OM&PM/Class 2a3 Economies of Scale versus Diseconomies of Flexibility/Complexity
OM&PM/Class 2a4 Class 1b Learning Objectives How do a strategic operational audit Relationship between process choice and strategy –operational focus Price vs. Variety Competition –trade off scale economies with variety diseconomies
OM&PM/Class 2a5 Classification of Processes by process architecture Project Job Shop Batch Line Flow Continuous Flow Job Shop Flow Shop
OM&PM/Class 2a6 Characteristics of Processes: Job Shop vs. Batch vs. Flow Shop
OM&PM/Class 2a7 Matching Products and Processes with the Product-Process Matrix Capital Investment for big chunk capacity, Technological Change, Vertical Integration Product Process Jumbled Flow. Process segments loosely linked. Disconnected Line Flow/Jumbled Flow but a dominant flow exists. JOB SHOP (Commercial Printer) BATCH (Heavy Equipment) LINE FLOWS (Auto Assembly) CONTINUOUS FLOW (Oil Refinery) Low volume Low Standardization One of a kind Low volume Many Products Higher volume Few Major Products High volume High Standardization Commodity Products Connected Line Flow (assembly line) Continuous, automated, rigid line flow. Process segments tightly linked. Bidding, delivery, product design flexibility Quality & Product Differentiation, output volume flexibility Price Scheduling, Materials Handling, Shifting Bottlenecks Worker Motivation, Balance, Maintaining Flexibility Managerial Challenges Opportunity Costs Out-of-pocket Costs
OM&PM/Class 2a8 Michigan Manufacturing Corp.: using the Product-Process Matrix
OM&PM/Class 2a9 Classification of Processes: by Positioning Strategy Functional Focus: Product Focus: AB CD Product 1 Product 2 ADB CBA Product 1 Product 2 = resource pool (e.g., X-ray dept, billing)
OM&PM/Class 2a10 Classification of Processes: by Customer Interface Make to Stock Make to Order
OM&PM/Class 2a11 How can operations help a company compete? The changing sources of competitive advantage Low Cost & Scale Economies (< 1960s) –You can have any color you want as long as it is black Focused Factories (mid 1960s) Flexible Factories and Product variety (1970s) –A car for every taste and purse. Quality (1980s) –Quality is free. Time (late 1980s-1990s) –We love your product but where is it? –Don’t sell what you produce. produce what sells.
OM&PM/Class 2a12 Relating operational measures (flow time T, throughput R & inventory I) with Little’s Law Inventory = Throughput x Flow Time I = R x T Turnover = Throughput / Inventory = 1/ T Inventory I [units] Flow rate/Throughput R [units/hr]... Flow Time T [hrs]
OM&PM/Class 2a13 Process Flow Examples Customer Flow: Taco Bell processes on average 1,500 customers per day (15 hours). On average there are 75 customers in the restaurant (waiting to place the order, waiting for the order to arrive, eating etc.). How long does an average customer spend at Taco Bell and what is the average customer turnover? Job Flow: The Travelers Insurance Company processes 10,000 claims per year. The average processing time is 3 weeks. Assuming 50 weeks in a year, what is the average number of claims “in process”. Material Flow: Wendy’s processes an average of 5,000 lb. of hamburgers per week. The typical inventory of raw meat is 2,500 lb. What is the average hamburger’s cycle time and Wendy’s turnover?
OM&PM/Class 2a14 Process Flow Examples Cash Flow: Motorola sells $300 million worth of cellular equipment per year. The average accounts receivable in the cellular group is $45 million. What is the average billing to collection process cycle time? Question: A general manager at Baxter states that her inventory turns three times a year. She also states that everything that Baxter buys gets processed and leaves the docks within six weeks. Are these statements consistent?
OM&PM/Class 2a16 Case: CRU Computer Rentals Flow Chart Customer ReceivingRepairs Pre-Config Parts places order Receives from Supplier Repairs Status 40 Status 24 Status 41 Status 42 Status 20 Config 30% 70% 15% Ship Status 32 Ship
OM&PM/Class 2a17 CRU Situation in 1996: Customer term = 8 wks, Demand = 1000 units/wk
OM&PM/Class 2a18 CRU Situation in 1996: Financial Performance Number of units on rent = 8,000 Total number of units = 14,405 Utilization = 0.56 (56%) Revenue rate = 8,000 x 30 = $240,000/wk Variable Cost rate = 25 x 1,000 (R) + 25 x 1,000 (S) + 4 x 700 x.85 + 150 x 405 = $113,130/wk Contribution Margin = $126,870/wk Depreciation = 14,405 x ($1000/156wks) = $92,340/wk –bottomline =
OM&PM/Class 2a20 CRU Situation in 1997: Financials buffer sizes unchanged, Demand = 1400 units/wk Number of units on rent = 8,000 Total number of units = 15,205 Utilization = 0.53 (53%) Revenue = 4,800 x 30 + 3,200 x 35 = $256,000/wk Cost = 25 x 1,400 (R) + 25 x 1,400 (S) + 4 x 980 x.85 + 150 x 567 = $158,382/wk Contribution Margin = $97,618/wk Depreciation = 15,205 x (1000/156) = $97,468/wk –bottomline =
OM&PM/Class 2a21 CRU Situation in 1997: flow times unchanged, Demand = 1400 units/wk
OM&PM/Class 2a22 CRU Situation in 1997: flow times unchanged, Demand = 1400 units/wk Number of units on rent = 8,000 Total number of units = 16,967 Utilization = 0.47 (47%) Revenue = 4,800 x 30 + 3,200 x 35 = $256,000/wk Cost = 25 x 1,400 (R) + 25 x 1,400 (S) + 4 x 980 x.85 + 150 x 567 = $158,382/wk Contribution Margin= $97,618/wk Depreciation = 16,967 x (1000/156) = $108,763/wk –bottomline =
OM&PM/Class 2a23 CRU Potential situation in 1997: without sales drive, Demand = 600 units/wk
OM&PM/Class 2a24 CRU Potential situation in 1997: without sales drive, Demand = 600 units/wk Number of units on rent = 4,800 Total number of units = 8,643 Utilization = 0.56 (56%) Revenue = 4,800 x 30 = $144,000/wk Cost = 25 x 600 (R) + 25 x 600 (S) + 4 x 420 x.85 + 150 x 243 = $67,878/wk Contribution Margin = $76,122/wk Depreciation = 8,643 x (1000/156) = $55,404/wk –bottomline =
OM&PM/Class 2a25 Lecture 2a Learning Objectives Classification of processes –Match with strategy Process Measures: time, inventory, and throughput What is an improvement? –Link financial measures to operational ones –Good operational measures are leading indicators of financial performance Using Little’s law for process flow analysis