Cycle Counting in Inventory Management

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Table of Contents

Introduction
Cycle Counting
Benefits of the Technique
Key Issues in Designing and Implementing this Technique
The Main Requirements of Effective Implementation
Examples of UAE
Conclusion
Bibliography

Introduction

Improvements in production and inventory control translate directly into customer value through better product quality, cost, and delivery. This impact dictates that the production and inventory system must be a focal point in a manufacturing firm’s strategy to compete through customer value. Chemical industry is one of those industries that demanded innovative inventory management techniques which help it solve daily problems and maintain quality control.

The inability of many firms to achieve good production and inventory stems from fundamental deficiencies in certain organizational systems. The role of management must be that of identifying and improving those systems. The purpose of inventory is to insulate each step of the production process from the other steps and to protect the production process from the suppliers and the customers. Cycle Counting is an innovative technique that allows chemical plants to manage accuracy and procedure execution.

Cycle Counting

Following Stahl (2007): “the accuracy of inventory records is the “product” of the inventory keeping practices. The records are only as accurate as the record transactions themselves. As such, procedures must be in place not only to assure that expected results are achieved, but to provide feedback so that causes of error can be identified”. Although this protective barrier may make life easier in the short run, it is an obstacle to understanding and improving the systems involved. Inventory holding costs, as traditionally measured, are so incomplete in their representation of the real cost of inventory that they are misleading.

For example, consider two consecutive steps in a manufacturing process in which defects in items produced in the first step cannot be detected until the items are used in the second step. In the absence of accuracy between the two steps, defects will interrupt production, thus making the problem very visible (Bragg 2004). The absence of inventory will also permit defects to be discovered as they occur. This facilitates tracking the causes of defects, so that process improvement becomes easier.

Thus, any inventory between these two steps is a barrier to improvement, and a cost of inventory is incurred. Inventory makes it possible to live with machine breakdowns, unreliable vendors, inaccurate inventory records, unpredictable yields, long and unpredictable setup times, absenteeism, unreliable forecasts, long and unpredictable lead times in processing customers’ orders, poor quality, and poor scheduling. (It has often been said, not completely in jest, that every evil known to manufacture manifests itself in some form of inventory (Chase & Jacobs 2003).

The main types of cycle counting are

“geared at identifying the causes of error, and
measuring the level of conformance to expectation.

These cycle counting types are Control Group Cycle Counting and “Random” Cycle Counting” (Stahl 2007). Accuracy and procedure execution make quality problems more visible. For example, in the absence of buffer inventories, the production of a batch of defective parts may cause a shutdown of the entire production process. Although this may appear to be a costly experience (and maybe in the short run), in the long run it can actually be a very profitable experience.

The shutdown may draw attention to the problems that cause the defects and, therefore, lead to the problems being fixed. In fact, the knowledge that a batch of defective parts can shut down the process may be sufficient to stimulate proactive efforts to improve quality. On the other hand, when large buffer stocks are present, a batch of defective parts may simply be scrapped while the production process continues uninterrupted. Thus, the problems that caused the defects remain invisible and unsolved while losses resulting from poor quality accumulate. These losses may, in the long run, far exceed the cost of a shutdown.

Scheduling begins with a master schedule, which is the planned weekly production of finished products, for several weeks into the future. This schedule is converted into gross requirements for level one items (items used directly to make the finished product) (Lin & Esogbue 1999). The existing inventory and scheduled receipts of these level one component items are then subtracted from the gross requirements to weekly net requirements.

These weekly net requirements determine the offset by an estimated lead time (consisting of queue time, setup time, run time, and move time) to yield the weekly production levels for the level one component items. This production schedule for the level one items is then used to develop a schedule for items two steps removed from the finished product, or level two items. This process is repeated at successively higher levels until weekly production schedules for all manufactured items and weekly planned order releases for raw materials have been obtained (Bragg, 2004).

The main steps in Control Group Cycle Counting are selection, counting, comparing the records, error identification, strategy development and announcement of the results. This technique allows quicker detection of quality problems. In many production processes, defects introduced at one step of a process may not be detectable until a later step of the process. If there is a week’s worth of buffer inventories between these two steps in the process, the possibility exists for a week’s worth of defective inventory to be produced at the former step before the problem is detected at the latter step (Bragg, 2004).

Even if such an extreme problem is unlikely, other equally expensive (in the long run) problems may exist. Problems occurring at the first step will be detected, at best, one week later. By this time the events that caused the problem are typically forgotten. The same problem occurs repeatedly, but its causes are never isolated and corrected. A less obvious but very important impact of improved production scheduling and inventory control is the facilitation of statistical process control activities (Lin & Esogbue 1999).

Benefits of the Technique

In chemical plants, error problems are very costly, but they are sometimes difficult to see and impossible to solve in the presence of large inventories. There is a popular analogy relating inventory to water in a river and the problems with the rocks below the surface. Each rock is a hazard to navigation. In order to cope with the rocks, one can add more water (increase inventories) or attempt to navigate around the rocks (use a more expensive and complex production scheduling system). These methods of coping with the rocks are the more expensive ways. The rocks can only be discovered and removed by gradually lowering the water until they are exposed (Lin & Esogbue 1999).

For many chemical plants, cycle counting is more effective because other inventory systems in using this predetermined schedule to coordinate operations, have an inherent weakness in dealing with variation. For example, even though a particular operation may be behind schedule, the MRP schedule continues to push inventory toward that operation. Enhancements such as closed-loop MRP and input-output control are reactive mechanisms that make periodic adjustments to the schedule, in an attempt to compensate for variation (Bragg, 2004).

As feedback loops, these mechanisms are supposed to reduce variation but have the capability of increasing variation if improperly used. In contrast to cycle counting, these systems also ignore capacity constraints, or, in the case of finite loading MRP, they only deal with them in an ad hoc manner by making heuristic adjustments to the master schedule. Thus, the schedule derived may load some operations beyond capacity (i.e., the schedule may not be feasible).

In practice, such problems are often managed by building extra lead times into the schedule, so that the inevitable delays at various overloaded operations will not disrupt the overall schedule. However these long lead times translate into high inventories. “Research shows that the more often a product is received or shipped, the less accurate its computer stock balance. This makes sense. Every time someone goes to the bin is an opportunity for a mistake (or to coin the new term, an “equality event”) to occur” (Schreibfeder 1997).

The assumed benefit of cycle counting is that it provides protection against idle work center capacity. Long production runs of similar items and the resulting inventories reduce the frequency of downtime for setups. Buffer stock inventories between work centers protect each work center from the variation at other steps that could cause downtime owing to starvation and blockages in the workflow. This variation may be due to unpredictable yields, unscheduled downtime, unreliable vendors, unpredictable run times, unpredictable quality, inaccurate inventory records or absenteeism. The assumption of such benefits ignores two important facts.

First, the production system is amenable to improvements that can reduce the time required for setups and reduce the sources of variation. Thus, there are alternative means of eliminating wasted capacity. For example, the time saved by eliminating a setup at a work center that is not needed at full capacity becomes either idle time or, even worse, time used to produce unneeded inventory. The essential focus in these efforts is achieving an understanding of the sources of variation in quality attributes. Among the potential sources of variation are the materials, methods, equipment people, and environment (Muller, 2002).

Identifying the sources of variation is difficult when it is not possible to identify for a given item the conditions encountered at each upstream step of the process. This identification is impossible when items sit idle for long periods of time between steps of the processor when the production order of the items becomes scrambled between steps in the production process. On the other hand, it is much easier when items that exit the process together not only have gone through each upstream step of the process together, but have done so very recently (Lin & Esogbue 1999).

Key Issues in Designing and Implementing this Technique

The key issues in designing and implementation are stock-level information and management support. These tools reflect an attitude that the manager’s role is to optimize the performance of the existing system, rather than to improve the system. For example, there is a major focus on tools for coping with variation, such as safety stock formulas (or, equivalently, safety lead time formulas), input and output control systems, and closed-loop material requirements planning systems.

These tools are designed to provide a cushion against variation or to make continual corrections to compensate for variation (Piasecki, 2003). There is little focus on reducing the variation. Similarly, lot sizing has focused on determining the best lot size, given a setup time required, rather than on reducing the time required for setups. There has been a similar focus on developing long-term forecasts of demand (a typically futile undertaking) rather than on reducing lead time so that production can be, planned from customers’ orders (Muller, 2002)

Traditional means of performance measurement tend to bias the decision-making process in the direction of higher inventories. These means include the cost accounting system as well as the local measures of performance used on the floor. A similar distortion occurs with inventory. When a machine is shut down owing to a stockout, it is immediately and clearly visible. Furthermore, the resulting drop in utilization shows up as a tangible “cost” in the monthly utilization and efficiency reports (Piasecki, 2003).

On the other hand, the costs of having too much inventory are more insidious. For example, the cost of losing customers owing to poor quality and long lead times is not immediately visible and certainly not recognizable as inventory holding cost. Hence, a “better to have inventory and not need it, than to need it and not have it” mentality evolves (Slack et al 2003). Inventory control is typically viewed as a process that makes tradeoffs between high inventory costs and low utilization of capacity.

This tradeoff is an important aspect of all production scheduling techniques. For example, material requirements planning relies on long predetermined lead times between operations that create buffer stocks as cushions against variation. Just-in-Time systems use less-than-capacity scheduling of equipment (in conjunction with worker flexibility).

The slack capacity and the ability of the workers to do more than one job serve as a cushion against variation. The problem of inventory control should not be viewed as one of simply making a tradeoff between the evils of wasted capacity and the evils of excess inventory. This tradeoff is necessary because of variation in the process. Inventories can be reduced and capacity utilization can be improved simultaneously through a reduction in variation. Cycle counting increases manufacturing lead times. This fact is often counter to intuition. Typically, the response to a demand for shorter customer lead times is to increase inventory levels. However, inventory located upstream from an operation represents a waiting line through which each order must pass. The longer the line, the longer the order will take to get through the system (Piasecki, 2003).

For example, if a manufacturing process has six weeks of work-in-progress inventory, then materials released at the first step of the process will exit as finished goods, on average, six weeks later. This means that in order to produce customers’ orders, the customer lead time must be six weeks, that is, orders must be placed at least six weeks in advance. If the customer lead time is to be shortened without reducing the work in progress, the production schedule must be developed from a forecast that may be very unreliable (Slack et al 2003).

The Main Requirements of Effective Implementation

Many chemical plants hold an excessive amount of inventory, as much as a third of their total assets. One reason for such large inventories is that firms have not undertaken the improvement efforts eventually required in inventory reduction. Even so, most firms hold more inventory than is necessary, even in the absence of these improvements. Indeed, a reduction in inventory is typically a needed first step in the improvement process.

Since most manufacturing organizations do not have a policy of intentionally holding excessive inventories, there is an obvious question as to why high inventories consistently evolve. There obviously must be organizational biases toward higher inventories. The most fundamental reason, discussed in the following section, relates to the way managers perceive their role in organizations (Piasecki, 2003).

The main companies this technique is ideally suited for are plants with large production cycles and frequent shipment. In such companies, variation will result in either large inventories, wasted capacity, or both, regardless of the production scheduling mechanism used. If the cycle counting is biased toward high inventories, then there will be high inventories. However, the production scheduling mechanism used will have an impact on the ability to locate systemic problems and implement improvements. It can be instrumental in exposing the sources of variation or the bias in the performance measurement system.

Production scheduling systems can be classified into three categories: push systems, pull systems, and drum-buffer-rope. In push systems, for example, material requirements planning (MRP), production is coordinated through a detailed (normally computer-based) production. The items are pushed through the system by this schedule. In pull systems, items are produced in response to downstream consumption of that item. Thus, items are pulled through the system in response to downstream demand (Piasecki, 2003).

Cycle counting will be less effective in small plants relying on internal controls and with short product cycles. In cycle counting, systems the production of component items are triggered by downstream demand. A predetermined schedule is developed only for the finished product. For example, production items may be moved to downstream usage points in standard containers each of which holds twenty pieces (Greasley, 2005).

When a container is emptied at a usage point, it is sent back upstream to the operation where the item is produced. An empty container acts as a work order requesting the production of twenty more pieces of the given item. Similar coordination is achieved with a card system in which cards attached to production items are passed back upstream when the items are used. Cycle counting responds immediately to variation, that is, variation in the production rate is immediately communicated to upstream operations. Cycle counting also facilitates deliberate reduction of inventory (by simply reducing the number of cards and containers), which is necessary to expose problems and improve systems (Christopher 2005).

Examples of UAE

The main chemical plants successfully using this technique are Chlor-alkali plant in Dubai, a chemical plant in Abu Dhabi, and a state-owned Safewater Chemicals LLC. At these plants, accuracy of all operations is the main priority. The excess capacity at these operations can be profitably used for inventory reduction through smaller lot sizes and reduced buffer stocks. With excess capacity the machine time used for extra setups does not reduce the overall production rate of the process and is, therefore, virtually free (The Industry (UAE), n.d.).

Similarly, since the work center can use its excess capacity to recover from disruptions to its schedule, it can also accommodate periodic starvation caused by upstream disruptions. Hence, an upstream buffer may not be necessary (Bragg, 2004). The drum-buffer-rope approach differs from other scheduling methods in several important aspects. Scheduling priorities are determined by the capacity constraints. Lead times for each operation are not predetermined (whereas in MRP lead times are specified before the schedule is derived).

Process batches are broken down into smaller transfer batches in order to allow the operations on the batch to overlap in time. Lot sizes are not fixed but vary from time to time and from operation to operation. The random cycle counting approach assumes that the process has a small number of operations that are actually bottlenecks and that these bottleneck operations do not change with the normal variations in the order mix. The method also assumes that it is possible to identify these bottleneck operations and to accurately specify setup times and run times so that realistic schedules for the bottlenecks can be developed. Obviously, these assumptions are not valid for all manufacturing processes (Christopher 2005).

Accuracy typical for cycle counting manifests itself in the ability of logistics to create value for the customer through the uniqueness and distinctiveness of logistical service. The logistics process has several unique characteristics. First, it is comprehensive, extending from the original source of raw materials to the location of the final customer. In fact, the logistics process can, and does, span organizational boundaries in terms of encompassing very comprehensive, industry-wide channels of supply and distribution (Chase & Jacobs 2003).

The second characteristic is that it pertains to the flows of both product and information, and considers each as essential to the value-creating process. This concept has received broad acceptance and acknowledges the critical role of logistics in the overall area of information processing and management. Third is that logistics represents a viable means to satisfy and create value for the external customer(s) of the firm and/or the channel of distribution.

It is this dimension that truly justifies the recent attention directed toward the new role of logistics management. Third, considerable attention has been directed toward the integrative aspects of logistics, and the fact that the length and consistency of the customer “order cycle” are emerging as a key concern of firm-wide interest. In effect, the integrative aspects of logistics (to be discussed subsequently) have qualified this area to be a major contributor to the creation of customer value. As a result, managers have been actively repositioning their efforts in the interests of facilitating cross-functional coordination in order to best serve the customer (Bragg 2004).

It is encouraging to see logistics managers work closely and consistently with their counterparts in areas such as marketing, manufacturing, finance, and general management. This type of activity is a significant factor in helping to eliminate the so-called functional-silo syndrome that has been so negative and characteristic of the past. Accuracy increases the organization’s ability to provide the desired product/service mix at a level of cost that is acceptable to the customer.

This concept implicitly identifies the need for logistics to manage its resources wisely and to leverage expense into customer value whenever possible. The interests of efficiency are well served, for example, by the current interest in and trend toward the use of activity-based cost management systems. Workers in high-performance systems expect to participate proportionately in the economic product of their efforts, but if they have no trust or confidence in their management (Greasley, 2005).

Conclusion

Cycle counting decisions influence prices, middlemen activities, and margins. They strongly affect inventory situations and production fluctuations, as well as marketing policies in such areas as advertising, branding, product lines, personnel selling, and physical distribution. Where plants are concentrated for mass production while raw materials and finished product markets are geographically dispersed, then warehousing becomes extremely important.

As plants are decentralized and located near material sources and markets, then warehousing requirements are relatively less important. The related point is the impact of market development; markets expand, volume increases, and plant decentralization is feasible. A benefit of cycle counting is that they focus attention on the need for accuracy of the inputs — inventory records, customers’ orders, forecasts bills of materials, and yield formulas. Thus, companies that have gone through the implementation of these methods tend to have a more complete database, a higher level of data integrity, and a better ability to monitor the inventory flows in the production process.

Bibliography

Bragg SM, 2004, Inventory Best Practices, Wiley Hobokan New Jersey.
Chase R.B., Jacobs R.F. 2003, Operations Management for Competitive Advantage, Hill/Irwin; 10 edition.
Christopher M, 2005, Logistics and Supply Chain Management, Creating Value-adding Networks, Pearson Edinburgh Gate.
Greasley, A. 2005, Operations Management. John Wiley & Sons.
The Industry (UAE). n.d. Web.
Lin B, Esogbue A, 1999, Decision Criteria for Optimal Inventory Processes, Kluwer International Series, Boston.
Muller, M.2002. Essentials of Inventory Management, AMACOM.
Piasecki, D. 2003, Inventory Accuracy: People, Processes, & Technology, Ops Publication.
Schreibfeder, J. Cycle Counting Can Eliminate Your Annual Physical Inventory! Web.
Slack N., Chambers S. Johnston R. 2003, Operations Management FT Prentice Hall.
Stahl, R. A. Inventory Record Accuracy. 2007. Web.

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