LOGM 3220 Contemporary Logistics Demand, Order, and Customer Service Management 1 Plan for today • How to determine what the customer wants? • Demand Management • Undersupply & oversupply does not satisfy customer • How to satisfy customer’s requirements? • Order management and customer service • If the firm can’t fulfill the order, customer is not satisfied LOGM 3220 2 Demand Management • Demand management can be defined as “the creation across the supply chain and its markets of a coordinated flow of demand.”1 • Demand is the total number of requests for a resource. • Demand management is all about making choices. • What choices? LOGM 3220 3 Demand Management • Make-to-stock or make-to-order? • The process of ensuring that market demand and the company’s capabilities are in synchronization • Recognizing all demands for products and services to support the marketplace. • Doing what is required to help make the demand happen • Prioritizing demand when supply is lacking. • Planning and using resources for profitable business results LOGM 3220 4 Demand Management • Optimize business processes for demand fulfillment and improve efficiency of services delivered • Complete projects on time • Reduce costs • Increase client satisfaction • Assess the availability and skill sets of resources LOGM 3220 5 Demand Management • Demand Management is based on “forecast” and plans. • In DM, forecasts of the quantities and timing of customer demand are developed. • What do we actually plan to deliver to customers each period is the output of the process. • Forecasting: Process of projecting the values of one or more variables into the future LOGM 3220 6 Why is forecasting important? • Demand for products and services is usually uncertain. • Forecasting can be used for… • Strategic planning (long range planning) • Finance and accounting (budgets and cost controls) • Marketing (future sales, new products) • Production and operations LOGM 3220 7 Principles of Forecasting • Many types of forecasting models that differ in complexity and amount of data & way they generate forecasts: 1. Forecasts are rarely perfect 2. Forecasts are more accurate for grouped data than for individual items 3. Forecast are more accurate for shorter than longer time periods LOGM 3220 8 Before we go any further… • A forecast is only as good as the information included in the forecast (past data) • History is not a perfect predictor of the future (i.e.: there is no such thing as a perfect forecast) REMEMBER: Forecasting is based on the assumption that the past predicts the future! When forecasting, think carefully whether or not the past is strongly related to what you expect to see in the future… LOGM 3220 9 Example: Mercedes E-class vs. M-class Sales Month E-class Sales M-class Sales Jan 23,345 - Feb 22,034 - Mar 21,453 - Apr 24,897 - May 23,561 - Jun 22,684 - Jul ? ? Question: Can we predict the new model M-class sales based on the data in the the table? Answer: Maybe... We need to consider how much the two markets have in common LOGM 3220 10 Basic Concepts in Forecasting • Forecast planning horizon • Planning horizon: Length of time on which a forecast is based • Spans from short-range forecasts with a planning horizon of under 3 months to long-range forecasts of 1 to 10 years • Time bucket: Unit of measure for the time period used in a forecast LOGM 3220 11 Types of Forecasting Methods • Decide what needs to be forecast • Level of detail, units of analysis & time horizon required • Evaluate and analyze appropriate data • Identify needed data & whether it’s available • Select and test the forecasting model • Cost, ease of use & accuracy • Generate the forecast • Monitor forecast accuracy over time LOGM 3220 12 Demand forecasting models Causal Model Trend Quantitative Stationary Time series Trend Forecasting Expert Judgment Qualitative Trend + Seasonality Delphi Method Grassroots LOGM 3220 13 Demand forecasting models • Qualitative methods – judgmental methods • Forecasts generated subjectively by the forecaster • Educated guesses • Quantitative methods – based on mathematical modeling: • Forecasts generated through mathematical modeling LOGM 3220 14 Qualitative Methods Type Characteristics Strengths Executive A group of managers Good for strategic or opinion meet & come up with a new-product forecast forecasting Market research Delphi method Uses surveys & Good determinant of interviews to identify customer preferences customer preferences Seeks to develop a consensus among a group of experts Excellent for forecasting long-term product demand, technological changes, and scientific advances LOGM 3220 Weaknesses One person's opinion can dominate the forecast It can be difficult to develop a good questionnaire Time consuming to develop 15 Quantitative Methods • Time Series Models: • Assumes information needed to generate a forecast is contained in a time series of data • Assumes the future will follow same patterns as the past • Causal Models or Associative Models • Explores cause-and-effect relationships • Uses leading indicators to predict the future • Housing starts and appliance sales LOGM 3220 16 Time Series Models • Forecaster looks for data patterns as • Data = historic pattern + random variation • Historic pattern to be forecasted: • Level (long-term average) – data fluctuates around a constant mean • Trend – data exhibits an increasing or decreasing pattern • Seasonality – any pattern that regularly repeats itself and is of a constant length • Cycle – patterns created by economic fluctuations • Random Variation cannot be predicted LOGM 3220 17 Time Series Patterns Cycle Trend Random movement Time Seasonal pattern Demand Time Trend with seasonal pattern Time Time LOGM 3220 18 Time Series Models Ft +1 = At • Naive: • The forecast is equal to the actual value observed during the last period – good for level patterns F = A / n • Simple Mean: • The average of all available data - good for level patterns F = A / n • Moving Average: • The average value over a set time period (e.g.: the last four weeks) • Each new forecast drops the oldest data point & adds a new observation • More responsive to a trend but still lags behind actual data t +1 t t +1 t LOGM 3220 19 Simple Moving Average • Moving average (MA) forecast: Average of the most recent k observations in a time series Ft +1 = A t / n • Ft+1 = ∑(most recent k observations)/k = = (At + At–1 + At–2 + ... + At–k+1)/k • MA methods work best for short planning horizons when there is no major trend, seasonal, or business cycle pattern • As the value of k increases, the forecast reacts slowly to recent changes in the time series data  LOGM 3220 20 Simple Moving Average - Example • Kroger sells (among other stuff) bottled spring water • What will the sales be for July? LOGM 3220 Month Jan Feb Mar Apr May Jun Jul Bottles 1,325 1,353 1,305 1,275 1,210 1,195 ? 21 Simple Moving Average - Example • What if we use a 3-month simple moving average? FJul = AJun + AMay + AApr 3 = 1,227 • What if we use a 5-month simple moving average? FJul = AJun + AMay + AApr + AMar + AFeb = 1,268 5 LOGM 3220 22 Simple Moving Average - Example 1400 1350 5-month MA forecast 1300 1250 1200 3-month MA forecast 1150 1100 1050 1000 0 1 2 3 4 5 6 7 8 5-month average smoothes data more; 3-month average more responsive LOGM 3220 23 Weighted Moving Average Ft +1 =  C t A t • Weighted Moving Average: • All weights (Ct) must add to 100% or 1.00 e.g. Ct =.5, Ct-1 =.3, Ct-2 =.2 (weights add to 1.0) • Allows emphasizing one period over others; above indicates more weight on recent data (Ct=.5) • Differs from the simple moving average that weighs all periods equally - more responsive to trends LOGM 3220 24 Weighted Moving Average - Example • Kroger sells (among other stuff) bottled spring water • What will the sales be for July? LOGM 3220 Month Jan Feb Mar Apr May Jun Jul Bottles 1,325 1,353 1,305 1,275 1,210 1,195 ? 25 Weighted Moving Average - Example • 6-month simple moving average… FJul = AJun + AMay + AApr + AMar + AFeb + AJan 6 = 1,277 • In other words, because we used equal weights, a slight downward trend that actually exists is not observed… LOGM 3220 26 Weighted Moving Average - Example • What if we use a weighted moving average? • Make the weights for the last three months more than the first three months… July Forecast 6-month SMA WMA 40% / 60% WMA 30% / 70% WMA 20% / 80% 1,277 1,267 1,257 1,247 • The higher the importance we give to recent data, the more we pick up the declining trend in our forecast. LOGM 3220 27 Weighted Moving Average • Weights are selected: • Depending on the importance that we feel past data has • Depending on known seasonality (weights of past data can also be zero). • WMA is better than SMA because of the ability to vary the weights! LOGM 3220 28 Causal Models • Causal models establish a cause-and-effect relationship between independent and dependent variables • Also referred to as associative forecasting • Assumes that one or more factors are related to demand and that the relationship between cause and effect can be used to estimate future demand • Some techniques include: • Simple regression • Multiple regression LOGM 3220 29 Regression as a Forecasting Approach • Regression analysis: Method for building a statistical model that defines a relationship between numerical variables, such as: • Single dependent • One or more independent • Y = a + bX • Simple linear regression finds the best values of a and b using the method of least squares dependent variable = a + b  (independent variable) LOGM 3220 30 Linear Regression • Identify dependent (y) and independent (x) variables • Solve for the slope of the line XY − n X Y  b=  X − nX 2 2 • Solve for the y intercept a = Y − bX • Develop your equation for the trend line Y = a + bX LOGM 3220 31 Linear Regression Problem • A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year. LOGM 3220 Sales $ (Y) Adv.$ (X) 1 130 32 2 151 52 3 150 50 4 158 55 5 ? 53 Tot 589 189 32 Linear Regression Problem XY − n XY  b=  X − nX 2 b= Sales $ (Y) Adv.$ (X) XY 1 130 32 4160 2304 16,900 2 151 52 7852 2704 22,801 3 150 50 7500 2500 22,500 4 158 55 8690 3025 24964 5 153.85 53 Tot 589 189 2 28202 − 4(47.25 )(147.25 ) 9253 − 4(47.25 ) 2 = 1.15 a = Y − b X = 147.25 − 1.15(47.25) a = 92.9 Y = a + bX = 92.9 + 1.15X Y = 92.9 + 1.15(53 ) = 153.85 X^2 Y^2 28202 9253 87165 Avg 147.25 47.25 LOGM 3220 33 Excel’s Regression Analysis option • Use Excel’s Data Analysis Tookpak SUMMARY OUTPUT Regression Statistics Multiple R 0.992425824 R Square 0.984909015 Adjusted R Square 0.977363523 Standard Error 1.811188226 Observations 4 ANOVA df Regression Residual Total Intercept X Variable 1 Significan SS MS F ce F 1 428.1892 428.1892 130.5295 0.007574 2 6.560806 3.280403 3 434.75 Standard Lower Upper Lower Coefficients Error t Stat P-value 95% 95% 95.0% 92.82649109 4.848883 19.14389 0.002717 71.96343 113.6896 71.96343 1.151820294 0.100816 11.42495 0.007574 0.718043 1.585598 0.718043 LOGM 3220 Upper 95.0% 113.6896 1.585598 34 Excel’s Add Trendline Option • Excel provides a tool to find the best-fitting regression model for a time series by selecting the add trendline option from the chart menu Sales $ (Y) 180 y = 1.1518x + 92.826 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 LOGM 3220 60 35 Causal Forecasting with Multiple Regression • Multiple linear regression model: Has more than one independent variable • Other independent variables that influence the time series • Economic indexes • Demographic factors LOGM 3220 36 Multiple Regression • An extension of linear regression but: • Multiple regression develops a relationship between a dependent variable and multiple independent variables. The general formula is: Y = B0 + B1X1 + B2X2 + … + BkXk LOGM 3220 37 Forecast Accuracy • Forecasts are never perfect • Need to know how much we should rely on our chosen forecasting method • Measuring forecast error: Et = At - Ft • Note that over-forecasts = negative errors under-forecasts = positive errors LOGM 3220 38 Demand forecasting issues • Selection of forecasting technique(s) depends on many factors • Selecting an inappropriate technique will reduce forecast accuracy • Forecast accuracy can have important logistical implications • Computer forecasting software unable to completely eliminate forecast errors LOGM 3220 39 Selecting the Right Forecasting Model • The amount & type of available data • Some methods require more data than others • Degree of accuracy required • Increasing accuracy means more data • Length of forecast horizon • Different models for 3 month vs. 10 years • Presence of data patterns • Lagging will occur when a forecasting model meant for a level pattern is applied with a trend LOGM 3220 40 Selecting the Right Forecasting Model • Selecting an inappropriate model will reduce forecast accuracy • Forecast accuracy can have important logistical implications • Computer forecasting software unable to completely eliminate forecast errors LOGM 3220 41 Forecasting Software • Spreadsheets • Microsoft Excel, Quattro Pro, Lotus 1-2-3 • Limited statistical analysis of forecast data • Statistical packages • SPSS, SAS, NCSS, Minitab • Forecasting plus statistical and graphics • Specialty forecasting packages • Forecast Master, Forecast Pro, Autobox, SCA LOGM 3220 42 Order Management LOGM 3220 43 Order Management • Order management refers to management of the various activities associated with the order cycle • Order cycle (replenishment cycle or lead time) refers to the time from when a customer places an order to when goods are received • Order to cash cycle refers to the length of time it takes an organization to receive payment for an order • Some organizations include order to cash cycle in their order management model LOGM 3220 44 Order Management • Order Process Customer Invoice Order Order Shipping Sales Department Inventory LOGM 3220 45 Order Process Flow 1. 2. 3. 4. 5. 6. 7. 8. Customer request order Enter order Book order Schedule Release order Ship product and update inventory Invoice the customer Complete LOGM 3220 46 Order Management • Four stages of the order cycle include: 1. Order transmittal 2. Order processing 3. Order picking and assembly 4. Order delivery LOGM 3220 47 Order transmittal • Refers to the time from when the customer places an order until the seller receives the order • Methods of order transmittal • In person • Mail • Telephone • Fax • Electronically LOGM 3220 48 Order processing • Refers to the time from when the seller receives an order until an appropriate location (i.e., warehouse) is authorized to fill the order LOGM 3220 49 Order processing • Order processing activities include: • Checking for completeness and accuracy • Checking the buyer’s ability to purchase • Order entry into the computer system • Crediting salesperson with the sale • Recording the transaction • Determining inventory location • Arranging for outbound transportation LOGM 3220 50 Order processing flowchart LOGM 3220 51 Order picking and assembly • Includes all activities from when an appropriate location is authorized to fill the order until goods are loaded aboard an outbound carrier • Often represents the best opportunity to improve the effectiveness and efficiency of an order cycle • Can account for up to two-thirds of a facility’s operating cost and time LOGM 3220 52 Order delivery • Refers to the time from when a transportation carrier picks up the shipment until it is received by the customer • Three key order delivery issues: • Variety of options in terms of transit time are now available, such as delivery by 12 noon and delivery by 4:30 p.m. • A number of shippers are emphasizing both elapsed transit time as well as transit time reliability • Transportation carriers are revamping their operations to provide faster transit times to customers LOGM 3220 53 Customer Service • Defined as “the ability of logistics management to satisfy users in terms of time, dependability, communication, and convenience.”2 • Unhappy customers -> Not communicate with the firm, but with friends (and will switch to a different organization) • Customer service is much more difficult for competitors to imitate than other marketing mix variables such as price and promotion LOGM 3220 54 Customer Service • What matters to the buyer • Shorter order cycle (= less inventory) • Simplified order placement (= lower headcount) • Simplified order receiving (= lower headcount) • What matters to the seller • Smooth workload and asset utilization • Repeat customers • Simplified order processing = lower headcount LOGM 3220 55 Customer Service • Four dimensions of customer service include: • Time • Dependability • Communication • Convenience LOGM 3220 56 Customer Service • Time • Refers to the period between successive events (e.g., order cycle, inquiry response) • Target is to reduce order cycle time • Dependability • Refers to the reliability of the service encounter • Consists of three elements: • Consistent order cycles • Safe delivery • Complete delivery (order fill rate) LOGM 3220 57 Customer Service • Communication • To be effective, should be a two-way exchange between seller and customer • Goal is to keep both parties informed • Requires correct parties to be involved in the process • Convenience • Focuses on the ease of doing business with a seller • Not the same for all (e.g. fast food vs. eat-in customer) • Costs may be associated with convenience (e.g. delivery cost) • Multichannel (omnichannel) marketing systems LOGM 3220 58 Managing Customer Service • Four specific customer service considerations include: • Establishing customer service objectives • Measuring customer service • Customer profitability analysis (CPA) • Service failure and recovery LOGM 3220 59 Establishing customer service objectives • Means by which goals are to be achieved • Should be “SMART”: • Specific • Measurable • Achievable • Realistic • Timely LOGM 3220 60 Establishing customer service objectives • Determine the customer’s viewpoint • Customer service is a competitive tool – So benchmark it! • Compare against competitors and best-in-industry • Performance benchmarking (quantitative values) • Process benchmarking (qualitative values) • Nature of the products: if monopoly, no need of CS • Where the product in its life cycle (introduction, mature, etc) LOGM 3220 61 Measuring Customer Service • Need to measure in order to manage • Where to collect data? Internal or external? (better if both) • Internal (e.g. credit memos -> no. of error free activities) • External (e.g. from customer surveys) • What factors to measure? • Easiest to measure? (might not be important to customer) • What ‘they believe’ is important? (might not be actual) • Measure at least: • Time, dependability, communication, convenience LOGM 3220 62 Measuring Customer Service • Time • Order cycle time • Inquiry response time • Dependability • Perfect order • On-time delivery • Communication • Customer complaints • Order status information • Convenience • Returns process • Response to emergency situations • Selected metrics should be relevant and important to customers • E.g. In a call center, what metric should be measured? • Call duration? • # of problems solved per day? LOGM 3220 63 Customer Profitability Analysis • Allocation of revenues and costs to customer segments (or individual customers) • To calculate the profitability of segment of customers • Not all customers are similar • Identify customers on profitability perspective • Four groups of customers • High revenue/ high costs • High revenue/ low costs -> Most attractive • Low revenue/ high costs -> Least attractive • Low revenue/ low costs • Choose different levels of customer service LOGM 3220 64 Service Failure and Recovery • Service failures will happen • E.g. lost/ late/ damaged delivery, incorrect quantity, etc. • Need to have service recovery decisions • Service recovery: process of making customer happy after a service failure • A good service recovery can ‘improve’ the image of a firm • Firm can learn from the failure and become a better performing organization • No set formula LOGM 3220 65 What should you do next • Review presentation 7 • Work on Assignment 5 • Prepare for Exam 1 • On Oct. 3rd LOGM 3220 66 Exam 1 • Open book • MCQ, Short answer, Long answer • 90 minutes • Includes topics up-to (including) Procurement • Similar to quiz and assignment questions LOGM 3220 67
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