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 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 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 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

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

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, 1987: $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 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 Example 8.1

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 = = 90 + 110 + 130 3 = 110 orders for Nov Example 8.1

Simple Moving Average ORDERS THREE-MONTH 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 Example 8.1

Simple Moving Average  Di MA5 = = 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 MA5 = 5 i = 1  Di = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Example 8.1

Simple Moving Average ORDERS THREE-MONTH FIVE-MONTH 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 Example 8.1

Smoothing Effects 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Orders Month Figure 8.2

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 Figure 8.2

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 Figure 8.2

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 Figure 8.2

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 Example 8.2

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) = 103.4 orders 3 Month = 110 5 month = 91

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 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 S1 = = = 0.28 D1 D 42.0 148.7 S2 = = = 0.20 D2 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 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 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 Si 0.28 0.20 0.15 0.37

Seasonal Adjustment 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 Si 0.28 0.20 0.15 0.37 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)

MAD Example PERIOD DEMAND, Dt Ft ( =0.3) 1 37 37.00 2 40 37.00 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, Dt Ft ( =0.3)

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

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