TTOM – Logistics
Risk-Pooling Strategies
Francisco Furtado Raul Pires
Risk-Pooling Strategies to Reduce and Hedge Uncertainty Redesign the supply chain, production process or product to either reduce the uncertainty a firm faces or hedge uncertainty so that the firm is in a better position to mitigate the consequences of uncertainty. (Cachon Gérard, et all; Matching Supply with Demand) In other words, risk pooling consists in several pooling strategies either through centralizing inventories (pooling inventories/stockpile), having universal designs (pooling products for different segments), delaying time of deliveries to the client (lead time pooling) or introducing flexibility in manufacturing (pooling different plants, with different capabilities so that they can produce the same final product) to decrease the effects of uncertainty (reduce the coefficients of variation - better match supply and demand).
Objectives • Reduce Inventory while maintaining the same service; • Increase Service while holding the same inventory; • Combination of the above improvements.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Risk-Pooling Strategies to Reduce and Hedge Uncertainty Topics • Location Pooling
• Virtual Pooling • Product Pooling • Correlation Effect • Lead Time Pooling • Consolidated Distribution • Delayed Differentiation • Capacity Pooling with Flexible Manufacturing Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Location Pooling (1/3) In how many different locations should the firm store inventory to serve demand? Single Distribution Center Inventories per Store
Several Distribution Centers by pooling regions, no store Inventories
How can performance be improved by pooling territories having several distribution centers for each “pooled” territory ? • Reducing individual stores inventories; • By reducing demand variability, decreasing the coefficient of variation for the demand function for the “pooled” territory. Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Location Pooling – Measuring the Impact (2/3) Assumptions: • Each single Store Demand is a Normal Distribution (we have 6 stores); • Average daily demand is 3 items and 1 for the Standard Deviation (equal in all); • Using order-up-to model with a target in-stock probability of 99.9%; • Lead Time of two days (equal in all). Expected Inventory Pooled Stores 1 2 3 4 5 6
Pooled Stores Expected Demand
Order Up to level(S)
6 12 18 24 30 36
10 17 24 31 37 44
Units Days of Demand 4,00 0,67 5,00 0,42 6,00 0,33 7,00 0,29 7,00 0,23 8,00 0,22
Coeficient of Variation(σ/μ) 0,17 0,12 0,10 0,08 0,07 0,07
Total (E) Inventory 24 21 18 15 11 8
With inventories in each store total inventory would be 24, pooling them all gives us 8. It is a 66.7% reduction in Inventory. Without reduction of level of service!
Virtual Pooling No physical pooling of inventories. Inventory information is shared between stores so that each store can obtain inventory from the Supplier/Central distribution center and any other store with excess inventory. Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Location Pooling – Measuring the Impact (3/3) Relation Expected Inventory - Coef. Variation 0,80
0,18
0,70
0,16
0,60
0,14 0,12
0,50
0,10
0,40
0,08
0,30
0,06
0,20
0,04
0,10
0,02
0,00
0,00 1
2
3
4
5
Days of Demand Coeficient of Variation(σ/μ)
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Comments on results: • As the mean of the Normal distribution (and sample size) increases, it´s coefficient of variation (σ/μ) decreases, that is, the distribution becomes less variable (because the Mean grows faster then the Standard Deviation); • But Std Deviation decreases at a decreasing rate, each incremental increase as a proportionally smaller impact on the Coef. Of Variation; • Doesn´t affect Pipeline Inventory (depends on average not variability); • Reduces expected inventory investment achieve a target service level.
needed to
• Better scale economies (shipment and warehouses). Downside: • Explicit Storage costs; • Inventory moves further away from Demand; • transshipment between stores and decide when inventory can move from store A to B (Virtual P.) Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Product Pooling (1/3) Serve demand with fewer products. Combine different products in only one, universal design.
Example: • 2 Products, made by the same firm (same Demand and Economics); • Demand with Normal Distribution (μ=3190; σ=1181). For each one expected Profit is $191 760, and total profit is $383 520; • If we combine the Product then: • μ = 3190×2 = 6384; σ=1181×√ 2= 1670; • Expected Profit is $402 116. • Pooling the Product can potentially increase profit by 4.85%!
There is a decrease in demand variability, in the first scenario we have 1181/3190=0.37, with the pooled product we have 1670/6384=0.26. This way we reduce the mismatch cost (newsvendor model) and achieve higher expected profits.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Product Pooling – Correlation Effect (2/3) To Pool demand we have been considering independence between different products and regions, but is that so? What is the influence if Demands are not independent?
With a negative correlation total demand is stable. With a positive, total demand varies. More variability with positive correlations.
Ideal outcome is a point, when demand for each product happens to equal its order quantity.
With the Universal product (and negative correlation) ideal outcome is a downward-sloping, not a single point.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Product Pooling – Correlation Effect (3/3) This effect applies also to location pooling and lead time pooling. If correlation is equal to 1 (perfect positive correlation) there is no benefit in pooling (we do not decrease uncertainty/variability).
Remarks • May not provide the needed functionality (Bike example); • Can be more expensive to produce (but also more cheaper, there can be scale economies); • May eliminate some brand/price segmentation; • “Customer Focused” deliriums, no benefits in splitting a fixed customer base into smaller pieces. Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Lead Time Pooling – Consolidated Distribution (1/2) Address the main problem with location pooling – distance between Inventory and Customers. Firms face two kinds of uncertainty (even with a single product): Total demand and Allocation of that demand. Consolidated Distribution aims at keeping inventory close to clients and at the same type reduce allocation uncertainty. • Instead of being directly served by the supplier, stores are served by a Central distribution center (but still keep some storage capacity); • Reduced lead times to replenish stores, stores are able to reduce drastically their storage capacity (other gains in economies of scale); • The more then 100 connections between Supplier and Stores are pooled into only one between the Supplier and the Central Distribution Center, this way uncertainty lies only in the total demand and not in were to allocate that total demand.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Lead Time Pooling – Consolidated Distribution (2/2) Example
• More effective when total demand is less variable than demand at individual stores (negative correlation between stores); • Lead time is bigger between before Distribution Center, than the lead time after the distribution center; • Cost of a Central Distribution Center (DC); • Extra transportation costs from DC to stores; • Easier to store a large buy (take advantage of price fluctuations); • Diminishes “less-then-load” problems, more frequent shipments (hub like concept). Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Lead Time Pooling – Delayed Differentiation (1/2) • Addresses the problem of uncertainty associated with product variety by making it possible to differentiate the product in the last stages before it reaches the customers.
• Idea is to have a generic base product that can be differentiated in the end and thus still have two different products, a strategy that can be important to achieve better sales.
• Also, differentiation is made at a point where better demand information is available.
• No inventory for finished goods but only for the generic good.
• Different from Product pooling: • eliminates variety by creating a single product, with no differentiation in the final outcome. • does not require a significant modification in the production process.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Lead Time Pooling – Delayed Differentiation (2/2) • To be successful : • Resolve technical issues regarding last stage differentiation • No substantial delay between request and shipment process • It is an ideal strategy when: 1. Customers demand many versions, variety is important 2. There is less uncertainty for total demand than there is for individual versions 3. Variety is created in the late stages of the production process 4. Variety can be added cheaply and quickly 5. The components that create variety are inexpensive compared to the generic component • Examples: • Packaging differences of Black&Decker drill between Wal-Mart and Home Depot • Retail paint colors • Fast/Slow printers with no additional processing • Resembles make-to-order strategy • Main difference concerns production steps required to a finished unit
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Capacity Pooling with Flexible Manufacturing (1/3) • What degree of flexibility should the manufacturing process of a factory have so that shortage of production of fast selling products of a company can be overcome by other factories? • If no flexibility exists, then if the demand for a given product is higher than the capacity to produce it, the excess demand sales will be lost. This becomes even more evident if, for instance, the same company also produces a certain product whose demand is lower than the capacity of the factory that produces it. • So, there can be a compensation between them by the introduction of flexibility so that the one whose capacity is not fulfilled can produce the product for which the capacity is not available. • The problem is that with flexibility comes higher costs in the manufacturing process, as it is more expensive to have flexible processes and equipment instead of just dedicated ones. • Therefore, how do we decide the level of flexibility and where? And when does it make sense to invest in it instead of in increasing capacity?
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Capacity Pooling with Flexible Manufacturing (2/3)
• Auto car maker GM example. • Each plant can produce 100 vehicles • Results produced by simulation • Demand 20 to 180 vehicles for each model • As we add links, capacity utilization rate increases as well as expected sales. • But, as with location pooling, the difference between total flexibility and 20 links flexibility only increases capacity utilization rate (and expected sales) by around 1%! • Links configuration -> Chaining effect
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Capacity Pooling with Flexible Manufacturing (3/3) • Pooled capacity flexibility also affected by 2 important issues: • Correlation • Uncertainty in total demand is more important than individual demand which implies that negative correlation of demand increases success of flexibility since increase in one product means decrease in another and so compensation can be accomplished. • It is not mandatory that a plant can produce two negatively correlated products, it is enough that they are in the same chain. • Vital information that can reduce costs because, instead of having a plant capable of producing these two different products, we can just connect them through the chain.
• Total Capacity • If plants capacity is either very large or very small then no use in having flexibility because either they are already at full capacity or they have more then enough capacity to cover more demand.
Flexibility is more valuable at intermediate levels of capacity and they are substitutes because capacity increase can cover less flexibility and vice versa. Depends on costs of each.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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Conclusions • There are different risk pooling strategies to better match supply with demand, each one with its own strengths and limitations. • Risk Pooling should be carefully measured. Substantial improvements are obtained in the first iterations and in most cases yield nearly the same level of gains as using risk pooling to its maximum. • Risk Pooling strategies are more effective in the existence of negative correlated demands due to the fact that uncertainty with total demand is less than with individual. • They can be used with 3 objectives: 1. reduce inventory while maintaining same level of service 2. Increase level of service while maintaining same inventory 3. Combination of 1. and 2. • Ford, Chrysler, Nokia use some kind of risk pooling in their processes • Contract Manufacturers is an emerging business.
Master’s Program on Complex Transport Infrastructure Systems (CTIS)
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