IE 3265 Production & Operations Management Slide Series 2

Topics for discussion Product Mix and Product Lifecycle – as they affect the Capacity Planning Problem The Make or Buy Decision –Its more than $ and ¢! –Break Even Analysis, how we filter in costs Capacity Planning –When, where and How Much

Product Issues related to capacity planning Typical Product Lifecycle help many companies make planning decisions Facility can be designed for Product Families and the organization tries to match lifecycle demands to keep capacitiy utilized

The Product Life-Cycle Curve

The Product/Process Matrix

Product Mix (Families) Typically Demand Different Production Capacity Design Is product Typically “One-Off” –These systems have little standardization and require high marketing investment per product –Typically ‘whatever can be made in house’ will be made ‘in house’ –Most designs are highly private and guarded as competitive advantages Multiple Products in Low Volume –Standard components are made in volume or purchased –Shops use a mixture of flow and fixed site manufacturing layouts

Product Mix (Families) Typically Demand Different Production Capacity Design Few Major (discrete) Products in Higher Volume –Purchase most components (its worth standardizing nearly all components) –Make what is highly specialized or provides a competitive advantage –Make decisions are highly dependent of capacity issues High Volume & Standardized “Commodity” Products –Flow processing all feed products purchased –Manufacturing practices are carefully guarded ‘Trade Secrets’

Make-Buy Decisions A difficult problem address by the M-B matrix Typically requires an analysis of the issues related to People, Processes, and Capacity Ultimately the problem is addressed economically

Make – Buy Decision Process Secondary QuestionsPrimary QuestionDecision 1.Is the Item Available? 2.Will our Union Allow us to buy? 3.Is outside Quality Acceptable? 4.Are Reliable Sources Available? NO = MAKE (if yes continue down) 1.Is Manufacturing Consistent with our objectives? 2.Do we have Technical Expertise? 3.Is L & MFG capacity available? 4.Must we MFG to utilize existing capacity? NO = Buy (if yes continue down) Can Item be Purchased? NO YES NO YES Can Item be Made?

Make – Buy Decision Process Secondary QuestionsPrimary QuestionDecision 1.What Alternatives are available to MFG? 2.What is future demand? 3.What are MFG costs? 4.What are Reliability issues that influence purchase or MFG? NO = Buy (if yes continue down) 1.What other opportunities are avail. For Capital? 2.What are the future investment implications if item is MFG? 3.What are costs of receiving external Financing? NO = Buy YES = MAKE Is it cheaper to make than buy? NO YES NO YES Is Capital Available To Made?

Break-even Curves for the Make or Buy Problem Cost to Buy = c 1 x Cost to make=K+c 2 x K Break-even quantity

Example M-B Analysis Fixed Costs to Purchase consist of: –Vendor Service Costs: Purchasing Agents Time Quality/QA Testing Equipment Overhead/Inventory Set Asides Fixed Costs to Make (Manufacture) –Machine Overhead Invested $’s Machine Depreciation Maintenance Costs –Order Related Costs (for materials purchase and storage issues)

Example M-B Analysis BUY Variable Costs: –Simply the purchase price Make Variable Costs –Labor/Machine time –Material Consumed –Tooling Costs (consumed)

Example M-B Analysis Make or Buy a Machined Component Purchase: Fixed Costs for Component: $4000 annually ($20000 over 5 years) Purchase Price: $38.00 each Make Using MFG Process A Fixed Costs: $145,750 machine system Variable cost of labor/overhead is 4 $36.50/hr: $2.43 Material Costs: $5.05/piece Total Variable costs: $7.48/each

Example M-B Analysis Make on MFG. Process B: Fixed Cost of Machine System: $312,500 Variable Labor/overhead cost is 45.00/hr: $0.45 Material Costs: $5.05 Formula for Breakeven: F a + V a X = F b + V b X X is Break even quantity F i is Fixed cost of Option i V i is Variable cost of Option i

Example M-B Analysis Buy vs MFG1: BE is {( )/( )} = 4120 units Buy vs MFG2: BE is {( )/(38-5.5)} = 9000 units MFG1 vs MFG2: BE is {( )/( )} = units

Capacity Strategy Fundamental issues: –Amount. When adding capacity, what is the optimal amount to add? Too little means that more capacity will have to be added shortly afterwards. Too much means that capital will be wasted. –Timing. What is the optimal time between adding new capacity? –Type. Level of flexibility, automation, layout, process, level of customization, outsourcing, etc.

Three Approaches to Capacity Strategy Policy A: Try not to run short. Here capacity must lead demand, so on average there will be excess capacity. Policy B: Build to forecast. Capacity additions should be timed so that the firm has excess capacity half the time and is short half the time. Policy C: Maximize capacity utilization. Capacity additions lag demand, so that average demand is never met.

Capacity Leading and Lagging Demand

Determinants of Capacity Strategy Highly competitive industries (commodities, large number of suppliers, limited functional difference in products, time sensitive customers) – here shortages are very costly. Use Type A Policy. Monopolistic environment where manufacturer has power over the industry: Use Type C Policy. (Intel, Lockheed/Martin). Products that obsolete quickly, such as computer products. Want type C policy, but in competitive industry, such as computers, you will be gone if you cannot meet customer demand. Need best of both worlds: Dell Computer.

Mathematical Model for Timing of Capacity Additions Let D = Annual Increase in Demand x = Time interval between adding capacity r = annual discount rate (compounded continuously) f(y) = Cost of operating a plant of capacity y Let C(x) be the total discounted cost of all capacity additions over an infinite horizon if new plants are built every x units of time. Then

Mathematical Model (continued A typical form for the cost function f(y) is: Where k is a constant of proportionality, and a measures the ratio of incremental to average cost of a unit of plant capacity. A typical value is a=0.6. Note that a<1 implies economies of scale in plant construction, since

Mathematical Model (continued) Hence, It can be shown that this function is minimized at x that satisfies the equation: This is known as a transcendental equation, and has no algebraic solution. However, using the graph on the next slide, one can find the optimal value of x or any value of a (0 < a < 1)

Issues in Plant Location Size of the facility. Product lines. Process technology. Labor requirements. Utilities requirements Environmental issues. International considerations Tax Incentives.