By instructing that parts and sub-assemblies are only produced to meet that demand MRP seeks to meet projected customer demand. Using the bill of materials to calculate how many are needed and when they must be made MRP back schedules demand for part and sub-assemblies. This is how MRP connects customer demand with the internal and external supply network. Although JIT planning and control has similar objectives. Derived from end-customer demand Pull scheduling parts only move in response to matched and synchronised signals (Slack, 2004).
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MRP is driven by the MPS that identifies future end-item demand. Using a computer to calculate how many of, and when, each part should be made a fixed lead-time environment is modelled. Its output is of time-phased environment plans that are centrally calculated and coordinated. Regardless of whether the next process can take or actually need the part at that time it is made in response to central instructions. The authority of MRP is undermine and make the plans unworkable at the shop floor level due to day-to-day disturbances, such as quality problems and inaccurate stock records. While MRP is excellent at planning, it is weak at control (Slack, 2004).
MRP II is more precise than MRP because it instigates production of a variety of components, releases orders and offsets inventory reductions. “MRP II grasps the final product by its parts, orders their delivery to operators, keeps track of inventory positions in all stages of production, and determines what is needed to add to existing inventories” (Karmarkar, 1989).
Through such products as SAP and Oracle, MRP II has led to additional advancements such as ERP. An ERP system consists of a suite of software modules, where each module is usually responsible for a separate business function. Its functionality has increased by new software capabilities; however the core planning and control assumptions that underline these packages have developed less rapidly (Stevenson, 2005).
Case Study: Rolls Royce
One of the world’s largest manufacturers of the gas turbines is Rolls Royce. Their products are used to in civil aircraft, military aircraft, fast ships and power generation in addition to many other uses. Typically each product has around 25,000 parts as they are exceptionally complex products, and have hundreds of assemblies and sub-assemblies. Moreover, their production is equally complex with thousands of work centres in many different locations and over 600 external suppliers. Rolls Royce was one of the earliest users of computers to help with the task due to the complexity of planning and controlling their manufacture. Conventionally the company developed its won systems and software.
The company then decided on implementing a standard ERP system, which was supplied by SAP and was their best selling R/3 product. This system offered a number of advantages over the approach previously used within the company. Significantly, it was an off-the-shelf system that would force the company to implement a standardised and disciplined approach.
Ultimately the entire organisation would use a singular modular database that would reduce duplication and errors.
“There was an extensive data clean up to ensure accuracy and integrity of existing information, and all existing processes were reviewed and standardised….Within operations we have already seen a significant reduction in inventory, improved customer service, and substantially improved business information and controls.” (Slack, 2004)
However, a significant problem with MRP is its inherent assumption that real production parameters can be determined or fixed such as delivery lead-time and lot size. Accurate lead-time estimation and lot size optimisation are the important components to maintain proficient operation of an MRP system (Benton, 1998).
Moreover, the many assumptions made by MRP the most troublesome is the assumption of fixed lead times, with production lead times varying depending on the loading and degree of congestion within the shop (Karmarkar, 1989). This lack of ability of MRP to relate lead times to capacity loading can lead to poor on-time and lead time performance as well as high work-in-process (Karmarkar, 1986).
For all situations faced on the floor an MRP system must use a single lead-time number. Subsequently to cover all variations the lead-time number has to be set up to the worst case. Moreover, “people have an incentive to increase the planned lead time if an order is ever late, so that the delay does not occur again” (Karmarkar, 1989). For example if a commuter encounters a traffic jam, on their next journey they will leave early to reduce the affect of such contingencies. Equally, orders will regularly be released too early and will frequently complete early, thus increasing inventories in the system (Karmarkar, 1989).
Incentives for Improvement.
Conceivably the most harmful aspect of MRP is the elimination of any responsibility for reducing lead-time on the shop floor. Moreover, there are no incentives to reduce lead times within MRP, as there are no rewards for finishing work earlier than MRP’s fixed standards say (Karmarkar, 1989)
Another major problem with MRP is its unnecessarily intricate and centralised nature. With MRP systems, producing order releases to the shop-floor in addition to planning and coordinating material flow. However, in many circumstances, the shop-floor can in fact be more flexible than the MRP system (Karmarkar, 1989).
For example, if parts are not available for a current schedule an assembly group may want to change its build schedule. However, change is thwarted because the appropriate paperwork will not be available until the MRP is next run. Moreover, running an MRP system everyday often makes no practical sense. As collecting and distributing the entire data involved can take a long time (Karmarkar, 1989).
Several MRP enhancements have tried to fix these problems. Shop floor control modules have been created by MRP vendors that track progress on the shop floor. Conceivably ‘rough-cut capacity planning’ is the best known of these systems. This method examines the load that MRP order releases generate on the shop floor. Moreover, if this load surpasses the capacity of a work centre, the repercussions are that the work will not be done in the shop within the allowed time (Karmarkar, 1989)
Case C MRP+JIT (Mazany, 1995)
The company is a small knitwear manufacturer that produces approximately 90,000 garments per year, together for local and export markets. The company’s Yearly sales are around NZ$4.5 million. Furthermore, 75 staff are employed with the company wholly operated within New Zealand.
They manufacture for corporate contracts along with stock garments. In off-peak periods, their corporate contracts are often used as a way of smoothing demand fluctuations. 60 styles at present are produced in the summer and winter ranges. Moreover, each style, has four to five sizes and four to eleven colours. At present, the company produces eighty per cent to order, with the rest being to stock.
Main problem areas within the company are high inventory, long lead-time and poor communications.
Problems met by the company were deep-rooted in the established manufacturing philosophy employed by management. Many problems and inefficiencies have hidden high WIP, finished goods and stock holdings in raw materials. This was due to large minimum order quantities, raw material levels being high, joint with long lead times. Due to the production, processes of the traditional system being inefficient WIP levels were high. This led to, total stock averages of over thirty per cent of sales, which for a company of this size is an extremely high figure.
Furthermore, in many levels within the organisation communication was lacking due to the traditional manufacturing environment. Within the two main manufacturing departments, this lack of communication allowed large buffer stocks to build up. In addition, raw materials and finished goods inventories accumulated due to overruns of old styles, cancelled orders and surplus stock. Lead-times are affected by several factors. Some of these factors are:
type of garment
time of year
order size
urgency of order or others in process
availability of raw materials
efficiency of the production process.
Moreover, the primary objectives of the company without any compromise in quality were to reduce lead-time levels in addition to inventory levels by fifty per cent.
Once the company had decided to integrate JIT with their MRP system, the JIT implementation initially begun with making technical process improvements. Conversely, this approach achieved little. As it was realised that the actual barriers to implementation were related to people and not technical issues. Therefore, to overcome this problem concerned actually involving the workers in designing and driving the changes not simply overcoming the resistance to change (Mazany, 1995).
JIT has much to offer SMEs; clear evidence is the considerable productivity gains that this company has made over the past three years. Such as reduction in lead-time, a increase in staff involvement and an improvement in teamwork. Conversely, it must be noted that after three years from the implementation of JIT, the company is only now starting to achieve the advantages that it wanted. This has shown that a high level of commitment in the early stages of JIT to maintain the process is needed before the results become apparent (Mazany, 1995).
Kanban Is Reactive.
JIT pull scheduling aspires to meet demand instantaneously. This is achieved through a simple control system based on Kanban. When independent demand has been levelled and dependent demand, synchronised JIT works best. While JIT may be good at control, it is not good on planning (Slack, 2004).
However, where variations are too vast or too inflexible to be disciplined easily, Kanban encounters difficulties that show up particularly when it operates in complex operations (Karmarkar, 1989).
Furthermore, compared to MRP, when a kanban system is implemented in a situation beset with variations in supply and demand, kanban is less likely to operate in a stockless manner. To avoid back orders and cover variability extra Kanban cards used as buffers have to be introduced (Karmarkar, 1989).
Changes in demand level filter slowly from stage to stage due to the reactive nature of the system. There is no standard way to get ready for the situation, even if it is completely obvious that demand is rising. Some U.S. assemblers working with Japanese suppliers using pull systems can take up to six months to adjust to steep changes in demand levels. The suppliers can encounter many problems pending the system reaching smooth operation again (Karmarkar, 1989).
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Finally, JIT is less capable of responding instantaneously to changes in demand. Consequently, JIT production systems favour designs based on simpler product structures with high part commonality. Whereas, MRP is better at dealing with complexity, as measured by numbers of parts and finished products. It can handle detailed parts requirements, even for products that are made infrequently and in low volumes (Slack, 2004).
Case A MRP+JIT (Spencer, 1995)
John Deere Engine Works is a significantly automated repetitive manufacturer of diesel engines. Most major components of their engine are machined in group technology cells and assembled along a computerised assembly line (Spencer, 1995).
A final build schedule determines what engines are produced in addition to triggering when finished components are delivered to the assembly line. The components for individual engines are feed by sequencing their subassemblies. Moreover, the entire process must be closely coordinated and any variation from the build sequence is most troublesome to the manufacturing environment and amplifes the risk of failing to match a build sequence (Spencer, 1995).
The just-in-time programme
The factory elected “not to use a card trigger system to pull production through the facility and from vendors, but instead uses a computer generated ‘pick’ list from the master production schedule” (Spencer, 1995).
However, results were rather unexpected and interesting as of the JIT programme. With the major focus on inventory reduction early in the factory’s JIT programme. Furthermore, total work-in-process inventory was reduced by around 35% and 50% in finished goods. Conversely, this result was thought to be because of enhanced scheduling performance in the MRP system alongside improved machine availability and not due to setup reductions or other JIT methods (Spencer, 1995).
Therefore, one conclusion that was recognised by the factory’s management was, most of the reductions occurred because of internally generated information, mainly from the workforce. Moreover, for reducing setup times the original machine tool seller were not a reliable source of information. Furthermore, in a repetitive environment in a machine line, the whole line must be observed for coordinated setup reductions not just each separate machine tool. For example, a 40 % reduction in setup time on one important machine centre could be rapidly absorbed by a downstream centre creating a new bottleneck. Therefore, rather than the capacity limiting machine centre remaining constant it could shift around the production line (Spencer, 1995).
Rather than merely replacing the MRP system as originally thought, the JIT approach would have to be implemented to fit the current Engine Works MRP operating environment. Conversely, it also became clear that the approach would necessitate virtually all staff and line departments’ participation and a degree of organisation not previously called for using the original MRP system (Spencer, 1995).
The revised JIT/MRP programme
JIT efforts can be integrated into the production environment essentially supporting the assembly lines that are now connected to the vehicle assembly lines (Spencer, 1995).
The framework in operation
However, for the JIT/MRP framework to be effective, it must be solid enough to deal with changes external to the factory. For example, schedule modification, internal changes to the factory such as introducing a new part, being able to assist the continued migration to a full JIT production mode anywhere it can be warranted (Spencer, 1995).
Conclusions
From this case study care should be taken in drawing conclusions from the experience of a particular factory. Conversely, for production planning and control, there seems to be support for the thinking that JIT methods enhance rather than replace MRP in a repetitive environment. The factory approached the JIT programme with the anticipation of replacing their MRP system. Conversely, as their implementation unfolded extra methods of planning using the current MRP system were developed (Spencer, 1995).
Finally, it was found that a continued dependence on planning and control practices using computer integrated manufacturing (CIM) is needed in this environment. However, this factory has integrated the JIT aspects into their programme that emerged to yield the greatest benefits. With management indicating little wish to diminish the computer for production planning as the integration of JIT into MRP appears successful for the factory (Spencer, 1995).
Clearly, there can be no ogeneralisation of these results to the whole production planning field. Conversely, the results from one factory functioning in a repetitive environment do show that a combination of approaches to PPC is an substitute to the implementation of a single approach (Spencer, 1995).
Tailored Controls, Hybrid Systems.
The fact is that an organisation does not need to choose between a push or pull system. Each has its pros and cons, with these methods not being mutually exclusive. The strengths of both systems are often utalised in a hybrid system (Karmarkar, 1989). “Putting the relative advantages and disadvantages of JIT and MRP together suggests how the two approaches can be blended” (Slack, 2004).
Furthermore, because pull methods do not need computerisation they are likely to be cheaper. Therefore, at a local level leaving control and responsibility and offering good incentives for lead-time management. Whereas, MRP provides a basis for inter-functional communication along with data management and is good at materials planning and coordination. Even if MRP is weak on timing, when it comes to work release, it is good at computing quantities. A successful hybrid system can utalise each approach to its greatest benefit (Karmarkar, 1989).
In a repetitive manufacturing environment that has fairly stable but varying schedules, MRP II and JIT methods can be combined for the materials planning. Although, “order release may require MRP calculations if changes are frequent or if it is necessary to coordinate with long lead times or complex materials supply and acquisition” (Karmarkar, 1989). With pull methods working best on the shop floor.
However, in a more variable contexts such as job shop manufacturing, MRP becomes an critical part of the planning and release. Whereas, pull techniques find it difficult to handle increasing demand and lead-time variability. Furthermore, shop floor control necessitates higher levels of tracking and scheduling complexity. With materials flow too complex for strict JIT (Karmarkar, 1989).
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