1. Managing Retail Supply Chain by Effective Replenishment System with Forecasting Capability Supervised by: Mrs. Gayathri Ranasinghe REPSFORSYS Author: Chinthaka C. Palliyahguru University of Westminster – UK Informatics Institute of Technology REPSFORSYS REPSFORSYS REPSFORSYS REPSFORSYS
5. Business Case – Problem Domain Did you know ? Findings of the Industry Survey Rs. 22,000
6. Business Case – Problem Domain Cont Findings of the Industry Survey Business Need to Address Both Issues
7. To develop an automated replenishment system in order to reduce the risk of losing sales and keeping unnecessary stocks at the outlets and thereby increasing profits of the retail stores and super markets. AIM REPSFORSYS
12. Design– Order Generation ORDER = { [AVERAGE SALES] + [CRITICAL STOCK COVER] } – [LATEST STOCK ] To generate accurate orders for the branch outlets based on demand which will be eliminating “ No Stocks” or “Excess Stocks” at branch outlets
19. Conclusion – Key Points Contribution to the Society Contribution to the Business Value Creation Gaining and maintaining the Market Growth Reduce Business Risk and Enhanced Business Opportunities Improved Operational performance and efficiency Improved attraction and retention of our work force Enhanced Brand Recognition and Reputation Enhance ability to strategically plan for the long term Maintain Security of the Operations
21. THANK YOU ! REPSF ORSYS Chinthaka C. Palliyahguru E-mail: [email_address] Mobile: (+94) 77 2907 488 Blog: http://www.chintho.com
Hinweis der Redaktion
Hello. My name Chinthaka and I’m going to talk to you today about the research which I have carried out with regarding to Managing Retail Supply Chain by Effective Replenishment System with Forecasting Capability.
This slide will give you a bird eye view of what I’m going to cover in this presentation. As you can see I have alienated my entire project into 3 main categories.
So let’s move onto the Business Case
I have mainly focused on the SL retail industry. And based on the fact that all the retail outlets now in a era of practicing super market concepts. Furthermore I have narrowed down my research specially focusing the FMCG industry.
So before Moving head from this point onwards I just wanted to know how many of you all have experienced the out of situations @ the retail stores / Super Markets in you life? Is it many times or quite few. ? To find out the exact facts and figures to this question I’ve carried out an industry survey by getting filled questionnaires from the customers who are going to super markets to buy products. When I filtered out my consumer research by filtering people who has experienced any sort of OOS situation. Results that I’ve got were quite strange and scary. 32% has confirmed that they will never turn back in to this outlets simply because it didn’t has the product that they were looking for. Furthermore I wanted to find out the quantitative figure in term of Rupees that super markets loosing due to OOS situations. So I went to this particular outlet and was focusing on four OOS product for 3 weeks time. That particular outlet has loose Rs. 22K due to the OOS.
So. I have identify the strong relation ship between Store Forecasting and Store Ordering and out of stock situation. So then I started to find out the root causes which will lead to OOS situation. Results was amazing. Surprisingly, almost three-quarters of stock-outs are the direct result of retail store practices such as ordering and forecasting. All most all the super market has their own EPOS system, Which means they have their sales data saved in computer. But t here is a business need, to generate high accurate purchase orders while considering the past sales data and trends
So I developed and deployed an aim which is followed by key objectives to my project in order to overcome above mentioned root causes.
To generate accurate orders for the branch outlets based on demand which will be eliminating “No Stocks” or “Excess Stocks” at branch outlets. To establish centralized sales order processing system based on past sales performance and to provide an effective service to all branches by providing accurate sales forecasting which will finally lead to increase in profits. To eliminate unnecessary use of man-hours by eliminating manual interventions which can also lead to erroneous sales forecasting and thereby losing profits
Even this kind of system will be available only in large scale supermarkets. Most of the supermarket juts order from the sales rep. Branches sending their orders weekly basis to the CPU and the CPU manger will process the orders MANUALLY. (gut feeling) Problem occurs Stock control package generates orders based on maximum cases should be available at warehouse. Prepare One single order to supplier after checking the remaining stocks at CPU.
So the proposed system is having the capability of automated forecasting while considering the trends (past sales data). There will be one main Database server + Application and web server Directly taking data from EPOS (since EPOS is available at all most every super market). transfer to web server and from their it will transferred the orders to CPU and from there to suppliers and copy to branch managers.
WAMP – As a development environment Adobe Dream Weaver – As a GUI Designer Microsoft Office – As a documenting and presenting tool MySQL – As a database management tool UML – As a system design tool
The basic function of a replenishment system is to decide, when to order which amount Characteristics of replenishment systems with make-to-stock policy (i.e. the product is already built when the order comes in) can therefore be captured with four elements. The replenishment logic defines the business rules that are followed in order to decide when to order (Silver, Pyke et al. 1998). To be able to decide which quantity to order, it is obviously necessary to know how many items are on stock. This is called inventory visibility , the ability to track the status of inventory in a supply chain tier or even across the entire supply chain. Some systems take into account order restrictions as well. For example, if the system can only order items in six-packs, an order of 10 units must not be placed. The last element is the forecasting part . Some systems try to anticipate future consumer demand by computing forecasts.
First of all, the quantitative examination in this thesis concentrates on a single grocery retailer. It could be proven that the introduction of the replenishment systems was indeed beneficial for this retailer, as long as the replenishment logic was set correctly. The next logical step would be to have another empirical examination with several retailers to prove that this was not just an exception but that indeed all retailers can benefit from this approach. Second, the present research focused on grocery retailers and chemists. Yet from several interviews it is known that this topic is also relevant for other types of retailer. Further research could hence concentrate on the transfer of the present results to other types of products. The question of the contribution of suppliers to the success of ASR systems is still to be answered, as most of the retailers examined in this thesis carried the implementation on their own. The models developed in this work only regard the last tiers of the supply chain. The next topic which could only be examined on the surface is the influence of ASR introduction for human agency. One possible approach would be a social psychological one. It would be interesting to see how the satisfaction of the individual store employee changes after the introduction of ASR systems and how long it takes to return to its former level. Finally, a topic that also requires further research is the one of the multiple tradeoffs a retailer is facing when dealing with the setting of the replenishment system. It was a remarkable result to prove that ASR systems are able to reduce OOS rate and inventory level at the same time. Yet, it is not clear what exactly the relationship is between the two. At some point, the store manager will have to decide on a tradeoff: further reducing inventory will raise the OOS level and vice versa. Besides, there are many other parameters that have influence on the total replenishment costs and the handling workload in the store, such as shelf space, case order size and data quality.
