Syllabus Class 1 (Aug 18): chap 1; chap 2, case study

Syllabus Class 1 (Aug 18): chap 1; chap 2, case study
Class 3: (Sep 1) chap 10, chap 11, Chap 17(Take home exam) Class 4: (Sep 8) Chap 5, Chap 7 Class 5: (Sep 15); Chap 9, Chap 12, The Beer Game Class 6: (Sep 22) no class Class 7: (Sep 29) Cabela’s Tour Class 8: (Oct 6) Chap 15, Reverse Logistics – need “The Forklifts Have Nothing To Do!” Available in the Lewis and Clark Bookstore, 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 and Quizzes

Supply Chain Management
Chapter 5 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:

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.

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 In Theory:
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

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

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 How does single source differ from sole source?
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?

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 Federal Express Only rail carrier to receive outstanding supplier award - 2 years in a row Toyota’s North American Parts and Logistics Division (NAPLD) Rail Carrier of the Year – 3 consecutive years Schneider Carrier of the Year – 3 consecutive years KIA Carrier of the Year

Trucking 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)

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

Chapter 7 Forecasting

Fortune Council survey, Nov 2005
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

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 Long-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 Cycle Seasonal pattern
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
Time (a) Trend (d) Trend with seasonal pattern (c) Seasonal pattern (b) Cycle Demand Random movement Figure 8.1

Forecasting Methods Time series Qualitative methods Delphi method
Regression or causal modeling Qualitative methods Management judgment, expertise, opinion Use management, marketing, purchasing, engineering Delphi method Solicit forecasts from experts

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 ORDERS MONTH PER MONTH 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 Daug+Dsep+Doct MAnov = 3 90 + 110 + 130 = 3
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 Daug+Dsep+Doct MAnov = = 3 = 110 orders for Nov

Simple Moving Average ORDERS THREE-MONTH
Jan 120 – Feb 90 – Mar 100 – Apr May June July Aug Sept Oct Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE

Simple Moving Average  Di MA5 = = 91 orders for Nov Jan 120 –
Feb 90 – Mar 100 – Apr May June July Aug Sept Oct Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE MA5 = 5 i = 1  Di = 5 = 91 orders for Nov

Simple Moving Average ORDERS THREE-MONTH FIVE-MONTH
Jan 120 – – Feb 90 – – Mar 100 – – Apr – May – June July Aug Sept Oct Nov – ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE

Smoothing Effects 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders
| | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Orders Month

Smoothing Effects 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders Actual
| | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Orders Month Actual

Smoothing Effects 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders 3-month
| | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov 3-month Actual Orders Month

Smoothing Effects 150 – 125 – 100 – 5-month 75 – 50 – 25 – 0 – Orders
| | | | | | | | | | | 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
WMAn = i = 1  Wi Di where Wi = the weight for period i, between 0 and 100 percent  Wi = 1.00 Adjusts moving average method to more closely reflect data fluctuations

Weighted Moving Average Example
MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90

Weighted Moving Average Example
MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 November forecast WMA3 = 3 i = 1  Wi Di = (0.50)(90) + (0.33)(110) + (0.17)(130) = orders 3 Month = month = 91

Exponential Smoothing
Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method

Ft +1 = Dt + (1 - )Ft where Ft +1 = forecast for next period Dt = actual demand for present period Ft = 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

FORECAST, Ft + 1 PERIOD MONTH DEMAND ( = 0.3) ( = 0.5) 1 Jan 37 – – 2 Feb 3 Mar 4 Apr 5 May 6 Jun 7 Jul 8 Aug 9 Sep 10 Oct 11 Nov 12 Dec 13 Jan –

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

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 Seasonal factor = Si = Di D = demand for period/sum of demand

Seasonal Adjustment DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total
Total DEMAND (1000’S PER QUARTER) YEAR Total

Seasonal Adjustment S1 = = = 0.28 D1 D 42.0 148.7 S2 = = = 0.20 D2
Total DEMAND (1000’S PER QUARTER) YEAR Total S1 = = = 0.28 D1 D 42.0 148.7 S2 = = = 0.20 D2 29.5 S4 = = = 0.37 D4 55.3 S3 = = = 0.15 D3 21.9

Seasonal Adjustment DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total
Total DEMAND (1000’S PER QUARTER) YEAR Total Si

Seasonal Adjustment Forecast for 1st qtr 2002
Total DEMAND (1000’S PER QUARTER) YEAR Total Si Forecast for 1st qtr 2002 Forecast for 2002 using simple 3 year moving ave

Forecast Accuracy Find a method which minimizes error
Error = Actual - Forecast Mean Absolute Deviation (MAD)

Mean Absolute Deviation (MAD)
 Dt - Ft  n MAD = where t = the period number Dt = demand in period t Ft = the forecast for period t n = the total number of periods  = the absolute value

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 Tracking signal = = (Dt - Ft) MAD E Use control limits of +/- 2 to +/- 5 MAD

Tracking Signal Values
– – – DEMAND FORECAST, ERROR E = PERIOD Dt Ft Dt - Ft (Dt - Ft) MAD

– – – DEMAND FORECAST, ERROR E = PERIOD Dt Ft Dt - Ft (Dt - Ft) MAD TS3 = = 2.00 6.10 3.05 Tracking signal for period 3

– – – – DEMAND FORECAST, ERROR E = TRACKING PERIOD Dt Ft Dt - Ft (Dt - Ft) MAD SIGNAL

Forecasting Long Term – location, capacity, new product design
Short Term – production, inventory control, labor levels, cost controls Questions?

Next Week Chap 9 Chapter 12 The Beer Game

Syllabus Class 1 (Aug 18): chap 1; chap 2, case study

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