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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. Note - Visitors shall not be able to comment on this article until they are logged in.

Decision making is the inevitable truth of the life of every practicing manager. The best of the corporate leaders constantly emphasize on the fact that the litmus test of a manager is to take correct decisions in the difficult times. Management styles of the managers have varied widely and there has been always a contest between the merit of data driven decision making and the intuitive method of decision making. So any attempt to reach a conclusion about the greater efficacy of one over the other or the chance of success of one over the other lies in accessing the arguments made in support of each of them. The intuitive managers would argue that this style of decision making is justified because it is not a fluke. Rather it is the gut feeling of the manager which he has acquired out of experience. Further such decision making is quick and less rigorous to reach conclusively for the decision maker. Also there are examples which prove that this method has produced unexpectedly great results. But does this certify the method beyond doubt? Even when claim of intuitive manager regarding generation of unprecedented, unexpected great result is accepted yet this argument is incomplete. This is because the frequency of the success rate in the above stated intuitive method can always be questioned. The decisions taken intuitively are equally likely to produce highly good and highly bad outcomes. On the other hand the decisions taken based on data are likely to produce outcomes with lesser variance. In their case the outcomes are expected to be far more standard. An analogy can be drawn with the individuals trained in the discipline of management and individuals who do the business based on their experience and not on the basis of tools, techniques or methods taught to them. While the latter might produce very high outcomes, the former can be expected to produce standard set of expected outcomes on all instances. The risk involved in the case of the former is bracketed and is well within bounds. This helps us further argue the case that the data driven decision making helps organization to create a standard process for carrying out decisions making. This makes the company less dependent on skillsets of a given manager rather it makes the skills ubiquitous through the designed processes. We have examples of successes in decision where the manager is guided by gut feeling and intuition. Hitler in the battlefield, a manager at war or Indian cricket captain M.S. Dhoni, a manager in the stadium or Steve Jobs, a manager spinning the invention wheel without any empirical evidence of demand are all such examples. But there are also examples that did just the contrary like Napoleon on the battlefield, Nate Silver in the baseball match and modern India’s entrepreneur like Snapdeal founder Kunal Bahl. It should be concluded here that data driven decision making helps create a framework which allows validation of a standard method by repetition. Data driven decision making has produced remarkable results in several domains and has helped to solve historically unsolved problems. In the field of education, the top notch management colleges have expressed concern on not being able to create the best of peer groups that could maximize peer learning. The problem was essentially a consequence of intuitively formed groups with seldom any recognition of the educational background, past academic performance; data based analysis of individual’s strength and weaknesses etc. Modern research has shown that application of decision based system has resulted in remarkable increase in the peer learning. This study was based on the assumption of keeping all the externalities equal and the examination results were taken as the proxy for the improvement in performance. In the field of crime mitigation and managing police administration the USA has shared reports that could startle any intuitive manager. The manager in this case is Anne Milgram, former Attorney General, State of New Jersey who was trying to put a check on the amount of crime rates. For all the time prior to Anne the decision was based on intuitive skills of the officers concerned. Anne revolutionized the entire methodology. She instructed to collect data in greater detail and based on the profiling of the people and the locality she redesigned the entire schedule for the police petrol and beet police. The impact was a sizable decrement in the crime rates. Another example that validates the claim is the research of Prof. David Pall of Wharton School. In relation to e-commerce the professor says that attempt to push sales by an online enterprise should be based on some empirical findings. He says that the information about the social capital and the buying behavior of the customers are positively correlated with a correlation of 0.4. This correlation might appear to be small but even this much insight into the buying behavior of the customers has far reaching consequences on the company’s strategy. This gives the sales team a sizable knowledge that where their efforts in selling goods are more likely to fructify. This example becomes clearer when we see it in the light of the failure of an e-commerce company that worked without any regard to data based decision making. This company, boo.com based in U.K. had established offices in the elitist of the locations for which it spent more money than Amazon and eBay and yet failed. The reason primarily was making decisions that weren’t corroborated by data and facts. They launched products unmindful of regulations in respective countries, did absolutely no market research on the expectations of target population in terms of getting the touch and feel of the products that they offered. The final nail in the coffin came when they expected very high volume of customer orders on the Christmas when they actually received just eight orders. This clearly shows that data based decision making has greater significance and are likely to have a strong impact on business. Note - Visitors shall not be able to comment on this article until they are logged in.

Once when asked what he does, if the data does not support his decision, John Maynard Keynes replied – “I change my opinion. What do you do?†In a haystack of information today, that one thing which helps organizations take sound decisions is the ‘analysis of data’. Often, companies find themselves in situations where from a variety of choices, they need to pick one. In such cases, data-driven decision making enables following a systematic procedure. A successful completion of any process begins from a decision well made. It forms the first step of any execution process, and is thereafter followed by modification, as and when changes in information arise. The question that one would ask then is – ‘If it is data that is needed, then exactly how much of it?’. Authors like James Taylor and Stephen Covey, in their writings explain keeping ‘the end in mind' before undertaking a course of action. They say that the goal is never to build on the data; rather it is to use the facts to make work easier. Thus, the perfect quantity would be one which helps an organization make ‘timely’ as well as ‘correct’ choices. But even a manager’s power to predict can do this job, isn’t it? On digging deeper we realize that the ‘intuition' we are referring to is nothing but the gut feeling that arises based on a manager’s experiences of the past, and thus its own roots lie in data as well. What people actually follow is ‘informed intuition' – that uses previous occurrences as its basis. This is justified, since not only is complete information necessary, but also alongside is corporate alignment and clarity. These days, a term that managers often hear is ‘big data’ – which refers not only to the volume of data available, but also to the variety of it and the rapid pace at which it alters. Big data brings with itself the complexities of processing and interpretation, causing confusion and delays. It is here that just-the-right-kind of filtration is needed, to separate what is relevant and what is not.Infact sometimes, even lesser amounts of data can lead to better decisions being taken. As they say, the ‘first impression’ can indeed be beneficial if taken as the ‘last impression’. This is exactly where the use of instinct comes into picture. Whether it be studying a consumer insight or predicting the future, it is a blend of analysis and a manager’s intuition that leads to the apt solution. In a post in Forbes, Robert Carraway, a professor at the Darden School of Business said that big data and the increasing use of frameworks require not less, but a higher amount of managerial insight to accompany them. There have been faults based on judgment (remember Google claiming to overtake Firefox when launching Chrome?) ; and so exist popular crisis due to over-reliance on data. The idea hence is to strike a balance between these two seemingly different ways that managers use to reach a conclusion. The more an association can accommodate diversity in terms of style, emotions and experiences, the higher is the probability of improved performance. In a nutshell, if a corporation can make sure that it has as members both left-brain and right-brain thinkers – it can strike the nail on its head! (*source for the cartoon : Google images) Note - Visitors shall not be able to comment on this article until they are logged in.