ssinghal

Hi Abhinav, Thankyou for your interest in the article. Below i have tried to answer your query regarding the challenges faced while implementing DDDM. 1. Accuracy of data used for D3M 2. Performance of data analytics algorithms 3. Data filtering so as to feed relevant data only in the system 4. Availability of data online and to the right people 5. User friendliness of the system All these are the factors that need consideration for a sound D3M mechanism in place. If not, then benefits of D3M would be negated. Thanks.

Thankyou so much for appreciating the submission.

Thankyou for appreciating the submission.

Hi Rishita, Thankyou very much.

Hi Gopala, Thanks for appreciating the submission. I would try and write mores such informative articles.

Hi Harneet, Thankyou for sharing your views about the article.

Hi Sonhal, Thanks for bringing forth an interesting perspective that whether DDDM can be applied for small scale family business or not. The decision for applyiing DDDM is based on various reaqsons. Some of them are: 1. Increase the scale- Current Coverage and Potential capacity 2. Huge data to analyse 3. Earlier insights into future situations 4. Increase efficiency and thus drive profits 5. Sound backing for decisions taken If these are the reasons present in any case then DDDM can be applied in the case as these can be eaily fulfilled. For a small scale business, DDDM can even help further to increase its scale to medium and large at later stages. The software packages available for data analytics can be used for any amount of data and would help in predicting outcomes accordingly. One can go for customized software packages so as to suit the requirements and save on investment as well. Thanks.

Hi Abhijeet, Thankyou for liking the article and I would try and write more on varied other concepts as well. Thanks.

Hi Tejinder, Thankyou for appreciating the submission,

Hi Sonhal, Thankyou for liking the article.

Hi Anubhav, Thanks for showing interest towards this submission.

Hi Sagar, Thanks for appreciating the article and that it helped you in understanding the concept of DDDM. Thanks.

Hi Ankit, Thanks very much. Hope it helped. Thanks.

Hi Soubhik, Thanks for your interest in the submission. The points mentioned above that DDDM is based on past sales, production and procurement data is true and its benefits are immense. The article highlights the advantages that could have been achieved in the two cases occurred in past where in the manager had taken decisions based on gut feeling instead of D3M approach. Experience along with data backed analysis gives much more efficient results and reduces uncertainties to minimum. So, D3M approach should be deployed as far as possible. Thanks.

Hi Jayati, Thanks for your query. Below I have tried to answer the same. D3M process is performed largely with the help of data analytics software packages. In these systems, data is fed and they deploy predictive models such as Bayesian Plots to predict future demands and supplies. Example: JDA provides supply chain softwares for Data Forecasting and Replenishment. It considers the historical data,current balance on hand, on order to provide forecasts for placing orders. Its underlying mechanism is D3M only. Also, the example stated by you of E3 systems which are automatic warehouse replenishment systems are also a good example of D3M as they perform the calculations on the basis of data. Thanks.

Hi Kunal, Thanks for appreciating the article. Below I have tried to answer your query about the scenarios where D3M finds less relevance: 1. IMMEDIATE DELIVERY When there is time constraint and delivery takes precedence over analysis then D3M approach can not be applied. 2. OPERATIONAL TASKS When the manager has to carry out operational tasks which are of day to day nature, then D3M might delay the process as it takes time in analysis. 3. RESOURCE CRUNCH When the resources involved in the process are not available easily then the manager has to take a call and proceed with the available means and not apply D3M. There could be more such situations where trade off needs to be done between time and delivery, then whether D3M should be applied or not largely depends on the factor which holds more relevance. Thanks.

Hi Saumyadeep. Thanks for your interest in this article. Below I have tried to answer your query that how DDDM can help in unforeseen circumstances. DDDM is essentially a field associated with data analytics in which past data is gathered and used in predictive models. These models take into consideration various environmental variables and based on past data give a probabilistic numbers to unforeseen situations. These probabilistic algorithms give us some confidence level and find out what can go wrong. Thus, with data a manager can achieve a considerable insight into futuristic situations. Though there is always scope for inefficiency but that could also help in long term as that data would also be captured and would become a part of DDDM bringing greater accuracy the next time. Thankyou.

Hi Raja, Thankyou for your interest in this article. Below I have tried to answer the query on various levels of application of DDDM in a supply chain. There are three kinds of levels which a supply chain manager has to take decisions: 1. Operational Level 2. Tactical Level 3. Strategic Level a. Application of DDDM is least in Operational decisions as these involve day to day standardized process purchasing which involves a fixed set of processes and needs no major decision taking. b. Tactical decisions involve the role executed by a buyer including commercial and negotiation skills. Here also DDDM plays less role as the scope is fixed to a great extent. c. DDDM has the highest role to play in case of strategic decisions where the company wants to restructure or improve its supply chain strategies involving sourcing, supplier selection, materials management and such areas. DDDM can help to a great extent to analyse the most profitable and efficient alternatives to be adopted. These decisions also give the maximum efficiency as these get converted to tactical and operational ones in later stages so application of DDDM at this level assumes greater importance. Thanks.

Hi Gaurav, Thanks for bringing forth this perspective of how new firms can enjoy the benefits of DDDM. Below I have tried to answer the same. Data driven decision making can be performed by analyzing data collected from two broad sources: 1. Internal data 2. External data In case of a new firm, the internal data would not be available. In that scenario,the firm has to rely upon trusted sources of data analytics which provide an in depth dissected information about the field. From those data sources, the company can build its own business intelligence and take decisions using DDDM approach. The company can also go for services of outsourcing firms who can perform the task of customized data mining for them. Taking the point of accuracy of data, an organization's age in terms of market presence does affect the level of data it possesses. But at the same time, data's accuracy and relevance relate to the firm's age only when insights collected are regularly used to update the database. For Example: A credit card company can deploy data analytics to track which services are being used frequently by its customer and then the company can link him to the offers(if any) which are associated with those services. This way company can do profit making using DDDM. A new firm also after a period of time can enjoy the benefits of DDDM from its own database and before that globally trusted sources in the form of government provided and consultancy reports can be used. Thanks.

Hi Bharanidharan, Thankyou for your interest in this submission. Below I have tried to put forward some points for the queries: 1. Handling Anomalies in the data Step 1: Defining the criteria for data anomaly Before detecting the anomaly in the data, a manager has to define the yardsticks against which the data needs to be compared. Some of the possible measuring points could be : 1. Market Trends 2. World wide global reports 3. Competitor's position in the market Step 2: Detecting the data anomaly After the anomaly's criteria is defined the next stage is to identify the area which is leading to the anomaly. It can be performed by a 360 degree analysis of all the work fields. For Example: A company manufactures Product A whose sale is on a continuous decline for the past 3 months. The database available with the company showed that the consumers are liking its Product B which is the substitute of Product A. This gives company an inference that overall consumer market is shifting towards Product type B. On the contrary, when whole market is analysed it is found that the demand of other brands of Product type A showed an upward trend. Here, market trend was used as a yardstick and the data anomaly was identified by comparing the in-house and market data. 2. Ensuring that the data is up to date To ensure that the insights offered by the data are not obsolete, the precondition is updated database from where data is analysed for decision making. To keep an updated database, a company can go for either of the two methods: a. Market Intelligence Team Deploy an in-house market intelligence team that continuously takes insights from all the sources and reflects it back into company;s data resources. b. Using the services of Outsourcing companies These days outsourcing firms are helping the organisations in making their supply chains robust by providing them with the latest data about their filed of operation. By using their real time specialized services companies can take right decisions. Thanks.

@Abhishek Sharma Thankyou .

Hi Anuj, Thank You for bringing this important aspect of requirement of mutual collaboration in DDDM. When data is used for analysis, one should ensure that the data is exhaustive and collected from all the concerned areas i.e the area of application and its source as well. Mutual integration between supplier and buyer is an important aspect for successful implementation of DDDM in a supply chain. To achieve this integration, both the sides need to work for each other and help in mutual growth. Purchase department in supply chains is slowly transforming into a function called as Procurement. Though the two terms might sound synonymous, they are different in actual application. On one side purchasing implies buying whatever is offered by the supplier while in case of procurement , the manager works in close coordination with the supplier giving him the required product specifications and also extending support in manufacturing the same. One can visualize a Procurement Manager to be the Deputed Production Manager at the supplier's side. This brings cost cutting, time savings and operational leverage as supplier gives the exact items required by the buyer and buyer also needs not to recheck the supplied material. With this approach, data collection and sharing becomes easy and DDDM becomes much more effective. Thanks.

@Vaibhav. Thanks for your interest towards this submission. A supply chain model can either be of the following two types: fulfill the request as and when it arrives or have a long term view and use sourcing as the strategy. In first case JIT becomes the method and in the second one it gets converted into an approach giving broad guidelines for operations. When JIT is the method of execution, there is no emphasis on inventory instead delivery to customer takes precedence over it. So, there is a tendency to overbuy which leads to inefficiency. While using JIT as an approach one has to take into consideration the variability factors analysed on the basis of DDDM. It would help combat uncertainties by using estimations. It would in turn keep inventory and other costs in control while giving strategic and operational leverage at the same time. Thus, DDDM would prove beneficial for the supply chain. Thank You.

@Gaurav Marda.. Thankyou..

Hey Pranesh, Thankyou for liking the submission. Below is the answer to your query. I have tried my best to justify the same. When a company faces supply chain issues due to supplier side variability, data driven decision making can come to its rescue. A purchase manager can analyse the data and track the reasons for the inconsistency. Supplier side variability can arise due to two issues: Logistics based variability or Production based variability. For example: A batch supply fell short by 5% out of which 3% was due to damage in transporting and 2% due to production shortages. This analysis was possible only with the help of accurate data. Thus,with data analysis; the exact reason among the two can be ascertained and then the client can actually help the supplier in fixing those issues. This in turn would help the client himself as the supply chain would become more robust. Thank You.