Supply Chain Management Supply Chain Management
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Supply Chain Management Supply Chain Management

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Supply Chain Management Supply Chain Management Supply Chain Management Supply Chain Management Presentation Transcript

  • Syllabus
    • Class 1 (Jan 5): chap 1; chap 2, case study
    • Class 2: (Jan 12) No Class
    • Class 3: (Jan 19) Chap 6, Chap 8
    • Class 4: (Jan 26) chap 10, chap 11, Chap 17(Take home exam)
    • Class 5: (Feb 2) Chap 5, Chap 7
    • Class 6: (Feb 9) Chap 9, Chap 12, 14
    • Class 7: (Feb 16) Chap 15, Reverse Logistics – need “The Forklifts Have Nothing To Do!” Available in the Lewis and Clark Bookstore
    • Class 8: (Feb 23) Cabela’s Tour
    • Class 9: (Mar 2) Chap 13; Chap 16, Chap 4 (take home exam)
    • Other requirements:
    • -> visit Harley-Davidson Plant in Kansas City to see operations management in practice and write a 3-5 page paper comparing the class slides and readings to the Harley operations
    • -> Home Work
  • Supply Chain Management
  • Supply Chain Management
    • First appearance – Financial Times
    • Importance -
    • -> Inventory ~ 14% of GDP
    • -> GDP ~ $12 trillion
    • -> Warehousing/Trans ~ 9% of GDP
    • -> Rule of Thumb - $12 increase in sales to = $1 savings in Supply Chain
    • 1982 Peter Drucker – last frontier
    • Supply Chain problems can cause ≤ 11% drop in stock price
    • Customer perception of company
  • SCOR Reference: www.supply-chain.org
  • Supply Chain
    • All activities associated with the flow and transformation of goods and services from raw materials to the end user, the customer
    • A sequence of business activities from suppliers through customers that provide the products, services, and information to achieve customer satisfaction
  • Supply Chain
    • “ The global network used to deliver products and services from raw materials to end customers through an engineered flow of information, physical distribution, and cash.”
    • APICS Dictionary, 10th ed.
  • Supply Chain Management
    • Synchronization of activities required to achieve maximum competitive benefits
    • Coordination, cooperation, and communication
    • Rapid flow of information
    • Vertical integration
  • Supply Chain Uncertainty
    • Forecasting, lead times, batch ordering, price fluctuations, and inflated orders contribute to variability
    • Inventory is a form of insurance
    • Distorted information is one of the main causes of uncertainty Bullwhip effect
  • Information in the Supply Chain
    • Centralized coordination of information flows
    • Integration of transportation, distribution, ordering, and production
    • Direct access to domestic and global transportation and distribution channels
    • Locating and tracking the movement of every item in the supply chain - RFID
  • Information in the Supply Chain
    • Consolidation of purchasing from all suppliers
    • Intercompany and intracompany information access
    • Electronic Data Interchange
    • Data acquisition at the point of origin and point of sale
    • Instantaneous updating of inventory levels
    • Visibility
  • Electronic Business
    • Replacement of physical processes with electronic ones
    • Cost and price reductions
    • Reduction or elimination of intermediaries
    • Shortening transaction times for ordering and delivery
    • Wider presence and increased visibility
    In Theory:
  • Electronic Business
    • Greater choices and more information for customers
    • Improved service
    • Collection and analysis of customer data and preferences
    • Virtual companies with lower prices
    • Leveling the playing field for smaller companies
    • Gain global access to markets & customers
  • Electronic Data Interchange
    • Computer-to-computer exchange of business documents in a standard format
    • Quick access, better customer service, less paperwork, better communication, increased productivity, improved tracing and expediting, improves billing and cost efficiency
  • Bar Codes
    • Computer readable codes attached to items flowing through the supply chain
    • Generates point-of-sale data which is useful for determining sales trends, ordering, production scheduling, and deliver plans
    1234 5678
  • IT Issues
    • Increased benefits and sophistication come with increased costs
    • Efficient web sites do not necessarily mean the rest of the supply chain will be as efficient
    • Security problems are very real – camera phones, cell phones, thumb drives
    • Collaboration and trust are important elements that may be new to business relationships
  • Suppliers
    • Purchased materials account for about half of manufacturing costs
    • Materials, parts, and service must be delivered on time, of high quality, and low cost
    • Suppliers should be integrated into their customers’ supply chains
    • Partnerships should be established
    • On-demand delivery (JIT) is a frequent requirement - what is JIT and does it work?
  • Sourcing
    • Relationship between customers and suppliers focuses on collaboration and cooperation
    • Outsourcing has become a long-term strategic decision
    • Organizations focus on core competencies
    • Single-sourcing is increasingly a part of supplier relations
    How does single source differ from sole source?
  • E-Procurement
    • Business-to-business commerce conducted on the Internet
    • Benefits include lower transaction costs, lower prices, reduce clerical labor costs, and faster ordering and delivery times
    • Currently used more for indirect goods
    • E-Marketplaces service industry-specific companies and suppliers
  • Distribution
    • The actual movement of products and materials between locations
    • Handling of materials and products at receiving docks, storing products, packaging, and shipping
    • Often called logistics
    • Driving force today is speed
    • Particularly important for Internet dot-coms
  • Distribution Centers and Warehousing
    • DCs are some of the largest business facilities in the United States
    • Trend is for more frequent orders in smaller quantities
    • Flow-through facilities and automated material handling
    • Final assembly and product configuration (postponement) may be done at the DC
  • Warehouse Management Systems
    • Highly automated systems
    • A good system will control item slotting, pick lists, packing, and shipping
    • Most newer systems include transportation management (load management/configuration), order management, yard management, labor management, warehouse optimization
  • Vendor-Managed Inventory
    • Not a new concept – same process used by bread deliveries to stores for decades
    • Reduces need for warehousing
    • Increased speed, reduced errors, and improved service
    • Onus is on the supplier to keep the shelves full or assembly lines running
    • variation of JIT
    • Proctor&Gamble - Wal-Mart
    • DLA – moving from a manager of supplies to a manager of suppliers
    • Direct Vendor Deliveries – loss of visibility
  • Collaborative Distribution and Outsourcing
    • Collaborative planning, forecasting, and replenishment (CPFR) started by Nabisco
    • Allows suppliers to know what is really needed and when
    • Electronic-based exchange of data and information
    • Significant decrease in inventory levels and more efficient logistics - maybe not!
    • Companies work together for benefit of all of the supply chain
  • Transportation
    • Common methods are railroads, trucking, water, air, intermodal, package carriers, and pipelines
  • Railroads
    • 150,000 miles in US
    • Low cost, high-volume
    • Improving flexibility
      • intermodal service
      • double stacking
    Complaints: slow, inflexible, large loads Advantages: large/bulky loads, intermodal
  • Award-Winning Service Recognition United Parcel Service 99.5% failure free, damage free and on-time rating from United Parcel Service every year since 1995 Wal-Mart Stores, Inc. Carrier of the Year – 5 years in a row Target Only rail carrier to receive the Vice President’s Award American Honda Motor Company Premier Partner – 4 consecutive years Toyota’s North American Parts and Logistics Division (NAPLD) Rail Carrier of the Year – 3 consecutive years KIA Carrier of the Year Schneider Carrier of the Year – 3 consecutive years Federal Express Only rail carrier to receive outstanding supplier award - 2 years in a row
    • Most used mode in US -75% of total freight (not total weight)
    • Flexible, small loads
    • Consolidation, Internet load match sites
    • Single sourcing reduces number of trucking firms serving a company
    • Truck load (TL) vs. Less Than Truck Load (LTL)
    Trucking
  • Air
    • Rapidly growing segment of transportation industry
    • Lightweight, small items
    • Quick, reliable, expensive (relatively expensive depending on costs of not getting item there)
    • Major airlines and US Postal Service, UPS, FedEx, DHL
  • Package Carriers
    • FedEx, UPS, US Postal Service, DHL
    • Significant growth driven by e-businesses and the move to smaller shipments and consumer desire to have it NOW
    • Use several modes of transportation
    • Expensive - relative!!
    • Fast and reliable - relative!!
    • Innovative use of technologies in some cases
    • Online tracking – some better than others
  • Intermodal
    • Combination of several modes of transportation
    • Most common are truck/rail/truck and truck/water/rail/truck
    • Enabled by the use of containers – the development of the 20 and 40 foot containers significantly changed the face of shipping
    • ~2% of all US cargo via intermodal
  • Water
    • One of oldest means of transport
    • Low-cost, high-volume, slow (relative)
    • Security - sheer volume - millions of containers annually
    • Bulky, heavy and/or large items
    • Standardized shipping containers improve service
    • The most common form of international shipping
  • Pipelines
    • Primarily for oil & refined oil products
    • Slurry lines carry coal or kaolin
    • High initial capital investment
    • Low operating costs
    • Can cross difficult terrain
  • Global Supply Chain
    • Free trade & global opportunities
    • Nations form trading groups
    • No tariffs or duties
    • Freely transport goods across borders
    • Security!!
  • Global Supply Chain Problems
    • National and regional differences
    • Customs, business practices, and regulations
    • Foreign markets are not homogeneous
    • Quality can be a major issue
  • Security
    • ~ 10+ million containers annually
    • Customs-Trade Partnership Against Terrorism (C-TPAT)
    • Port Security – SAFE Ports Act; Scanning of all Containers
    • Cost - $2 billion closing of major port
    • 66% of all goods into US comes through 20 major ports
    • 44% through LA/Long Beach
    • Cost of attack on major port estimated at $20 Billion
  •  
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  • Chapter 11 Forecasting
  • Forecasting Survey
    • How far into the future do you typically project when trying to forecast the health of your industry?  less than 4 months 3%  4-6 months 12%  7-12 months 28%  > 12 months 57%
    Fortune Council survey, Nov 2005
  • Indices to forecast health of industry
    • Consumer price index 51%
    • Consumer Confidence index 44%
    • Durable goods orders 20%
    • Gross Domestic Product 35%
    • Manufacturing and trade inventories and sales 27%
    • Price of oil/barrel 34%
    • Strength of US $ 46%
    • Unemployment rate 53%
    • Interest rates/fed funds 59%
    Fortune Council survey, Nov 2005
  • Forecasting Importance
    • Improving customer demand forecasting and sharing the information downstream will allow more efficient scheduling and inventory management
    • Boeing, 1997: $2.6 billion write down due to “raw material shortages, internal and supplier parts shortages” Wall Street Journal, Oct 23, 1987
  • Forecasting Importance
    • “ Second Quarter sales at US Surgical Corporation decline 25%, resulting in a $22 mil loss…attributed to larger than anticipated inventories on shelves of hospitals.” US Surgical Quarterly, Jul 1993
    • “ IBM sells out new Aetna PC; shortage may cost millions in potential revenue.” Wall Street Journal, Oct 7, 1994
  • Principles of Forecasting
    • Forecasts are usually wrong
    • every forecast should include an estimate of error
    • Forecasts are more accurate for families or groups
    • Forecasts are more accurate for nearer periods.
  • Important Factors to Improve Forecasting
    • Record Data in the same terms as needed in the forecast – production data for production forecasts; time periods
    • Record circumstances related to the data
    • Record the demand separately for different customer groups
  • Forecast Techniques
    • Extrinsic Techniques – projections based on indicators that relate to products – examples
    • Intrinsic – historical data used to forecast (most common)
  • Forecasting
    • Forecasting errors can increase the total cost of ownership for a product - inventory carrying costs
    • - obsolete inventory - lack of sufficient inventory - quality of products due to accepting marginal products to prevent stockout
  • Forecasting
    • Essential for smooth operations of business organizations
    • Estimates of the occurrence, timing, or magnitude of uncertain future events
    • Costs of forecasting: excess labor; excess materials; expediting costs; lost revenues
  • Forecasting
    • Predicting future events
    • Usually demand behavior over a time frame
    • Qualitative methods
      • Based on subjective methods
    • Quantitative methods
      • Based on mathematical formulas
  • Impact of Just-in-Time on Forecasting
    • Just in time as a inventory method
    • Just in time as a Continuous process improvement program
    • Just in time - one on the shelf
    • Usage factors
    • Single order vs. Case order
  • Strategic Role of Forecasting
    • Focus on supply chain management
      • Short term role of product demand
      • Long term role of new products, processes, and technologies
    • Focus on Total Quality Management
      • Satisfy customer demand
      • Uninterrupted product flow with no defective items
    • Necessary for strategic planning
  • Strategic Role of Forecasting
    • Focus on supply chain management
      • Short term role of product demand
      • Long term role of new products, processes, and technologies
    • Focus on Total Quality Management
      • Satisfy customer demand
      • Uninterrupted product flow with no defective items
    • Necessary for strategic planning
  • Total Quality Management
    • Management approach to long term success through customer satisfaction
    • Total Quality Control - process of creating and producing quality goods and services that meet the expectations of the customer
    • quality - conformance to requirements or fitness for use
  • Trumpet of Doom
    • As forecast horizon increases, so does the forecasting error (i.e., accuracy decreases) – shorten horizon by shortening of cycles or flow times
    • Law of Large Numbers – as volume increases, relative variability decreases – forecasting error is smaller: goal – forecast at aggregate levels; collaborate; standardize parts
    • Volume and activity increase at end of reporting periods – Krispy Kreme
  • Components of Forecasting Demand
    • Time Frame
      • Short-range, medium-range, long-range
    • Demand Behavior
      • Trends, cycles, seasonal patterns, random
  • Time Frame
    • Short-range to medium-range
      • Daily, weekly monthly forecasts of sales data
      • Up to 2 years into the future
    • Long-range
      • Strategic planning of goals, products, markets
      • Planning beyond 2 years into the future
  • Demand Behavior
    • Trend
      • gradual, long-term up or down movement
    • Cycle
      • up & down movement repeating over long time frame
    • Seasonal pattern
      • periodic oscillation in demand which repeats
    • Random movements follow no pattern
  • Forms of Forecast Movement Figure 8.1 Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Demand Demand Demand Random movement
  • Forecasting Methods
    • Time series
      • Regression or causal modeling
    • Qualitative methods
      • Management judgment, expertise, opinion
      • Use management, marketing, purchasing, engineering
    • Delphi method
      • Solicit forecasts from experts
  • Forecasting Process Figure 8.2 6. Check forecast accuracy with one or more measures 4. Select a forecast model that seems appropriate for data 5. Develop/compute forecast for period of historical data 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 1. Identify the purpose of forecast 3. Plot data and identify patterns 2. Collect historical data
  • Time Series Methods
    • Statistical methods using historical data
      • Moving average
      • Exponential smoothing
      • Linear trend line
    • Assume patterns will repeat
    • Naive forecasts
      • Forecast = data from last period
  • Moving Average
    • Average several periods of data
    • Dampen, smooth out changes
    • Use when demand is stable with no trend or seasonal pattern
    • stock market analysis - trend analysis
  • Moving Average
    • Average several periods of data
    • Dampen, smooth out changes
    • Use when demand is stable with no trend or seasonal pattern
    Sum of Demand In n Periods n
  • Simple Moving Average Example 8.1 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 ORDERS MONTH PER MONTH
  • Simple Moving Average Example 8.1 MA nov = = 110 orders for Nov D aug +D sep +D oct Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 ORDERS MONTH PER MONTH 3 = 90 + 110 + 130 3
  • Simple Moving Average Example 8.1 Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE
  • Simple Moving Average Example 8.1 = 91 orders for Nov Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE MA 5 = 5 i = 1  D i 5 = 90 + 110 + 130 + 75 + 50 5
  • Simple Moving Average Example 8.1 Jan 120 – – Feb 90 – – Mar 100 – – Apr 75 103.3 – May 110 88.3 – June 50 95.0 99.0 July 75 78.3 85.0 Aug 130 78.3 82.0 Sept 110 85.0 88.0 Oct 90 105.0 95.0 Nov – 110.0 91.0 ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE
  • Smoothing Effects Figure 8.2 150 – 125 – 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Orders Month
  • Smoothing Effects Figure 8.2 150 – 125 – 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Orders Month Actual
  • Smoothing Effects Figure 8.2 150 – 125 – 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov 3-month Actual Orders Month
  • Smoothing Effects Figure 8.2 150 – 125 – 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov 5-month 3-month Actual Orders Month
  • Weighted Moving Average
    • Adjusts moving average method to more closely reflect data fluctuations
  • Weighted Moving Average
    • Adjusts moving average method to more closely reflect data fluctuations
    WMA n = i = 1  W i D i where W i = the weight for period i , between 0 and 100 percent  W i = 1.00
  • Weighted Moving Average Example Example 8.2 MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90
  • Weighted Moving Average Example 3 Month = 110 5 month = 91 MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 November forecast WMA 3 = 3 i = 1  W i D i = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders
  • Exponential Smoothing
    • Averaging method
    • Weights most recent data more strongly
    • Reacts more to recent changes
    • Widely used, accurate method
  • Exponential Smoothing F t +1 =  D t + (1 -  ) F t where F t +1 = forecast for next period D t = actual demand for present period F t = previously determined forecast for present period   = weighting factor, smoothing constant
    • Averaging method
    • Weights most recent data more strongly
    • Reacts more to recent changes
    • Widely used, accurate method
  • Forecast for Next Period
    • Forecast = (weighting factor)x(actual demand for period)+(1-weighting factor)x(previously determined forecast for present period)
    0 >  <= 1 Lesser reaction to recent demand Greater reaction to recent demand
  • Exponential Smoothing Example 8.3 PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54
  • Exponential Smoothing PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 F 2 =  D 1 + (1 -  ) F 1 = (0.30)(37) + (0.70)(37) = 37 F 3 =  D 2 + (1 -  ) F 2 = (0.30)(40) + (0.70)(37) = 37.9 F 13 =  D 12 + (1 -  ) F 12 = (0.30)(54) + (0.70)(50.84) = 51.79
  • Exponential Smoothing Example 8.3 FORECAST, F t + 1 PERIOD MONTH DEMAND (  = 0.3) 1 Jan 37 – 2 Feb 40 37.00 3 Mar 41 37.90 4 Apr 37 38.83 5 May 45 38.28 6 Jun 50 40.29 7 Jul 43 43.20 8 Aug 47 43.14 9 Sep 56 44.30 10 Oct 52 47.81 11 Nov 55 49.06 12 Dec 54 50.84 13 Jan – 51.79
  • Exponential Smoothing Example 8.3 FORECAST, F t + 1 PERIOD MONTH DEMAND (  = 0.3) (  = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61
  • Exponential Smoothing Forecasts Figure 8.3 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Orders Month
  • Exponential Smoothing Forecasts Figure 8.3 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Actual Orders Month
  • Exponential Smoothing Forecasts Figure 8.3 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Actual Orders Month  = 0.30
  • Exponential Smoothing Forecasts Figure 8.3 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13  = 0.50 Actual Orders Month  = 0.30
  • Linear Trend Line y = a + bx where a = intercept (at period 0) b = slope of the line x = the time period y = forecast for demand for period x
  • Linear Trend Line y = a + bx where a = intercept (at period 0) b = slope of the line x = the time period y = forecast for demand for period x b = a = y - b x where n = number of periods x = = mean of the x values y = = mean of the y values  xy - nxy  x 2 - nx 2  x n  y n
  • Seasonal Adjustments
    • Repetitive increase/ decrease in demand
    • Use seasonal factor to adjust forecast
  • Seasonal Adjustments
    • Repetitive increase/ decrease in demand
    • Use seasonal factor to adjust forecast
    = demand for period/sum of demand Seasonal factor = S i = D i  D
  • Seasonal Adjustment 1999 12.6 8.6 6.3 17.5 45.0 2000 14.1 10.3 7.5 18.2 50.1 2001 15.3 10.6 8.1 19.6 53.6 Total 42.0 29.5 21.9 55.3 148.7 DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total
  • Seasonal Adjustment 1999 12.6 8.6 6.3 17.5 45.0 2000 14.1 10.3 7.5 18.2 50.1 2001 15.3 10.6 8.1 19.6 53.6 Total 42.0 29.5 21.9 55.3 148.7 DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total S 1 = = = 0.28 D 1  D 42.0 148.7 S 2 = = = 0.20 D 2  D 29.5 148.7 S 4 = = = 0.37 D 4  D 55.3 148.7 S 3 = = = 0.15 D 3  D 21.9 148.7
  • Seasonal Adjustment 1999 12.6 8.6 6.3 17.5 45.0 2000 14.1 10.3 7.5 18.2 50.1 2001 15.3 10.6 8.1 19.6 53.6 Total 42.0 29.5 21.9 55.3 148.7 DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total S i 0.28 0.20 0.15 0.37
  • Seasonal Adjustment Forecast for 2002 using simple 3 year moving ave Forecast for 1st qtr 2002 1999 12.6 8.6 6.3 17.5 45.0 2000 14.1 10.3 7.5 18.2 50.1 2001 15.3 10.6 8.1 19.6 53.6 Total 42.0 29.5 21.9 55.3 148.7 DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total S i 0.28 0.20 0.15 0.37
  • Forecast Accuracy
    • Find a method which minimizes error
    • Error = Actual - Forecast
    • Mean Absolute Deviation (MAD)
  • Mean Absolute Deviation (MAD) where t = the period number D t = demand in period t F t = the forecast for period t n = the total number of periods  = the absolute value
    •  D t - F t 
    • n
    MAD =
  • MAD Example 1 37 37.00 2 40 37.00 3 41 37.90 4 37 38.83 5 45 38.28 6 50 40.29 7 43 43.20 8 47 43.14 9 56 44.30 10 52 47.81 11 55 49.06 12 54 50.84 557 PERIOD DEMAND, D t F t (  =0.3)
  • MAD Example 1 37 37.00 – – 2 40 37.00 3.00 3.00 3 41 37.90 3.10 3.10 4 37 38.83 -1.83 1.83 5 45 38.28 6.72 6.72 6 50 40.29 9.69 9.69 7 43 43.20 -0.20 0.20 8 47 43.14 3.86 3.86 9 56 44.30 11.70 11.70 10 52 47.81 4.19 4.19 11 55 49.06 5.94 5.94 12 54 50.84 3.15 3.15 557 49.31 53.39 PERIOD DEMAND, D t F t (  =0.3) ( D t - F t ) | D t - F t |
  • MAD Example 1 37 37.00 – – 2 40 37.00 3.00 3.00 3 41 37.90 3.10 3.10 4 37 38.83 -1.83 1.83 5 45 38.28 6.72 6.72 6 50 40.29 9.69 9.69 7 43 43.20 -0.20 0.20 8 47 43.14 3.86 3.86 9 56 44.30 11.70 11.70 10 52 47.81 4.19 4.19 11 55 49.06 5.94 5.94 12 54 50.84 3.15 3.15 557 49.31 53.39 PERIOD DEMAND, D t F t (  =0.3) ( D t - F t ) | D t - F t |
    •  D t - F t 
    • n
    MAD = = = 4.85 53.39 11
  • Mean Absolute Deviation
  • Forecast Control
    • Reasons for out-of-control forecasts
      • Change in trend
      • Appearance of cycle
      • Weather changes
      • Promotions
      • Competition
      • Politics
  • Tracking Signal
    • Tracking Signal establishes control limits - usually +/- 3 MAD
    • The greater the tracking signal the more the demand exceeds the forecast
    • Sum(Demand-Forecast)/Mean Absolute Deviation
    • Sometimes called Running Sum of Forecasting Error
  • Tracking Signal
    • Compute each period
    • Compare to control limits
    • Forecast is in control if within limits
    Use control limits of +/- 2 to +/- 5 MAD Tracking signal = =  ( D t - F t ) MAD E MAD
  • Tracking Signal Values 1 37 37.00 – – – 2 40 37.00 3.00 3.00 3.00 3 41 37.90 3.10 6.10 3.05 4 37 38.83 -1.83 4.27 2.64 5 45 38.28 6.72 10.99 3.66 6 50 40.29 9.69 20.68 4.87 7 43 43.20 -0.20 20.48 4.09 8 47 43.14 3.86 24.34 4.06 9 56 44.30 11.70 36.04 5.01 10 52 47.81 4.19 40.23 4.92 11 55 49.06 5.94 46.17 5.02 12 54 50.84 3.15 49.32 4.85 DEMAND FORECAST, ERROR  E = PERIOD D t F t D t - F t  ( D t - F t ) MAD
  • Tracking Signal Values 1 37 37.00 – – – 2 40 37.00 3.00 3.00 3.00 3 41 37.90 3.10 6.10 3.05 4 37 38.83 -1.83 4.27 2.64 5 45 38.28 6.72 10.99 3.66 6 50 40.29 9.69 20.68 4.87 7 43 43.20 -0.20 20.48 4.09 8 47 43.14 3.86 24.34 4.06 9 56 44.30 11.70 36.04 5.01 10 52 47.81 4.19 40.23 4.92 11 55 49.06 5.94 46.17 5.02 12 54 50.84 3.15 49.32 4.85 DEMAND FORECAST, ERROR  E = PERIOD D t F t D t - F t  ( D t - F t ) MAD TS 3 = = 2.00 6.10 3.05 Tracking signal for period 3
  • Tracking Signal Values 1 37 37.00 – – – – 2 40 37.00 3.00 3.00 3.00 1.00 3 41 37.90 3.10 6.10 3.05 2.00 4 37 38.83 -1.83 4.27 2.64 1.62 5 45 38.28 6.72 10.99 3.66 3.00 6 50 40.29 9.69 20.68 4.87 4.25 7 43 43.20 -0.20 20.48 4.09 5.01 8 47 43.14 3.86 24.34 4.06 6.00 9 56 44.30 11.70 36.04 5.01 7.19 10 52 47.81 4.19 40.23 4.92 8.18 11 55 49.06 5.94 46.17 5.02 9.20 12 54 50.84 3.15 49.32 4.85 10.17 DEMAND FORECAST, ERROR  E = TRACKING PERIOD D t F t D t - F t  ( D t - F t ) MAD SIGNAL
  • Tracking Signal Plot 3  – 2  – 1  – 0  – -1  – -2  – -3  – | | | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 11 12 Tracking signal (MAD) Period
  • Tracking Signal Plot 3  – 2  – 1  – 0  – -1  – -2  – -3  – | | | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 11 12 Tracking signal (MAD) Period Exponential smoothing (  = 0.30)
  • Tracking Signal Plot 3  – 2  – 1  – 0  – -1  – -2  – -3  – | | | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 11 12 Tracking signal (MAD) Period Exponential smoothing (  = 0.30) Linear trend line
  • Forecasting
    • Long Term – location, capacity, new product design
    • Short Term – production, inventory control, labor levels, cost controls
    Questions?
  • Next Week
    • Chap 9
    • Chapter 14