Theory of Time 2024 (Universal Theory for Everything)
KONE SPA
1. Kone Elective Assignment
Advanced Supply Chain Planning LAB
A.Y. 2015/2016
Prof. Giovanni Miragliotta
Group 3
Federico Edoardo Pantanella – 837908
Fabio Parisi – 838093
Danilo Torretta – 837978
Elective assignment #3
Company Tutors
Ing. A. Zanini
Ing. P. Citraro
2. 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
3. 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.
4. 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)
5. Inventory model
In 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.
7. Estimation of SKUs’ future demand
In 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.
8. 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
9. 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”
10. 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.
11. Determination of safety stock (2/2)
From Excel sheet
“Re-order Model”
From Excel sheet
“Re-order Model”
From Excel sheet
“Historical Data”
12. Net order quantity
Since 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).
13. Annual holding and ordering cost
Eventually, 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.
