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Title: Product and Process Design, Sourcing, Equipment Selection and Capacity Planning


1
Product and Process Design,Sourcing, Equipment
Selection and Capacity Planning
2
Major Topics
  • Product and Process Design
  • Documenting Product and Process Design
  • Sourcing Decisions
  • A simple Make or Buy model
  • Decision Trees A scenario-based approach
  • Equipment Selection and Capacity Planning

3
Product Selection and Development
Stages(borrowed from Heizer Render)
4
Quality Function Deployment (DFD) and the House
of Quality
  • QFD The process of
  • Determining what are the customer requirements
    / wants, and
  • Translating those desires into the target product
    design.
  • House of quality A graphic, yet systematic
    technique for defining the relationship between
    customer desires and the developed product (or
    service)

5
House of Quality Example(borrowed from Heizer
Render)
6
The House of Quality Chain(borrowed from
Heizer Render)
7
Concurrent Engineering The current approach for
organizing the product and process development
  • The traditional US approach (department-based)
  • Research Development gt Engineering gt
    Manufacturing gt
  • Production
  • Clear-cut responsibilities but lack of
    communication and forward thinking!
  • The currently prevailing approach
    (cross-functional team-based)
  • Product development (or design for
    manufacturability, or value engineering) teams
    Include representatives from
  • Marketing
  • Manufacturing
  • Purchasing
  • Quality assurance
  • Field service
  • (even from) vendors
  • Concurrent engineering Less costly and more
    expedient product development

8
The time factor Time-based competition
  • Some advantages of getting first a new product to
    the market
  • Setting the standard (higher market control)
  • Larger market share
  • Higher prices and profit margins
  • Currently, product life cycles get shorter and
    product technological sophistication increases gt
    more money is funneled to the product development
    and the relative risks become higher.
  • The pressures resulting from time-based
    competition have led to higher levels of
    integrations through strategic partnerships, but
    also through mergers and acquisitions.

9
Additional concerns in contemporary product and
process design
  • promote robust design practices
  • Robustness the insensitivity of the product
    performance to small variations in the production
    or assembly process gt ability to support product
    quality more reliably and cost-effectively.
  • Control the product complexity
  • Improve the product maintainability /
    serviceability
  • (further) standardize the employed components
  • Modularity the structuring of the end product
    through easily segmented components that can
    also be easily interchanged or replaced gt
    ability to support flexible production and
    product customizationincreased product
    serviceability.
  • Improve job design and job safety
  • Environmental friendliness safe and
    environmentally sound products, minimizing waste
    of raw materials and energy, complying with
    environmental regulations, ability for reuse,
    being recognized as good corporate citizen.

10
Documenting Product Designs
  • Engineering Drawing a drawing that shows the
    dimensions, tolerances, materials and finishes of
    a component. (Fig. 5.9)
  • Bill of Material (BOM) A listing of the
    components, their description and the quantity of
    each required to make a unit of a given product.
    (Fig. 5.10)
  • Assembly drawing An exploded view of the
    product, usually via a three-dimensional or
    isometric drawing. (Fig. 5.12)
  • Assembly chart A graphic means of identifying
    how components flow into subassemblies and
    ultimately into the final product. (Fig. 5.12)
  • Route sheet A listing of the operations
    necessary to produce the component with the
    material specified in the bill of materials.
  • Engineering change notice (ECN) a correction or
    modification of an engineering drawing or BOM.
  • Configuration Management A system by which a
    products planned and changing components are
    accurately identified and for which control of
    accountability of change are maintained

11
Documenting Product Designs (cont.)
  • Work order An instruction to make a given
    quantity (known as production lot or batch) of a
    particular item, usually to a given schedule.
  • Group technology A product and component coding
    system that specifies the type of processing and
    the involved parameters, allowing thus the
    identification of processing similarities and the
    systematic grouping/classification of similar
    products. Some efficiencies associated with group
    technology are
  • Improved design (since the focus can be placed on
    a few critical components
  • Reduced raw material and purchases
  • Improved layout, routing and machine loading
  • Reduced tooling setup time, work-in-process and
    production time
  • Simplified production planning and control

12
Engineering Drawing Example(borrowed from Heizer
Render)
13
Bill of Material (BOM) Example(borrowed from
Heizer Render)
14
Assembly Drawing Chart Examples(borrowed from
Heizer Render)
15
Operation Process Chart Example(borrowed from
Francis et. al.)
16
Route Sheet Example(borrowed from Francis et.
al.)
17
Make-or-buy decisions
  • Deciding whether to produce a product component
    in-house, or purchase/procure it from an
    outside source.
  • Issues to be considered while making this
    decision
  • Quality of the externally procured part
  • Reliability of the supplier in terms of both item
    quality and delivery times
  • Criticality of the considered component for the
    performance/quality of the entire product
  • Potential for development of new core
    competencies of strategic significance to the
    company
  • Existing patents on this item
  • Costs of deploying and operating the necessary
    infrastructure

18
A simple economic trade-off model for the Make
or Buy problem
  • Model parameters
  • c1 (/unit) cost per unit when item is
    outsourced (item price, ordering and receiving
    costs)
  • C () required capital investment in order to
    support internal production
  • c2 (/unit) variable production cost for
    internal production (materials, labor,variable
    overhead charges)
  • Assume that c2 lt c1
  • X total quantity of the item to be outsourced
    or produced internally

c1X
Total cost as a function of X
Cc2X
C
X
X0 C / (c1-c2)
19
Example Introducing a new (stabilizing) bracket
for an existing product
  • Machine capacity available
  • Required infrastructure for in-house production
  • new tooling 12,500
  • Hiring and training an additional worker 1,000
  • Internal variable production (raw material
    labor) cost 1.12 / unit
  • Vendor-quoted price 1.55 / unit
  • Forecasted demand 10,000 units/year for next 2
    years
  • ?
  • X0 (12,5001,000)/(1.55-1.12) 31,395 gt
    20,000
  • ?
  • Buy!

20
Evaluating Alternatives through Decision Trees
  • Decision Trees A mechanism for systematically
    pricing all options / alternatives under
    consideration, while taking into account various
    uncertainties underlying the considered
    operational context.
  • Example
  • An engineering consulting company (ECC) has been
    offered the design of a new product.The price
    offered by the customer is 60,000.
  • If the design is done in-house, some new software
    must be purchased at the price of 20,000, and
    two new engineers must be trained for this effort
    at the cost of 15,000 per engineer.
  • Alternatively, this task can be outsourced to an
    engineering service provider (ESP) for the cost
    of 40,000. However, there is a 20 chance that
    this ESP will fail to meet the due date requested
    by the customer, in which case, the ECC will
    experience a penalty of 15,000. The ESP offers
    also the possibility of sharing the above penalty
    at an extra cost of 5,000 for the ECC.
  • Find the option that maximizes the expected
    profit for the ECC.

21
Decision Trees Example
22
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

23
Production Capacity
  • Design capacity the theoretical maximum output
    of a system, typically stated as a rate, i.e.,
    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 prod. rate / design
    capacity
  • Plant efficiency actual prod. rate / (effective
    capacity x
  • design capacity)
  • Notice that
  • actual prod. rate (design capacity) x
    (utilization)
  • (design capacity) x (effective capacity) x
    (efficiency)

24
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).
  • Frequently, 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.

25
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.
  • Typically, the above calculations are based on
    net present values (NPVs) of the expected costs
    and revenues, which take into consideration the
    cost of money NPV (Expense or Revenue) /
    (1i)N
  • where i is the applying interest rate and N the
    time period of the considered expense.
  • Possible methods used include
  • Break-even analysis, similar to that applied to
    the make or buy problem, that seeks to
    minimizes the total (fixed variable) cost.
  • 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.
  • Mathematical Programming formulations which allow
    the optimized selection of technology mixes.

26
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)

27
The MP formulation
28
Reading Assignment
  • Chapter 1 Section 1.11 and Appendix 1-A.
  • Also you are encouraged to read Chapter
    1Section1.10.
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