Facilities design

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

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.

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

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

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)

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.

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 / (1+i) N (Chpt 7: Table 7.4and Examples 10, 11) –Mathematical Programming formulations which allow the optimized selection of technology mixes.

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)

The MP formulation

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

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.

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

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