Production systems : in the middle there are two rails for the shuttle to move
pallets between machining centers (there are also FMS which use
AGVs), in front of each machining center there is a buffer and in left we have a shelf for storing pallets. Usually in the back there is a similar system for managing the set of
tools required for different
machining operations. A production system comprises both technological elements (machines and tools) and
organizational behavior (division of labor and
information flow) needed to produce goods and services. An individual production system is usually analyzed in the literature referring to a single business; therefore it is usually improper to include in a given production system the operations necessary to process goods that are obtained by
purchasing or the operations carried by the
customer on the sold products, the reason being simply that since businesses need to design their own production systems this then becomes the focus of
analysis,
modeling and
decision making (also called "configuring" a production system).
Classification A first possible distinction in production systems (technological classification) is between continuous process production and discrete part production (
manufacturing). • Process production means that the product undergoes physical-chemical transformations and lacks assembly operations, and therefore the original raw materials cannot easily be obtained from the final product. Examples include:
paper,
cement,
nylon and
petroleum products. • Part production (e.g. cars and ovens) comprises both
fabrication systems and
assembly systems. In the first category are
job shops,
manufacturing cells,
flexible manufacturing systems and
transfer lines. In the assembly category there
fixed position systems,
assembly lines and
assembly shops (both manual and automated operations). Another possible classification is one based on
lead time (manufacturing lead time vs delivery lead time):
engineer to order (ETO),
purchase to order (PTO),
make to order (MTO),
assemble to order (ATO) and
make to stock (MTS). According to this classification different kinds of systems will have different customer order decoupling points (CODP), meaning that
work in progress (WIP) cycle stock levels are practically nonexistent regarding operations located after the CODP (except for
WIP due to queues). (See
Order fulfillment.) The concept of production systems can be expanded to the
service sector world keeping in mind that services have some fundamental differences in respect to material goods: intangibility, client always present during transformation processes, no stocks for "finished goods". Services can be classified according to a service process matrix: degree of labor intensity (volume) vs degree of customization (variety). With a high degree of labor intensity there are mass services (e.g.,
commercial banking bill payments and
state schools) and professional services (e.g., personal
physicians and
lawyers), while with a low degree of labor intensity there are service factories (e.g.,
airlines and
hotels) and service shops (e.g.,
hospitals and
auto mechanics). The systems described above are
ideal types: real systems may present themselves as hybrids of those categories. Consider, for example, that the production of
jeans involves initially
carding,
spinning,
dyeing and
weaving, then cutting the fabric in different shapes and assembling the parts in pants or jackets by combining the fabric with thread, zippers and buttons, finally
finishing and
distressing the pants/jackets before being shipped to stores. The beginning can be seen as process production, the middle as part production and the end again as process production: it is unlikely that a single company will keep all the stages of production under a single roof, therefore the problem of
vertical integration and
outsourcing arises. Most products require,
from a supply chain perspective, both process production and part production.
Operations systems If a production system is concerned with the
production of goods and services, an operations system is concerned with
provisioning them. Please note that this section does not particularly include "Professional Services Firms" and the professional services practiced from this expertise (specialized training and education within). According to Fitzsimmons, Fitzsimmons and Bordoloi (2014) differences between manufactured goods and services are as follows: •
Simultaneous production and consumption. High contact services (e.g. health care) must be produced in the presence of the customer, since they are consumed as produced. As a result, services cannot be produced in one location and transported to another, like goods. Service operations are therefore highly dispersed geographically close to the customers. Furthermore, simultaneous production and consumption allows the possibility of self-service involving the customer at the point of consumption (e.g. gas stations). Only low-contact services produced in the "backroom" (e.g., check clearing) can be provided away from the customer. •
Perishable. Since services are perishable, they cannot be stored for later use. In manufacturing companies, inventory can be used to buffer supply and demand. Since buffering is not possible in services, highly variable demand must be met by operations or demand modified to meet supply. •
Ownership. In manufacturing, ownership is transferred to the customer. Ownership is not transferred for service. As a result, services cannot be owned or resold. •
Tangibility. A service is intangible making it difficult for a customer to evaluate the service in advance. In the case of a manufactured good, customers can see it and evaluate it. Assurance of quality service is often done by licensing, government regulation, and branding to assure customers they will receive a quality service. These four comparisons indicate how management of service operations are quite different from manufacturing regarding such issues as capacity requirements (highly variable), quality assurance (hard to quantify), location of facilities (dispersed), and interaction with the customer during delivery of the service (product and process design). While there are differences there are also many similarities. For example, quality management approaches used in manufacturing such as the Baldrige Award, and
Six Sigma have been widely applied to services. Likewise,
lean service principles and practices have also been applied in service operations. The important difference being the customer is in the system while the service is being provided and needs to be considered when applying these practices. One important difference is service recovery. When an error occurs in service delivery, the recovery must be delivered on the spot by the service provider. If a waiter in a restaurant spills soup on the customer's lap, then the recovery could include a free meal and a promise of free dry cleaning. Another difference is in planning capacity. Since the product cannot be stored, the service facility must be managed to peak demand which requires more flexibility than manufacturing. Location of facilities must be near the customers and scale economics can be lacking. Scheduling must consider the customer can be waiting in line. Queuing theory has been devised to assist in design of service facilities waiting lines. Revenue management is important for service operations, since empty seats on an airplane are lost revenue when the plane departs and cannot be stored for future use.
Metrics: efficiency and effectiveness Operations strategy concerns policies and plans of use of the firm productive resources with the aim of supporting long term competitive strategy. Metrics in operations management can be broadly classified into
efficiency metrics and
effectiveness metrics. Effectiveness metrics involve: •
Price (actually fixed by marketing, but lower bounded by production cost): purchase price, use costs, maintenance costs, upgrade costs, disposal costs •
Quality: specification and compliance •
Time: productive
lead time, information lead time,
punctuality •
Flexibility: mix (capacity to change the
proportions between quantities produced in the system), volume (capacity to increase system
output), gamma (capacity to expand the product family in the system) • Stock
availability • Ecological Soundness: biological and
environmental impacts of the system under study. A more recent approach, introduced by Terry Hill, involves distinguishing competitive variables in order winner and order qualifiers when defining operations strategy. Order winners are variables which permit differentiating the company from competitors, while order qualifiers are prerequisites for engaging in a transaction. This view can be seen as a unifying approach between operations management and
marketing (see
segmentation and
positioning).
Productivity is a standard efficiency metric for evaluation of production systems, broadly speaking a ratio between outputs and inputs, and can assume many specific forms, for example: machine productivity, workforce productivity, raw material productivity, warehouse productivity (=
inventory turnover). It is also useful to break up productivity in use U (productive percentage of total time) and yield η (ratio between produced volume and productive time) to better evaluate production systems performances. Cycle times can be modeled through
manufacturing engineering if the individual operations are heavily automated, if the manual component is the prevalent one, methods used include:
time and motion study,
predetermined motion time systems and
work sampling.
ABC analysis is a method for analyzing inventory based on
Pareto distribution, it posits that since revenue from items on inventory will be
power law distributed then it makes sense to manage items differently based on their position on a revenue-inventory level matrix, 3 classes are constructed (A, B and C) from cumulative item revenues, so in a matrix each item will have a letter (A, B or C) assigned for revenue and inventory. This method posits that items away from the diagonal should be managed differently: items in the upper part are subject to risk of obsolescence, items in the lower part are subject to risk of
stockout.
Throughput is a variable which quantifies the number of parts produced in the unit of time. Although estimating throughput for a single process maybe fairly simple, doing so for an entire production system involves an additional difficulty due to the presence of queues which can come from: machine
breakdowns, processing time variability, scraps, setups,
maintenance time, lack of orders, lack of materials,
strikes, bad coordination between resources, mix variability, plus all these inefficiencies tend to compound depending on the nature of the production system. One important example of how system throughput is tied to system design are
bottlenecks: in job shops bottlenecks are typically dynamic and dependent on scheduling while on transfer lines it makes sense to speak of "the bottleneck" since it can be univocally associated with a specific station on the line. This leads to the problem of how to define
capacity measures, that is an estimation of the maximum output of a given production system, and
capacity utilization.
Overall equipment effectiveness (OEE) is defined as the product between system availability, cycle time efficiency and quality rate. OEE is typically used as key performance indicator (KPI) in conjunction with the lean manufacturing approach.
Configuration and management Designing the
configuration of production systems involves both
technological and
organizational variables. Choices in production technology involve: dimensioning
capacity, fractioning capacity, capacity location,
outsourcing processes, process technology,
automation of operations, trade-off between volume and variety (see
Hayes-Wheelwright matrix). Choices in the organizational area involve: defining worker
skills and
responsibilities, team coordination, worker incentives and information flow. In
production planning, there is a basic distinction between the
push approach and the
pull approach, with the later including the singular approach of
just in time. Pull means that the production system authorizes production based on inventory level; push means that production occurs based on demand (forecasted or present, that is
purchase orders). An individual production system can be both push and pull; for example activities before the CODP may work under a pull system, while activities after the CODP may work under a push system. (red). Total cost (green) admits a
global optimum. The traditional pull approach to
inventory control, a number of techniques have been developed based on the work of Ford W. Harris (EPQ) differs from the EOQ model only in that it assumes a constant fill rate for the part being produced, instead of the instantaneous refilling of the EOQ model. , rough-cut capacity planning, MPS,
capacity requirements planning, traditional MRP planning, control (bottom) concerned with
scheduling.
Joseph Orlickly and others at IBM developed a
push approach to inventory control and production planning, now known as
material requirements planning (MRP), which takes as input both the
master production schedule (MPS) and the
bill of materials (BOM) and gives as output a schedule for the materials (components) needed in the production process. MRP therefore is a planning tool to manage
purchase orders and production orders (also called jobs). The MPS can be seen as a kind of aggregate planning for production coming in two fundamentally opposing varieties: plans which try to
chase demand and
level plans which try to keep uniform capacity utilization. Many models have been proposed to solve MPS problems: • Analytical models (e.g. Magee Boodman model) • Exact optimization algorithmic models (e.g.
LP and
ILP) •
Heuristic models (e.g. Aucamp model). •
Resource profit models MRP can be briefly described as a 3s procedure: sum (different orders), split (in lots), shift (in time according to item lead time). To avoid an "explosion" of data processing in MRP (number of BOMs required in input)
planning bills (such as family bills or super bills) can be useful since they allow a rationalization of input data into common codes. MRP had some notorious problems such as infinite
capacity and fixed
lead times, which influenced successive modifications of the original software architecture in the form of
MRP II,
enterprise resource planning (ERP) and
advanced planning and scheduling (APS). In this context problems of
scheduling (sequencing of production), loading (tools to use), part type selection (parts to work on) and applications of
operations research have a significant role to play.
Lean manufacturing is an approach to production which arose in
Toyota between the end of World War II and the seventies. It comes mainly from the ideas of
Taiichi Ohno and
Sakichi Toyoda which are centered on the complementary notions of
just in time and
autonomation (jidoka), all aimed at reducing waste (usually applied in
PDCA style). Some additional elements are also fundamental: production smoothing (Heijunka), capacity buffers, setup reduction, cross-training and plant layout. •
Heijunka: production smoothing presupposes a level strategy for the
MPS and a
final assembly schedule developed from the MPS by smoothing aggregate production requirements in smaller time buckets and sequencing final assembly to achieve repetitive manufacturing.
If these conditions are met,
expected throughput can be equaled to the inverse of
takt time. Besides volume, heijunka also means attaining
mixed-model production, which however may only be feasible through set-up reduction. A standard tool for achieving this is the
Heijunka box. • Capacity buffers: ideally a JIT system would work with zero breakdowns, this however is very hard to achieve in practice, nonetheless Toyota favors acquiring extra capacity over extra WIP to deal with starvation. •
Set-up reduction: typically necessary to achieve mixed-model production, a key distinction can be made between internal and external setup. Internal setups (e.g. removing a die) refers to tasks when the machine is not working, while external setups can be completed while the machine is running (ex:transporting dies). •
Cross training: important as an element of Autonomation, Toyota cross trained their employees through rotation, this served as an element of production flexibility, holistic thinking and reducing boredom. •
Layout: U-shaped lines or cells are common in the lean approach since they allow for minimum walking, greater worker efficiency and flexible capacity. A series of tools have been developed mainly with the objective of replicating Toyota success: a very common implementation involves small cards known as
kanbans; these also come in some varieties: reorder kanbans, alarm kanbans, triangular kanbans, etc. In the classic kanban procedure with one card: • Parts are kept in containers with their respective kanbans • The downstream station moves the kanban to the upstream station and starts producing the part at the downstream station • The upstream operator takes the most urgent kanban from his list (compare to
queue discipline from queue theory) and produces it and attach its respective kanban The two-card kanban procedure differs a bit: • The downstream operator takes the production kanban from his list • If required parts are available he removes the move kanban and places them in another box, otherwise he chooses another production card • He produces the part and attach its respective production kanban • Periodically a mover picks up the move kanbans in upstream stations and search for the respective parts, when found he exchanges production kanbans for move kanbans and move the parts to downstream stations Since the number of kanbans in the production system is set by managers as a constant number, the kanban procedure works as
WIP controlling device, which for a given arrival rate, per
Little's law, works as a lead time controlling device. , a representation of materials and information flows inside a company, mainly used in the lean manufacturing approach. The calculation of the time-line (bottom) usually involves using
Little's law to derive lead time from stock levels and
takt time. In Toyota the TPS represented more of a philosophy of production than a set of specific lean tools, the latter would include: •
SMED: a method for reducing changeover times •
Value stream mapping: a graphical method for analyzing the current state and designing a future state • lot-size reduction • elimination of time batching •
Rank order clustering: an algorithm which groups machines and product families together, used for designing
manufacturing cells • single-point
scheduling, the opposite of the traditional push approach •
multi-process handling: when one operator is responsible for operating several machines or processes •
poka-yoke: any mechanism in lean manufacturing that helps an equipment operator avoid (
yokeru) mistakes (
poka) •
5S: describes how to organize a work space for efficiency and effectiveness by identifying and storing the items used, maintaining the area and items, and sustaining the new order •
backflush accounting: a product costing approach in which costing is delayed until goods are finished Seen more broadly, JIT can include methods such as: product standardization and
modularity,
group technology,
total productive maintenance,
job enlargement,
job enrichment,
flat organization and
vendor rating (JIT production is very sensitive to replenishment conditions). In heavily
automated production systems production planning and information gathering may be executed via the
control system, attention should be paid however to avoid problems such as
deadlocks, as these can lead to productivity losses.
Project production management (PPM) applies the concepts of operations management to the execution of delivery of capital projects by viewing the sequence of activities in a project as a production system. Operations managements principles of variability reduction and management are applied by buffering through a combination of capacity, time and inventory.
Optimization modeling are systems in which single queues are connected by a routing network. In this image servers are represented by circles, queues by a series of rectangles and the routing network by arrows. In the study of queue networks one typically tries to obtain the
equilibrium distribution of the network. , a classical approach to solving
LP optimization problems and also
integer programming (ex:
branch and cut). This technique is mainly used in push approach but also in production system configuration. Computations of
safety stocks are usually based on modeling demand as a
normal distribution and MRP and some inventory problems can be formulated using
optimal control. When analytical models are not enough, managers may resort to using
simulation. Simulation has been traditionally done through the
discrete event simulation paradigm, where the simulation model possesses a state which can only change when a discrete event happens, which consists of a clock and list of events. The more recent
transaction-level modeling paradigm consists of a set of resources and a set of transactions: transactions move through a network of resources (nodes) according to a code, called a process. output variable is modeled by a
probability density function and for each
statistic of the
sample an upper control line and lower control line are fixed. When the statistic moves out of bounds, an alarm is given and possible causes are investigated. In this drawing the statistic of choice is the
mean and red points represent alarm points. Since real production processes are always affected by disturbances in both inputs and outputs, many companies implement some form of
quality management or
quality control. The
Seven Basic Tools of Quality designation provides a summary of commonly used tools: •
check sheets •
Pareto charts •
Ishikawa diagrams (Cause-and-effect diagram) •
control charts •
histogram •
scatter diagram •
stratification These are used in approaches like
total quality management and
Six Sigma. Keeping quality under control is relevant to both increasing customer satisfaction and reducing processing waste. Operations management
textbooks usually cover
demand forecasting, even though it is not strictly speaking an operations problem, because demand is related to some production systems variables. For example, a classic approach in dimensioning
safety stocks requires calculating the
standard deviation of
forecast errors.
Demand forecasting is also a critical part of push systems, since order releases have to be planned ahead of actual clients’ orders. Also, any serious discussion of
capacity planning involves adjusting company outputs with market demands.
Safety, risk and maintenance Other important
management problems involve
maintenance policies (see also
reliability engineering and
maintenance philosophy),
safety management systems (see also
safety engineering and
risk management),
facility management and
supply chain integration. ==Organizations==