PSG COLLEGE OF TECHNOLOGY

COIMBATORE










AUGUST 2014




DEPARTMENT OF PRODUCTION ENGINEERING
PSG COLLEGE OF TECHNOLOGY
(Autonomous Institution)
COIMBATORE 641 004





TOPIC:

SAFETY INVENTORY CONTROL






ASSINGNMENT WORK DONE BY:

V.ASWIN KUMAR(10P607)
Department of production engineering
Inventory Management System at Distribution Company

Effective distribution company management revolves around balancing
the three key dimensions of inventory, cost, and service. Managing these trade-
offs efficiently is typical inventory management task which can result for
company in improving business performance and driving competitive
advantage. The explored topicality of inventory management system defines the
goal of the case study aimed at analysis of the inventory management system in
a distribution company (King Coffee Service (KSC), a leader in field of coffee
sales and renting of coffee machines in Latvia. Comparing with others, the
company offers to its customers full coffee concept starting from stirrers and
paper cups to premium coffee machines, which in total make several hundreds
of positions in inventory list. Therefore the analysis of current inventory
management system and its enhancement is a high priority for the company.
However, the object of the case study is not the inventory management itself,
but modeling approaches which can be applied for inventory management task.
Following is the structure of the paper. In the next part, the description of the
company KCS inventory management system is presented including ABC
analysis. Then, main inventory models are discussed and applied for the case of
the company. In the fourth part of the paper, simulation models for above
discussed inventory models are developed. An application of optimization
model for calculating inventory models settings is presented as the last
modeling approach within current approach

SUPPLY CHAIN:
KCS is an international company with head office located in Riga
(Latvia) and representatives in Lithuania and Estonia. All material flows are
managed through Latvia. The company receives goods from manufacturers
directly, skipping wholesaler echelon from typical supply chain structure. The
inventory is hold in companys private warehouse. However the company rents
storage space in a public warehouse in order to achieve best service to its main
customer, who has storage space in same place. Other customers, as well as
KCS representatives in Baltic are served from KCSs private warehouse. Lead
time from KCS company to the customer is one day, i.e. goods ordered today
will be delivered tomorrow. However there are agreements with main customers
about certain dates when order can be placed. The goal of KCS in respect to
customer is to support 100% service level. KCS works with several suppliers
located in Europe, i.e. Finland, Poland, Swiss, Italy, Holland, France, and
Germany. The lead time varies for each supplier, and is not proportional to a
distance to customer. Every stock keeping unit has its own lead time despite
they are served by one supplier.


INVENTORY MANAGEMENT:
Inventory of KCS consists of hundreds stock keeping units and can be
divided in two main groups: (1) coffee machines and spare parts, and (2)
ingredients and raw materials for coffee drinks. First group is not fast moving,
so there is no sufficient inventory kept for this group. Inventory management for
the first group belong to pull approach. Second group is very dynamic with
intensive consumption that is why it is selected as an object of the current case
study. All goods within second group can be divided into ingredients (including
coffee beans, milk powder, chocolate powder, coffee syrups and sugar pre-
packed) and raw materials (i.e. paper cups, plastic lids for cups, stirrers, plastic
juice cups). The current inventory management for this group belongs to push
approach. Inventory replenishment is organized as continuous order review with
a period of one day. However, as delivery from suppliers is performed monthly,
periodic.

ABC analysis:
The company operates with hundreds of items, it is decided to cut
quantity of goods to the most essential ones. Undoubtedly, that main product is
coffee beans, as sales of others depends on consumption of coffee. In order to
prove this, as well as for selecting one item from a variety of coffee beans, ABC
classification analysis is performed [1]. Within current research following key
indicators are selected: total year revenue, one item sales price and total amount
of sales. The list of SKU consists of 44 items (some items are grouped before
the analysis, as well some seldom and special items are ignored as not typical in
daily operations). ABC classification by total year revenue is very essential for
the company as it shows items which require most assets into inventory. These
items should be controlled as tight as possible, i.e. low inventory levels and
safety stocks to minimize costs. Performing ABC classification by price is less
important than total year revenue however is still useful. As group A needs a
high level of safety to protect it from any damage (this is very actual for first
group goods, i.e. coffee machines). ABC classification by demand has a similar
nature with classification by total year revenue. Besides, group A items should
be held in the most accessible place in warehouse as they are the most
demanded.Comparing analyses, the conclusion is that both ABC analysis by
total year revenue and demand show almost the same result, which is close to
theoretical ABC breakdown. In spite of the items in both Groups A are almost
the same, they have different ranking. ABC analysis by items total price shows
different results as the nature of key parameter is different. The most essential
items for the company based on ABC analysis are SP12 Main, Coffee 3200,
Topping, Choco powder, Lids 85 mm, SP12 KCS and SP16 Main. However,
only three of them are used furthermore, i.e. SP12 Main, Coffee 3200 and
Topping.



ANALYSIS OF DEMAND:
To make the analysis of demand for the selected items, statistical
Information is analyzed over the period of year 2009. A weekly demand for all
products is unstable. All customers have one free of charge delivery per month.
Almost all of them place orders at one time, but there are still some orders
during the rest time. Although in terms of a month, fluctuation of demand is
smaller Demand for considered products is seasonal, i.e. demand for hot drinks
rises in colder months, and falls during summer time. The fall of demand in
January is explained by extra stocks made in December approaching Christmas
holidays. Topping, Coffee3200, SP12 Main are dependent products, i.e. sales of
each product are mutually related. The correlation test is performed by means of
Excel Spreadsheet function CORREL. The results show that correlation
between Coffe 3200 and Topping is 0,89, and correlation between Coffee 3200
and SP12Main is 0,82. This means that Coffee 3200, Topping and SP12Main
closely vary
together in the same direction. Moreover, the correlation between Coffe 3200
and Toppin is higher than correlation between Coffee 3200 and SP12. It can be
explained by the fact that majority of coffee drinks made from Coffee3200 are
filled in SP12Main cups. Topping, a milk powder, is not always used for
preparing coffee, as some customers prefer black coffee. However, at this
research it is assumed that products are independent. Moreover the rest of the
paper will focus on Coffee3200 product only.



CONCLUSION:

Inventory management is an important sector of logistics and economic
spheres, company's growth and success is strictly dependent on it. Even empiric
experience may help to manage inventory well, application of managerial theory
allows analyzing future improvements. There is a variety of inventory
management strategies all answering same questions, i.e. When to order? and
How much to order? To answer them, different approaches can be applied
namely inventory models, simulation, and optimisation. Traditional inventory
strategies expressed by means of analytic formulas are the most popular.
However, a complexity of analytic inventory models increases if stochastic data
appears. If demand and lead time are stochastic (as they use to be in practice),
computation of the optimal order quantity will be more complex. Application of
analytical formulas is only then useful, if there is no necessity for complex
adoption inventory models for any certain case which requires from a manager
good mathematical skills and creativity. In order to check and evaluate results of
system operation under defined inventory models settings (found either by using
inventory models or empirically) before implementing them to the company a
simulation model can be used.