Strategic issues to be considered Make or buy Technology selection Make-to-stock vs. Make-to-order

Make or Buy In general, parts that directly relate to the core competencies of the company are usually produced internally. For parts that could be outsourced, some additional concerns are: –the quality of product and service guaranteed by the vendor –the stability of the vendor in terms of responsiveness and prices –What could be the benefits and/or drawbacks if this unit was produced in-house (e,g,, expand the company’s technology base or strain too much its human resources)? When the above more qualitative considerations fail to resolve the issue, it can boil down to an economic comparison of the different scenaria.

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 < c1 X: total quantity of the item to be outsourced or produced internally X Total cost as a function of X C C+c2*X c1*X X0 = C / (c1-c2)

Model Enhancements Demand uncertainty Quantity-based discounts Stair-step capacity costs Nonlinear variable production costs Supplier limitations

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 developments (e.g., economic, legal, 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

Model Parameters and Decision Variables 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: number of units of technology i used to produce product j per period (nonnegative real, i.e., it can be fractional)

Minimizing the total cost

Make-to-Order vs. Make-to-Stock Make-to-Order: Orders are produced or procured only upon placement Make-to-stock: Demand is met from pre-built inventories, which are replenished periodically, through the production / procurement of a new lot of some predefined size Q. Advantages for make-to-order / Disadvantages for make-to-stock No need to tie capital in inventories and storage facilities Guards against obsolescence and spoilage Enhances the ability to support customization Disadvantages for make-to-order / Advantages for make-to-stock Introduces and element of backordering in the company operations => negative psychology to customers => loss of market share (especially if quoted delivery times are too long) Increases the “pressure” in the company operations and might fail to take advantage of efficiencies that can result from early and good planning, like optimizing the production / procurement lot sizes taking advantage of low prices of raw materials or quantity discounts and using expensive production options like overtime and outsourcing rather than using the existing slack capacity.

Characterizing the operational cost under the “make-to-order” regime Model parameters –D: expected demand per period (e.g., year) –Q_ns: average order quantity under non-stocking option –A: setup / ordering cost per production lot / order –  : backorder cost experienced every time we need to order under no stocking (includes goodwill loss due to slower delivery of the final product to the customer) –(C: unit variable cost) Resulting cost per period: (A +  ) * (D / Q_ns) + C*D Cost per order Number of orders per period

Characterizing the operational cost under the “make-to-stock” regime Model parameters and assumptions: –D: expected demand per period (e.g., year) –A: setup / ordering cost per production lot / replenishment order –(C: unit variable cost) –h: inventory holding cost per unit per period (typically, h = i*C, where i is an interest rate per period) –C_s: inventory managing costs per period –Q: production batch / replenishment order quantity –Assuming instantaneous replenishment: t Inventory position Q T Replenishment cycle or inventory turn

Characterizing the operational cost under the “make-to-stock” regime (cont.) Resulting cost per period: TC(Q) = A*(D/Q) + h*(Q/2) + C*D + C_s Setup/Ordering cost per period Holding cost per period Variable Item cost per period Minimizing cost per period through the selection of Q: Economic Order Quantity (EOQ) Q* =  ( 2*A*D / h ) Resulting optimal cost per period TC(Q*) =  ( 2*A*D*h ) + C*D + C_s

Cost comparison (A +  ) * (D / Q_ns) + C*D >=<>=<  ( 2*A*D*h ) + C*D + C_s