logisticsandtransportationpart7.pptxlogisticsandtransportationpart7

logisticsandtransportationpart7

1. Demand Management, Order Management, and Customer Service

2. Important Dates ▶ Midterm 8/16/2024 ▶ Assignment 8/23/2023 ▶ Final 8/27/2023 This Photo by Unknown Author is licensed under CC BY-SA-NC

3. Terminal Learning Objectives ▶ To explain demand management and demand forecasting models ▶ To examine the order cycle and its four components ▶ To understand the four dimensions of customer service as they pertain to logistics ▶ To familiarize you with select managerial issues associated with customer service

4. Demand Management ▶ Demand management can be defined as “the creation across the supply chain and its markets of a coordinated flow of demand.”1 A key component in demand management is demand (sales) forecasting, which refers to an effort to project future demand. Without question, demand forecasting is helpful in make-to-stock situations (when finished goods are produced prior to receiving a customer order). However, demand forecasting can also be helpful in make-to-order situations (when finished goods are produced after receiving a customer order). This Photo by Unknown Author is licensed under CC BY-SA

5. Demand Management ▶ Demand management is a critical aspect of business operations, particularly in supply chain management, production planning, and service delivery. It involves forecasting, planning, and controlling the demand for products or services to ensure that a company can meet customer needs while optimizing resources and minimizing costs. It is essential for aligning a company's operations with market needs, optimizing resources, and driving profitability. It requires a combination of accurate forecasting, strategic planning, and real-time adjustments to navigate the complexities of supply and demand. This Photo by Unknown Author is licensed under CC BY-SA

6. Demand Management Key Concepts in Demand Management Demand Forecasting: Predicting future customer demand using historical data, market trends, and statistical methods. Forecasts can be short-term, medium-term, or long-term depending on the business needs. Accuracy in forecasting is crucial for efficient inventory management and production planning. Demand Planning: Integrating demand forecasts with business operations to create a plan that meets customer demand. Involves collaboration between different departments like sales, marketing, finance, and operations. The goal is to balance demand with supply while minimizing costs and maximizing customer satisfaction. Demand Shaping: Actively influencing or managing customer demand through pricing strategies, promotions, marketing campaigns, or product variations. Helps in aligning demand with supply capabilities or strategic business goals. This Photo by Unknown Author is licensed under CC BY-SA

7. Demand Management Key Concepts in Demand Management Inventory Management: Ensuring that the right amount of inventory is available to meet demand without overstocking or understocking. Techniques like Just-In-Time (JIT), Economic Order Quantity (EOQ), and safety stock calculations are used. Capacity Planning: Ensuring that production or service capacity can meet demand forecasts. Involves managing resources like labor, equipment, and facilities to handle variations in demand. Sales and Operations Planning (S&OP): A process that aligns demand planning with supply chain management, production, and finance. It ensures that business strategies and operations are in sync with market demand and business goals. Demand Control: Monitoring actual demand against forecasts and adjusting plans in real time. Helps in dealing with unexpected changes in demand, such as sudden spikes or drops. This Photo by Unknown Author is licensed under CC BY-SA

8. Demand Management Importance of Demand Management Customer Satisfaction: By accurately predicting and meeting demand, companies can ensure that products are available when customers want them, leading to higher satisfaction and loyalty. Cost Efficiency: Effective demand management minimizes the costs associated with overproduction, excess inventory, or lost sales due to stockouts. Resource Optimization: Aligning demand with production and supply capabilities ensures that resources are used efficiently, reducing waste and improving profitability. Strategic Decision-Making: Demand management provides critical insights for long-term planning, helping companies make informed decisions about product development, market expansion, and capacity investments. This Photo by Unknown Author is licensed under CC BY-SA

9. Demand Management Challenges in Demand Management Demand Variability: Fluctuations in demand due to seasonality, market trends, or economic conditions can make forecasting and planning difficult. Data Accuracy: Inaccurate or incomplete data can lead to poor demand forecasts and planning decisions. Supply Chain Disruptions: External factors like supplier issues, natural disasters, or geopolitical events can disrupt supply chains, making it harder to meet demand. Cross-Departmental Coordination: Effective demand management requires close collaboration across multiple departments, which can be challenging in large or complex organizations. This Photo by Unknown Author is licensed under CC BY-SA

10. Demand Management Tools and Techniques Statistical Forecasting Models: Methods like moving averages, exponential smoothing, and regression analysis to predict demand. Software Solutions: Advanced demand planning software that integrates data from various sources and provides real-time analytics and forecasting. Collaborative Planning, Forecasting, and Replenishment (CPFR): A business practice that combines the intelligence of multiple partners in the supply chain to improve demand forecasting and inventory management. This Photo by Unknown Author is licensed under CC BY-SA

11. Demand Forecasting Models ▶ The three basic types of forecasting models are (1) judgmental, (2) time series, and (3) cause and effect. Judgmental forecasting involves using judgment or intuition and is preferred in situations where there is limited or no historical data, such as with a new product introduction. Time series forecasting is that future demand is solely dependent on past demand. For example, if this year’s sales were 7 percent higher than last year’s sales, a time series forecast for next year’s sales would be this year’s sales plus 7 percent. Cause-and-effect forecasting (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. This Photo by Unknown Author is licensed under CC BY

12. Demand Forecasting Models ▶ Judgmental forecasting is a method of predicting future events or trends based on the opinions, intuition, and experience of individuals, rather than purely on quantitative data or statistical models. It is often used when historical data is scarce, unreliable, or when the situation involves unique or unprecedented circumstances that cannot be captured by standard forecasting methods. In practice, judgmental forecasting is often used in conjunction with quantitative methods to improve accuracy. For example, a company might use statistical models to generate a baseline forecast and then adjust it based on expert judgment to account for factors that the model may not have captured. This hybrid approach can leverage the strengths of both methods—quantitative data’s objectivity and judgmental forecasting’s flexibility and human insight—to produce more reliable forecasts. This Photo by Unknown Author is licensed under CC BY

13. Demand Forecasting Models Key Features of Judgmental Forecasting Expert Opinion: Relies heavily on the insights of individuals with expertise in a specific area, such as market analysts, industry experts, or experienced managers. These experts use their knowledge and intuition to make predictions about future trends. Subjectivity: Unlike quantitative forecasting methods that rely on objective data, judgmental forecasting involves a subjective element. The forecasts are influenced by personal biases, perceptions, and experiences of the forecaster. This Photo by Unknown Author is licensed under CC BY

14. Demand Forecasting Models Key Features of Judgmental Forecasting Flexibility: Judgmental forecasting can adapt to new information quickly, as it is not bound by rigid statistical models. It is particularly useful in dynamic environments where conditions change rapidly, and data may become outdated. Use in Uncertain or New Situations: This method is valuable in situations where there is little historical data to base forecasts on, such as launching a new product, entering a new market, or during times of economic uncertainty. It is also used in fields like politics, where human behavior and decision-making can significantly influence outcomes. This Photo by Unknown Author is licensed under CC BY

15. Demand Forecasting Models Techniques in Judgmental Forecasting Delphi Method: A structured approach where a panel of experts is consulted in multiple rounds. After each round, the forecasts are aggregated and shared with the panel for further refinement. The process continues until a consensus is reached or the forecasts converge. Scenario Planning: Experts develop a range of possible future scenarios based on different assumptions about key variables (e.g., economic conditions, technological advancements). Forecasts are made for each scenario to prepare for different potential outcomes. This Photo by Unknown Author is licensed under CC BY

16. Demand Forecasting Models Techniques in Judgmental Forecasting Consensus Forecasting: Involves bringing together multiple experts to discuss and agree on a single forecast. The aim is to combine different perspectives to arrive at a more reliable prediction. Intuition-Based Forecasting: Relies purely on the gut feeling or instinct of the forecaster, often based on their deep experience and understanding of the market or situation. Analogies: Using similar past situations to predict future outcomes. For example, forecasting the success of a new product by comparing it to the launch of a similar product in the past. This Photo by Unknown Author is licensed under CC BY

17. Demand Forecasting Models Advantages of Judgmental Forecasting Flexibility: Can incorporate new information and adapt to changing circumstances quickly. Human Insight: Utilizes the experience and intuition of experts, which can be particularly valuable in complex or unprecedented situations. Applicability in Data-Scarce Environments: Effective when there is little or no historical data available. This Photo by Unknown Author is licensed under CC BY

18. Demand Forecasting Models Disadvantages of Judgmental Forecasting Subjectivity and Bias: Forecasts may be influenced by the forecaster’s personal biases, leading to less accurate predictions. Inconsistency: Different experts may provide varying forecasts, leading to inconsistency and potential conflicts in decision-making. Lack of Transparency: The reasoning behind judgmental forecasts can be difficult to explain or justify, particularly in comparison to statistical models. This Photo by Unknown Author is licensed under CC BY

19. Demand Forecasting Models When to Use Judgmental Forecasting New Product Launches: When launching a product without historical sales data. Market Disruptions: During times of economic uncertainty, political instability, or technological disruption where historical data may not be reliable. Strategic Planning: For long-term planning where qualitative factors (e.g., social trends, regulatory changes) are critical. Small Sample Sizes: When working with limited data that may not be sufficient for statistical forecasting methods. This Photo by Unknown Author is licensed under CC BY

20. Demand Forecasting Models Time series forecasting is a method used to predict future values based on past data points that are collected over time at regular intervals. This approach is particularly valuable when the data exhibits patterns such as trends, seasonality, cycles, or other temporal dependencies. Key Concepts in Time Series Forecasting Time Series Data: A sequence of data points collected or recorded at specific time intervals (e.g., daily, monthly, annually). Examples include stock prices, monthly sales data, temperature records, and economic indicators. This Photo by Unknown Author is licensed under CC BY

21. Demand Forecasting Models Components of Time Series: Trend: The long-term movement or direction in the data over time. It can be upward, downward, or stationary. Seasonality: Regular, repeating patterns or cycles in data due to seasonal factors. For example, retail sales may increase during the holiday season. Cyclic Patterns: Fluctuations in data with no fixed period, usually influenced by economic conditions or business cycles. Noise: Random variation in the data that cannot be explained by the trend, seasonality, or cycles. Stationarity: A time series is considered stationary if its statistical properties, like mean and variance, do not change over time. Many forecasting methods require the series to be stationary or involve techniques to transform non-stationary data into stationary form. Lag: The idea of using past data points to predict future values. For example, a lag of 1 would mean using the previous time period's data to predict the next period. This Photo by Unknown Author is licensed under CC BY

22. Demand Forecasting Models Demand shaping refers to strategies used to influence or modify customer demand for a product or service. This can be achieved through various tactics such as promotions, pricing adjustments, altering product features, or improving availability. The goal is to align demand with supply capabilities, improve efficiency, and maximize revenue or customer satisfaction. For example, a company might offer discounts on off-peak products to encourage purchases during slower periods, or they might enhance product features to attract more customers. This Photo by Unknown Author is licensed under CC BY

23. Demand Forecasting Models In the context of supply chain management, demand shaping is a key strategy to balance supply and demand, optimize inventory levels, and improve overall efficiency. Here are some ways demand shaping is applied in supply chains: Promotions and Discounts : Offering special deals or discounts during periods of lower demand can help even out sales and reduce excess inventory. Product Bundling : Combining products into bundles can encourage customers to buy more or purchase items that might otherwise be less popular. Pricing Strategies : Adjusting prices dynamically based on demand patterns can help manage inventory levels and maximize revenue. Lead Time Management : By adjusting lead times and order quantities, companies can better align their production schedules with actual demand. This Photo by Unknown Author is licensed under CC BY

24. Demand Forecasting Models Customer Segmentation: Targeting different customer segments with tailored offers or products can help match supply with specific demand patterns. Capacity Planning: Investing in flexible production capabilities or adjusting workforce levels can help meet varying demand without overextending resources. Demand Forecasting: Improving the accuracy of demand forecasts through advanced analytics and machine learning can help better align supply with anticipated demand. By implementing these strategies, companies can reduce costs associated with excess inventory or stockouts and improve their overall supply chain efficiency. This Photo by Unknown Author is licensed under CC BY

25. Demand Forecasting Issues ▶ It’s important to recognize that the selection of a forecasting technique (or techniques) depends on a variety of factors, such as the situation at hand, forecasting costs in terms of time and money, and the accuracy of various forecasting techniques. Forecasting accuracy refers to the relationship between actual and forecasted demand, and accuracy can be affected by various considerations. One of the challenges with the analog technique is selecting the appropriate analog, because an inappropriate selection will reduce forecast accuracy. Forecast accuracy can have important logistical implications, as illustrated at Lighthouse Foods, where improved forecasting resulted in substantial reductions in the amount of finished goods inventory. This Photo by Unknown Author is licensed under CC BY-SA-NC

26. Demand Forecasting Issues ▶ Collaborative planning, forecasting, and replenishment (CPFR) concept, is when supply chain partners share planning and forecasting data to better match up supply and demand. Conceptually, CPFR suggests that supply chain partners will be working from a collectively agreed-to single demand forecast number as opposed to each member working off its own demand forecast projection. CPFR also suggests that supply chain partners will be working from a collectively agreed-to order forecast, which considers both forecasted demand and current inventory levels. This Photo by Unknown Author is licensed under CC BY

27. Demand Forecasting Issues ▶ A great deal of demand forecasting currently involves the use of computer software packages. Although this software can provide fast and detailed data, software packages should not be viewed as a panacea to an organization’s demand forecasting. For example, enterprise resource planning (ERP) systems conceptually should lead to much lower forecasting errors. However, the Grocery Manufacturers Association found forecasting errors that averaged more than 20% among companies that had implemented ERP-based forecasts.5 It is important to keep in mind that no software package—regardless of its sophistication and cost—is capable of totally eliminating forecast errors.

28. Order Management ▶ Order management refers to management of the various activities associated with the order cycle; the order cycle (which can also be referred to as the replenishment cycle or lead time) refers to the time from when a customer places an order to when the goods are received. The order cycle should be analyzed not only in terms of total cycle time but in cycle time variability (reliability) Order management has been profoundly affected by advances in information systems. This Photo by Unknown Author is licensed under CC BY-SA-NC

29. Order Management ▶ Order cycle as consisting of four components or stages: order transmittal, order processing, order picking and assembly, and order delivery. Order transmittal refers to the time from when the customer places an order until the seller receives the order. In general, there are five possible ways to transmit orders: in person, by mail, by telephone, by facsimile (fax) machine, and electronically. Order processing refers to the time from when the seller receives an order until an appropriate location (such as a warehouse) is authorized to fill the order Order picking and assembly is the next stage of the order management process, and it includes all activities from when an appropriate location (such as a warehouse) is authorized to fill the order until goods are loaded aboard an outbound carrier. . The final phase of the order cycle is order delivery, which refers to the time from when a transportation carrier picks up the shipment until it is received by the customer. 

30. Amazon Deliveries and Returning ▶ It takes 1-800-Flowers.com a year to prepare for Mother’s Day. The business depends on more than 5,000 local florist and fulfillment centers to get orders of items like flowers, bouquets and even teddy bears to customers. How did the online florist become one of the leaders to e-commerce? The following video takes a look behind the complicated logistics of delivering 23 million flowers on the company’s most profitable day of the year.

31. How 23 million Flowers Are Delivered From Far to Doorstep

32. Customer Service ▶ Customers are important to organizations, and organizations that view customers as a “nuisance” may not last very long in today’s highly competitive business environment. Customer service can be an excellent competitive weapon. It is more difficult for competitors to imitate than other marketing mix variables such as price and promotion. Macroenvironmental changes, such as globalization and advances in technology, are causing organizations and individuals to demand higher levels of customer service. Furthermore, the increased use of vendor quality-control programs necessitates higher levels of customer service. customer service can be defined as “the ability of logistics management to satisfy users in terms of time, dependability, communication, and convenience.”.

33. Time ▶ Time refers to the period between successive events, and clearly the order cycle is an excellent example of the time dimension of customer service. At the risk of sounding redundant, many businesses today are looking to reduce order cycle times—longer cycle times translate into higher inventory requirements. Moreover, some customers now expect nearly instantaneous gratification—which explains why Amazon continues to add to the number of locations where it offers one-hour delivery for online orders.

34. Time ▶ In customer service, "time" is a crucial metric that can significantly impact customer satisfaction and operational efficiency. Here are a few key time-related metrics often considered: Response Time: The time it takes for a customer service representative to respond to a customer's initial inquiry. Faster response times typically lead to higher customer satisfaction. Resolution Time: The time it takes to resolve a customer's issue or complete their request from start to finish. Shorter resolution times are generally preferred as they indicate efficiency and effectiveness in handling customer issues. Handle Time: The total time a representative spends interacting with a customer, including both talk time and any follow-up work. Optimizing handle time helps improve productivity and customer experience.

35. Time First Contact Resolution (FCR): The percentage of issues resolved on the first contact without the need for follow-up. High FCR rates are indicative of effective problem-solving and customer satisfaction. Average Speed of Answer (ASA): The average time it takes for a customer to be connected to a representative after initiating a contact. Lower ASA values often reflect better service levels. Time to Resolution (TTR): The average time taken to resolve an issue from the moment it is reported. This metric helps assess how quickly problems are addressed and fixed. Efficient management of these time-related metrics can enhance the overall customer experience, streamline operations, and improve service quality.

36. Dependability ▶ Dependability refers to the reliability of the service encounter. It consists of three elements, namely, consistent order cycles, safe delivery, and complete delivery.13 Our earlier discussion of the order cycle highlighted the importance of consistency (reliability/dependability)—inconsistent order cycles necessitate higher inventory requirements. And although order cycle time is important, an increasing number of companies are trading off order cycle speed for order cycle consistency. More specifically, these companies are willing to accept a slower order cycle so long as it exhibits a high level of consistency.

37. Dependability ▶ Dependability in customer service refers to the reliability and consistency with which a service meets customer expectations. It encompasses several key aspects: Consistency : Providing the same high level of service across all interactions, whether through phone, email, chat, or in-person. This includes maintaining consistent quality, response times, and problem-solving capabilities. Reliability : Being dependable in fulfilling promises and commitments. This means delivering on time, accurately addressing customer needs, and ensuring that solutions are effective and lasting. Availability : Being accessible when customers need assistance. This includes having adequate coverage during business hours, as well as providing support through various channels like phone, email, live chat, and social media. Accuracy : Providing correct information and solutions. Dependability involves ensuring that the advice and solutions provided are accurate and well-informed.

38. Dependability Trustworthiness : Building and maintaining customer trust through transparent communication, ethical practices, and by keeping personal and sensitive information secure. Responsiveness : Reacting promptly to customer inquiries and issues, and following up to ensure that concerns are fully addressed. Problem-Solving : Effectively resolving issues in a way that meets or exceeds customer expectations. Dependability means addressing problems thoroughly and finding satisfactory resolutions. By focusing on these aspects, companies can build a reputation for being dependable, which can enhance customer loyalty, satisfaction, and overall business success.

39. Convenience ▶ The convenience component of customer service focuses on the ease of doing business with a seller. Having said this, different customers may have different perceptions of the “ease of doing business” concept. For example, for a college student the “ease of doing business” with a bank might mean access to automatic teller machines, whereas for a small business owner it might mean bank tellers who specifically focus on commercial deposits and withdrawals. As such, sellers should have an understanding of their customer segments and how each segment views the “ease of doing business.”

40. Convenience ▶ Convenience in customer service is about making it easy and hassle-free for customers to interact with a company and get their needs met. Key elements of convenience in customer service include: Multiple Contact Channels: Providing various ways for customers to reach support, such as phone, email, live chat, social media, and self-service options. This ensures customers can choose the method that best suits their preferences. Accessibility: Ensuring that customer service is available during convenient hours and considering extended hours or 24/7 support for global customers or high-demand periods. User-Friendly Interfaces : Designing intuitive and easy-to-navigate websites, apps, or support portals so customers can quickly find the information or assistance they need. Self-Service Options: Offering tools like FAQs, knowledge bases, and online troubleshooting guides that allow customers to resolve issues on their own without needing to contact support.

41. Convenience Streamlined Processes: Simplifying processes such as returns, exchanges, and claims to reduce the time and effort required by customers. Personalization: Tailoring interactions based on customer history and preferences to make service more relevant and efficient. Speed and Efficiency: Minimizing wait times and reducing the number of steps required to resolve an issue or complete a request. This includes quick response times and effective problem-solving. Proactive Support: Anticipating customer needs and addressing potential issues before they become problems. This might include notifying customers of service updates or potential issues with their orders. By focusing on these aspects, companies can enhance the convenience of their customer service, leading to improved customer satisfaction and loyalty.

42. Managing Customer Service ▶ In addition to understanding what customer service is, the logistician faces multiple managerial considerations with customer service. The remainder of this chapter will discuss four specific considerations—establishing customer service objectives ; measuring customer service ; customer profitability analysis ; and service failure and recover .

43. Establishing Customer Service Objectives ▶ Because customer service standards can significantly affect a firm’s overall sales success, establishing goals and objectives is an important management decision. A central element in establishing customer service goals and objectives is determining the customer’s viewpoint. Because customer service is a competitive tool, it is also important to learn how the customer evaluates the service levels of competing sellers. Many companies evaluate their service performance through benchmarking, which refers to a process that continuously identifies, understands, and adapts outstanding processes found inside and outside an organization. This Photo by Unknown Author is licensed under CC BY-SA

44. Establishing Customer Service Objectives ▶ The nature of the product also affects the level of the customer service that should be offered. Substitutability, which refers to the number of products from which a firm’s customers can choose to meet their needs, is one aspect. Another product-related consideration when establishing customer service goals and objectives is where the product is in its product life cycle. Establishing minimum acceptable order sizes is an ever-present customer service problem because many customers want to order smaller quantities at more frequent intervals. This Photo by Unknown Author is licensed under CC BY-SA

45. Measuring Customer Service ▶ Grandiose statements and platitudes regarding a firm’s level of customer service represent little more than rhetoric unless the customer service standards to support them are actually implemented. Several key issues are associated with measuring customer service, one of which involves determining the data sources to be used. Ideally, an organization might want to collect measurement data from both internal and external sources. A second key issue associated with customer service measurement is determining what factors to measure.

46. Measuring Customer Service Measuring customer service involves evaluating how well your service meets or exceeds customer expectations. Effective measurement helps identify strengths, areas for improvement, and overall performance. Here are key metrics and methods for measuring customer service: Customer Satisfaction (CSAT) Definition : A measure of how satisfied customers are with a specific interaction or overall experience. Method : Typically measured using post-interaction surveys with questions like, "How satisfied were you with our service?" Often rated on a scale from 1 to 5 or 1 to 10. Net Promoter Score (NPS) Definition : Measures customer loyalty and the likelihood of customers recommending your service to others. Method : Customers are asked, "On a scale of 0 to 10, how likely are you to recommend our company to a friend or colleague?" Responses are categorized into Promoters (9-10), Passives (7-8), and Detractors (0-6). NPS is calculated by subtracting the percentage of Detractors from the percentage of Promoters.

47. Measuring Customer Service Customer Effort Score (CES) Definition : Evaluates the ease of interaction and problem resolution. Method : Customers are asked, "How easy was it to get your issue resolved?" or "How much effort did you have to put in to get your issue resolved?" Responses are usually on a scale from "Very Easy" to "Very Difficult.“ First Contact Resolution (FCR) Definition : Measures the percentage of issues resolved on the first interaction. Method : Track how often customer issues are resolved during the initial contact without the need for follow-ups or additional interactions. Average Handle Time (AHT) Definition : The average time spent handling a customer’s request, including talk time and follow-up work. Method : Calculate the total time spent on customer interactions divided by the number of interactions.

48. Measuring Customer Service Service Level Definition : Measures the percentage of calls or inquiries answered within a specified time frame. Method : For example, "What percentage of calls are answered within 30 seconds?" or "What percentage of emails are responded to within 24 hours?“ Customer Retention Rate Definition : The percentage of customers who continue to do business with a company over a specific period. Method : Calculate the number of repeat customers divided by the total number of customers, usually over a defined period. Customer Churn Rate Definition : The percentage of customers who stop using your service or product over a specific period. Method : Calculate the number of customers lost during a period divided by the total number of customers at the start of the period.

49. Measuring Customer Service Customer Satisfaction Index Definition : A composite metric that combines various aspects of customer satisfaction. Method : Combine results from CSAT, NPS, CES, and other relevant metrics to get a holistic view of customer satisfaction. Quality Assurance (QA) Scores Definition : Measures the quality of customer service interactions based on predefined standards and criteria. Method : Review and score customer service interactions according to a set of quality criteria, such as accuracy, professionalism, and adherence to processes.

50. Measuring Customer Service Feedback and Complaints Definition : Direct insights from customers about their experiences and issues. Method : Monitor and analyze customer feedback and complaints to identify common issues and areas for improvement. Employee Satisfaction Definition : Measures how satisfied customer service employees are with their roles and work environment. Method : Conduct employee surveys to understand their job satisfaction, which can impact their performance and customer interactions. By using these metrics and methods, you can gain a comprehensive understanding of your customer service performance, identify areas for improvement, and implement strategies to enhance overall customer satisfaction.

51. Select Customer Service Measures ▶ Table 7.2 provides representative customer service measures for each of these four dimensions.

52. Customer Profitability Analysis ▶ Customer profitability analysis (CPA) refers to the allocation of revenues and costs to customer segments or individual customers to calculate the profitability of the segments or customers. Customer profitability analysis suggests that different customers (segments) consume differing amounts and types of resources; for example, some customers might require telephone-based communication with an organization, whereas other customers are able to communicate electronically with an organization.

53. Customer Profitability Analysis ▶ Customer profitability analysis explicitly recognizes that all customers are not the same, and some customers are more valuable than others to an organization. CPA can be used to identify different groups of customers from a profitability perspective, and such a grouping can better help in allocating an organization’s resources. Thorough customer profitability analysis only works if it is grounded in activity-based costing in the sense that activity-based costing suggests that different products are characterized by differences in the amount and types of resources consumed. This Photo by Unknown Author is licensed under CC BY

54. Customer Profitability Analysis Customer Profitability Analysis involves evaluating the financial contribution of individual customers or customer segments to a company's bottom line. This analysis helps businesses understand which customers are most valuable and how to allocate resources effectively. Here’s an overview of the process: Data Collection Gather data on: Revenue : Total revenue generated by each customer or customer segment. Cost : Costs associated with serving each customer, including direct costs (e.g., product or service costs) and indirect costs (e.g., customer service, marketing). Customer Lifetime Value (CLV) : The total profit a customer is expected to generate during their relationship with the company. Analyze Revenue Determine how much revenue each customer or segment generates. This includes: Purchasing Frequency : How often the customer makes a purchase. Average Order Value : The typical amount spent per transaction. Product or Service Mix : Which products or services the customer buys and their respective margins. This Photo by Unknown Author is licensed under CC BY

55. Customer Profitability Analysis Assess Costs Evaluate the costs associated with each customer: Acquisition Costs : Costs related to acquiring new customers (e.g., marketing, sales expenses). Service Costs : Costs incurred to serve the customer, including support and fulfillment. Retention Costs : Expenses related to keeping the customer, such as loyalty programs or personalized service. Calculate Profitability Subtract the total costs from the total revenue for each customer or segment to determine profitability. This can be done using: Gross Profit : Revenue minus the cost of goods sold (COGS). Net Profit : Gross profit minus all other expenses associated with serving the customer. This Photo by Unknown Author is licensed under CC BY

56. Customer Profitability Analysis Segment Analysis Segment customers based on profitability to identify patterns and trends. Common segments include: High-Value Customers : Those who contribute significantly to profit with relatively low service costs. Low-Value Customers : Those who generate little profit or incur high costs. Profitability Risks : Customers whose costs are rising or whose purchasing frequency is declining. Strategic Actions Based on the analysis, consider actions such as: Targeted Marketing : Focus marketing efforts on high-value customers or segments with potential for growth. Cost Management : Reduce costs associated with low-value or unprofitable customers, such as by adjusting service levels or pricing. Customer Retention : Invest in retaining high-value customers through loyalty programs or personalized service. Product or Service Adjustments : Tailor offerings to better meet the needs of profitable segments. This Photo by Unknown Author is licensed under CC BY

57. Customer Profitability Analysis Continuous Monitoring Regularly update the analysis to reflect changes in customer behavior, market conditions, and business strategies. This helps ensure that resources are allocated effectively, and strategies remain aligned with profitability goals. By understanding customer profitability, companies can make informed decisions about where to invest their resources and how to enhance overall business performance. This Photo by Unknown Author is licensed under CC BY

58. Service Failure and Recovery ▶ Regardless of how well run an organization is, some situations will occur in which actual performance does not meet the customer’s expected performance (i.e., a service failure). Service failure has emerged as a prominent business issue in recent years, in part because organizations have learned that customers can easily become disaffected. For example, it has been estimated that poor customer experiences cost U.S. business in excess of $40 billion per year.

59. Service Failure and Recovery ▶ Service recovery refers to a process for returning a customer to a state of satisfaction after a service or product has failed to live up to expectations. Excellent response to a service failure can sometimes result in the service recovery paradox, in which a customer holds the responsible company in higher regard after the service recovery than if a service failure had not occurred in the first place. There is no set formula for service recovery, in part because each service failure is unique in its impact on a particular customer.

60. Service Failure and Recovery Service Failure Service failure occurs when a service does not meet customer expectations or fails to deliver as promised. This can include: Poor Quality : When the service provided is substandard compared to what was promised. Delayed Service : Instances where services are not delivered within the expected time frame. Incorrect Information : Providing customers with inaccurate or misleading information. Unfulfilled Promises : When a company fails to deliver on commitments or guarantees. Negative Interactions : Unprofessional behavior from service representatives or unsatisfactory communication.

61. Service Failure and Recovery Service Recovery Service recovery involves the actions a company takes to address and rectify service failures. Effective recovery strategies include: Acknowledge the Issue : Recognize and admit the problem to the customer. Avoiding denial or deflection is crucial. Apologize Sincerely : Offer a genuine apology for the inconvenience or mistake. Acknowledging the impact on the customer can go a long way in mending the relationship. Provide Solutions : Offer a clear and practical solution to address the issue. This could be a replacement, refund, discount, or any other form of compensation. Act Quickly : Resolve the issue as promptly as possible to minimize customer frustration and demonstrate a commitment to service.

62. Service Failure and Recovery Service Recovery Service recovery involves the actions a company takes to address and rectify service failures. Effective recovery strategies include: Follow Up : After resolving the issue, follow up with the customer to ensure they are satisfied with the resolution and to reaffirm their importance to the company. Learn and Improve : Analyze the failure to understand what went wrong and implement changes to prevent similar issues in the future. This might include staff training, process improvements, or system upgrades. Empower Employees : Ensure that service representatives have the authority and tools to handle issues effectively and make decisions that benefit the customer. Communicate Clearly : Keep customers informed throughout the recovery process, explaining what happened and what steps are being taken to resolve the issue.

63. Conclusion ▶ Demand management deals with determining what customers want, and a key component involves demand forecasting. The chapter discussed basic demand forecasting models along with select forecasting issues such as cost and accuracy. The chapter also looked at order management and the order cycle, which refers to the period of time from when the order is placed until it is received. Four components of an order cycle—order transmittal, order processing, order picking and assembly, and order delivery—were identified and discussed in some detail. Customer service was the third major topic addressed in this chapter. The four dimensions of customer service—time, dependability, communication, and convenience—were discussed. The chapter also looked at managing customer service, with a specific focus on establishing customer service objectives, measuring customer service, customer profitability analysis, and service failure and service recovery.

64. Amazon Deliveries and Returning ▶ Customers are important to organizations and have come to expect one day delivery due to macroenvironmental changes, such as globalization and advances in technology. These changes have caused organizations and individuals to demand higher levels of customer service. Amazon has created a convenience for customers when buying and selling. That simplicity comes at a cost. The next video from CNBC takes you on an exclusive tour inside a Liquidity Services returns warehouse outside Dallas, Texas, where unwanted goods from Amazon and Target are stacked to the ceiling before being resold on Liquidation.com or a variety of other marketplaces. This Photo by Unknown Author is licensed under CC BY-NC-ND

65. The Truth About Amazon Deliveries and Returns