bring much benefit, if any, to ETO manufacturers. However, such companies' product families often include products that require one or two unique and expensive components in addition of their share of common parts, which could benefit from flow methods of smoothing spikes in demand.
In fact, with appropriate changes in workflow management and the appropriate software to help manage the approach, even companies operating in particularly complex environments can realize significant benefits. For instance, smaller make-to-order (MTO) companies, those that make large or complex products in small quantities or one-at-a-time, and those unwilling or unable to rearrange the plant for flow manufacturing should still be able to reap the primary lean benefits of smaller lots, shorter lead times, reduced inventory and work-in-process (WIP), and higher quality. Infor, for example, claims that dozens of its customers, operating in a similar environment, have had significant improvements in performance and profitability within two or three months. As long as management buys in, the methods and the tools are available. There is some relativism to be considered, however, since the improvements may not be on par with those obtained by high volume, repetitive manufacturers. Nonetheless, relative to industry competition, results could be quite impressive.
In a nutshell, flow systems cannot handle demand variability, variable product mix, shared resource constraints, or complex products with long lead times. This limits flow's applicability to items where variability is only at the end item mix, and not with frequent content variations of option mixes. For this reason, as well as all the above reasons, most manufacturers implement this method gradually and use flow manufacturing to make one product family at the time. This necessitates the use of enterprise resource planning (ERP), MRP, or advanced planning and scheduling (APS) for the rest of the business (see Best Manufacturing Scheduling Systems).
While flow manufacturing may have limits in terms of the complexity it can handle, MRP is not without its drawbacks either. MRP is a set of techniques that uses bills of material (BOM) data, inventory data, and the master production schedule (MPS) to calculate requirements for materials so as to make recommendations to release replenishment orders for materials. Because MRP is time-phased, it makes recommendations to reschedule open orders even when due dates and need dates are not in phase. MRP will, by default, create orders with specific due dates for products. Consequently, to manufacture these orders, companies prioritize resources based on these calculated due dates. The unfortunate result is that other orders, perhaps more important orders, are neglected, which often leads to overtime in the factory. Therefore, slack needs to be built into the schedule through conservative, often unjustifiably pessimistic lead times.
Combined with information from actual customer orders, MRP is still the tool most widely used in manufacturing industries to track, monitor, and order the volumes of components needed to make a certain product. However, for the above reasons, many manufacturing environments have discovered that MRP has trouble controlling stock levels, which results in poor delivery performance.
Moreover, MRP is incapable of handling demand-driven, ever-changing manufacturing, since it works especially well when demand for a particular product is constant and predictable. If there is any variation in demand, however, MRP loses many of its advantages and the benefits of using alternative planning approaches increase. In fact, the main flaw with MRP is that it is too deterministic—it does not allow for the natural variation that occurs in real life (e.g., people get sick or go on strike, trucks or shipments get delayed, machines malfunction, quality issues require scrap or rework, and customers do not always, if ever, order according to forecasts). In other words, MRP is a static model of a stochastic reality. Manufacturing requirements change all the time, according to customer orders, available parts, and so on; thus, MRP attempts to apply a high degree of precision to something that is inherently imprecise.
However, Just-in-time, Lean, and Flow Are Not Universal Panaceas
The challenge in using lean and flow manufacturing as a panacea for the shortcomings of MRP is often in setting the number of kanban cards in the system and the size of the kanban. Even with a help of computerized systems, this can become complex if the demand for each product varies significantly and the production layout is not line- or cell-based.
The just-in-time (JIT) approach normally begins with limiting inventory in the system using a two-bin kanban method. This prevents the shop floor from being flooded with inventory and WIP, and the result is shorter production cycle times and improved inventory control. With JIT, production planning centers efforts on takt time, and the result is that production volumes are determined by the market rate of pull. Process improvement is achieved by gradually reducing kanban size and monitoring decreased inventory, but JIT is useful mainly where demand is relatively stable and there is single-piece flow production feasibility.
In more of a job shop environment, however, the kanban JIT approach no longer makes sense, since the product mix by product type, routings, and process times becomes widely divergent, causing the prediction of kanban sizes to be impractical and temporary, or wandering bottlenecks to appear all over the shop floor. Indeed, where the order mix changes or not all resources are dedicated to lean flow manufacturing, then kanban sizes must continuously be reevaluated. In these situations, a theory of constraints (TOC) approach is often more appropriate.
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