Supply chain industry is flooded with streams of data received from scores of interactions everyday. From point-of-sale to warehouse management, each touchpoint accumulates an intensive amount of structured and unstructured data for the business. And since long, organisations have struggled to store, manage and leverage this humungous volume of data in their favour.
However, with the advent of Big Data solution tables have turned for the supply chain industry. The massive volume of data has become an asset instead of liability, allowing the business to not only accumulate all the scattered data but transform it into relevant information.
Earlier, as the departments worked in silos, information flow between the departments was slow and inconsistent. For instance, if the product manufacturing experienced a delay or halt, then it took days for the business to communicate this information to the point-of-sale store owners.
Introduction of Big Data Analytics reduced this gap by providing stakeholders with real-time information access. And with the cloud hosting, accessing all the information from anywhere at any time became a matter of few clicks. Stakeholders could easily track the complete product cycle on their mobile devices, starting from manufacturing to the POS.
Having said that, things are not as rosy as it seems. “Lack of big data understanding to improve the business” was identified as one of the core obstacles in the effective application of big data solutions, according to a survey conducted by MIT.
Thus, only knowing that big data can help your business will not suffice, as stakeholders, you should be deft in implementing big data strategies in your supply chain process. And it starts from being vivid about how and where this advanced analytics technology can fit in your supply chain needs. Here let’s understand how different stages of supply chain processes can make use of big data technology.
1. Sales, Inventory and Operations Planning
Planning stage of the supply chain process utilises the big data analytics to the maximum. With real-time access to point-of-sale, inventory and manufacturing data, preparing for the upcoming demand and supply gap is easier than ever before.
If you have 1000 stores in a city, each having a 1000 customer footfall, then you have 1000000 probable data distributions every day to analyse.
This enormous volume of data collected every day gives you a peek into the details as well as provides you with a broader view of the ongoing trends. That means, the more customer interactions you capture, the more accurately you can forecast.
Analytics has introduced many critical parameters into the supplier selection process, which were earlier neglected or not considered crucial.
It follows a data-driven approach to choose a supplier. Instead of awarding contracts on low price quotes, prior connections or recommendations, you can now assess vendors on aggressive parameters such as performance trend, risk management strategies, competitive pricing and more.
Well-informed businesses are also taking into account detailed parameters such as minimum shipping time, history of man-handling errors, and the followed shipping routes of different suppliers.
Analytics in the manufacturing process can curtail production errors, increase process efficiency, and upgrade production quality.
For instance, Intel used to run 19,000 quality tests on every chip that came out of the manufacturing unit. With the volume of chips created by Intel every day, the quality checks were extensive and time-consuming.
To optimize this process, Intel started analyzing all the historical manufacturing data and strategized testing at the wafer level. This augmented the efficiency of the testing process by performing extensive tests on selective chips, saving $3 million in the manufacturing costs.
Analytics information such as demand-supply gap, competitor pricing, ongoing trends, transportation time and offered value helps in determining the pricing of the product. Recently, the mode of payment or the customer’s preferred payment platform data has also been a crucial factor in the process.
Transportation cost has been a constant source of worry for the supply chain department. Unexpected vehicle breakdown, adverse weather conditions, route complexities were a few of the problems that damaged the transportation efficiency and delayed the timely delivery.
Big Data here equips the transportation process with all the right information before and during the process. Details such as delivery sequence, traffic conditions, weather forecast, route congestion are continuously monitored to eliminate any bottleneck that might hamper the smooth flow.
6. Point of Sale
POS is the most crucial among all the stages of the supply chain. Due to which retailers are the most adept at utilizing analytics in their favour. They’re assessing customer trends to decide the placement of the products in the store, finalize the discounts, and keep a check on inventory.
The collected data also helps to estimate and forecast the future demands of the different products. Few of the forecasting analysis which can be done through POS data are,
1. Product demand by end customers
2. Stock on-hand and replenishment orders
3. Parameters such as display stock, pack size, minimum order quantity affects the time and amount of the orders.
Take the Leap
According to a research, ninety-seven per cent of supply chain executives report that they know how big data analytics can benefit their supply chain, yet only 17 per cent have implemented analytics in one or more supply chain functions. It is startling to see that after realizing the potential of Big Data analytics, businesses are still holding back to make the shift.
Lack of structured approach and capabilities have been the major roadblocks in the big data analytics adoption. Here, the supply chain industry needs to understand that analytics is more than a trend. It is the need of the hour. Supply chain industry needs the Big Data leap to minimise the demand-supply challenges faced for years. It is time to harness the advantages of analytics and start striding towards the data-driven approach.