INAD. Conversion Factory STOCKOP INVENTORY OPTIMIZATION & INVENTORY POOLING

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1 Conversion Factory INAD STOCKOP INVENTORY OPTIMIZATION & INVENTORY POOLING Executive Summary Steel is one of the most used materials in the construction industry and the economic situation has direct impact on the various stakeholders in the steel supply chain. Demand for steel in the Netherlands has declined from 5,7 million ton in 2008 to 2,6 million ton in Innovation and collaboration between parties involved in the steel supply chain is necessary in order to cope with this crisis. Steel stockholders are an important echelon in the steel supply chain. Keeping inventories low, maximizing stock turnover rate, reducing stock-outs and creating efficient operations are essential to keep the steel stockholder business profitable. INAD Industrie Software has developed StockOP; stock optimisation applications to facilitate these objectives. The advantages of StockOp for management: Simulations over 2012 with data of INAD clients have shown the following results: Yearly cost reduction of stock 19-31% Inventory reduction 38 47% The Bleeder Identification tool showed that on average 30% of the inventory consists of bleeders. In the pilot more than 60% of the bleeders can be pooled with other stockholders. Management can evaluate the cost impact of different scenarios by changing the required input parameters. Less dependency on buyer knowledge and intuition. StockOp calculates the optimal buying strategy based on historical information. All SKU s (Stock Keeping Unit) can be ordered automatically since the pro-gram calculates the minimal stock levels. The buyer can concentrate on more complex products and projects. The objective of StockOp is to help the buyer making decisions on: How much to stock When to reorder How much to reorder Which products to stock and which to buy via the StockOp Pooling program The application StockOp consists of two programs; StockOp Local Optimization and StockOp Pooling Optimization. STOCKOP LOCAL OPTIMIZATION StockOp Local Optimization will help steel stockholders reducing the total stocking cost, which includes ordering costs, inventory holding costs and stock-out costs. Nowadays stock management is based on work experience of planners and buyers. StockOp Simulation compares the actual stock levels and buying activities over a certain period of time with a simulation over the same period by using the calcu-lated reorder point (s) and the fixed reorder quantity (Q). The tool takes parameters into account such as minimum order quantities, batch sizes and variable lead-times. This will give management insight in the possible savings. StockOp Production recalculates on a daily basis the policy parameters s and Q of each SKU. It adjusts the inventory levels for the operation, advising the purchase department

2 about how much to stock, when to reorder and how much to reorder. STOCKOP POOLING OPTIMIZATION Presently stockholders buy products from different colleagues when they run out of stock. This creates pressure on promised delivery times that are normally 24/48 hrs. Therefore security stocks are high. With an inventory-pooling program in place buyers are certain that stock is available. StockOp Pooling Optimization generates additional savings for stockholders when pooling non-strategic and bleeder products with colleagues/competitors. Only stockholders that have implemented StockOp can participate because it is necessary to predict the behaviour of stocks for having a consistent inventory pooling strategy between stockholders (Figure 1). The result is a completely decentralized inventory pooling strategy. The Stock Pooling program consists of three tools: Identification of Bleeders, Maximum Price to Buy and Minimum Price to Sell. The Bleeder ID tool will identify the SKU s in stock which are not profitable. These are likely to be shared. The stockholder has two options: Remove the SKU from stock and buy as a Cross Dock product from a colleague when needed or Keep additional stock to fulfil the demand of other interested colleagues. Because of the additional sales a bleeder can become a profitable SKU. With the Maximum and Minimum Price tools the buying stockholder can define the maximum price to pay in case of not keeping stock. The seller can define the minimum price for selling stock. Both profit from the negotiations by reducing loss or increasing profit compared with the actual situation. Figure 1. Stock Pooling with competitors Main obstacle in this strategy is trust. Stockholders are not always willing to cooperate with other stockholders, even if there are proven benefits. To overcome this obstacle we designed a strategy that will give stockholders freedom to choose with whom to share stocks. In that way willingness of cooperation and trust between parties become more feasible. Figure 2. Feasibility of a Deal If the minimum selling price is lower than the maximum buying price, an arrangement between parties is feasible. A price in between is a win-win situation for both stockholders.

3 STOCKOP LOCAL OPTIMIZATION Inventory management is about determining when to order necessary products. How much to order, how to ensure that you have the right quantity of the right product at the right location at the right time. The main objective is to minimize the total relevant cost. As shown in Figure 3 the total cost consists of: holding cost, ordering cost and stock-out cost. Inventory Policy After evaluating the different inventory policies and carrying out numerous simulations with the scientific staff of the Technical University Eindhoven, the continuous review, order-point, orderquantity (s,q) policy is chosen (Figure 4). In this policy a quantity Q is ordered whenever the inventory position drops to the reorder point s or lower. Figure 3. Total Cost Composition Holding or inventory carrying cost relate to the physical stock in the warehouse (onhand stock). This consists of two parts: Capital Cost: interest paid on the capital or the return of investment expected. Warehouse Cost: costs associated with the warehouse (renting, taxes, services, etc.). Handling costs are not included. Ordering cost are related to the purchasing process and inventory control, such as personnel and related expenses. Stock-out cost is the penalty to pay when running out of stock. The stockholder has to buy the missing material from a colleague at a higher price. The main objective is to have the right amount of stock in order to minimize the total cost. This is difficult to achieve. As inventory levels increase, costs of carrying inventory also increase, but stock-out cost decrease. So the inventory policy design must balance out the three groups of costs in order to reach the lowest total cost. Figure 4. (s,q) Policy On-Hand Stock is stock that is physically on the shelf; it can never be negative. This quantity is relevant for determining whether a particular customer demand is available directly from the shelf. Inventory Position consists of the on-hand stock plus bought material which is not yet received at the stocking point. Lead time (LT) is the timespan between the date of placing the order and when the material is received. The s,q policy has the advantage to be quite simple to understand, easy to implement and errors are not likely to occur. How often should the inventory status be determined? Because of practical reasons we are assuming a review period equal to 1 day. This means that the inventory status of each SKU is reviewed every day and the replenishment decisions are taken at that moment. The buyer will check the inventory

4 status every day and take all the replenishment decisions. When should a replenishment order be placed? The two factors that determine the right order point (s) are the expected demand during the lead time (LT) and the safety stock, which is the minimum level of inventory to avoid shortages. s = Expected Demand during lead time + Safety Stock The lead time in the steel industry is based on steel plants rolling schedules because they are producing product families (e.g. beams) in large volume batches in regular intervals. As shown in Figure 5 the total lead time for receiving an order is composed of the time to the next production batch plus the delivery lead time (DLT). The DLT is fixed and known by stockholders, but the time for next production (TP) may vary depending on which point of time we are. Figure 5. Lead Time The LT can be calculated (min, average or max) from historical information but if necessary the buyer can overrule this information. Expected Demand during lead time is not known, therefore there is a chance of not being able to deliver directly out of stock. But since it is simulated from historical information this will be as accurate as possible. The safety stock is the amount of stock to minimize risk of stock-outs due to uncertain demand. The B2 costing method is chosen to calculate the stock-out cost. It is a fractional charge per unit short. So every time there is a stock-out the stockholder has to buy at a price that is B2 higher than the price of the regular supplier. How large should the replenishment order be? Once the inventory position reaches the reorder point, a replenishment order of a fixed size Q is triggered. The following factors are taken into account: Minimum Purchase Quantity: the suppliers require a minimum order size per SKU. Batch or bundle Size: the order of each SKU must be a multiple of the batch size. Anticipation feature When stockholders have the opportunity to procure a SKU at reduced unit cost or want to replenish before a price rise, StockOp gives the optimal Q to maximize profit in relation to the % of the price increase. Then the buyer can motivate his decision. StockOp Production Program The input needed to calculate the policy parameters s & Q and then the expected holding, ordering and stock-out costs are: Total Year Demand (D) Ordering Cost (A) Inventory Holding Cost (r) Product Price per Unit (v) Shortage Fractional Charge (B2) As shown in Figure 6, the parameters (s,q) are recalculated every working day, taking into account the demand over the past year. Recalculating the parameters every day assures to follow the variations in the demand over time. At the same time the

5 changes in the parameters over the days are going to be smooth by leaving the demand of one day out and incorporating the demand of another day. R1Capital cost: This represents the cost of investment made in buying the inventory. Interest paid on the capital or the return of investment expected depending on the source of the capital. r 2 Warehouse cost: These are the cost associated with the warehouse (renting, taxes, services). The handling cost is not included in this concept. A 1 Ordering cost (purchasing side): This contains the cost incurred for the purchasing team. Figure 6. Moving Demand Initial Input The data can be divided in two major groups (Figure 7). The first group contains data introduced by the stockholder, the second group contains the data extracted from the ERP system. A 2 Ordering cost (inventory control side): This contains the cost incurred for the inventory control team. B 2 Fractional charge per unit short: This is the surcharge in % on top of the supplier price, when buying form another stockholder. This is used to calculate the stock-out cost. B 2 Fictitious Fractional charge per unit short: This is the surcharge in % on top of the Fractional charge that raises the stockout costs, therefore reduces the stock-outs. NB: this has as a result that the stock level raises and the total cost increases! Q min Minimum Purchase Quantity: When buying from a supplier, the order size must be at least of the Minimum Purchase size. Q batch Batch Size: When buying from a supplier, the order size must be an integer multiple of the batch size. Figure 7. Initial Input In the first group there are some fields with an *. These fields are optional because they can be calculated from the ERP system databases or replaced with manual input.

6 Figure 8. Detailed Steps of the Tool The second group is divided in 3 databases. The first database is the usage database and contains all the necessary demand information. The second database contains all the necessary purchase information. Finally the last database contains the onhand stock position during the year. STEPS IN DETAIL Figure 8 describes the steps followed to reach the final objective of the programs in detail. The Simulation program is used for calculating the economical benefits and the Production program is used for the day-today optimization of the parameters. Step 1 Simulation & Operation programs The first step is the same for both programs. It consists in calculating the necessary parameters for the analysis from the input data described in the previous subsection. Step 2 Simulation Program The second step of the Simulation program consists in generating 100 years of demand based on Year N-1 Demand. StockOp generates workdays of demand which is the equivalent of 100 years of Demand. We choose to simulate 100 years in order to have an acceptable low standard deviation in the total demand between the different simulations. Since the demand is lumpy and irregular, one cannot use the standard deviation method. Step 3 Simulation Program / Step 2 Operation program In this step StockOp calculates the EOQ (economical order quantity) for each day taking into account the effect of the batch size and nimum purchase quantity.

7 Step 4 Simulation Program / Step 3 Operation program Once the Q per day of the past year demand is known, the corresponding optimal reorder point for each day can be calculated. Step 5 - Simulation Program Once the optimal s&q for each day is known, the total costs can be calculated using the Simulation model Simulation (s,q) which is also done over workdays to have a stability in the total demand between simulations. This Simulation gives the average yearly on hand stock, the average orders per year and the average stock-outs in function of the optimal s and Q calculated per day. Then the Holding, Ordering and Stock-out Cost per year are calculated. Step 6 and 7 Simulation Program As shown in Figure 8, once the total expected cost with the optimal stock policy parameters (Q and s) is calculated, the program proceeds to calculate the real cost that the stockholder had during that year (Year N-1). The real Holding Costs are calculated multiplying the Purchase Price (v) of Year N-1 times the real average on-hand stock of the same year. This average on hand stock is calculated from the data of the On-Hand Inventory position Year N-1 database. The average reorder quantity is also calculated from this database. Finally the Stock-out Cost is calculated by summing all the purchases that belong to the channel Crossdock. This is the channel that indicates that is a line purchased from another stockholder, because of a stock-out and multiplying it by the respective purchase price of that SKU and the penalty cost subsequently. Finally, once the total cost of both scenarios are calculated, they are compared in order to know the possible savings if we implement the (s,q) inventory policy. Figure 9. Simulation Program - Possible Savings Calculation

8 Future price increase: a special opportunity to procure StockOp also pays attention to an important situation regularly faced by the steel stockholders. The one-time opportunity to purchase from suppliers before the steel price increases. To maximize profits Stockop calculates the new optimal Q. The profit maximization is a trade-off between the additional revenue due to a price increase and the additional cost of carrying additional stock. PILOT RESULTS OPTIMIZATION The pilot results of running StockOp Optimization Program and calculation of the possible savings of implementing the (s,q) stock policy during year 2012 has shown the following: Yearly total cost reduction % Average inventory reduction 38-47% There is a significant reduction in holding cost due to reduction of the average inventory. Less stock implies more orders therefore the ordering cost increases. Stock-out costs increase substantially, but the total cost (holding + ordering + stockout) is reduced. One of the reasons of this increase is that the main objective of stock policy proposed is to minimize the total cost. This means that sometimes it is cheaper to have a stock-out. It is possible to decrease the amount of stock-outs by increasing the B 2 (Fictitious Fractional charge per unit short) parameter, but the total cost will be higher. This gives the stockholder the possibility of adapting the model to his business strategy. The increases in stock-outs can also be the result of missing historical information since not all the stock-outs are registered. Stockholders can evaluate the cost impact of different scenarios by changing the required input parameters. The inventory policy design is implemented at the SKU level. This allows a partial implementation if desired. One can start with the implementation for less critical SKU s and after evaluating the new performance the more critical SKU s. Many products in the purchase database have a very high lead time, This is because stockholders often purchase an SKU in advance and ask to deliver it on a certain day. This considerably exceeds regular leadtimes. STOCKOP POOLING PROGRAM The objective of our StockOp Pooling program is to generate additional stock reduction while maintaining the actual service levels of the participating stockholders. A cooperative approach (shown in Figure 10) could be a win-win situation for every participant. Several interviews with stockholders have given a deeper insight in the feasibility of implementing an inventory pooling strategy. The main conclusion was that the most critical success factor is trust. Generally stockholders trust German or Belgian stockholders because they have no interest in the Dutch market. It is also possible to trust stockholders who are not direct competitors, have another business approach or are specialized in other types of steel products. Therefore an inventory pooling strategy is developed that will give the stockholders the freedom to choose with whom they want to share stocks.

9 To summarize the following requirements have to be fulfilled: The strategy must generate savings. Freedom of choice with whom to share stock. Stockholders have to decide to stock or to buy products Same delivery conditions to customers must be met. In the inventory pooling strategy as shown in Figure 10 Stockholder 2 will be the owner of stock. He will provide the requested SKU in the agreed lead-time. The arranged price per unit will include transport. A centrally coordinated strategy may give more savings but requires more trust. This can be seen as a potential improvement for the future. transport + purchase costs) are higher than the revenues obtained from sales. These SKU s are potential pooling products and have to be evaluated. Sometimes they are necessary because they are complementary to non-bleeders. Maximum Price This is the price the buyer has to pay in case of not stocking an SKU. With this tool the stockholder defines the maximum price he wants to pay for each unit. If this price is lower than the present cost he will have more profit or less loss, depending on the case. Minimum Price This is the price the supplier has to receive to keep the stock for interested stockholders. Both prices include transport. The price also depends on a possible stockout situation at the seller. The first option is where the seller is responsible for delivery in case of his stockout. This situation is called with stock-outs. Figure 10. Inventory Pooling Proposed To facilitate this inventory pooling strategy design 3 tools are developed: Identification of Bleeders This tool detects which SKU s are not profitable. This means that the costs (holding + ordering + stock-out + handling + The second option is where the seller is not responsible of providing products in case of his stock-out. This situation is called without stock-outs. The last option will reduce the risk of the seller in case of unexpected demand and therefore the selling prices are lower and the number of pooled SKU s is always higher. Transport cost calculation The transport costs will be calculated with the total quantity of Kg per day transported to the buyer(s). The next step is to calculate the minimum transport cost. This way of transport depends on the quantity of trucks needed per day and the load type of each truck.

10 The following load strategies are possible: Full truck Load (FTL): A full truck will be send. The minimum required load, maximum load, cost per trip. Less than a truck Load (LTL): Is there a truck going in the direction of the buyer, how many trucks, maximum load, additional hours needed to add a stop, cost per hour. The simulation will always check the combination (FTL / LTL) that minimizes the cost. PILOT RESULTS POOLING The Bleeder Identification tool showed that on average >30% of the inventory consists of bleeders. Here we are using the same parameters of the StockOp local optimization program. More than 60% of the bleeders in the pilot can be pooled with other stockholders. In the pilot we simulated 6 pooling scenarios between 3 stockholders. We calculated the quantity of SKU s that can be shared when the stockholder is in the position of buyer or seller. For this analysis we assumed that there must be at least a price difference of 0,02/kg between the Maximum and Minimum prices and that the arranged price is in the middle. To link the SKU s of the different stockholders we used the Uniforme Artikel Classificatie (UAC) system of the Staalfederatie Nederland (SFN). At the moment approximately 200 SKU s could be shared, mostly in the beam product group. This accounts for >40% of the revenue of the stockholders. In Table 1 and 2 the possibilities of Inventory Pooling between Stockholder X and Stockholder Y are shown with and without stock-outs. In both cases the percentage of bleeders that can be pooled, is always higher than 60%. This shows that in general the bleeder SKU s are more likely to be shared. The approximate possible savings with the inventory pooling strategy varies from to per year. SH 1 is buyer, SH 2 is seller. With Stockouts Without Stockouts SKUs Pooled Bleeders Savings x Year SKUs Pooled Bleeders Savings x Year SH 1 X 16 94% % SH 2 Y % % Total % 8% Table 1. Inventory Pooling Possibilities between Stockholder X (SH1) and Stockholder Y (SH2) With Stockouts Without Stockouts SKUs Pooled Bleeders Savings x Year SKUs Pooled Bleeders Savings x Year SH 1 Y % % SH 2 X 23 64% % Total % 18% Table 2. Inventory Pooling Possibilities between Stockholder Y (SH1) and Stockholder X (SH2)

11 Dinalog, the Dutch Institute for Advanced Logistics is a public private partnership of the logistics sector in the Netherlands in which logistics service providers, government, port authorities and knowledge institutions work together to realize the Dutch National Innovation program Logistics and Supply Chain Management to add value to the Dutch economic competitiveness. Dinalog drives open innovation in logistics and supply chain management. For more information about the project: INAD INDUSTRIE SOFTWARE B.V. KRONEHOEFSTRAAT AC EINDHOVEN Tel.: +31 (0) Fax: +31(0) adres: info@inad.nl KvK This results was realized through cooperation of: