Facilities design

Main Topics Discrete vs. Continuous Flow and Repetitive Manufacturing Process vs. Product-focused designs and the other currently used variations Designing Layouts –Systematic Layout Planning (SLP) for Process-focused layouts –Flow Patterns for Product-focused layouts (Assembly) Line Balancing Cell Formation (Warehousing and its design issues – in a sequel presentation)

Operation Process Chart Example for discrete part manufacturing (borrowed from Francis et. al.)

Discrete vs. Continuous Flow and Repetitive Manufacturing Systems (Figures borrowed from Heizer and Render)

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

Synchronous Transfer Lines: Examples (Pictures borrowed from Heragu)

Major Layout Types (borrowed from Francis et. al.)

Advantages and Limitations of the various layout types (borrowed from Francis et. al.)

Advantages and Limitations of the various layout types (cont. - borrowed from Francis et. al.)

Selecting an appropriate layout (borrowed from Francis et. al.)

The product-process matrix Jumbled flow (job Shop) Disconnected line flow (batch) Connected line flow (assembly Line) Continuous flow (chemical plants) Process type Production volume & mix Low volume, low standardi- zation Multiple products, low volume Few major products, high volume High volume, high standardization, commodities Commercial printer Heavy Equipment Auto assembly Sugar refinery Void

Cell formation in group technology: A clustering problem 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

Clustering Algorithms for Cellular Manufacturing Row & Column Masking

Clustering Algorithms for Cellular Manufacturing: Similarity Coefficients - Motivation 1 1

Clustering Algorithms for Cellular Manufacturing: Similarity Coefficients - Definitions P(Mi) = set of parts supported by machine Mi |P(Mi)| = cardinality of P(Mi), i.e., the number of elements of this set SC(Mi,Mj) = |P(Mi)  P(Mj)| / |P(Mi)  P(Mj)| = |P(Mi)  P(Mj)| / (|P(Mi)|+|P(Mj)|-|P(Mi)  P(Mj)|) Notice that:0  SC(Mi,Mj)  1.0, and the closer this value is to 1.0 the greater the similarity among the part sets supported by machines Mi and Mj. By picking a desired threshold, one can cluster together all machines that have a similarity coefficient greater than or equal to this threshold.

Flow Patterns for Product-focused Layouts (borrowed from Francis et. al.)

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.

Prevailing Methodology: Systematic Layout Planning (SLP) Departments  Activities 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

Example on SLP Developed in class – c.f. your class notes!

Synchronous Transfer Lines: Examples (Pictures borrowed from Heragu)

Balancing 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.

Heuristics for Assembly Line Balancing Developed in class – c.f. your class notes!

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

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

The G/G/1 model TH Station Parameters: (m: number of machines) Production rate / Throughput: TH Mean effective processing time: t e St. deviation of effective processing time:  e Coefficient of variation (CV) of effective processing time: c e =  e / t e Machine utilization u = TH * t e (TH*t e / m) Coefficient of variation of inter-arrival times: c a Coefficient of variation of inter-departure times: c d Evaluating the key performance measures: CT q = [(c a 2 + c e 2 ) / 2 ]*[u / (1-u)] * t e [(c a 2 + c e 2 ) / 2 ]*[u  (2(m+1))-1 /(m (1-u))] * t e CT = CT q + t e WIP q = TH * CT q WIP = TH * CT = WIP q + u WIP q + m*u c d 2 = u 2 * c e 2 + (1-u 2 ) * c a 2 1+(1-u 2 )(c a 2 -1)+u 2 (c e 2 -1)/  m

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.

Taking into consideration machine failures Definitions: Base machine processing time: t 0 Coefficient of variation for base processing time: c 0 =  0 / t 0 Mean time to failure: m f Mean time to repair: m r Coefficient of variation of repair times: c r =  r / m r Machine Availability A = m f / (m f + m r ) Then, t e = t 0 / A (or equivalently 1/t e = A * (1/t 0 ) )  e 2 = (  0 /A) 2 + (m r 2 +  r 2 )(1-A)(t 0 /A) c e 2 =  e 2 / t e 2 = c (1+c r 2 )A(1-A)m r /t 0

Example on the Analysis and Design of Asynchronous Production Lines through the presented G/G/m model Developed in class – c.f. your class notes!

Reading Assignment From your textbook: –Chapter 1: Section 1.9 –Chapter 10: Sections , 10.6 –Chapter 8: Section 8.10 –(for those of you interested to see something more, read also Sections 10.4, 10.5 and 10.7)