Facilities design

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Facilities design
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Main Topics

• Process vs. Product-focused designs and the other
  currently used variations
• Technology selection and capacity planning
• Layout design
• (Assembly) Line Balancing
• Cell Formation (?)
• Layout issues in warehousing

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Process vs. Product-focused designs and the
remaining variations

• Process and product-focused designs advantages
  and disadvantages, based on Figures 2.18 and
  Table 2.2 of Francis, McGinnis and White (pgs
  58-60)
• The classification of the manufacturing systems
  to Discrete and Continuous, and its implications
  for the adopted facility strategy. (Figure 7.4
  textbook)
• The concept of repetitive manufacturing the
  contemporary implementation of product-focused
  facility design in discrete part manufacturing.
  (Figure 7.3 textbook)
• The role of cellular manufacturing for
  facilitating the involved material flows and
  simplifying the complexity of the underlying
  production planning and scheduling problems.
• Process re-engineering a systematic
  re-evaluation and redesign of the production
  process and the associated facility to increase
  its efficiencies, by controlling the operational
  waste and costs.

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A typical (logical) Organization of the
Production Activity in Repetitive Manufacturing
Assembly Line 1 Product Family 1
Raw Material Comp. Inventory
Finished Item Inventory
S1,1
S1,n
S1,i
S1,2
Fabrication (or Backend Operations)
Dept. 1
Dept. 2
Dept. k
Dept. j
S2,1
S2,2
S2,m
S2,i
Assembly Line 2 Product Family 2
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Technology selection

• The selected technology must be able to support
  the quality standards set by the corporate /
  manufacturing strategy
• This decision must take into consideration future
  expansion plans of the company in terms of
• production capacity (i.e., support volume
  flexibility)
• product portfolio (i.e., support product
  flexibility)
• It must also consider the overall technological
  trends in the industry, as well as additional
  issues (e.g., environmental and other legal
  concerns, operational safety etc.) that might
  affect the viability of certain choices
• For the candidates satisfying the above concerns,
  the final objective is the minimization of the
  total (i.e., deployment plus operational) cost

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Production Capacity

• Design capacity the theoretical maximum output
  of a system, typically stated as a rate, i.e., x
  product units / unit time.
• Effective capacity The percentage of the design
  capacity that the system can actually achieve
  under the given operational constraints, e.g.,
  running product mix, quality requirements,
  employee availability, scheduling methods, etc.
• Plant utilization actual output / design
  capacity
• Plant efficiency actual output / (effective
  capacity x
• design capacity)
• Also
• actual production
• (design capacity) x (effective capacity) x
  (efficiency)

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Capacity Planning

• Capacity planning seeks to determine
• the number of units of the selected technology
  that needs to be deployed in order to match the
  plant (effective) capacity with the forecasted
  demand, and if necessary,
• a capacity expansion plan that will indicate the
  time-phased deployment of additional modules /
  units, in order to support a growing product
  demand, or more general expansion plans of the
  company (e.g., undertaking the production of a
  new product in the considered product family).
  (c.f. Figure 7.10)
• In general, technology selection and capacity
  planning are addressed simultaneously, since the
  required capacity affects the economic viability
  of a certain technological option, while the
  operational characteristics of a given technology
  define the production rate per unit deployed and
  aspects like the possibility of modular
  deployment.

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Quantitative Approaches to Technology Selection
and Capacity Planning

• All these approaches try to select a technology
  (mix) and determine the capacity to be deployed
  in a way that it maximizes the expected profit
  over the entire life-span of the considered
  product (family).
• Expected profit is defined as expected revenues
  minus deployment and operational costs.
• Possible methods used include
• Decision trees which allow the modeling of
  problem uncertainties like uncertain market
  behavior, etc., and can determine a strategy as a
  reaction to these unknown factors. (Chpt 7
  Example 6)
• Break-even analysis and crossover charts which
  allow the selection of a technology option in a
  way that minimizes the total (fixed variable)
  cost. (Chpt 7 Figures 7.12 and 7.13)
• Net present value analysis which takes into
  consideration the cost of money P F / (1i)N
  (Chpt 7 Table 7.4and Examples 10, 11)
• Mathematical Programming formulations which allow
  the optimized selection of technology mixes.

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Technology Selection and Capacity Planning
through Mathematical Programming (MP)

• Model Parameters
• i ? 1,,m technology options
• j?? 1,,n product (families) to be supported
  in the considered plant
• D_j forecasted demand per period for product j
  over the considered planning horizon
• C_i fixed production cost per period for one
  unit of technology option i
• v_ij variable production cost for of using one
  unit of technology i for one (full) period
  to produce (just) product j
• a_ij number of units of product j that can be
  produced in one period by one unit of
  technology option i.
• Model DecisionVariables
• y_i number of units of technology i to be
  deployed (nonnegative integer)
• x_ij production capacity of technology i used at
  each period to produce product j
  (nonnegative real, i.e., it can be fractional)

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The MP formulation
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Design of Process-based layouts

• Arrange spatially the facility departments in a
  way that
• facilitates the flow of parts through the
  facility by minimizing the material handling /
  traveling effort
• observes additional practical constraints
  arising from, e.g.,
• processing/operational requirements
• safety/health considerations
• aesthetics
• building features
• etc.

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Prevailing MethodologySystematic Layout
Planning (SLP)
1. Material Flows
2. Activity Relationships
3. REL Chart
4. REL Diagram
5. Space Requirements
6. Space REL Diagram
7. Space Availability
8. Layout Alternatives
Departments ? Activities
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Assembly Line Balancing for Synchronous Transfer
Lines

• Given
• a set of m tasks, each requiring a certain
  (nominal) processing time t_i, and
• a set of precedence constraints regarding the
  execution of these m tasks,
• assign these tasks to a sequence of k
  workstations, in a way that
• the total amount of work assigned to each
  workstation does not exceed a pre-defined cycle
  time c, (constraint I)
• the precedence constraints are observed,
  (constraint II)
• while the number of the employed workstations k
  is minimized. (objective)
• Remark The problem is hard to solve optimally,
  and quite often it is addressed through
  heuristics.

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Asynchronous Production Lines

• Each part moves to the next station upon
  finishing processing at its current station,
  provided that there is available buffering
  capacity at the next station, without
  coordinating its movement with other parts in the
  system.
• Some reasons for adopting an asynchronous
  operational mode
• Lack / High cost of synchronizing material
  handling equipment
• (Highly) variable processing times at or among
  the different stations
• Frequent equipment failures

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Buffers, WIP and Congestion

• Typical quantities of interest
• Times spent at different part of the system
  (cycle times)
• Material accumulated at different parts of the
  system (WIP)
• Estimates for these quantities can be obtained
  either through
• Queueing theory (G/G/1 models), or
• Simulation

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The G/G/1 model

• Station Parameters (m number of machines)
• Production rate / Throughput TH
• Mean effective processing time te
• St. deviation of effective processing time ?e
• Coefficient of variation (CV) of effective
  processing time ce ?e / te
• Machine utilization u TH te (THte / m)
• Coefficient of variation of inter-arrival times
  ca
• Coefficient of variation of inter-departure
  times cd
• Evaluating the key performance measures
• CTq (ca2 ce2) / 2u / (1-u) te
  (ca2 ce2) / 2u?(2(m1))-1 /(m (1-u)) te
• CT CTq te
• WIPq TH CTq
• WIP TH CT WIPq u WIPq mu
• cd2 u2 ce2 (1-u2) ca2 1(1-u2)(ca2-1)u2
  (ce2-1)/?m

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Evaluating an entire Production Line
TH

• Key observations
• For a stable system, the average production rate
  of every station
• will be equal to TH.
• For every pair of stations, the inter-departure
  times of the first
• constitute the inter-arrival times of the
  second.
• Then, the entire line can be evaluated on a
  station by station basis,
• working from the first station to the last,
  and using the equations for
• the basic G/G/1 model.

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Taking into consideration machine failures

• Definitions
• Base machine processing time t0
• Coefficient of variation for base processing
  time c0 ?0 / t0
• Mean time to failure mf
• Mean time to repair mr
• Coefficient of variation of repair times cr
  ?r / mr
• Machine Availability A mf / (mf mr)
• Then,
• te t0 / A (or equivalently 1/te A (1/t0)
  )
• ?e2 (?0/A)2 (mr2 ?r2)(1-A)(t0/A)
• ce2 ?e2 / te2 c02 (1cr2)A(1-A)mr/t0

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The underlying clustering problem for cell
formation in group technology
Partition the entire set of parts to be produced
on the plant-floor into a set of part families,
with parts in each family characterized by
similar processing requirements, and therefore,
supported by the same cell.
Part-Machine Indicator Matrix