Big Data Analytics

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Technology & Automation

Big Data Analytics

Big data is surely becoming one of the most powerful tools to help leading organizations transform the performance of their supply chains. However, technology experts sight that lack of capabilities and the lack of a structured approach to supply chain big data is holding many companies back. For big data and advanced analytical tools to deliver greater benefits for more companies, the organizations need a more systematic approach to their adoption. This perspective story aims to empower readers on the criticality of BIG DATA in SUPPLY CHAIN and how to make the most of it…

Devadas Nair

Devadas Nair, Chief Supply Chain Officer, Shoppers Stop

Effective SCM operations are those that react quickly to business demand in the least possible time, faster movement of merchandise from source to end point, that too with the cost effective solution. To achieve these objectives, Data analytics will be very effective in following areas:

  • To drive volatility management by accurately forecasting business cycles. To predict Demand cycles for a geography, Time Cycle and by Merchandise so that Material and SCM operations are well planned.
  • To ensure right merchandise is present in right quantum in an appropriate Retail outlets in a given time cycle.
  • To identify weak links in terms of external sources like trading partners, any logistics operator, also internal links like our own DCs / Retail outlets, specific personnel within our SCM operations. Information will come with facts and figures so that effective measures can be taken to tackle that issues from root cause.
  • Eliminate huge inventory holding, thereby reducing blocking of cash, eliminate margin reduction to a great extent due to discount offerings.
  • For an effective Suppliers Risk Management and providing incoming goods projection
  • For an effective Warehouse management, which includes picking and storing space allocation, manpower allocation to pick zones, proper replenishment of primary and storing locations, workload optimization, optimum utilization of Space, infrastructure and manpower.
  • For an effective transportation by mapping delivery schedules as per customer / retail outlet order patterns, dynamic routing, etc.
  • Possible out-of-stock detection, shelf space optimization, employee scheduling, to name a few.
Pradeep Chaudhary

Pradeep Chaudhary, Domain Consultant, Tata Consultancy Services Ltd

Data is the oil, which makes businesses work. High speed GPUs, availability of cloud environment, competitive costs of cloud processing and storage are some of the leading factors, which are driving the rapid adoption of Machine learning based data analytics. Today it is not about analyzing past data alone. We are moving from reactive to predictive to prescriptive analytics. Reactive analytics is just post mortem. It is good to analyze past data to learn from it. But now, we are seeing adoption of predictive and prescriptive analytics, which tell a business what will happen and what should one do to mitigate a potential problem. This is a real value add as it helps in nipping potential problems in the bud. Imagine a reefer container carrying goods worth lakhs of rupees. If the temperature readings in the reefer unit were analyzed on real time basis (along with orthogonal data) while the trailer is on the move, it is possible to predict what will be the temperature – say after 30 minutes. If any fluctuations are observed and the predictive algorithm detects a trend in the fluctuation, then an alert could be sent to the concerned personnel and the driver to look into the matter. So, real time data analytics can actually prevent a problem from becoming a full-blown problem. In our case, the refrigerated cargo worth lakhs of rupees can be saved through timely intervention.

Manav Verma

Manav Verma, Chief Marketing Officer, DHL SmarTrucking India

Big Data, and the ability to interpret it, has the potential to optimize supply chains in many ways. Analyzing this data can help companies in planning and scheduling shipments, monitoring goods throughout the process, responding quickly to unforeseen events, managing seasonal and variations in inventory, streamlining ordering and inventory replenishment, forecasting and planning demand, and making other vital decisions quickly and correctly.

DHL SmarTrucking has been in operation for a year now. During this time, we have accumulated a lot of data as well as learnings. We have real time visibility of our fleet. We are able to analyse the overall operations performance on a regular basis, and compare it to the performance at any point in the past. Data analytics helps us highlight operational gaps that can be plugged with monitoring and data-driven governance. Insights gleaned from the accumulating data, and our learnings, together have helped us see and analyse demand patterns, and predict the availability of our fleet in the near future. These insights also facilitate reduction in time between orders, and help our teams plan effective sales and operational strategies.

Manjunath SR

Manjunath SR, Senior Director, Industrial Supply Chain Consulting, JLL India

Supply Chain is a tricky business. One missing entity or a lack of synchronization can break the entire chain and mean millions in losses for a company. One of the main drivers of collecting and analysing data for companies today remains cost reduction. However, the use of analytics in the supply chain is resolving several pain points in supply chain management at the strategic, tactical and operational levels. Supply Chain Analytics brings data-driven intelligence to business, reducing the overall cost to serve and improving service levels. For supply chain professionals, it can only mean one thing – to upskill to be able to use advanced analytics to improve operational efficiency and make data-driven decisions Supply chain analytics turn data into real insights. Solid operational knowledge allows an analyst to understand or interpret the results of the analysis and to communicate those findings in a manner that would allow the findings to be actionable. Three ingredients are key to getting an advanced analytics initiative underway: having the right people; collecting high quality data and; obtaining the best tools at the right price.

Data analytics can enhance customer satisfaction dramatically, as it allows supervisors to pick the ideal shipping methods, utilize the best carriers, reduce the potential for damage and halt delays – all leading to improved service. The trend will continue to expand, and the cost-savings alone in efficiently re-structuring supply chains are potentially enough for not only significant additional profit but also for efficient, streamlined operations moving forward.


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