9th September 2010 16:54
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Benchmarking Valio Logistics Strategy against customer needs (flexibil
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Problem Statement
How far the logistics development plan meets the expectations and needs of the customers. Is there any logistical flexibility in the market. |
Improvement Approach
The seeking of the answers to these questions was done with a customer questioning. Statistical Multivariate Clustering was used to create logistics profiles to the customers. |
Benefits
The main benefit of the analysis is to find out homogenous clustering from the customers. Using the number of criteria which best describe the wanted context we can build up the Customer Profiles. These Profiles operate as a tool for the development of |
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Valio customers are located round the country. Customers using Valio logistics services are mainly grouped in chains or are working together with alliance. Cooperation with client sets out to help them succeed. A profound customer orientation requires Valio and its clients – the RI (Retail Industry / Trade), Horeca (Hotel, Restaurant, Catering) and industry – work together in analyzing markets, product groups, service company customers and consumers. Such analyses create a foundation on which to construct operations that support the business idea of clients and seek success
The deliveries from industry to customers are managed by Valio Logistics Services using own equipments or equipments of the logistics partners. The responsibility for the Customer logistics Satisfaction belongs to the logistics managers. Customer operations are managed daily and continuously and new point of views are needed and generated all the time.
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Customers have to be grouped some way. Traditional ways are to use different factors found in the ERP - systems. Mainly they are based on demographic factors and accounting data. Mostly this kind of categorizing is made using one factor at the time. This leads in some cases to the misunderstanding of the customer situation if there are many criteria, which have to been taken in to the consideration |
To create better logistics solutions to the customers they have to be grouped in some way. Traditional ways are to use different factors found in the ERP - systems. Mostly this kind of categorizing is made using one factor at the time. This leads to too narrow eyesight of the customer needs - sometimes even directs to wrong decisions. There are always many criteria, which have to been taken in to the consideration when developing logistics services to the customers.
Multivariate Statistical Methods offers very many good choices and ways to evaluate statistically customer entries. We used the information available in the Research Data Base based to the customer questioning made in the beginning of year 2004. Data was collected for the "profiles" of customers in B-to-B business in order to better serve the customer needs.
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In this project there was developed a new dynamic way to group customers using several criteria at the same time. The new grouping mechanism allows one to better direct right operations to right customers at the right time. |
The main benefit of the analysis was to find out homogenous clustering from the customers. Using the number of criteria which best described the wanted context we could build up the Customer Profiles.
The Multivariate Statistical Methods based approach enables the logistics management to analyze the customer profiles and better understand customer's expectations and experiences.
In this approach the Manager can consider the customers with homogenous groups and plan development efforts effective. With the clustering method we can utilize the information of all variables needed at the same time to build up the Customer Profiles.
These Profiles operate as a tool for the development of logistics service operations for the customers.
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