Kone Elective AssignmentAdvanced Supply Chain Planning LABA.Y. 2015/2016Prof. Giovanni Miragliotta

Group 3Federico Edoardo Pantanella – 837908 Fabio Parisi – 838093 Danilo Torretta – 837978

Elective assignment #3

Company Tutors Ing. A. ZaniniIng. P. Citraro

Agenda

• Executive summary• Project objectives• Inventory model• Excel tool’s logical framework• Estimation of SKUs’ future demand• Determination of the optimal order interval• Determination of safety stock• Net order quantity• Annual holding and ordering cost

Executive summary• In order to help Kone in managing orders from Meroni F.lli, the "periodic review“ inventory

model seems to be the most appropriate option. Indeed, it simplifies the inventory control and allows an easier reordering process. Accordingly, a user-friendly Excel tool based on this inventory management model has been created.

• Since the model needs as a input the expected monthly consumption of each SKU, we integrated the Excel tool with some formulas that allow to foresee the future components’ consumption starting from a daily production target of finished products (i.e. doors and cars). The logic behind these additional formulas is exactly the same as the one currently implemented by Kone.

• The tool will compute the most suitable order interval (valid for all items purchased from Meroni), order quantity and safety stock level for each SKU, with the aim of minimising procurement and inventory-holding costs (still maintaining the service level desired by the company).

• Finally, the model has been adjusted in order to cope with a possible components’ consumption different from expected, i.e. consumption of safety stock or excess cycle stock. The tool will take the aforementioned situations into account and adjust order quantities accordingly.

Project objectives Criticalities

• Possible inefficiencies related to an inventory level higher-then-needed

• Risk of discontinuities in manufacturing due to components’ unavailability

• Difficulty to predict the actual consumption of each SKU, due to the high variety of products offered

Objectives• Defining the optimal re-order policy to manage the supply from

Meroni F.lli, in terms of:1. Order interval (one for all SKUs)2. Order quantity (one for each SKU in each month)3. Safety stock (one for each SKU in each month)

Inventory modelIn order to solve the criticalities, we first decided to compare two different inventory models, trying to cluster all their advantages and disadvantages.

Reorder Point («quantità fissa»)

Periodic Review («periodo fisso»)

• Inventory level under control (continuous control)

• Minimization of the main costs

• The joint reorder of more than one item can be easily done

• The inventory level control is easy to be done (only when an order has to be placed)

• The joint reorder of more than one item is very hard to be done (as a consequence many orders have to be placed-even to the same supplier)

• The inventory level control has to be continuous and as a consequence expensive

• On average, the inventory level is higher than in the reorder point model

Eventually, we decided to implement the Periodic Review model as it represents the best fit in a situtation where multiple items are ordered from the same supplier.

Excel tool’s logical framework (2/2)

Excel ToolINPUTS OUTPUTS

- Monthly output (doors, cars)

- Monthly consumption (components)

- % inventory-holding cost- Ordering cost- Supplier’s LT- Desired service level- Unit cost (components)- Daily production target

(doors, cars)- Current inventory level

- Expected daily consumption (components)

- Safety Stock level- Order quantity- Optimal order interval- Inventory-holding cost- Procurement cost

Estimation of SKUs’ future demandIn order to assess the future demand of purchase items, we carried out the following steps:

Step 1

• Starting from historical data, we analysed the correlation between the production output of both doors and cars and the demand of each purchase item. Indeed, Kone computed an average consumption rate for each item using these data (doors output from September to May, cars output from September to April).

Step 2

• In order to have a more reliable estimate of the average consumption rate, we suggest assessing the correlation between output and items’ demand across a whole year, also to take into account seasonality. Our Excel tool allows to compute the average consumption rate using data from entire past years. Please refer to the “Historical Data” sheet.

Step 3

• Finally, we used the average consumption rate to estimate the future consumption of purchase items. In the Excel tool (“Re-order Model” sheet), Kone will set a daily production target for both cars and doors in any given month. Multiplying the daily target by the average consumption rate, the tool will come up with future daily demand of all SKUs.

Determination of the optimal order interval (1/2)

Considering the company should apply a Periodic Review model, KONE could minimize the total holding and ordering cost by calculating an optimal order interval (Topt) and issue orders to the supplier Meroni every Topt.

We referred to Harris-Wilson model in order to measure Topt in a multi-item context.

• OrderingCost is the cost for issuing an order to the supplier

• %InventoryHoldingCost is the cost of capital, obsolescence, storage and insurance for an item in stock in percentage of its value

• Vi is the unit purchasing cost of an item• Dyi is the annual demand of an item

Determination of the optimal order interval (2/2)

Using input data, the Excel tool will automatically compute Topt [days], i.e. the optimal order interval, and then formulate the quantities to order every Topt for every item in any given month. These quantities, which are meant to replenish the cycle stock, are equal to an item’s daily demand multiplied by Topt. The model, as it is presented, is static and assumes that order quantities are fully consumed during the order interval.

From Excel sheet “Re-order Model”

Determination of safety stock (1/2)The amount of safety stock to be kept for a given item was computed using the following formula:

We assumed the supplier’s lead time to be quite reliable (standard deviation equal to zero) and we the standard deviation of monthly demand in the past year (data provided by the company) for the other calculations.

We set k equal to the standard normal distribution’s quantile giving the desired service level (SL) as a result. For instance, if KONE admits a 10% stock-out probability (SL=90%), k is the 90th quantile, i.e. 1.28.

Determination of safety stock (2/2)

From Excel sheet “Re-order Model”

From Excel sheet “Re-order Model”

From Excel sheet “Historical Data”

Net order quantitySince the model assumes that the actual consumption of an SKU is equal to the order quantity, it has been adjusted in order to avoid: An inventory level higher than the safety stock’s amount (if the order quantity has

not been fully consumed during the past order interval) An inventory level lower than the safety stock’s amount (if it has been consumed

more than what has been ordered)

For this reason, we introduced in the Excel tool (“Re-order Model” sheet) the column “Current Stock Level”: by simply inputting data about the current stock quantity for each SKU, the model will suggest the proper order quantity (“Net order quantity”), which optimises the inventory level (i.e. maintaining the inventory level equal to safety stock).

Annual holding and ordering costEventually, we computed the annual procurement and inventory-holding costs, in order to enable benchmarking with respect to the as-is situation. Please refer to the Excel sheet «Costs».

For a given purchase item «i»:

The annual average inventory level was computed as the mean of monthly averages:

Dividing the order quantity by 2 means assessing the average level of cycle stock across a period of time that is equal to the order interval.

We arrived at ground

floor!

Thank you for your

attention