Today with the growing competition, customers are demanding quick response with minimum cost. Lead-time reduction has long been recognized as an important metric to improve the responsiveness. Lead-time as it is defined, is, “The total time required to complete one unit of a product or service”. Management of time specifically lead-time, is a competitive advantage, and delivery lead-time reduction within the logistic network is the mechanism for time-based competition .
In the current project work, Inventory Optimization is done to a Distribution Center to reduce its lead time and improve its responsiveness towards the customers. Pre-positioning of inventory to optimize it is one way of reducing the lead time.
The aim of the project is, to reduce the lead time of the customer orders for a distribution center. The current system which is being followed consists of a long waiting time for the customers. With the present inventory arrangement, the lead time for the orders being processed is high which is making the process slow. The distribution center consists of levels into which the inventory is arranged .There is no specific criteria following currently for this process. So when a customer places an order and after the order receipt is received, the customer is made to wait for a long time until the order is being processed.
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After the receipt for an order is received, the employees have to manually fill the order by picking the products from the stock, which is randomly placed in the entire distribution center. Even for a single product the employee has to travel in the entire levels for filling it, which is creating a lot of unwanted motion. This is contributing a high lead time which is resulting in customer unsatisfaction.
Major emphasis is done on the inventory arrangement so as to reduce the time taken to fill the orders, through which the lead time can be reduced considerably. The flaws in the current system are studied and data is collected relevantly to apply the required tools. This is discussed in the later sections.
The customer orders data is collected. Data mining techniques are applied to analyze the data. The frequently repeated items together were found out from the customer orders. With the frequent item sets the inventory is rearranged as clusters. Sku Clustering is done in the inventory arrangement in the entire stocks. Now with the clustering of the sku’s there is more chance that, more likely ordered products are nearby. So this reduced the time taken to fill the orders. So as the products are near by the orders are filled faster than earlier. Through which the customer waiting time is reduced. The lead time is reduced considerably which resulted in increased customer satisfaction.
So the Inventory principles are applied to reduce the lead time. Even though this may not be the entire solution to the problem, but to some extent this is a solution which improves the efficiency considerably.
Today with the growing competition, customer is demanding quick solutions with minimum cost and with no compromise in the quality. Now customers are the one who evaluate the supplier’s performance based on five factors:
ƒ Product performance
ƒ Responsiveness to the customer demands
Among all these often timely delivery and responsiveness are considered as the important deciding factors in getting new customers and keeping old ones . Customers are more powerful than ever before. They have a wealth of choices, unprecedented access to information, and demand excellent quality at a reasonable price. Through which earning customer satisfaction has become a challenge for organizations. For which improving the performance of business processes has been started widely within the logistic channels.
Management of time, specifically lead time is a competitive advantage, and lead-time reduction within the supply chain is the mechanism for time-based competition . Lead time reduction has been recognized as an important metric to improve the responsiveness. A great emphasis to reduce the lead time is, to lower the safety stock, reduce the loss caused by stock out, and increase the service level to the customer and gain competitive advantages in business. Time-based-competition among the organizations is also forcing firms to analyze and optimize time as a competitive advantage .
In today’s market conditions, time is equivalent to money, and reducing lead time with in the network is same as reducing the different costs incurred and thereby increasing the profits.
The cost, profit and price equation has been changed in the recent years. Earlier the companies used to set their prices according to the formula,
Cost + Profit margin = Price.
But now it is no longer true. The profit equation now is as follows,
Price (fixed) – Cost = Profit.
So, now the only way to improve profits for organizations is to cut down the costs . So now the challenge for the companies is to continually improve quality and customer choices while reducing cost. Improving quality is to reduce wastes in production.
2.1.1 Waiting Time:
It is the time elapsed in the work flow, other than the processing time. It may be employees waiting for the material to be delivered or waiting for machine to process a part or for a line stoppage to be cleared and likewise. Delays in processing increases the waiting time. The total time taken to manufacture a product includes waiting time along with the processing time. In some cases the wait time may encounter more than 95% of the total time. That is Muda, the non-value adding .
2.1.2 Lead Time and Cycle Time:
The Lead time is, the total time required to complete one unit of a product or service. It is the time between the customers placing an order and receiving it. It may be defined as follows,
Lead time = Processing time + Retention time (Wait time)
Delays increase retention time, sometimes it far exceeds the processing time .
The Cycle time is total time from the starting to end of the process. It includes process time, during which a unit is acted to bring it closer to an output, and delay time, during which a unit of action is made to wait for the next action. Cycle time is the total elapsed time to move a unit of work from the beginning to the end of a process.
By this, we understand that a lot of time is wasted in waiting other than processing. As, long cycle times cause high inventories, higher cost and poor customer service, many manufacturers are streamlining internal and external supply operations to reduce order-to-delivery cycle time. It is often considered as a good place to start in the overall effort of improving operations because many times it can be done with a little capital investment. As processes are improved cycle time is reduced .
Reducing lead times may not only involve speeding up equipment or getting plant personal to work faster. It involves rapid fulfillment of customer orders and rapid transformation of raw materials into quality products in the shortest possible time.
Inventory is either a list of goods and materials, or those goods and materials themselves, held in stock available for a business. The various forms of inventory are raw materials, goods in process and finished goods. All the stock usually falls into any of these three categories.
In a literal sense inventory refers to stocks of anything necessary to do business. These stocks consume a large portion of business investment and should be managed well to maximize profits. Each type of inventory represents money tied up until they are transformed as finished goods and leaves the stock room. Unless inventories are controlled, they are unreliable, inefficient and costly. Tracking and adjusting inventory to meet customer demand is critical .
2.2.1 Inventory Wastes:
The Inventory waste is the one of the seven forms of Muda, as defined by Taichi Ohno. The wastes of inventory may be, over-production of goods, too many goods in stock other than required, too many raw-materials for the production like the accessory goods, supplies and likewise. All these excess inventory stems up to a lot of waste and a lot of inventory costs.
An example of a poor inventory arrangement is the one with too much inventory in a stock room in an unorganized and messed up form. This type of inventory arrangement creates a lot of wastes in the manufacturing in the forms of, high lead times due to unavailability of stock at the required times, reordering the same goods as they are unable to track when they are needed, unwanted employee motions to the stock room, lot of floor space occupied as so much of stock is thrown on the floor, else that space can be used to some other useful purpose, and likewise.
With the poor inventory arrangement, it’s hard to track the stock when it is exactly needed, which results in high costs.
2.2.2 Inventory Management:
Successful inventory management involves, balancing the costs of inventory with the benefits of inventory. It is primarily about placement of stocked goods as per the determined size. It is required within multiple locations of a supply chain network or at different locations of a facility to plan and manage the random variations in running out of stock .
The main objective of Inventory Management is to have appropriate amounts of stock in the right place, at the right time, and at low cost. It tracks and manages all the functions related to the material. It includes the monitoring of material moved in and out of stockroom and the reconciling of the inventory balances. It also concerns about the replenishment lead times, inventory carrying costs, inventory forecasting, space available for inventory, and all other aspects related to the inventory within the stockroom.
Due to, the time lags present from supplier to user at every stage in the supply chain and uncertainties in supply and demand, a certain amount of inventories are maintained as buffers .
2.2.3 Inventory Optimization:
Optimizing inventory to match customer demands, with minimal costs, is crucial for any successful business. Inventory optimization is to balance the inventory levels in the supply chain without having stock out costs and without high inventory handling costs. A certain amount of buffer stock has to be maintained to avoid stock out costs. And it shouldn’t be too high so as it increases the unwanted inventory in the facility which in turn increases the inventory carrying costs.
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The reality of inventories is that, they consume capital, requires storage space, incurs taxes, can be lost and stolen, damaged, outdated, or obsolete. The inventory Optimization techniques balances all these. Inventory forecasting is a proactive strategy aimed at providing estimated stock level to meet customer demand at a particular point of time. Forecasting is estimating what is exactly needed based on certain assumptions. Both inventory management and forecasting are strategic issues. Companies which recognize this fact will provide higher service levels to their customers and turn in higher profits .
Inventory Optimization reduces the lead times and eliminates the costs and wastes incurred with it. With the estimated forecast results, the inventories will be arranged so as every stock is available when exactly needed in required amounts.
The inventory optimization may be of different kinds, based on the requirement of a particular facility. Some may need placing exactly the stock needed there by reducing the inventory holding costs, some may focus on eliminating stock out costs by placing the required buffer stock by forecasting techniques. Some may place inventory in some random patterns so as to increase the service levels and reduce lead times .
For every particular requirement a specific forecasting technique can be used to create a pattern or output. For instance, for a certain data of inventory, data mining techniques can be implemented to find out any hidden patterns, which attain improved service levels, higher customer satisfaction, lead time reduction and considerable cost reduction, through prepositioning of inventory.
2.3 APPROACH TO REDUCE LEADTIME:
Lead time reduction is required in all fields of business. Manufacturing companies, service companies continually put efforts to reduce lead time in various aspects.
2.3.1 Reduction of Lead Time in a Manufacturing Company:
Lead time reduction is a key concern of many industrial buyers of capital facilities with the current economic conditions. Supply chain initiatives in manufacturing settings have given expectations to owners that dramatic reductions of lead time can be possible in every phase of their business, also in the delivery of capital materials.
A food product manufacturing company has a large number of manufacturing facilities and has a high utilization at all facilities. Increase in production capacity is required for new product roll-outs and large promotions and that can only be gained by adding a new capital facility. One more difficulty the Company often experienced was completing projects by the scheduled date. These issues drove a change in the Company’s management of construction supply chain relationships. Most projects generally involve the change and expansion of existing facilities. The focus is mainly on the supply chain for stainless steel pipe and pipe fittings for a new facility construction.
Simulation is used as a tool to consider tradeoffs for multiple inventory locations so as to better match the needs. Simulation of companies supply chain is performed to analyze the impact of inventory placement on pipe arrival time to the job site. The objective of simulation is to better match demand patterns with materials delivery. The alternatives suggested includes, all inventory held at the mill, Inventory pre-positioned at a distributor and also Inventory pre-positioned at mill, pipe manufacturer and distributor.
The discrete event simulation environment SIMPHONY is used and resulted in a reasonable match to the expected performance on observed lead times .
Data Mining refers to extracting or mining knowledge or interesting hidden patterns from large amount of available data which can be further used in many applications . The data mining tools perform data analysis and uncover important data patterns contributing greatly to business strategies and researches.
2.4.1 DATAMINING APPROACH:
In general data mining involves the earlier stages of data selection and data transformation and the subsequent stages of validation and interpretation. It aims to provide an alternative to the traditional scientific method . The aim of data mining is to find intelligible patterns, which are not predicted by established theory.
2.4.2 Data Mining in the Insurance Industry:
Data mining methodology often can improve traditional statistical approaches for solving business solutions. In the insurance industry, applying data mining techniques helps to gain business advantage like, companies can fully exploit data about customer’s buying patterns and behavior, gain a greater understanding of customer motivations to help reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition. The Insurance industry regulators require easily interpretable models and model parameters. Data mining improves existing models by finding additional important variables, by identifying interaction terms and by detecting nonlinear relationships. Models that predict relationships and behaviors more accurately lead to greater profits . Linear Regression is used in the Insurance Industry which helps in, Establishing rates, Acquiring new customers, Retaining customers, Developing new product lines, Detecting fraudulent claims, and likewise.
Clustering is the process of grouping the data into classes or clusters. The objects within a cluster have high similarity to one another and are dissimilar to objects in other clusters. Some typical applications of clustering can be found in data mining, biology, business.
In business clustering helps marketers discover distinct patterns in their customer bases and characterize customer groups based on purchasing patterns . It is used in biology to categorize genes which function in similarity. It is also used in the identification of groups of automobile insurance policy holders with a specific parameter, as well as in identifying the groups of houses in a city according to house type and geographical location. Likewise its applications extend from small needs to high business functions.
If the clustering process is used to find which groups or sets of items are purchased by customers, it is used to implement two different strategies by the retailers. In one strategy, the frequently purchased items are placed in close proximity, which further encourages the sale of such items together. In another strategy, placing these items away at different ends encourages customers to pick different items along the way. It also aids retailers in planning to put items on sale at reduced prices.
2.5.1 Clustering Approach: Topic Detection by Clustering Keywords
Clustering is used in finding the set of most prominent topics in a collection of documents. The problem of identifying and characterizing a topic is considered as the integral part of the task within the given list of topics. The approach followed consists of two steps: First a list of most informative keywords is extracted. Subsequently clusters of keywords are identified, for which a center is defined and this is taken as the representation of a topic . Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. The experimental results show that topic detection by clustering a set of keywords works fairly well.
In the current work different tools were used to reduce the lead time and optimize the inventory. Lead time is reduced by optimizing the inventory by rearranging it in the stockroom. Data mining techniques were used to analyze the data and find a pattern out of it. Market Basket Analysis is used to find the frequent item sets of the customer orders using Oracle packages as the tool for data mining. With the resulted item sets inventory is rearranged as clusters thus optimizing the inventory and reducing lead time through it.
3.1 DATA MINING:
Data Mining is the process of selecting, exploring, and modeling large amounts of data to uncover unknown patterns . The abundance of data, coupled with the need for powerful data analysis tools, has led for a need to call for the data mining tools, else important decisions are not made based on the information rich data stored in data bases but on a decision maker’s intuition. The aim of data mining is to find interesting and useful patterns from data using data mining techniques. These techniques are used by different people and organizations like the commercial entities to help discover something about their consumers .
3.2 DATA MINING TECHNIQUES:
Different techniques are used to analyze different types of data and find out the important information from it. Association rule mining is one data mining technique which aids in finding interesting association or correlation relationships among the set of data items. The discovery of relationships among huge amounts of business transaction records can help in many business decision making processes, such as catalog design, inventory optimization through rearrangement of inventory. Market basket analysis is an example of association rule mining through which inventory optimization is attained .
3.3 MARKET BASKET ANALYSIS:
Market basket analysis is one form of association rule mining. This process analyzes customer buying habits by finding association between the different items customers place in their baskets. Retailers develop marketing strategies by discovering such associations by gaining insight into which items are frequently purchased together by customers. For instance, if customers are buying milk, how likely are they also to buy bread. Such information leads to increased sales by helping retailers to do selective marketing and plan their shelf space . For example placing the frequent items together within close proximity may further encourage the sale of these items together or reduces the time taken to fill the order from different locations.
3.3.1 FREQUENT ITEMSETS:
The market basket analysis returns the frequently purchased items together by the customers. The frequent item sets or groups are clusters of the most repeated pattern of items. These are used for further data analysis.
The figure shows the market basket analysis and the frequent item sets. The items purchased by different customers are analyzed to find out the patterns. Different tools are used for analyzing the data and to find out the pattern and frequent item sets.
3.4 DBMS_FREQUENT_ITEMSET Package:
The pattern recognition for frequent item sets can be done by software tools. Many different tools are available to mine the data for data mining. One of those is the Oracle tool through Sql. The Oracle software through Sql has many different inbuilt packages to implement for specific operations.
One of those kinds is the DBMS_FREQUENT_ITEMSET package. It is an inbuilt package of Oracle software which enables frequent item set counting. It quickly scans sets of data and returns if any items frequently appear in combinations. The important application of this package is in the market basket analysis, which upon implementing one can find which products are often bought together by the customers.
The DBMS_FREQUENT_ITEMSET package has been built with stored functions and procedures which perform the data mining operations for the Oracle software. One such function used for the market basket analysis is the FI TRANSACTIONAL Function. It counts all the frequent item sets for given input data. The FI function in turn contains many procedure parameters which aids in the implementation of the package. Some important parameters are:
Support: It displays the number of transactions in which the frequent item set occurs.
Itemset: This is the collection of the items which are computed as frequent item set.
Length: It displays the number of items or products in the item set.
All the stored functions and procedure parameters of the package are inbuilt with the structured query language (SQL) to perform the specific tasks. The required program is written and given under the specific procedure name, which upon implementation returns the desired values.
3.5 CLUSTER ANALYSIS:
The process of grouping a set of physical or abstract objects into classes of identical objects is termed as Clustering. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. In many applications, a cluster of data objects is treated collectively as one group .
Clustering has its roots in many areas like data mining, statistics, biology, and machine learning. It has been widely used in numerous applications, including pattern recognition, data analysis, image processing, and market research. By clustering one can identify dense and sparse regions and, therefore, discover overall distribution patterns and interesting correlations among data attributes.
In data mining, pattern recognition returns clusters of data for the frequently ordered items together. These clusters can be used in many ways for business improvements, like, marketing or advertising strategies, to design different store layouts.
3.6 INVENTORY OPTIMIZATION:
Inventory optimization can be achieved through rearranging the inventory with the available clusters in the stock house. The clusters are the most frequently ordered together products. Placing these clusters allows for the employees to pick the products faster and fill the orders early. It improves the efficiency by reducing the lead time, cost savings and many more.
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