So, having seen the different success factors of ERP implementation, let's switch gears slightly to talk about different strategies that are being used in ERP implementation. There are two main ways of implementing ERP systems. One is to use the Big Bang strategy. In contrast to that, we have the phase rollout. In the Big Bang strategy what happens is that the implementation team would take time to tweak, customize, and to test the system, at the same time teach and allow their users to learn how to operate the system. Upon a specified go live day, the implementers would make the system accessible to all the departments and all users in the company. A phased rollout is quite different from the Big Bang approach and the implementers would choose to release parts of the systems to different users at different times. It could well be that a certain feature within the ERP system is released in the first phase, or it could be that the implementers could choose to have different departments experience and use different systems at different times. What we know from practice is that in large companies is that in the case of 3M that has operations across different countries, they have chosen to use a phased rollout approach based on different countries that they have operations in. Let's also consider the different pros and cons of using the Big Bang and phased rollout approaches. In the Big Bang approach, because of the fact that everything goes live on a single day, and assuming that goes well, we could see that the Big Bang approach would bring about lower causes because the overall time needed to implement it's much shorter and the time for a return to be realized by a company would be much faster. However, this would bring a lot of risk to the company because in a large ERP system, many of these modules and processeses are interlinked. So, that could be a risk in which a single unit is not functioning and there could be bots across the system. So, upon turning on the big switch, there could be risks that the entire system might not work. As opposed to the Big Bang approach, the phased rollout will be seeing lower races because they are taking the roll out of different modules or processes in a piecemeal fashion. So that whenever an error or a bug is found in a single module, a quick fix could be done so that it doesn't affect subsequent modules. However, by taking on this rolling out approach, we could see that the length needed to finish the implementation will be much longer, and as a result of a longer time needed to implement system using the phased approach, we would expect higher causes and slower returns to the company. So, I'll leave you to understand and to go through a quiz in this video to look at certain scenarios where the Big Bang approach is appropriate and also scenarios where the phased rollout approach is appropriate. Another concept that we need to be aware of during implementation is a concept of an ERP system instance. So, let us go through the next few slides to allow you to have a better understanding of what that is. If everyone in the company uses one single server to store a copy of the ERP application, be it SAP or Oracle, and also using the same server to store the data that is needed to represent business and entities in the company and also all of its configuration, this is known as a single instance approach. An example of that is General Mills. General Mills is a large food company based in the US. Running their business, they have chosen to adopt the single instance strategy where they store the copy of the SAP along with its data all in a single database. In contrast to the single instant approach, there are cases where business units can be very different. As a result, they might need to store the data and configuration in two separate data basis. An example of that in the US is the Nordstrom's business. Nordstrom's is a business that deals with fashion and retail. At the same time Nordstrom's also runs a subsidiary company where they are dealing with credit card businesses. So, seeing that these two lines of business are very different, Nordstrom's have decided to utilize a duo instance approach where the retail side of the business is being stored on one instance, and the credit card subsidiary information along with its configuration are stored on a second instance. So, let us also take a look at some of the advantages and disadvantages of utilizing single versus multiple instances. For a single instance, one of the main advantages of doing that is that it makes it easy for the company to consolidate its data and also to have a overall view of all of its business activities. This is especially relevant in the US context because we have the Sarbanes Oxley Act which requires senior management in companies to produce timely and accurate reporting of all of the earnings and business activities in their companies. So, companies that have multiple business units and subsidiary, if they want to store their data in different instances, that will make it very hard for them to achieve this reporting. At the same time, another advantage is that having everything stored in a single database allows the implementers to have an easier time. There'll be a lower implementation cost and at the same time to maintain a system there is lower cost because there's only one database involved. A final advantage of having a single instance is that at the top management level because they're able to see all of these different data and activities on in a single place, it allows them to construct and develop strategic visions more easily for the entire company. However, on the flip side of things, if a company were to utilize a single instance but if this company is large and has different business units, they would lose the flexibility to customize their systems or they have different operating rules to cater for unique business requirements across these subsidiaries. At the same time because if they're having multiple databases to pull data together and to have a consistent view, that would mean that it will be putting more pressure and more time for the implementers and also for the users to integrate different pieces of data across systems. Finally, naturally, having different systems and different databases, we will run into issues of implementation.
