Week 1

A supply chain is a goal-oriented network of processes and stockpoints used to deliver goods and services to customers

Processes represent the individual activities involved in producing and distributing goods and services. They could be manufacturing operations, service operations, engineering design functions or even legal proceedings. But, since our focus is on the overall performance of the supply chain, we will concentrate primarily on the on the flow of goods and services.

Represent locations in the supply chain where inventories are held. These inventories may be the result of deliberate policy decisions (e.g., as in the case of retail stocks) or the consequence of problems in the system (e.g., as in the case of a backlog of defective items awaiting repair, i.e. queueing).

• Decoupling (anticipation, safety stocks)

• Queueing (you don’t have capacity)

  • Congestion

  • Assembly

  • Transport

  • Setup

• Decoupling: it has not yet been decided in which production order the stocked item will be processed next

• Queueing: This decision has already been made

• COOP is also relevant here

describes the various paths by which goods and services can flow through a supply chain

• They exist only to support business activities and therefore must be evaluated in business terms. Usually this means that the fundamental objective of a supply chain is to contribute to long term profitability.

• But profitability (or cost effectiveness) is too general to serve as a metric for guiding the design and control of supply chains. Therefore, a key starting point for a supply chain science is a description of the strategic objectives the system should support

Copying best practices, generally called benchmarking, can only partially ensure that an operations system fits its strategic goals (i.e., because the benchmarked system can only approximate the system under consideration).

single process fed by a single stockpoint as a station.

• Is a sequence of stations used to generate a product or service

• Is important because while a station behavior is important as a building block, few products are actually produced in a single station

• Can be a manufacturing line or a sequence of clerks required to process a loan

• Quality vs. Cost

• Speed vs. Cost

• Service vs. Cost

• Flexibility vs. Cost

• Ensuring operational fit with strategic objectives.

• Achieving maximal efficiency within the constraints established by strategy

efficient frontiers represent the most efficient (lowest cost) system for achieving a given performance level. Points below these curves are infeasible given current technology. Points above them are inefficient

• Cost

• Quality

• Speed

• Service

• Flexibility

• Other books say flexibility and dependability

• or OTIF (on time in full)

1. Strategic Problem. Determine where on the efficient frontier to locate.

2. Operational Problem. Design a system that achieves performance on the efficient frontier.

• It is copying best practices of other firms. But this approach can only partially ensure that a system achieves its strategic goals (i.e., because the benchmarked system can only approximate the system under consideration). Furthermore, benchmarking cannot provide a way to move efficiency beyond historical levels, because it is by nature imitative. Efficient frontiers are not static, so achieving world-class efficiency requires something beyond benchmarking.

Use science to push out the boundaries of efficiency

• Identify the areas of greatest leverage;

• Determine which policies are likely to be effective in a given system;

• Enable practices and insights developed for one type of environment to be generalized to another environment;

• Make quantitative trade-offs between the costs and benefits of a particular action;

• Synthesize the various perspectives of a manufacturing or service system, including those of logistics, product design, human resources, accounting, and management strategy

• The flow of entities through processes made up of servers.

  • The entities can be parts in

    • a manufacturing system

    • people in a service system

    • jobs in a computer system

    • transactions in a financial system.

  • The processes can be

    • machining centers

    • bank tellers

    • computer CPU’s

    • manual workstations.

  • The flows typically follow routings that define the sequences of processes visited by the entities.

Throughput

Work in Process (WIP)

Cycle Time

the rate at which entities are processed by the system

The number of entities in the system, which can be measured in physical units (e.g., parts, people, jobs) or financial units (e.g., dollar value of entities in system)

The time it takes an entity to traverse the system, including any rework, restarts due to yield loss, or other disruptions

the objective is to have throughput high but WIP and cycle time low. The extent to which a given system achieves this is a function of the system’s overall efficiency

Inventory turns

the system’s capacity

Capacity is defined as the maximum average rate at which entities can flow through the system, and is therefore a function of the capacities of each process in the system

• (General)The bottleneck of a routing is the process with the highest utilization.

• The process that constrains the capacity of the overall system

• Often, this is the slowest process. But not necesarily

• If because of losses the next stations receives less parts (rate into station), utilization drops, regardless of the lowest capacity and can therefore not be the bottleneck

• However, in systems where different types of entities follow different paths (routings) through the system, where yield loss causes fallout, or the routings require some entities to visit some stations more than once (either for rework or because of the nature of the processing requirements), then the slowest process need not be the system bottleneck. The reason is that the amount of work arriving at each station may not be the same.

The output of a system cannot equal or exceed its capacity.

The system is running at 120 percent of an arbitrarily defined “capacity”, representing one shift with no overtime, normal staffing levels, a historical average rate, or whatever. But it does not represent the true limiting rate of the system, or we could not be exceeding it.

• You don’t want to overload the bottleneck

• Variability can lead to too low or too high WIP

• When release rate is the same as the production rate, the WIP level will stay high for a long time because there is no slack capacity to use to catch up.

• In contrast, if we set the release rate below capacity, the system stabilizes

• Cycle time increases in utilization and does so sharply as utilization approaches 100%

• As we have seen, when utilization is low, the system can easily keep up with the arrival of work (e.g., Figure 1.4) but when utilization becomes high the system will get behind any time there is any kind of temporary slowdown in production

• the machine cannot “save up” production when it is ready but there is no WIP, the times the machine is starved do not make up for the times it is swamped. Therefore you cannot just use averages

The only way the machine can be always busy is to have a large enough pile of WIP in front of it so that it never starves. If we set the WIP level to anything less than infinity there is always a sequence of variations in process times, outages, setups, etc. that will exaust the supply of WIP. Hence, achieving higher and higher utilization levels requires more and more WIP

• Because (a) maximizing throughput is desirable

• (b) estimating true theoretical capacity is difficult, managers tend to set releases into the plant close to or even above theoretical capacity

• This causes cycle times to increase, which in turn causes late orders and excessive WIP

• When the situation becomes bad enough, management authorizes overtime, which changes the capacity of the system → cycle time goes down

A supply chain is a goal-oriented network of processes and stockpoints used to deliver goods and services to customers

1. it refers to long-term averages

   1. Restriction (1) simply means that Little’s law need not necessarily hold for daily WIP, throughput, and cycle time, but for averages taken over a period of weeks or months it will hold

2. the process must be stable

   1. Restriction (2) means that the process cannot be exhibiting a systematic trend during the interval over which data is collected

However, as long as WIP, throughput, and cycle time are measured in consistent units, it can be applied to an entire line, a plant, a warehouse, or any other operation through which entities flow.

• (1) specification of consistent and appropriate measures of variability

• (2) development of the cause-and-effect roles of variability in logistical systems

Book: “Random variables with CVs substantially below 1 are said to have low variability, while those with CVs substantially above 1 have high variability. Random variables with CVs around 1 (say between 0.75 and 1.33) have moderate variability.”

M. Land: can be relative; for a job shop, a CV of 1.33 is low

Effective process times & interarrival times

Interarrival times are simply the times between the arrival of entities to the process, which can be affected by vendor quality, scheduling policies, variability in upstream processes, and other factors

Standing in front of the process with a stopwatch and logging the times between arrivals. If two entities arrive at the same time (e.g., as would be the case if two customers arrived to a fast food restaurant in the same car), then we record the interarrival time between these as zero.

Interestingly, if we have a large collection of independent customers arriving to a server (e.g., toll booths, calls to 9-1 1) the CV will always be close to one. Such arrival processes are called Poisson and fall right between the high variability (CV > 1) and low variability (CV < 1) cases.

• Effective process times are measured as the time from when an entity reaches the head of the line (i.e., there is space in the process for it) and when it is finished. Notice that under this definition, effective process times include detractors, such as machine failures, setup times, operator breaks, or anything that extends the time required to complete processing of the entity

• is the study of waiting line phenomena

• In a operations system, entities queue up behind processes, so that where delay represents the time entities spend in the system not being processed.

  
  

  Delay represents the time entities spend in the system not being processed.

Queueing delay, in which entities are ready for processing but must wait for a resource to become available to start processing

At a single station with no limit on the number of entities that can queue up, the delay due to queuing is given by the VUT equation.

• V = a variability factor

  • the utilization factor will be proportional to 1/1−u, where u is the station utilization

  • This means that as utilization approaches 100 percent, delay will approach infinity.

  • The principle conclusion we can draw here is that unless WIP is capped (e.g., by the existence of a physical or logical limit), queueing delay will become extremely sensitive to utilization as the station is loaded close to its capacity

  • The variability factor is a function of both arrival and process variability, as measured by the CV’s of interarrival and process times.

  • The V factor is generally proportional to the squared coefficient of variation (SCV) of both interarrival and process times.

• U = a utilization factor

• T = average effective process time for an entity at the station

• Tells us that queueing delay will be V U multiples of the actual processing time T

• The first insight we can get from the VUT equation is that variability and utilization interact. High variability (V ) will be most damaging at stations with high utilization (U), that is, at bottlenecks.

• Variability reduction will be most effective at bottlenecks

because capacity is costly, high utilization is usually desirable. By the VUT equation, the only way to have high utilization without long delays is to have a low variability factor. For this reason, variability reduction is often the key to achieving high efficiency logistical systems.

• Good performance indicators

• Support is required for revealing the causes of a bad performance in the so-called diagnosis phase

It is important for the diagnosis phase that the supportive tool links performance indicators to those decisions that can affect the performance

production planning and control

• Input control (order acceptance/ delivery date promising, order release and priority dispatching)

• Decisions on output control (adjusting capacities).

• Lateness is defined as the conformity of a schedule to a given due date. It is measured by subtracting the promised delivery time from the realised throughput time.

• We can distinguish positive lateness (orders are delivered late) and negative lateness (orders are delivered early)

The percentage of orders delivered late can be decreased by reducing the average lateness (2b), and/or by reducing the variance of lateness (2c).

• The first input control decision distinguished is order acceptance and delivery date promising. This control decision deals with customer enquiries.

• The average lateness is the difference between the average realised throughput time and the average promised delivery time

• The next input control decision is the release of orders. Because capacity is often restrictive, it is important to select those orders for release that provide capacity groups in the shop with a good load balance.

• Balance of loads results in smooth flows on the shop floor and avoids congestion in front of certain capacity groups.

• Priority dispatching

• Once an accurate release decision has been made, priority dispatching has a limited influence on the average lateness and the variance of lateness.

• Output control decisions usually focus on controlling the average lateness of orders.

  • Capacity changes are generally triggered by large sets of orders, tending to be delivered late.

When for example an order arrives that needs 20 hours of work on a capacity group, the input curve increases with 20 hours at the time of arrival. The cumulative output curve increases by the hours of work completed for a capacity group at the time of completion

Controlled average throughput times are indicated by parallel input and output curves in the throughput diagram.

• The order progress diagram indicates the difference between the progress of an individual order and the average order progress pattern

• The vertical distance between the starting point of the curve and the horizontal axis shows the estimated lateness of an order at the time of acceptance, i.e., the resulting lateness if all stages would require exactly the average throughput time. If the acceptance dots are widely spread around the horizontal axis, this may indicate a first cause of a high variance of lateness

• The second dot in each curve represents the release decision. Order release can influence estimated lateness by varying the time between acceptance and release, the so-called pool time