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  • Data Driven Decision Making


    In the FMCG sector, our company is rated among the top 5 organisations both by the consumers and industry. Much of the success is attributed to the increased consumer confidence and round-the-clock shelf SKU availability of our product line. The role of our Purchase Division has always been at the core for achieving this mark. Over the years our team under your guidance has been able to keep the inventory at optimum levels thereby helping the Production Unit to make delivery on time. It is due to the careful execution of our fundamental function - 'Material Management' which ensures that raw material is procured from the suppliers at the right time and in right quantities. This is the key to success in our sector and thus any minuscule improvement towards this can play an important role in raising our organization's bar.

    Our well maintained database of daily Material Management activities can help us in this direction for making more accurate estimations. An approach-'Data Driven Decision Making' can be applied in which assessment data and background information can be used to take decisions related to planning activities. According to a study by MIT Center for Digital Business, organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. With data driven decision making we can deploy Just-In-Time as approach and Material Resource Planning as the method for waste minimization in the purchase cycle.

     

    To demonstrate the strategic, operational and financial advantages of DDDM approach, we can consider the following conventional process being carried out at our unit. Under usual circumstances, we have to keep a safety stock of inventory items so as to counter the market and supply chain uncertainties. These may include logistics delay, plant failures, supply side variability, demand side variability and others. This leads to estimations made with the help of brainstorming methods within the team and experiential knowledge that cumulatively determine the amount of inventory to be ordered. With the help of estimations, performance is delivered in terms of fulfilling various conditions like on time delivery, safety stock, safety time related to inventory items. But there could occur estimation errors during the process due to which stores or warehouses can have lower or excessive amounts of inventory. This can affect the overall process efficiency both in monetary and operational terms. To improve this scenario, Data driven decision making mode can be deployed. Considering the situation that we faced last month. The production unit had given a demand forecast of 100 packs for one of our products and keeping safety stock levels in mind, we had ordered 120 packs. But we received only 115 packs from the supplier due to production based variability at his end which had not been a factor of consideration in our process. Also, the actual demand turned out to be 118 packs which was closer to our safety stock levels. So, overall we faced a shortage of 3 packs. From this we can identify that there is a chance of around 4% variability in delivery from supplier's end. This data if incorporated in taking decisions before ordering again from the same supplier can help us in achieving more accurate outcomes. This was an example of countering lower inventory received from the supplier where DDDM could have helped otherwise. Considering another situation that occurred 6 months back where we had ordered around 200 packs where as the production had ordered for 120 packs only but finally picked up around 150 packs from the warehouse. We had a rough idea of up-scaling from demand side as well as in the costs from the supplier's end due to which we increased the order. Though a more accurate estimation using DDDM would have given an additional gain by reducing the wastage of the remaining underutilized 50 packs as well.

     

    Using DDDM as one of our modus operandi would be convenient and beneficial too as its output depends on the quality of data gathered and a well managed database already in place can help us in reaping maximum benefits out of this investment. Further its effectiveness depends on defining the questions to be considered before analyzing the data and with your experience level in this area, we can easily frame pertinent questions to get relevant results from the data. Data driven decision making would give us added advantages of faster processing, refocusing our resources to increase the yield, relevant data backing to explain our rationale behind purchase decisions to the management, foreseeing the opportunities and threats in the market and overall supply chain. Over a period of time, this approach can also lead to building a reliable group of suppliers giving us a competitive advantage gained by adopting a data backed strategic purchasing model. An early adoption of Data driven decision making would bring maturity to our supply chain infrastructure and resilience towards unforeseen circumstances so that we can quickly respond to them without compromising on financial and operational aspects.

     

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    Forecasting based on historical data is not a new concept and the probability of variation is high in this model also.That's why now a days,FMCG companies are using Agile supply chain concept which gives better and effective results and also companies can meet sudden change in the demand..


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    Hi Nikhil,


    Thankyou for sharing your perspective.


     


    Data driven decision making involves data from all the spheres-historical data, data from suppliers, real time data from market on various aspects, data from point-of-sales and point-of-use i.e. virtually from the entire supply chain. And this has become one of the main driving factors to implement an agile supply chain as well so as to respond to market sensitivities and uncertainties as quickly as possible. DDDM gives the purchase managers this required flexibility to strategically manage suppliers, logistics, latencies and demand fluctuations.


     


    To quote an example: Li & Fung company takes uncertainty a factor into its supplier strategy and its extent can easily be decided with the help of a data backed approach on varied factors such as supplier's performance in the past, alternative suppliers, raw material availability, prices and other factors.


     


    So, in essence DDDM stands essential for treading all the strategic moves of any organisation.


     


    Thanks.


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    Hi Nikhil,

    Thankyou for sharing your perspective.

     

    Data driven decision making involves data from all the spheres-historical data, data from suppliers, real time data from market on various aspects, data from point-of-sales and point-of-use i.e. virtually from the entire supply chain. And this has become one of the main driving factors to implement an agile supply chain as well so as to respond to market sensitivities and uncertainties as quickly as possible. DDDM gives the purchase managers this required flexibility to strategically manage suppliers, logistics, latencies and demand fluctuations.

     

    To quote an example: Li & Fung company takes uncertainty a factor into its supplier strategy and its extent can easily be decided with the help of a data backed approach on varied factors such as supplier's performance in the past, alternative suppliers, raw material availability, prices and other factors.

     

    So, in essence DDDM stands essential for treading all the strategic moves of any organisation.

     

    Thanks.

    U r absolutely ryt..btw nice submission..all d best :)

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    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.


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    Data driven decision making implementation requires reliable data for which one should work towards supply chain integration with suppliers and other associated partners. But I believe this often requires supplier-purchase department collaboration which might involve considerable effort. How should one go about enforcing such as collaboration at purchase department level?


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    @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.


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    Data driven decision making implementation requires reliable data for which one should work towards supply chain integration with suppliers and other associated partners. But I believe this often requires supplier-purchase department collaboration which might involve considerable effort. How should one go about enforcing such as collaboration at purchase department level?

     

    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.

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    Great info on DDDM.
    A firm which just entered the market will not  have much precise data so while taking decisons some errors may creep in , whereas a firm which is in the market for a long time will have relevant and accurate data to take correct decisions, so  does it mean that DDDM is actually more suitable for firms with a long history in the maket ?

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    Thanks a lot for this enlightening article Swatee. Could you throw some light on how organizations handle anomolies present in the data that is utilized for decision making? More over how do they constantly ensure the insights offered by the data are not obsolete?

     

    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.

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    Great info on DDDM.

    A firm which just entered the market will not  have much precise data so while taking decisons some errors may creep in , whereas a firm which is in the market for a long time will have relevant and accurate data to take correct decisions, so  does it mean that DDDM is actually more suitable for firms with a long history in the maket ?

     

    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.

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    Nice article...Can you please give some information on the various levels in a supply chain where DDDM can be implemented?

    Thanks

     

     

     

     

    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.

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