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Essay on Service Characteristics
| Date: |
05-16-00 2:52pm |
| Subject: |
Miscellaneous |
| Word Count: |
6189 |
| Page Count: |
24.76 |
Service Characteristics
1. Service Mechanism or Process:
For each category of customer it is necessary to describe the service process. This is usually expressed as a probability distribution for service times; for example, service times might be normally distributed or uniformly distributed. At the least, we need to state the average time it takes for a server to serve a customer and the variance or standard deviation of the service time. We decline
= average service rate possible per server in customers/unit time so 1/ is the average service time.
2. Queue Discipline
When customers are in the queue, the system must have operating rules that determine which customer to serve next; these rules are called the queue discipline. Frequently, we assume that the queue discipline is first-come-first-served (FCFS)
System Configuration
1. Number, Type, and Configuration of Servers
Number of servers in the system. Everything being equal, the more servers available, the less waiting time.
Type of servers used. We may choose between two types of machines, or combinations of people and machines, that have different average service rates and different variances in service times. Queuing analysis can help us determine whether a faster or less variable server is worth extra cost.
The configuration of servers. For example, should the system have dedicated servers – only certain customers can use certain servers.
2. Queue Capacity
Another design variable is the maximum number of customers that can be put in the queue before additional customers must be turned away. For example, a normal telephone has a queue capacity of zero. When the phone is being used (a caller is being served), additional incoming calls cannot enter the queue; they receive a busy signal and are turned away. Phone systems can, however, be designed so that incoming calls are put “on hold” in a queue.
3. Number of Queues
Whether each server has its own queue or all servers share a single queue can affect not only the average waiting time, Btu also the distribution and equity of waiting times as will be shown later.
Steady state: When the rate of departures from the system equals the rate of arrivals. This implies that any start-up or shutdown (called transient) effects are eliminated. For example, when a bank opens in the morning all servers are available, so the amount of waiting in the queue is reduced, but there will also be some time lag until customers begin to leave the system.
Utilization factor measures how much of the queuing system capacity is actually utilized serving
customers.
The simplest queuing system that includes randomness is the M/M/I system. It is based on the following assumptions.
1. Arrivals are generated by a Poisson process
2. Service times are exponentially distributed
3. There is one server
4. Any queue discipline can be used
5. Queue capacity is infinite
6. The customer population is homogenous and infinite in size
7. Customers are well behaved; no balking or reneging occurs.
Benefits of pooling servers into one system
If customers ore homogeneous (with respect to their service time distributions), then there will be less customer waiting on average if servers are pooled into one queuing system, rather than having a separate one-server system for each population of customers.
The Number of Queues for multiserver systems
Using a single waiting line for several servers is usually more efficient, and it is perceived by customers as being more equitable than having separate waiting lines for each server.
Even when a single queue is practical, it may not be most efficient, especially when the customers and servers are people. There are at least four factors that in some cases may make separate waiting lines for each server preferable to a single queue.
1. With separate queues, servers are sometimes able to serve to two customers at once
2. The time it takes for a customer to move from a common queue to the server may be longer than with individual queues for each server.
3. Servers sometimes work faster when they have their own queue, the customers in their queue are their customers and they are sensitive to the waiting incurred, whereas with a single queue the customer-server relationship is depersonalized until service begins and there is less sense of an obligation to serve customers quickly.
4. With separate queues, customers can choose their server. In some situations, servers can provide both faster and better-quality service to customers they have served before on a regular basis. Some customers prefer using the same server for this reason, and customer satisfaction should certainly be of primary importance in service systems.
Separate queues also make it possible to implement certain delay avoidance tactics. For example, requiring customers to use only cash in an express line at the grocery store means that the customers can obtain the benefits of a shorter wait only if they do something that also saves service time, which can translate into better service for everyone.
Finally, separate queues are sometimes necessary to implement other service improvement tactics. For example, when the population of customers is heterogeneous (customers require different types or amounts of service with substantially different average service times), it can be advantageous to have separate servers and queues for each type of customer.
Independent demand models are applicable to items, such as tools sold by a hardware store or common components used by a manufacturer, whose demand or usage rate can be treated in isolation from the demand for other items.
Dependent demand such as tires or transmissions used by an auto manufacturer, which depend directly on production of finished automobiles.
Reasons for holding inventories
Any materials that are held for future use can be considered inventories. There are at least four reasons to hold inventories:
1. To increase operating efficiency
2. To provide a quick response to customers
3. To provide safety against normal business uncertainties
4. To take advantage of unusual price opportunities or to protect against irregular business risks.
Economic efficiency
Inventories can improve operating efficiency in several ways.
1. Spreading the fixed costs of Procurement or Setups
If there is a fixed setup cost to produce a product, then the more units produced per production run the lower the setup cost per unit.
2. Decoupling of Production
In-process inventories make it possible to decouple one production stage from another, allowing grater scheduling and staffing flexibility. For example, if in-process inventories exist between stages, production at stage B could be stopped for a while without forcing stages A and C to stop. Although it is desirable to keep inventories of semifinished goods as low as possible, even JIT production systems allow for minimal amounts of inventories between stages.
3. Smoothing an Stabilizing Production
If demand for a product is seasonal, it is often less expensive to maintain a constant level of production and employment levels to match demand. During period of low demand production exceeds demand and inventories of the product increase, then during period of high demands, these inventories are depleted.
Quick customer response
1. By maintaining inventories of its final products, a company can respond immediately to customer demands, thereby providing more competitive service.
2. Raw material and in-process inventories can also be used to shorten the response time to customers. If raw materials or partially finished products are already available, a company can begin production to fill an order faster than if it had to acquire the raw materials.
Risk reduction and safety
Inventories play a major role in risk reduction for organizations
1. Uncertainties in the supply system. Inventories of raw materials can protect a company against late inventories, allowing the production process to operate until inventories are replenished.
2. Decoupling production stages. Inventories of semifinished products allow firms to decouple production stages. This not only allows production of other stages to continue while one stage stops for a product changeover or planned maintenance; it also protects against unplanned stoppages such as machine failures or employee absences.
3. Unexpected Surges in Demand. Inventories of final products can satisfy unexpected surges in demand. Inventories also put the company in a better competitive position, allowing it to supply new customers quickly when competitors are out of stock.
Exploiting or protecting against unusual events
Three types of inventories:
1. Speculative inventories are sometimes held to protect against unusual events or to take advantage of unusual opportunities.
2. Inventories held primarily to achieve economic efficiency normally increase and decrease in a planned cycle and are called cycling inventories.
3. In contrast, inventories that are held to protect the organization against normal business uncertainties and risks are called safety stocks.
Inventory-related costs
Three primary costs influence the inventory policy: holding costs, ordering or setup costs, and stockout or shortage costs.
The holding cost is the cost of actually keeping items in inventory. The primary components of this cost are (1) the opportunity cost of capital, (2) taxes and insurance, (3) breakage, spoilage, pilferage, and obsolescence, and (4) handling and storing.
Ordering or setup costs. It is important to note that Co should include only fixed costs (independent of order size) that are directly attributable to ordering or producing the product, general overhead cost such as corporate office expenses or maintenance should not be included. We consider fixed costs because the best number of units to order or produce at a time is function of the fixed cost of acquisition. The larger the order size or production lot size, the lower the fixed ordering or setup costs per nit because the fixed cost is spread over a larger number of units.
Shortage or stockout costs. When customers wish to buy a product and it is not available, the supplier incurs a cost, which might include not only lost profit but also lost future profits if the customer changes suppliers. Likewise, if a company runs out of a raw material, it may have to stop its production system, idling workers and possibly causing shortages of the final product. These costs are called stockout or shortage costs. Sometimes it is accepted practice in an industry to accept orders for future rather than immediate delivery. This is called backordering. Backordering is similar to having a negative inventory level and may involve a cost per unit time, usually due to loss of goodwill.
Hidden costs. Inventories also carry many hidden costs. Companies keep extra raw materials on hand to protect themselves against unreliable deliveries by suppliers. They maintain substantial in-process inventories to keep some activities operating when machines break or workers fail to show for work. They overproduce products in the expectation that some will be defective. Not only do excessive inventories hide production problems, they often amplify them.
Independent versus dependent demand. In contrast to dependent demand items, which occur almost exclusively in manufacturing systems, independent demand items occur extensively in both manufacturing and service systems.
Inventory review policies. A fundamental aspect of an inventory policy is deciding whether the inventory position (the number of units in inventory plus those on order) will be reviewed on a continuous or periodic basis. The organization reviews (counts) the inventory for the product and places orders at fixed time intervals. The amount ordered each time may vary according to the inventory position and the expected demand. A major disadvantage of this system is that between reviews the inventory can become dangerously low without the company’s knowledge until the next review occurs.
Continuous (or perpetual) review policies have become widespread. Continuous review systems involve less risk (and require less inventory) because the company always knows its current inventory position: as soon as inventories drop to a predetermined level, called the reorder point (RP), the company can place an order for some fixed amount. One way of differentiating the two systems is that periodic review systems typically have variable order quantities but fixed time intervals between order, whereas continuous reviews systems have fixed order quantities but variable time intervals between orders.
Basic economic order quantity (EOQ) model
The assumptions of the basic EOQ model are as follows:
1. The demand for or usage of the item is relatively constant over time at a rate of D units per unit time.
2. The item’s cost (price), p, is independent of the quantity ordered; that is, there are no quantity discounts.
3. There is a fixed cost, Co, for executing an order that is independent of the quantity ordered, Q.
4. The holding cost for inventories is proportional to the quantity stored; that is the holding cost per unit per unit time, Ch, is independent of the inventory level.
5. No shortages are allowed; all demand must be satisfied when requested.
6. The lead-time (LT) for deliveries, which is the time from when an order is placed until it is delivered, is known with certainty and is constant.
7. All items ordered are delivered at the same time; there are no split deliveries.
Reorder Point, RP, should be set equal to the number of units used during the lead time, called the demand during lead time (DDLT).
RP= DDLT = D x LT
Computing the optimal order quantity. To determine the optimal order quantity, we begin by expressing the firm’s total material cost (TMC) for the product during a unit of time (e.g., a year) as a function of the order quantity Q. Because shortages are not allowed, there appear to be three components to the material cost function: ordering costs, holding costs, and variable item costs.
Ordering cost per unit time = Co (D/Q)
Holding cost per unit time = Ch (Q/2)
TMC = Co (D/Q) + Ch(Q/2) + pD
But the item cost is not a function of the order quantity – there are no quantity discounts – so the amount spent on the items per unit time, pD, is a constant. Therefore, the value of Q that minimizes equation is the value that minimizes the sum of the ordering ad holding costs, called the total stocking cost (TSC)
The economic production lot-size model
A problem frequently encountered by manufacturers is to determine how many units of a product or a component to produce during a production run. This quantity is called the production lot size. The problem of determining the optimal lot size, called the economic lot size (ELS), is similar to the economic order quantity problems, with only minor differences.
ABC Classification of items and the pareto principle
In the nineteenth century, the Italian economist Vilfredo Pareto discovered empirically that wealth is maldistributed: for example, 80% of a nation’s wealth is typically owned by less than 20% of the population (and therefore, less than 20% of the nation’s wealth is owned by 80% of the population). This concept of maldistribution, called the Pareto principle.
ABC classification scheme = a way to set priorities on the amount of attention to devote to the inventories of various items. Items are divided into three categories according to their impact on the organization.
Causes of Inaccuracy. An open stockroom makes it easy for people to remove items from inventory without recording the withdrawal; even when the item is used for legitimate purposes, a discrepancy results. Other causes include the following:
1. Orders are recorded as received when they have not been or vice versa
2. The number or type of items received in a delivery do not match what was ordered, and the difference is not recognized
3. Items withdrawn form inventory may later be returned but not recorded (e.g. a mechanic may withdraw two sizes of a part to fix a machine and then return the one that does not fit)
4. Items may be stored in the wrong place – even though they exist, they are not of use because they can’t be found.
These and other types of errors can be costly, and they present opportunities for improvement.
An alternate approach is to use cycle counting of inventories. With this method all items are counted on a rotating basis. For example, each month 1/12th of the company’s items may be physically counted. This reduces disruptions and even makes it possible to have permanent staff devoted to physical counting and reconciliation of inventory records. Cycle counting also makes it easier to develop counting strategies that give differential attention to items according to their importance. Cycle counting can also be made more efficient by selecting items to count during a cycle using the following guidelines.
1. Count items when inventory records show a small quantity in stock, thereby reducing the number of objects to count.
2. Count items when inventory record show a positive level but a stockout is reported
3. Count items after an unusually large amount of inventory activity (receipts and withdrawals) for that item.
The two bin system
For small items, a simple way of identifying when the reorder point has been reached is to use a two-bin system for storage. In this system, units of a product are literally stored in two bins; the first bin hold a number of units equal to the reorder point quantity, and the second bin holds the remainder. Items are withdrawn form the second bin, this is a signal to place an order; until delivery is received, items are drawn from the first bin.
In recent years, companies have found that there are substantial benefits from establishing a long-term sole-supplier (sole-sourcing) relationship with vendors. By offering a supplier all of the customer’s purchases of an item for the next three to five years, the customer can insist on guarantees of reliable delivery, high quality, stab le or decreasing prices, and a share in productivity improvements.
In many situations, using a sole-supplier (sole-sourcing) method can reduce costs and enhance product innovation; the main problem is the risk of a supply interruption. Buying recycled and recyclable materials can be profitable as well as environmentally sound.
A popular method that uses to schedule production and purchasing of dependent demand items is material requirements planning (MRP)
MRP is a computer based information system for scheduling production and purchases of dependent demand items. It uses information about end product demands, product structure and component requirements, production and purchase lead times, and current inventory levels to develop cost-effective production and purchasing schedules.
MRP inputs. An MRP system requires four specific types of information: a schedule of requirements (or planned production) for each end product. A list of all components of the product according to the product’s hierarchical structure, expected lead times for producing or purchasing all components and materials, and information about current inventory levels. This information is maintained in three standard data files: the master schedule file, the bill-of-materials file, and the inventory record file. These files are used not only by the MRP system but also for product design, personnel scheduling, purchasing, shipping, and accounting activities.
The master schedule file contains the master production schedule for each product. The master production schedule (MPS) for a product specifies how much of the end product is needed or is to be produced and when. The MPS is derived from the aggregate production plan based on demand forecasts, customer orders, and capacity limitations. The MPS is divided into time period called time buckets. These time buckets are usually conventional units of time, most frequently weeks, although one-day, two-week, and one-month time buckets are not uncommon. The time buckets need not be the same for the entire schedule. Each product has its own MPS, and there is also an overall master schedule that synthesizes the requirements for ll products or a group of products that share facilities.
The bill-of-materials (BOM) file lists for each end product all assemblies, subassemblies, components, and raw materials necessary to produce the product. A BOM file includes information about how many units of each item are needed for each higher-level item in the product hierarchy (and possibly for the end product itself), whether the item produced internally or purchased, and the production or purchase lead-time necessary to acquire the item. A good way to visualize the hierarchical structure of the product is to use a product structure tree.
The BOM is then used to construct a material list, which combines and summarizes all the material needs for the product.
The inventory record file is a file listing the current inventories and outstanding purchase and production orders for each item. Although accuracy is important throughout the MRP system, the file whose accuracy is most crucial, and is most prone to error, is the inventory records file.
A simple heuristic method that dos this is the part-period balancing (PPB) method developed by DeMatteis. We first define a part period as a unit of measure that is equivalent to carrying one unit of an item (a part) in inventory for one period. Th PPB heuristic is based on the observation that in the basic EOQ model the optimal order quantity or lot size occurs when the total ordering or setup cost equals the total holding cost. Using this idea, the PPB algorithm first computes the economic part period (EPP)
EPP = (setup or ordering cost) /(holding cost/unit period)
An approach that does guarantee an optimal solution is the Wagner-Whitin algorithm. Wagner and Whitin formulated the lot-size and scheduling problem as a dynamic program that can be solved to find the optimal lot sizes over time. A dynamic program is a constrained optimization problem formulated in such a way that it can be solved using a special recursive (repetitive) technique. Specifically, the problem is broken into decision stages that correspond to time periods. The algorithm sequentially determines the best action to take in the last (first) time period (in this case, whether an order should be released and, if so, the lot size to use), then the best action to take in the last (first) two periods combined, the last (first) three periods and so forth. The solutions at each stage are optimal, and the method used to solve the problem at each state is moderately efficient, although much less efficient than linear programming. Because of the nature of dynamic programming, multiperiod problems such as the MRP lot-sizing problem are especially suitable for solution in this manner.
Although Wagner and Whitin first proposed their method in 1958, and although it provides an optimal solution for the given parameter values, it has not been widely used in practice. There may be two reasons. First, dynamic programming is far less widely known by managers, engineers, and computer scientists than other operations management modeling and solution methods. Second, for even moderate-sized problems, obtaining the optimal solution is computationally slow and cumbersome compared to other optimization models and algorithms.
A net-change system is one in which production and ordering plans are continually revised whenever the information on orders, production levels, and receipts is available. For example, if a customer asks to postpone delivery of a product by one week, the production and material requirement plans would be immediately revised to reflect this change. Rather than reproducing all the plans, however we would identify only the changes to the plan and disseminate them through a change report.
With a regenerative system information about order changes, material receipts, and actual production levels is gathered for a time period (say, a week). Then the MRP system is rerun, incorporating this new information and new material requirement plans and production schedules are generated. This type of updating is easier to manage because it can be done at the same time in every period. It is also less costly and creates a more stable operating atmosphere. The only risk is that if major changes occur (e.g. a large order is canceled), the delay in updating the production plans can be harmful. One way to handle this potential problem is to allow for emergency updates of the regenerative MRP system.
Time fences are periods of time during which changes to production and procurement plans are restricted. For example, a company may use two time fences: three weeks and six weeks. During the thee-week time fence the production schedule is essentially frozen, with only minor changes allowed: from three to six weeks into the future, larger changes in production and possibly resequencing of production runs may be allowed; beyond six weeks, unlimited changes may be allowed.
Like aggregate production planning, MRP uses a rolling horizon approach. That is, although a 15-week plan may be derived, only the first 1 or 2 weeks are implemented as planned. The plans for subsequent periods are revised and updated over time, but taking into account what is best over entire time horizon.
A far more efficient way to address variations in lead times (including production lead times) is to use safety time, that is, to place orders or schedule production earlier than necessary so that if there is a delay the items will still arrive when needed.
Because the production planning aspects of MRP ore related to most other functions of a company, the scope of MRRP has been expanded in recent years to integrate MRP with the order processing, billing, shop floor scheduling, and personnel and machine utilization activities of the company. These newer systems, called manufacturing resources planning or MRP II, contain the classical MRP scheduling function as their centerpiece. However, MRP II systems may include a module that collects sales and customer order data and generates an MPS for future end product requirements (e.g., using a forecasting model). In addition, an MRP II system may convert information from the material requirements plans into specific work schedules for departments and machine, evaluate department workloads and capacity conditions, generate shipping documents and customer invoices, and produce management reports on production and financial performance.
The benefits of using an MRP system
1. Low inventory Levels, Especially for In-Process Materials. Because materials are acquired or produced only when needed and in the quantities required, inventories are kept to a minimum.
2. Good Material Tracking and Production Scheduling. The material requirements plans for each item provide a quick summary of the status of each item used in production: how much is on hand, the status of outstanding orders, and the schedule for production.
3. A Method to Evaluate and Allocate Production Capacity. Tentative material requirement plans identify possible production bottlenecks and capacity problems. These plans can be used to decide whether to expand short-term capacity or reschedule production and how to reallocate production among time periods to stay within capacity limits.
Little JIT is a form of production scheduling and control whereby
1. Items are produced only to satisfy actual demand
2. Production is performed in small lot sizes
3. Production is “pulled” through the production system by the last production stage rather than “pushed” through by the first production stage. The consequences of Little JIT are smaller inventories and shorter throughput times
Bit JIT (or lean production) is a complete reengineering of the production process that emphasizes continuous improvement, quality management, reduced setup times, improved maintenance procedure, and cooperation with suppliers.
Two ways in which companies adapt to uncertainties in product demand are to
1. Improve their demand forecasting
2. Produce in anticipation of demand, that is, maintain final product inventories.
The first solution is never perfect and can still result in stockouts or late deliveries, and the second solution increases costs. Other deviations from the ideal world are accommodated by doing the following
1. Increasing inventories of raw materials to accommodate variations in delivery times and to take advantage of economies of scale in purchasing.
2. Increasing raw material, in-process and final product inventories, as well as overpurchasing materials and overproducing products, to make up for lost production due to defective raw materials and processing.
3. Scheduling large production runs and consequently holding large cycling inventories, to spread the fixed costs of machine setups over a larger number of units.
4. Maintaining large in-process inventories between production stages to keep operations running during product changeovers at other production stages and to protect against delays resulting from machine breakdowns and employee absences.
Production in classical systems is normally based on speculative demand. That is company forecasts the demand that is likely to occur in the future.
Materials are then ordered, and the first step in the production process is scheduled. Subsequent steps in the production process are also scheduled, but their execution will depend on when earlier production stages are completed in other words, production is initiated by scheduling the first production stage, and the output is “pushed” through the production system. This approach is commonly characterized as a push production system.
Production of a specific product is initiated by the final stage of production in response to actual or assured demand, and production is “pulled” through the system, the final stage pulls the needed materials from the preceding stage, which pulls from its preceding stage, and so on. As a result, JIT scheduling has been characterized as a pull production system.
Kanban (the Japanese word for “card” or “ticket”) to department 3. Department 3 puts that ticket in line for production and begins production on it within hours of receipt.
The number and size of kanbans
Several methods have been proposed for determining the best lot sizes (also called kanban sizes) and number of kanbans to use in a JIT system. In practice, the size of a kanbans is determined by the time and cost of a setup, the dead rate, and the number of units that can be conveniently stored and transported.
In 1950, while working for Toyo Kogo’s Mazda plant, Shigeo Shingo began his systematic study of production setups.
Shingo found that similar problems occurred frequently during setups. At that point he made the key discovery that would lead to faster setups: setup operations actually consisted of two different activities: what Shingo calls internal and external. Internal setup activities, such as mounting a die or changing the in cartridge on a photocopier, are those that can be performed only while the equipment is stopped. External setup activities, such as collecting all bolts needed to mount a die or unpacking an ink cartridge from its carton, are those that can be done while the equipment is operating.
Shingo’s two observations formed the foundation of a procedure for reducing setup times that he called single-minute exchange of dies (SMED) (named for consulting projects in which he reduced the time for die changes on large presses from several hours down to less than 10 minutes).
SMED uses the following four-step procedure
1. Observe and analyze how the setup is currently performed
2. Separate internal from external setup activities
3. Convert internal to external setup activities. Cleaning activities can often be transferred from internal to external setup. For example, by having two sets of tools or two processing vessels one can replace the dirty or contaminated one with a clean one quickly during internal setup, and then the dirty one can be cleaned after production has restarted. For fluid vessel cleaning rather than having two separate vessels a lower-cost solution is often to use plastic vessel liners that can be exchanged quickly.
4. Simplity and streamline activities
(a) Provide each work station with its own tools and store the tools conveniently
(b) Standardize the size and shape of dies and other parts that must be changed during setups
(c) Use the same fasteners, such as bolts, for each setup. The fasteners can stay with the machine they simply have to be loosened ad retightened for each setup, rather than removed, replaced, transported, and stored.
(d) Use fasteners that can be loosened and tightened with a single turn rather than those that require turning the fastener several revolutions.
(e) Reduce or eliminate adjustments by using fixed settings and markings on dies, tables, and guide bars.
There are at least four ways in which customers and suppliers can work together to provide reliable delivery to the customer at lower cost to both.
1. Share production scheduling plans quickly
2. Include suppliers in product design
3. Help suppliers improve their production methods
4. Have spatially close facilities
Causes of machine failures
1. Inadequate preventive maintenance
2. Overusing and operating machines at excessive speeds
3. Dirt, oil, and chemical damage
4. Collisions (e.g. a fork lift hitting a machine)
5. Incorrect machine setup for operation
6. Materials fed into machine or processed incorrectly.
The cost of machine breakdowns can be substantial, yet eliminating these six causes can be relatively inexpensive. That is the focus of the following maintenance strategy, which is sometimes called total productive maintenance (TPM)
More Maintenance Principles
Several other principles and actions can enhance equipment maintenance
1. As in product and process design, the secret of better machine maintenance is simplicity
2. To solve maintenance problems and to allocate maintenance resources efficiently it is important to collect useful data on the frequency and causes of machine failures
3. When some parts of a machine start to wear out, many other components are wearing out as well. It is better to rebuild the machine by replacing all worn parts at once instead or replacing them one by one, as it is sometimes done.
4. It is surprising how much time is wasted when a machine is shut down for overhaul and key components are not available when needed. Procurement should be carefully planned so that all parts are on hand when needed.
Institutionalizing this principle of continuous improvement (or kaizen in Japanese) is an integral part of any Big JIT production system. We have already seen several specific ways to do this SMED, TPM, and TQM are all way to generate continuous process improvement. The most successful continuous improvement systems are those that encourage employees themselves to find ways to improve production methods and to improve products. Three ways to promote this process are to use employee suggestion systems, quality circles, or autonomous work teams.
Employee suggestion systems have been used for over a century, and some companies have reported astounding success with them.
Quality circles are small groups of employees who met regularly to discuss and evaluate ways to improve productivity, quality, and safety.
Autonomous work teams are groups of employees who work together as a tam to perform some aspect of production. They are often evaluated and rewarded as a tram, and one aspect of the evaluation is improvement in performance. The development of both quality circles ad autonomous work teams was motivated by the continuous improvement philosophy.
JIT production
When to use JIT scheduling
The factors that most significantly affect the desirability of using JIT scheduling are the dead pattern, the product mix, and the structure of the production process.
1. Be patient
2. Customize implementation
3. Be flexible and adaptive
4. Maintain excess capacity
Synchronous (synchronized) production is a procedure for balancing the flow of production. It focuses on keeping the bottleneck stages fully utilized and forcing the other stages to produce in synch with the bottleneck. The mechanism for forcing the stages to produce at the same pace is called the drum-buffer-rope procedure.
Another approach to production scheduling in inventory management that is especially useful in job-shop environments is synchronous (or synchronized) production. Synchronous production has some similarities for JIT production, such as recognizing the harm resulting from production variations, trying to coordinate and pace production so all production stags are producing at the same rate, and using small batch sizes. However, synchronous production differs from JIT in two ways.
1. It focuses more on adapting efficiency to variations and imbalances in the system, rather than eliminating them
2. Production scheduling is hybrid of classical push and JIT pull approaches.
Synchronous production, developed by Eliyahu Goldratt, is based on the theory of constraints. This theory was developed from three empirical observations.
1. In multistage production systems not all stages have the same production capacity
2. Variations are randomness in production systems reduce effective capacity and output
3. The procedures used in classical production systems generally amplify rather than solve the problems created by capacity imbalance and production variations.
A bottleneck resource is any resource that limits the flow of production through the system.
A nonbottleneck resource is one hat has a capacity grater than the demand and therefore does not redistrict the system throughput.
A capacity-constrained resource is not a bottleneck but is operating close to capacity and could become a bottleneck if not operated efficiently
These facts lead to the key principle of the theory of constraints, to manage production effectively, one must focus on the constraining resources – the bottlenecks.
Goldratte. His key recommendation is a mechanism for coordinating production so that the flow of production (rather than the nameplate capacities) among the stages is balanced, and inventories are kept to a minimum except where they are really needed. This scheduling method is called the drum-buffer-rope method.
The drum is the mechanism that controls the pace of production; that is, the drum “beats” the rhythm of production.
In front of bottleneck operations a supply of safety stock is maintain to buffer or protect the bottleneck operation from material shortages, the bottlenecks must be kept operating.
The rope is the link between the bottleneck and preceding work stations that keeps them from running ahead of the bottleneck. In JIT scheduling the rope is the kanban pull mechanism. In synchronous production the rope can also be kanbans pulling production, or flow can be controlled by using a daily production schedule based on the production of the bottleneck.
JIT, synchronous production and MRP
All three can be integrate to varying degrees, depending on the situation. Synchronous production fits well with a JIT production system that has some imbalances and variations, which most do. The drum-buffer-rope method of controlling production flow is especially helpful in job-shop processes, where JIT scheduling may be impractical due to the variety of products and product routings.
One subsystem may best be driven with a JIT mechanism, while another may b better suited to MPR. (In fact, these three philosophies use different approaches to accomplish the same goals: maintain low inventories, deliver products on time, and avoid stockouts.
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