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Chapter 6: Supply Chain Management (SCM) IE 3265 – POM R. R. Lindeke UMD-MIE

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Topics For Discussion: Defining the issues of SCM Major Players How we can work in this new model Vender relationships Data Management Major Issues: Bullwhip effect Transportation problems Location issues

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What is SCM? “Supply Chain management deals with the control of materials, information, and financial flows in a network consisting of suppliers, manufacturers, distributors, and customers” (Stanford Supply Chain Forum Website) “Call it distribution or logistics or supply chain management... In industry after industry... executives have plucked this once dismal discipline off the loading dock and placed it near the top of the corporate agenda. Hard-pressed to knock out competitors on quality or price, companies are trying to gain an edge through their ability to deliver the right stuff in the right amount of time” (Fortune Magazine, 1994)

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Growing Interest in SCM – Why? As manufacturing becomes more efficient (or is outsourced), companies look for ways to reduce costs Several significant success stories: Efficient SCM at Walmart, HP, Dell Computer SCM considers the broad, integrated, view of materials management from purchasing through distribution The huge growth of interest in the web has spawned web-based models for supply chains: from “dot com” retailers to B-2-B business models

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Mass Customization: Designing Final Choices into Supply Chains Several companies have been able to cut costs and improve service by postponing the final configuration of the product until the latest possible point in the supply chain. Examples: Hewlett Packard printer configuration Postponement of final programming of semiconductor devices – all routines loaded, only certain ones activated Assemble to order rather than assemble to stock (Dell Computer)

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Design For Logistics: Many firms now consider SCM issues in the design phase of product development One example is IKEA whose furniture comes in simple to assemble kits that allows them to store the furniture in the same warehouse- like locations where they are displayed and sold Shipping container designs for FedEx and UPS – airfreight Dunnage control in Big Auto

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Efficient Design of the Supplier Base Part of streamlining the supply chain is reducing the number and variety of suppliers The Japanese have been very successful in this arena (they’re an Island – so getting materials there has always been a problem) In the mid 1980’s Xerox trimmed its number of suppliers from 5,000 to 400. Overseas suppliers were chosen based on cost Local suppliers were chosen based on delivery speed In 1996, Ford Motor reduced their supplier count by more than 60%

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Dell Designs the Ultimate Supply Chain! Dell Computer has been one of the most successful PC retailers. Why? To solve the problem of inventory becoming obsolete, Dell’s solution: Don’t keep any inventory! - All PC’s are made to order and parts shipped directly from manufacturers when possible. Compare to the experience of Compaq Corporation – initial success selling through low cost retail warehouses but they did not garner web-based sales

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Data Exchange – A Critical Idea EDI: Electronic Data Interchange Involves the Transmission of documents electronically in a predetermined format from company to company. (Not web based.) The formats are complex and expensive. It appears to be on the decline as web-based systems grow.

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Data and Products – E-Tailing E-tailing: Direct to customer sales on the web – the so-called Click & Mortor retail model Perhaps best known e-tailer is Amazon.com, originally a web-based discount book seller Today, Amazon.com sells a wide range of products (we can think of many, many similar organizations) Amazon and others spawned so called “dot com” stock explosion in the NASDAQ (1997 to April, 2000) Today, many traditional “bricks and mortar” retailers also offer sales over the web, often at lower prices

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Dealing with Data – the modern way B2B (business to business) supply chain management: While not as visible and “sexy” as E-tailing, it appears that B2B supply chain management is the true growth industry! Web searches yield over 80 matches for supply chain software providers. Some of the major players in this market segment include: Agile Software based in Silicon Valley. i2 Technologies based in Dallas. Ariba based in Silicon Valley

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Data Transfer in Supply Chains: Vendor Managed Inventory (the real solution?) Walmart and P & G Target and Pepsi/Coke But … Barilla SpA. An Italian pasta producer pioneered the use of VMI (Vendor Managed Inventory) They obtained sales data directly from distributors and decide on delivery sizes based on that information This is in opposition to allowing distributors (or even retailers) to independently decide on order sizes!

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Order Growth – The Bullwhip Effect – An Important issue

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Information Transfer in Supply Chains: cause of ‘The Bullwhip Effect’ First noticed by P&G executives examining the order patterns for Pampers disposable diapers. They noticed that order variation increased dramatically as one moved from retailers to distributors to the factory. The causes are not completely understood but have to do with batching of orders and building in safety stock at each level Problem: increases the difficulty of planning at the factory level

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There has been a Revitalization in the Analytical Tools needed to Support SCM Inventory management and demand forecasting models such as those discussed in this course The transportation problem and more general network formulations for describing flow of goods in a complex system Analytical methods for determining delivery routes for product distribution – optimal location of new resources

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Focusing on the Distribution Problem: The Goal is to reduce total transportation costs throughout the supply chain Usually solved with some approach to the “Transportation Problem” Our approach will be the Balanced Matrix model

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Lets do one, by example: \To From AlbuquerqueBostonClevelandCapacity Des Moines $5$4$3 100 Evansville $8$4$3 300 Ft. Lauderdale $9$7$5 300 Demand / / Cost of moving a unit of product from Row to column location

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In the Transportation Problem: We must have a Supply/Demand balance to solve In this problem that requirement is met If it is not met, we must create “Dummy” sources (at $0 move costs) or Dummy Sinks (also at $0 move costs) to achieve the require S/D balance

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The Transportation problem: Goal is to minimize the total cost of shipping We will allocate products to cells – any allocation means the row resource will ship product to the column demand The process is an iterative one that requires a feasible starting point Can start by using NW Corner approach Can start using a more structured VAM (Vogel Approximation method)

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Starting with a VAM Solution technique: Determine Row Penalty number (PN i ) –the difference between lowest and 2 nd lowest cost in row Here: R1 is 1; R2 is 1: R3 is 2 Determine Column Penalty Number (PN j ) – the difference between the lowest and 2 nd lowest col. Cost Here: C1: 3; C2: 0; C3: 0 Choose R or C with greatest penalty cost – here is C1 If there is a tie, break tie by choosing C or R with smallest costs Max out the allocation in chosen C or R at lowest cost cell then x-out the C or R And so on after allocation (after we recompute PN’s!)

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Phase 1 of VAM: StepABCPNr1PNr2PNr3 DM $5 100 $4$3 1 X-out -- E $8$4 200 $ x-out FL $9 200 $7$ PNc1300 PNc213 xout

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Costing The model: Current: 100* *9 +200* * *5 = $3900 Before proceeding, check if the Feasible solution is (or isn’t) degenerate: Number of allocation must be at least: m +n - 1 = = 5 (we have 5 is the above set so the solution is not degenerate! See next slide if it was) Now, we must determine if it’s optimal? We must continue to a second phase to determine this!

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Dealing with Degeneracy (when it is found) We must allocate a very small amount of material movement (call it ) to any independent cell An independent cell is any one where we can not complete a “stepping motion” of only horizontal and vertical movements through filled cells to return to the originating cell We call this the -path. (this would be done by alternating adding or subtracting assignments of material to any filled cell we step on) Note any cell were a path can be build is a dependent cell We would add sufficient ’s to reach allocated cells count of m + n – 1 number (make the solution non-degenerate) Here since R1C1 is independent – check for yourself – we will fill it with units – this makes our solution non- degenerate – 5 cells are allocated!

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Entering Phase II: Determining Optimality We will explore the MODI (modified distribution) algorithm After finding a non-degenerate initial solution, add a row of K j ’s and a column of R i ’s to the Matrix To begin, Assign a zero value to any R or K position For each allocated cell, the following expression must be satisfied: R i + K j + c i,j = 0

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Starting MODI with a possible R/K allocation: K j R i ABC -5-2 DM 0 $5 100 $4 +2$ E -2 $8 +1 $4 200 $ FL -4 $9 200 $7 +1 $ Demand: Indicator cost for this cell (= R+K+c)

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MODI continued: Examine all indicator values for empty cells – if all are non-negative the solution is optimal If some are negative then develop a -path beginning at most negative cell (here is R3C3) Complete the -path by stepping only to filled cells (and pivoting) while alternatively subtracting then adding allocation After completing the path, determine the “- ” cell with the smallest quantity and choose its value for “ ” – substitute it along the whole path Note here: all indicators are positive thus we have the optimal solution!

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Forming the -Path (starts in R2C3) K j R i ABC DM 0 $5 + $4 - 100 $3 Ind: E 0 $8 Ind: +3 $4 + 100 $3 - FL -4 $9 - 300 $7 Ind: -1 $5 + Ind: Demand: Smallest “- ” Allocated amount – if we had erroneously allocated as seen below and requiring an addition

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After 100 unit re-allocation – now recompute R’s and K’s & Indicators K j R i ABC Cap. -40 DM $5 +100 $4 +2$ E -3 $8 +1 $4 200 $ FL -5 $9 200 $7 +1$ Demand:300200

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Looking at this Matrix All indicators are now positive – this indicates an optimal solution! Note this agrees with optimal solution found earlier!!! Relax value to zero – makes cell 1,1 allocation 100 units Optimal transportation cost is: 5* * * * *100 = 3900

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Lets try one: /To Fr/ DEFGCap. A B C Demand 3334 /13 13/

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But the Transportation Problem can be solved by LP! Define an Objective Function: Subject to:

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Our Example (by LP Solver) VariablesXDM-AXDM-BXDM-CXE-AXE-BXE-CXFL-AXFL-BXFL-C Values: V*C OBJ.Fn. Costs: CapC CapC CapC DemC DemC DemC

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Applying Solver:

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Examining Results: Optimal Value = $3900 (as we found ‘by hand’!) Ship: 100 DM-A; 200 FL-A; 200 E-B; 100 E-C; 100 FL-C All as we found using the VAM Heuristic Much Faster and easier using LP!

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Expansion to Transshipment Problem When a system is allowed to use intermediate ‘warehousing’ sites – they are Source sites or even Sink sites in regular transportation problem – for reducing the total cost of transportation we call the problem the transshipment network problem We require more costs to be obtained but typically, in most complex S. Chains, companies find savings of from 7 to 15% (or more) in implementations that allow transshipment

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Expansion to Transshipment Problem In the general Transshipment problem the transport network is expanded to allow movement between sources and between sinks (and even back to other sources)

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Expansion to Transshipment Problem Extracted from: J. P. Ignizio, Linear Programming in Single- & Multiple- Objective Systems, Prentice Hall 1982 The original transportation problem

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Another Level of Transport – the delivery route problem This problem is usually one of very large scale (classically called the Traveling Salesman Problem) Because of this, we typically can not find an absolutely optimal solution but rather only near optimal solution – as seen in our textbook Here, the knowns are the costs of travel from point to point throughout the network and we try to save costs by “ganging up” trips Solution typically follows along a line of attack based on the “Assignment Problem” See Handout, focus on the Shortest Route Problem

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Delivery Optimization: Realistically, a delivery vehicle can only carry so much – so this may reduce effectiveness of solutions Delivery’s take Real Time – again this must be considered during scheduling and routing Loading of vehicles is very critical to control step 2 time – load in reverse delivery order!

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Looking at the Locating of New Facilities: Considerations: Labor Climate Transportation issues: Proximity to markets Proximity to suppliers & resources Proximity to parent company (sharing expertise, purchasing, drop routing) – could be plus or minus! Quality of transportation system

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Looking at the Locating of New Facilities: Consideration, cont. Costs to operate (utilities, taxes, real estate costs, construction) Expansion considerations Room available for growth? Construction to modify structure? Any local incentives to re-locate? Quality of Life (schools, recreational possibilities, health care – cost, availability)

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Looking at the Locating of New Facilities: Most organization compare several alternatives They identify weighting factors for the characteristics then narrow choices 1 st consider regions 2 nd narrow search to communities 3 rd consider specific sites Done by collecting data addressing the various factors under study After data is collected and weighted, make selection (typically by starting with quantitative decision follow with qualitative analysis)

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Location Decisions in SCM: In the final analysis the decision typically comes down to a “Center of Gravity Solution” that minimizes the total travel distance between the Facility and all possible Contact facilities Contact facilities may be sources of Raw Materials or other Suppliers or they may be destinations for Product stored in or made at the new facility under design

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Lets Consider an Example: (where should we put our new Distribution Center?)

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Given this information: StoreLocation (X,Y)C. Sales A(2.5, 2.5)5 B(2.5, 4.5)2 CaCa (5.5, 4.5)10 D(5, 2)7 E(8, 5)10 FbFb (7, 2)20 G(9, 3.5)14 a Site of Possible DC 1; b Site of Possible DC 2

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Solution is a Type of Transportation Minimization: Using Either Euclidian or Recta-linear offsets Euclidean Distance: Recta-Linear (RL) Distance:

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Now What? Best Location is the one that minimizes the sum of the total needs of all Demands times the travel distances involved:

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Leading to this analysis: DC DC 272 STOREX LocationY LocationD Euclid 1D Euclid 2D RL 1D RL 2DemandD*DE 1D*DE 2D*Drl 1D*Drl 2 A B C D E F G From the Analysis: DC 2 minimizes costs

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But is this the Optimal location? Perhaps we could place the ‘Center’ at the Median of all the current demand locations? In an ‘RL’ sense, form the Cumulative Weighting (Cum. Demand) 1 st: order each target location in increasing level of X and then Y Determine the average of this CumWt for X & Y and place location the is the same as the site that first exceeds this value

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C. Wt. Average is 34 StoreDem.X coor C. Wt. Sel.StoreDem.Y coor C. Wt. Sel A52.55D727 B2 7F20227 D7514A52.532* C *G ** F20744**B E10854C G14968E10568 Locate at: 7 (in X) and 3.5 (in Y) as a rule*

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Optimization using Euclidian Distances: 1st Compute the Center of Gravity:

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Optimization: Start with X *, Y * determined above as X cur, Y cur Compute: Stop iteration when X and Y stop changing

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Trends in Supply Chain Management Outsourcing of the logistics function (example: Saturn outsourced their logistics to Ryder Trucks. Outsourcing of manufacturing is a major trend these days) Moving towards more web based transactions systems Improving the information flows along the entire chain

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Global Concerns in SCM Moving manufacturing offshore to save direct costs complicates and adds expense to supply chain operations, due to: increased inventory in the pipeline Infrastructure problems Political problems Dealing with fluctuating exchange rates Obtaining skilled labor

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