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Demand Amplification in Supply Chain

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Presentation on theme: "Demand Amplification in Supply Chain"— Presentation transcript:

1 Demand Amplification in Supply Chain

2 Higher Variability in Orders Placed by Computer Retailer to Manufacturer Than Actual Sales
Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review

3 Increasing Variability of Orders Up the Supply Chain
Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review

4 The Bullwhip Effect and its Impact on the Supply Chain
Consider the order pattern of a single color television model sold by a large electronics manufacturer to one of its accounts, a national retailer.

5 The Bullwhip Effect and its Impact on the Supply Chain
Point-of-sales Data-Original POS Data After Removing Promotions

6 The Bullwhip Effect and its Impact on the Supply Chain
POS Data After Removing Promotion & Trend

7 We Conclude …. Order variability is amplified up the supply chain; upstream echelons face higher variability. What you see is not what they face.

8 What are the Causes…. Promotional sales
Forward buying Volume and transportation discounts Batching Inflated orders IBM Aptiva orders increased by 2-3 times when retailers thought that IBM would be out of stock over Christmas Motorola cell phones

9 What are the Causes…. Single retailer, single manufacturer.
Retailer observes customer demand, Dt. Retailer orders qt from manufacturer. What if there are promotional activities, no transportation discounts and no inflated order? Are we still going to see an increase in variability? Consider a simple supply chain with a single retailer and a single manufacturer Dt qt Retailer Manufacturer L

10 What are the Causes…. Promotional sales
Volume and transportation discounts Inflated orders Demand forecasting Order-up-to points are modified as forecasts change – orders increase more than forecasts Long cycle times Long lead times magnify this effect

11 What are the Causes…. Single retailer, single manufacturer.
Retailer observes customer demand, Dt. Retailer orders qt from manufacturer. Dt qt Retailer Manufacturer L

12 How big is the increase? Suppose a P period moving average is used.

13 Var(q)/Var(D): For Various Lead Times
14 L=5 L=5 12 10 8 L=3 L=3 6 4 L=1 L=1 2 5 10 15 20 25 30

14 Consequences…. Increased safety stock Reduced service level Inefficient allocation of resources Increased transportation costs

15 Multi-Stage Supply Chains
Consider a multi-stage supply chain: Stage i places order qi to stage i+1. Li is lead time between stage i and i+1. qo=D q1 Retailer Stage 1 Manufacturer Stage 2 q2 Supplier Stage 3 L1 L2

16 Multi stage systems Centralized: each stage bases orders on retailer’s forecast demand. Decentralized: each stage bases orders on previous stage’s demand

17 Multi-Stage Systems:Var(qk)/Var(D)
Dec, k=5 Cen, k=5 Dec, k=3 Cen, k=3 k=1

18 The Bullwhip Effect: Managerial Insights
Exists, in part, due to the retailer’s need to estimate the mean and variance of demand. The increase in variability is an increasing function of the lead time. The more complicated the demand models and the forecasting techniques, the greater the increase. Centralized demand information can significantly reduce the bullwhip effect, but will not eliminate it.

19 Coping with the Bullwhip Effect in Leading Companies
Reduce uncertainty POS Sharing information Sharing forecasts and policies Reduce variability Eliminate promotions Year-round low pricing Reduce lead times EDI Cross docking Strategic partnerships Vendor managed inventory Data sharing

20 Example: Quick Response at Benetton
Benetton, the Italian sportswear manufacturer, was founded in In 1975 Benetton had 200 stores across Italy. Ten years later, the company expanded to the U.S., Japan and Eastern Europe. Sales in 1991 reached 2 trillion. Many attribute Benetton’s success to successful use of communication and information technologies.

21 Example: Quick Response at Benetton
Benetton uses an effective strategy, referred to as Quick Response, in which manufacturing, warehousing, sales and retailers are linked together. In this strategy a Benetton retailer reorders a product through a direct link with Benetton’s mainframe computer in Italy. Using this strategy, Benetton is capable of shipping a new order in only four weeks, several week earlier than most of its competitors.

22 How Does Benetton Cope with the Bullwhip Effect?
1. Integrated Information Systems • Global EDI network that links agents with production and inventory information • EDI order transmission to HQ • EDI linkage with air carriers • Data linked to manufacturing 2. Coordinated Planning • Frequent review allows fast reaction • Integrated distribution strategy

23 Information for Effective Forecasts
Pricing, promotion, new products Different parties have this information Retailers may set pricing or promotion without telling distributor Distributor/Manufacturer might have new product or availability information Collaborative Forecasting addresses these issues.

24 Information for Coordination of Systems
Information is required to move from local to global optimization Questions: Who will optimize? How will savings be split? Information is needed : Production status and costs Transportation availability and costs Inventory information Capacity information Demand information

25 Locating Desired Products
How can demand be met if products are not in inventory? Locating products at other stores What about at other dealers? What level of customer service will be perceived?

26 Lead-Time Reduction Why? How? Customer orders are filled quickly
Bullwhip effect is reduced Forecasts are more accurate Inventory levels are reduced How? EDI POS data leading to anticipating incoming orders.

27 Information to Address Conflicts
Lot Size – Inventory: Advanced manufacturing systems POS data for advance warnings Inventory -- Transportation: Lead time reduction for batching Information systems for combining shipments Cross docking Advanced DSS Lead Time – Transportation: Lower transportation costs Improved forecasting Lower order lead times Product Variety – Inventory: Delayed differentiation Cost – Customer Service: Transshipment

28 The Effect of Lack of Coordination on Performance
Manufacturing cost (increases) Inventory cost (increases) Replenishment lead time (increases) Transportation cost (increases) Labor cost for shipping and receiving (increases) Level of product availability (decreases) Relationships across the supply chain (worsens) Profitability (decreases) The bullwhip effect reduces supply chain profitability by making it more expensive to provide a given level of product availability

29 Obstacles to Coordination in a Supply Chain
Incentive Obstacles Information Processing Obstacles Operational Obstacles Pricing Obstacles Behavioral Obstacles

30 Incentive Obstacles When incentives offered to different stages or participants in a supply chain lead to actions that increase variability and reduce total supply chain profits – misalignment of total supply chain objectives and individual objectives Local optimization within functions or stages of a supply chain Sales force incentives

31 Information Processing Obstacles
When demand information is distorted as it moves between different stages of the supply chain, leading to increased variability in orders within the supply chain Forecasting based on orders, not customer demand Forecasting demand based on orders magnifies demand fluctuations moving up the supply chain from retailer to manufacturer Lack of information sharing

32 Operational Obstacles
Actions taken in the course of placing and filling orders that lead to an increase in variability Ordering in large lots (much larger than dictated by demand) – Figure 17.2 Large replenishment lead times Rationing and shortage gaming (common in the computer industry because of periodic cycles of component shortages and surpluses)

33 Pricing Obstacles When pricing policies for a product lead to an increase in variability of orders placed Lot-size based quantity decisions Price fluctuations (resulting in forward buying) – Figure 17.3

34 Behavioral Obstacles Problems in learning, often related to communication in the supply chain and how the supply chain is structured Each stage of the supply chain views its actions locally and is unable to see the impact of its actions on other stages Different stages react to the current local situation rather than trying to identify the root causes Based on local analysis, different stages blame each other for the fluctuations, with successive stages becoming enemies rather than partners No stage learns from its actions over time because the most significant consequences of the actions of any one stage occur elsewhere, resulting in a vicious cycle of actions and blame Lack of trust results in opportunism, duplication of effort, and lack of information sharing

35 Managerial Levers to Achieve Coordination
Aligning Goals and Incentives Improving Information Accuracy Improving Operational Performance Designing Pricing Strategies to Stabilize Orders Building Strategic Partnerships and Trust

36 Aligning Goals and Incentives
Align incentives so that each participant has an incentive to do the things that will maximize total supply chain profits Align incentives across functions Pricing for coordination Alter sales force incentives from sell-in (to the retailer) to sell-through (by the retailer)

37 Improving Information Accuracy
Sharing point of sale data Collaborative forecasting and planning Single stage control of replenishment Continuous replenishment programs (CRP) Vendor managed inventory (VMI)

38 Improving Operational Performance
Reducing replenishment lead time Reduces uncertainty in demand EDI is useful Reducing lot sizes Computer-assisted ordering, B2B exchanges Shipping in LTL sizes by combining shipments Technology and other methods to simplify receiving Changing customer ordering behavior Rationing based on past sales and sharing information to limit gaming “Turn-and-earn” Information sharing

39 Designing Pricing Strategies to Stabilize Orders
Encouraging retailers to order in smaller lots and reduce forward buying Moving from lot size-based to volume-based quantity discounts (consider total purchases over a specified time period) Stabilizing pricing Eliminate promotions (everyday low pricing, EDLP) Limit quantity purchased during a promotion Tie promotion payments to sell-through rather than amount purchased Building strategic partnerships and trust – easier to implement these approaches if there is trust

40 Building Strategic Partnerships and Trust in a Supply Chain
Background Designing a Relationship with Cooperation and Trust Managing Supply Chain Relationships for Cooperation and Trust

41 Building Strategic Partnerships and Trust in a Supply Chain
Trust-based relationship Dependability Leap of faith Cooperation and trust work because: Alignment of incentives and goals Actions to achieve coordination are easier to implement Supply chain productivity improves by reducing duplication or allocation of effort to appropriate stage Greater information sharing results

42 Trust in the Supply Chain
Table 17.2 shows benefits Historically, supply chain relationships are based on power or trust Disadvantages of power-based relationship: Results in one stage maximizing profits, often at the expense of other stages Can hurt a company when balance of power changes Less powerful stages have sought ways to resist

43 Building Trust into a Supply Chain Relationship
Deterrence-based view Use formal contracts Parties behave in trusting manner out of self-interest Process-based view Trust and cooperation are built up over time as a result of a series of interactions Positive interactions strengthen the belief in cooperation of other party Neither view holds exclusively in all situations

44 Building Trust into a Supply Chain Relationship
Initially more reliance on deterrence-based view, then evolves to a process-based view Co-identification: ideal goal Two phases to a supply chain relationship Design phase Management phase

45 Designing a Relationship with Cooperation and Trust
Assessing the value of the relationship and its contributions Identifying operational roles and decision rights for each party Creating effective contracts Designing effective conflict resolution mechanisms

46 Assessing the Value of the Relationship and its Contributions
Identify the mutual benefit provided Identify the criteria used to evaluate the relationship (equity is important) Important to share benefits equitably Clarify contribution of each party and the benefits each party will receive

47 Creating Effective Contracts
Create contracts that encourage negotiation when unplanned contingencies arise It is impossible to define and plan for every possible occurrence Informal relationships and agreements can fill in the “gaps” in contracts Informal arrangements may eventually be formalized in later contracts

48 Designing Effective Conflict Resolution Mechanisms
Initial formal specification of rules and guidelines for procedures and transactions Regular, frequent meetings to promote communication Courts or other intermediaries

49 Managing Supply Chain Relationships for Cooperation and Trust
Effective management of a relationship is important for its success Top management is often involved in the design but not management of a relationship Figure process of alliance evolution Perceptions of reduced benefits or opportunistic actions can significantly impair a supply chain partnership

50 Achieving Coordination in Practice
Quantify the bullwhip effect Get top management commitment for coordination Devote resources to coordination Focus on communication with other stages Try to achieve coordination in the entire supply chain network Use technology to improve connectivity in the supply chain Share the benefits of coordination equitably

51 Summary of Learning Objectives
What are supply chain coordination and the bullwhip effect, and what are their effects on supply chain performance? What are the causes of the bullwhip effect, and what are obstacles to coordination in the supply chain? What are the managerial levers that help achieve coordination in the supply chain? What are actions that facilitate the building of strategic partnerships and trust in the supply chain?

52 Forecasting

53 Laws of Forecasting Three Laws of Forecasting
Forecasts are always wrong! Detailed forecasts are worst than aggregate forecasts! The further into the future, the less reliable the forecast will be!

54 Forecasting Starting point of all Production Planning systems
Qualitative Forecasting techniques Quantitative Forecasting techniques Decisions strive for Robustness while relying on forecasts (e.g., agile manufacturing)

55 Qualitative Forecasting
Relies on expertise of people Delphi Method Usually used for technological forecasts (long term forecasts)

56 Quantitative Forecasting
Causal models Predict a future parameter (e.g., demand for a product) as a function of other parameters (e.g., interest rates, marketing strategy). Time Series models Predict a future parameter as a function of past values of that parameter (e.g., historical demand).

57 Causal Forecasting Opening a fast food restaurant Demand forecast?
Predictable parameters Population in the vicinity Competition Use statistics (e.g., regression) to estimate the parameters Y = b0 + b1x1 + b2X2

58 Time Series Forecasting
Time period i = 1,2,…..t (most recent data) A(i): Actual observations f(t+λ): Forecasts for t + λ, λ = 1,2,……, F(t): smoothed estimate (current position of the process under consideration) T(t): smoothed trend A(i), i =1,2,…t Time Series Model f(t+λ), λ =1,2,3,…,

59 Time Series Forecasting
Moving-Average Model Exponential Smoothing Model Exponential Smoothing with a Linear Trend Model Winter’s Method (adds seasonal multipliers to the exponential smoothing with linear trend model)

60 Moving-Average Model Simply average the actual values to forecast future values Drawback-equal weights to all values Hence, m data points are chosen (user decides the value of m Higher m makes model more stable, but less responsive MA model ignores trends,T(t) = 0 Model underestimates rising trend, overestimates decreasing trend

61 Exponential Smoothing
Older the data point, lesser the weight α from 0 to 1 Lower values of α would make the model less responsive The model will underestimate the parameters with an increasing trend

62 Trend Models Exponential smoothing with a linear trend
Winter’s method for seasonality

63 Value of Information “In modern supply chains, information replaces inventory” Why is this true? Why is this false? Information is always better than no information. Why? Information Helps reduce variability Helps improve forecasts Enables coordination of systems and strategies Improves customer service Facilitates lead time reductions Enables firms to react more quickly to changing market conditions.

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