Managing Uncertainty in the Supply Chain: Safety Inventory

Managing Uncertainty in the Supply Chain: Safety Inventory

Role of Inventory in the Supply Chain
Cost Availability Responsiveness Efficiency Notes:

Different kinds of inventory
Cycle inventory: To satisfy demand between two replenishments. Safety inventory: To meet variability of demand – (1) daily demand variability, (2) variability of supply lead time. Seasonal inventory: To meet seasonal upsurge in demand.

The Role of Safety Inventory in a Supply Chain
Forecasts are rarely completely accurate If average demand is 1000 units per week, then half the time actual demand will be greater than 1000, and half the time actual demand will be less than 1000; what happens when actual demand is greater than 1000? If you kept only enough inventory in stock to satisfy average demand, half the time you would run out Safety inventory: Inventory carried for the purpose of satisfying demand that exceeds the amount forecasted in a given period. Safety inventory is the average inventory remaining when the replenishment lot arrives.

Role of Safety Inventory
Average inventory is therefore cycle inventory plus safety inventory There is a fundamental tradeoff: Raising the level of safety inventory provides higher levels of product availability and customer service and thus the margin captured from customer purchases. Raising the level of safety inventory also raises the level of average inventory and therefore increases holding costs. However, both the increased variety of products and the greater pressure for availability push firms to raise the level of safety inventory they hold. Very important in high-tech or other industries where obsolescence is a significant risk, where the product life cycles are short and demand is very volatile (where the value of inventory, such as PCs, can drop in value).

Two Questions to Inventory
What is the appropriate level of safety inventory to carry? What actions can be taken to improve product availability while reducing safety inventory? Compaq and Dell in PCs, in 1998, when prices dropped. Compaq carried 100 days of inventory compared to Dell which carried only 10 days of inventory. Declining prices hurt Compaq much more, given the extra inventory that it carried. Compaq did not make any profit in the first quarter of 1998. A key to Dell’s success has been its ability to provide a high level of product availability while carrying very low levels of safety inventory in its supply chain. Same is true for Wal-Mart and Seven-Eleven Japan.

Determining the Appropriate Level of Safety Inventory
Measuring demand uncertainty Measuring product availability Replenishment policies Evaluating cycle service level and fill rate Evaluating safety level given desired cycle service level or fill rate Impact of required product availability and uncertainty on safety inventory

Determining the Appropriate Level of Demand Uncertainty
Appropriate level of safety inventory is determined by: supply or demand uncertainty desired level of product availability Higher levels of supply or demand uncertainty require higher levels of safety inventory given a particular desired level of product availability. When a new Palm (PDA) model is introduced in the market, demand is highly uncertain, and the retailer B&M thus carries a much higher level of safety inventory relative to demand. As the market’s reaction to the new model becomes clearer, uncertainty is reduced and demand is easier to predict.

Determining the Appropriate Level of Demand Uncertainty
Higher levels of desired product availability require higher levels of safety inventory given a particular level of supply or demand uncertainty. If B&M targets a higher level of product availability for the new Palm model, it must carry a higher level of safety inventory for that model.

Measuring Demand Uncertainty
Demand has a systematic component and a random component. The goal of forecasting is to predict the systematic component and estimate the random component. The estimate of the random component is the measure of demand uncertainty Random component is usually estimated by the standard deviation of demand or forecast error. Notation: D = Average demand per period sD = standard deviation of demand per period L = lead time = time between when an order is placed and when it is received Uncertainty of demand during lead time, and not just a single period, is what is important

Measuring Demand Uncertainty
Assume that demand for each period i, i = 1,2,3,…,L is normally distributed. The total demand during L periods is normally distributed with a mean of P and a standard deviation of W. Also, assume that demand during each of L periods is independent and normally distributed with a mean of D and a standard deviation of sD. The total demand during the L periods is normally distributed with a mean DL and a standard deviation of s. P = demand during L periods = DL = LD W = std dev of demand during L periods = s = sDSqrt(L) Coefficient of variation = cv = m/s = mean/(std dev) = size of uncertainty relative to demand

Measuring Product Availability
Product availability: a firm’s ability to fill a customer’s order out of available inventory. Stockout: a customer order arrives when product is not available. Product fill rate (fr): fraction of the product’s demand that is satisfied from product in inventory. Assume that B&M provides Palms to 90% of its customers from inventory (10% lost to competitors), it achieves a fill rate of 90%.

Order fill rate: fraction of orders that are filled from available inventory, also, like fr, measured over a specified number of orders. In the case of B&M, a customer may order a Palm along with a calculator. The order is filled from inventory only if both the Palm and the calculator are available through the store. It tends to be lower than fr because all products must be in stock for an order to be filled (for single product situations, the two are same).

Cycle service level (CSL): fraction of replenishment cycles that end with all the customer demands being met. It is the probability of not having a stockout in a replenishment cycle. A replenishment cycle is the interval between two successive replenishment deliveries. If B&M manages inventory such that the store does not run out of inventory in 6 out of 10 replenishment cycles, the store achieves a CSL of 60%. However, even in the 40% of the cycles in which stockouts do occur, most of the customer demand is satisfied from inventory. Only a fraction towards the end of the cycle is lost when B&M is out of stock. As a result, the fill rate is much higher than 60%. However, the product fill rate and the order fill rate would differ. Tracking order fill rate is important when customers place a high value on the entire order being filled simultaneously.

Replenishment Policies
Replenishment policy: decisions regarding when to reorder and how much to reorder. These decisions determine the cycle and safety inventories along with fr and CSL. It takes the following two forms. Continuous review: inventory is continuously monitored and an order of size Q is placed when the inventory level reaches the reorder point (ROP). B&M managers orders 600 Palms when the inventory falls below 400. Size of the order remains constant, time between orders may fluctuate given variable demand. Periodic review: inventory is checked at regular (periodic) intervals and an order is placed to raise the inventory to a specified threshold (the “order-up-to” level). Every Saturday, employees at B&M check film inventory and the manager orders enough so that the available inventory and the size of the order total equals 1,000 films. Time between orders is fixed, size of each order, can fluctuate, given variable demand.

Average Inventory = Q/2 + ss
Continuous Review Policy: Safety Inventory and Cycle Service Level (weekly demand is normally distributed, mean D, SD D ) L: Lead time for replenishment D: Average demand per unit time D: Standard deviation of demand per period DL: Expected demand during lead time L: Standard deviation of demand during lead time CSL: Cycle service level ss: Safety inventory ROP: Reorder point Notes: Average Inventory = Q/2 + ss

Estimating Safety Inventory (Continuous Review Policy)
D = 2,500/week; D = 500 L = 2 weeks; Q = 10,000; ROP = 6,000 DL = DL = (2500)(2) = 5000 ss = ROP - DL = = 1000 Cycle inventory = Q/2 = 10000/2 = 5000 Average Inventory = cycle inventory + ss = = 6000 Average Flow Time = Avg inventory / throughput (weekly demand) = 6000/2500 = 2.4 weeks (Little’s Law) Notes:

Estimating Cycle Service Level (Continuous Review Policy)
D = 2,500/week; D = 500 L = 2 weeks; Q = 10,000; ROP = 6,000 Cycle service level, CSL = Prob (demand during lead time of L weeks ≤ ROP) = F(ROP=DL + ss, DL, L) = = NORMDIST (DL + ss, DL, L) = NORMDIST(6000,5000,707,1) = 0.92 (This value can also be determined from a Normal probability distribution table) ROP = DL + ss = DL + z L, thus, z = (ROP - DL)/ L = ( )/707 = This value of z corresponds to 92% probability

Evaluating safety inventory given CSL (Continuous Review Policy)
D = 2,500/week; D = 500 L = 2 weeks; Q = 10,000; CSL = 0.90 DL = DL = (2500)(2) = 5000 L = D sqrt L = 707 Z corresponding to CSL of 90% is 1.282 ss = z L = x 707 = ss = ROP – DL or z L = ROP – DL or ROP = DL + z L =

ESC = -ss{1-NORMDIST(ss/L, 0, 1, 1)} + L NORMDIST(ss/L, 0, 1, 0)
Fill Rate Proportion of customer demand satisfied from stock Stockout occurs when the demand during supply lead time exceeds the reorder point ESC is the expected shortage per replenishment cycle (average units of demand in excess of reorder point in each replenishment cycle) ss is the safety inventory Q is the order quantity Notes: ESC = -ss{1-NORMDIST(ss/L, 0, 1, 1)} + L NORMDIST(ss/L, 0, 1, 0)

Example 11.3: Evaluating Fill Rate
ss = 1,000, Q = 10,000, sL = 707, Fill Rate (fr) = ? ESC = -ss{1-NORMDIST(ss/L, 0, 1, 1)} + L NORMDIST(ss/L, 0, 1, 0) = -1,000{1-NORMDIST(1,000/707, 0, 1, 1)} NORMDIST(1,000/707, 0, 1, 0) = fr = (Q - ESC)/Q = (10, )/10,000 = Notes:

Factors Affecting Fill Rate
Safety inventory: Fill rate increases if safety inventory is increased. This also increases the cycle service level. Lot size: Fill rate increases on increasing the lot size even though cycle service level does not change. Notes:

Evaluating Safety Inventory Given CSL
D = 2,500/week; D = 500 L = 2 weeks; Q = 10,000; CSL = 0.90 DL = 5000, L = 707 (from earlier example) ss = FS-1(CSL)L = [NORMSINV(0.90)](707) = 906 (this value can also be determined from a Normal probability distribution table) ROP = DL + ss = = 5906

Evaluating Safety Inventory Given Desired Fill Rate
D = 2500, sD = 500, Q = If desired fill rate is fr = 0.975, how much safety inventory should be held? ESC = (1 - fr)Q = 250 Solve Notes:

Impact of Required Product Availability and Uncertainty on Safety Inventory
The two key factors that affect the required level of safety inventory are the desired level of product availability and uncertainty. As the desired product availability goes up, the required safety inventory also increases because the supply chain must now be able to accommodate uncommonly high demand or uncommonly low supply. For a Wal-Mart case, we get the following Table.

Evaluating Safety Inventory Given Fill Rate

It is observed that raising the fill rate from 97.5% to 98% requires an additional 116 units of safety inventory whereas raising the fill rate from 99.0% to 99.5% requires an additional 268 units of safety inventory. Thus, the marginal increase in safety inventory grows as product availability rises. This phenomenon highlights the importance of selecting suitable product availability levels. It is very important for a supply chain manager to be aware of the products that require a high level of availability and hold high safety inventories only for those products.

Desired product availability (cycle service level or fill rate) increases, required safety inventory increases As demand uncertainty, standard deviation of demand during the lead time, sL, increases, the required safety inventory increases linearly; the safety inventory, thus, is linearly proportional to the standard deviation of periodic demand, sD, and also proportional to the square root of the lead time, L.

Managerial levers to reduce safety inventory without reducing product availability (1): reduce supplier lead time, L (better relationships with suppliers): if lead time decreases by a factor of k, the required safety inventory decreases by a factor of square root of k. The only caveat here is that reducing supplier lead time requires significant effort from the supplier, whereas reduction in safety inventory occurs at the retailer, e.g., Wal- Mart, Seven-Eleven Japan.

Managerial levers to reduce safety inventory without reducing product availability (2): reduce uncertainty in demand, sD (better forecasts, better information collection and use): if sD decreases by a factor of k, the required safety inventory also decreases by a factor of k. Seven-Eleven Japan provides its store managers with detailed data about prior demand along with weather and other factors that may influence demand (market intelligence). A lot of the demand uncertainty exists only because each stage of the supply chain plans and forecasts independently. Both Dell and Seven Eleven Japan share demand information with their suppliers, reducing uncertainty and thus safety within the supply chain.

Impact of Supply Uncertainty
We consider the case in which lead time is uncertain and identify the impact of lead time uncertainties on safety inventories. Assume that customer demand per period for Dell computers and the replenishment lead time from the component supplier are normally distributed. We consider the safety inventory requirements given that Dell follows a continuous review policy to manage component inventory. Dell experiences a stockout of components if demand during the lead time exceeds the ROP; that is, the quantity on hand when places a replenishment order. Thus, we need to identify the distribution of customer demand during the lead time. Given that both lead time and periodic demand are uncertain, demand during the lead time is normally distributed with a mean DL and standard deviation sL.

D: Average demand per period D: Standard deviation of demand per period L: Average lead time sL: Standard deviation of lead time Notes:

D = 2,500/day; D = 500 L = 7 days; Q = 10,000; CSL = 0.90; sL = 7 days DL = DL = (2500)(7) = Thus, ss = F-1s(CSL)sL = NORMSINV(0.90) x = 22,491 = zsL = (corresponding to 90%) x 17,550

Safety inventory when sL = 0 is 1,695 Safety inventory when sL = 1 is 3,625 Safety inventory when sL = 2 is 6,628 Safety inventory when sL = 3 is 9,760 Safety inventory when sL = 4 is 12,927 Safety inventory when sL = 5 is 16,109 Safety inventory when sL = 6 is 19,298 Thus, a reduction in supply uncertainty can help dramatically reduce safety inventory required without hurting product availability. In practice, variability of supply lead time is caused by practices at both the supplier as well as the party receiving the order. Suppliers sometimes have poor planning tools that do not allow them to schedule production in a way that can be executed. In other instances, the behaviour of the part placing the order increases lead time variability.

Impact of Aggregation on Safety Inventory
Models of aggregation Information centralization Specialization Product substitution Component commonality Postponement

In practice, supply chains have varying degrees of inventory aggregation. For instance, HP sells computers through retail stores such as Best Buy with inventories distributed all over the country. Dell, in contrast, has a few centralized facilities from which all customer orders are shipped. Borders and Barnes & Noble sell books and music from retail stores with inventory geographically distributed across the country. Amazon.com, in contrast, ships all its books and music from a few facilities. Seven Eleven Japan has many small convenience stores densely distributed across Japan. In contrast, supermarkets tend to be much larger, with fewer outlets that are not as densely distributed. Our goal is to understand how aggregation in each of the aforementioned cases affects forecast accuracy and safety inventories.

Consider k regions, with demands in each region normally distributed with the following characteristics: Di: mean weekly demand in region i, i = 1,2,…,k i = standard deviation of weekly demand in region i r = correlation of weekly demand for regions i, j, 1 ≤ i ≠ j ≤ k There are two ways to serve demand in the k regions. One is to have local inventories in each region and the other is to aggregate all inventories into one centralized facility. Our goal ids to compare safety inventories in the two cases.

In disaggregated case, add all the individual safety inventories to get the total figure. The aggregated demand is normally distributed. In all k regions, the demand is identically distributed with mean D and standard deviation σ, therefore, the mean and standard deviation of aggregate weekly demand are given by DC = kD (for equal demand) and sDC. We assume that all k regions have demand that is independent, r = 0.

Impact of Aggregation

Impact of Aggregation Car Dealer : 4 dealership locations (disaggregated) D = 25 cars; sD = 5 cars; L = 2 weeks; desired CSL=0.90 What would the effect be on safety stock if the 4 outlets are consolidated into 1 large outlet (aggregated)? At each disaggregated outlet: For L = 2 weeks, sL = 7.07 cars (= 5 x sqrt 2) ss = Fs-1(CSL) x sL = Fs-1(0.9) x 7.07 = 9.06 Each outlet must carry 9 cars as safety stock inventory, so safety inventory for the 4 outlets in total is (4)(9) = 36 cars

Impact of Aggregation One outlet (aggregated option): DC = D1 + D2 + D3 + D4 = = 100 cars/wk sDC = Sqrt( ) = 10 sLC = sDC Sqrt(L) = (10)Sqrt(2) = (10)(1.414) = ss = Fs-1(CSL) x sLC = Fs-1(0.9) x =18.12 or about 18 cars If r does not equal 0 (demand is not completely independent), the impact of aggregation is not as great as evident from the following Table. Products such as heating oil are likely to have demand that is positively correlated across nearby geographic regions. In contrast, products such as milk and sugar is likely to have demand that is much more independent across regions.

Safety inventory in the disaggregate and aggregate options
r disaggregate aggregate safety inventory safety inventory

Impact of Aggregation The safety inventory savings on aggregation increase with the desired cycle service level, CSL The safety inventory savings on aggregation increase with the replenishment lead time L. The safety inventory savings on aggregation increase with the holding cost H. The safety inventory savings on aggregation decrease as the correlation coefficients increase. Aggregation reduces demand uncertainty and thus the required safety inventory as long as the demand being aggregated is not perfectly positively correlated.

Impact of Aggregation If demand in different geographic regions is about the same size and independent, aggregation reduces safety inventory by the square root of the number of areas aggregated. Thus, if number of independent stocking locations decreases by n, the expected level of safety inventory will be reduced by square root of n (square root law) Many e-commerce retailers attempt to take advantage of aggregation (Amazon) in terms of reduced inventories compared to bricks and mortar retailers (Borders). Amazon.com has aggregated its inventories of books and music in a few locations. As a result it has lower levels of book and music inventories than bookstore chains such as Borders and Barnes and Noble, which must keep inventory in every retail store.

Physical aggregation of inventories in one location: disadvantages
Aggregation has two major disadvantages: Increase in response time to customer order Increase in transportation cost to customer Both disadvantages result because the average distance between the inventory and the customer increases with aggregation. The Gap tends to have many smaller outlets distributed evenly in a region because this strategy reduces the distance that customers travel to reach a store. If The Gap had one centralized outlet, the average distance that customers need to travel would increase and thus the response time would increase. McMaster-Carr uses UPS for shipping product to customers. Because shipping charges are based on distance, having one large centralized warehouse increases the average shipping cost as well as the response time to the customers. Thus, McMaster-Carr has six warehouses that allow it to provide next-day delivery to a large fraction of the USA.

Information Centralization
McMaster-Carr uses information centralization to virtually aggregate all its inventories despite having six stocking locations. The company has set up an information system that allows access to current inventory records in all warehouses from each warehouse. Thus, inventory at all locations is available to all orders, no matter where they originate. Information Centralization allows McMaster-Carr to reduce the level of inventories required while providing a high level of product availability (most orders are filled from the warehouse closest to the customer) by virtually aggregating inventories (even if there is a stockout in any particular inventory for any particular item).

Information Centralization
Gap uses information centralization to virtually aggregate inventory across all retail stores even though the inventory is physically separated. Wal-Mart uses information centralization with a responsive transportation system to reduce the amount of safety inventory carried while providing a high level of product availability. Most orders are filled from closest warehouse In case of a stockout, another warehouse can fill the order Better responsiveness, lower transportation cost, higher product availability, but reduced safety inventory

Specialization Stock all items in each location or stock different items at different locations? Different products may have different demands in different locations (e.g., snow shovels would not sell in southern Florida) If aggregation reduces the required safety inventory for a product by a large amount, it is better to carry the product in one central location, but the reduction is by a small amount or there are small value items in the inventory, it may be better to carry the product in multiple decentralized locations to reduce response time and transportation cost.

Specialization Gap integrates its online channel with its retail stores. The retail stores carry fast- moving items (those with high demand and typically have low CV), and the customer is able to order slow-moving colour or size online (items with very low demand and typically having a high CV), thus providing increased variety with limited inventory.

Specialization Benefits of aggregation can be affected by:
coefficient of variation (CV) of demand: the higher the coefficient of variation of an item, the greater is the reduction in safety inventories as a result of centralization. For a product with very low CV, disaggregate demand can be forecast with accuracy. As a result, the benefit from aggregation is minimal. For products with high CV, disaggregate demand is very difficult to forecast. Aggregation improves forecast accuracy significantly, yielding greater reduction in safety inventory from centralization. For many supply chains, specializing the distribution network with fast- moving items stocked at decentralized locations close to the customer and slow-moving items stocked at a centralized location can significantly reduce the safety inventory carried without hurting customer response time or adding to transportation costs. Value of item (high value items provide more benefits from centralization) Let us now look at the following Table (Table 11.4)

Value of Aggregation at Grainger (Table 11.4)

Value of Aggregation at Grainger
The benefits from centralizing motors is much larger than benefits from centralizing cleaner. The company should stock cleaner at the stores and motors in the DC. Given that cleaner is a high-demand item, customers will be able to pick it up on the same day at the stores. Given that motors are a low-demand item, customers may be willing to wait the extra day that shipping from the DC would entail.

Product Substitution Substitution: use of one product to satisfy the demand for another product. Manufacturer-driven one-way substitution: the manufacturer or supplier makes the decision to substitute. Typically, the manufacturer substitutes a higher-value product for lower-value product that is not in inventory, e.g., Dell may install a 120 GB hard-drive into a customer order requiring a 100 GB drive, if the smaller drive is out of stock (reducing the company’s profit margin), instead of delaying or denying the customer order (potentially a lost sale or loss of future sales).

Product Substitution Manufacturer-driven substitution increases overall profitability for the manufacturer and allows the PC manufacturer to aggregate demand across components, reducing safety inventories required. The value of substitution increases as demand uncertainty increases. The desired degree of substitution is influenced by the cost differential between the higher-value and lower- value component. If the cost differential is very small, the PC manufacturer should aggregate most of the demand and carry most of its inventory in the form of the higher-value component. As the cost differential increases, the benefit of the substitution decreases.

Product Substitution Customer-driven two-way substitution: Consider W.W. Grainger selling two brands of motors – GE and SE, with very similar characteristics. Customers are generally willing to purchase either brand, depending on product availability. If the company’s managers do not recognize this customer-driven substitution, they will not encourage it and for a given level of product availability, they will have to carry high levels of safety inventory of each brand. If the company’s managers do recognize this customer-driven substitution, they can jointly manage inventories across substitutable products that allows a supply chain to reduce the required safety inventory while ensuring a high level of product availability. When a customer calls or goes online to place an order and the product she requests is not available, the customer is immediately told the availability of all equivalent products that she may substitute, prompting her to buy the substitute product. The greater the demand uncertainty, the greater is the benefit of substitution. The lower the correlation of demand between substitutable products, the greater is the benefit from exploiting substitution.

Component Commonality
Using common components in a variety of different products, but can be done in the product design stage only.. Can be an effective approach to exploit aggregation and reduce component inventories. Dell sells thousands of different PC configurations to customers. It designs products such that different combinations of the components result in different finished products. Without common components, the uncertainty of demand for any component is the same as the uncertainty of demand for the finished product in which it is used. Given the large number of components in each finished product, demand uncertainty will be very high, resulting in high levels of safety inventory. When products with common components are designed, the demand for each component is an aggregation of the demand for all finished product of which the component is a part. Component demand is thus more predictable than the demand for any one finished product. This fact reduces the component inventories carried in the supply chain. Automobiles – common platform – engine, transmission, electrical, instruments, upholstery, etc. Maruti 800 and Alto LX – same engine, electrical. Alto VX and Wagon R – same engine, brake system, fuel system

Example 11.9: Value of Component Commonality

Value of Component Commonality
The Table in the last slide evaluates the marginal benefit in terms of reduction in safety inventory as a result of increasing component commonality. Component commonality decreases the required safety inventory. The marginal benefit of commonality, however, declines as a component is used in more and more finished products. Further, as a component is used in more finished products, it needs to be more flexible. As a result, the cost of producing the component typically increases with increasing commonality.

Postponement This is the ability of a supply chain to delay product differentiation or customization until closer to the time the product is sold. The goal is to have common components in the supply chain for most of the push phase and move product differentiation as close to the pull phase as possible. In Dell, a PC is assembled in the pull phase of the supply chain, after the customer order arrives. Thus, Dell produces product variety only when demand is known with certainty. Postponement coupled with component commonality allows Dell (assemble-to-order direct sales model) to carry significantly lower safety inventories than a manufacturer such as HP (assemble-to-stock, sell through retailers model) that sells through retailers.

Postponement In Benetton, greige thread (that is not yet dyed) is purchased, knitted and assembled into garments before dyeing. The dyeing of the garment is done much closer to the selling season. In fact, part of the dyeing is done after the start of the selling season, where demand is known with great accuracy. Benetton has postponed the colour customization of the knitted garments. When thread is purchased, only the aggregate demand across all colours needs to be forecast (done much in advance, so least accurate forecast, advantage, therefore, in aggregation). The postponement allows Benetton to exploit aggregation and significantly reduce the level of safety inventory carried Without component commonality and postponement, product differentiation occurs early in the supply chain and most of the inventories are disaggregate. Postponement allows the supply chain to delay the product differentiation. As a result, most of the inventories in the supply chain are aggregate thus reducing safety inventories without hurting product availability.

Impact of Replenishment Policies on Safety Inventory
Continuous review policies Periodic review policies Periodic review replenishment policies require more safety inventory than continuous review policies for the same lead time and product availability.

Estimating and Managing safety Inventory in Practice
Account for the fact that supply chain demand is lumpy, raise the safety inventory. Adjust inventory policies if demand is seasonal. Both the mean and the standard deviation must be adjusted by the time of year to reflect changing demand. Use simulation to test inventory policies, as demand is most likely not normally distributed and may be seasonal. Start with a pilot programme of products that are representative of the entire set of products in inventory. Monitor service levels, track performance f th inventory policy. Focus on reducing safety inventories without hurting product availability that can significantly increase supply chain profitability.

Managing Uncertainty in the Supply Chain: Safety Inventory

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Managing Uncertainty in the Supply Chain: Safety Inventory

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