How Data Analytics Prevents Delayed Deliveries?

We know that having access to real-time data on inventory, supplier stocks, seasonal demands, etc. is a critical issue so that there would be no delays in deliveries. Real-time reporting is a regular feature in most industries. Business analysts, operations managers, inventory managers, business planners, customers, etc. all rely heavily on inventory data to not only run the business but also to plan future production and sales strategies.

When Business Intelligence (BI) combines with real-time reporting, you have the perfect tool with which you can pull out and analyze the raw data. Thus, Data Analytics (DA) involves data mining, processing the data accessed, querying the system for information, and producing analytical reports.

In the production industry, it involves cloud-computing software applications which enable the manufacturing units, warehouses, offices, and branches of an enterprise to access the same data remotely. This enables all the departments to make informed decisions regarding production and delivery schedules so that the customers are happy.


Delays in deliveries can have a major negative impact on your business. In a fiercely competitive business world, you cannot afford to lose customers due to frequent delays in deliveries. It is not a matter of just losing one customer but is a matter of brand value and reputation in the long run. So preventing delays is a matter of survival of the fittest.

Let us examine a few problems pertaining to delayed deliveries and examine how Data Analytics can solve them:

Problem 1: Peak seasons

Let’s say you are producing silverware gift items. Then the demand will be definitely at the peak during the festival seasons. Any delay in delivery will result in customer dissatisfaction and you will lose the customer. When people discuss this incident on social media platforms, it will soon become viral. This will increase your operational costs and it will lead to wasting precious time spent by the production and sales teams, and your brand loses value.

Data Analytics uses predictive methods and can help in reporting on possible delays in deliveries to top management. DA tracks information on customer experiences and collects a vast amount of data on their updates, likes, dislikes, comments, and shares on social media platforms. With this information, you can get a fair idea of the current demand for your products and also when the demand is expected to peak.

Problem 2: Dissatisfied customers

Customer service centers are the first port of call when buyers do not receive a product on time. So a high quality of customer interaction is necessary and post-sales services have to be effective. When was the item purchased, when was it supposed to reach the customer, and how many days have been delayed – these are the questions answered during the service call.

DA tracks the interactions between the customers and the service center. The tool analyzes the wait times during the call, the length of delay in deliverables, the solutions suggested by the service center representative, whether alternative products of the same brand were offered, what was the end result of the call, and other relevant information. Hence, with the help of DA, businesses can prevent delays in the future. It will give the company an opportunity to take quick action and solve the problem in order to retain the customer’s brand loyalty.

Problem 3: Unreliable suppliers

One particular supplier of your products is always late in delivering the goods ordered by buyers. Having many suppliers, it is difficult to keep track of how your suppliers behave towards your customers. In this case, the delay is not your fault, but you will definitely pay a heavy price by losing the customer and the person’s social media contacts.

The unreliable supplier is the systematic driver that triggers the dissatisfaction of customers. With digital data streams (DDS) in real-time taken from social media activities of these customers, Data Analytics can report on how much business you might lose due to delays in deliveries.

Problem 4: Irrelevant data

Tracking worldwide customers involves assimilation of vast amounts of real-time data. It is impossible to sift out the information that is not directly relevant to production, inventory, demand, and sales. So where do you begin separating the information that is vital for your organization from the one that isn’t?

Data Analytics focuses only on relevant information because reporting is based on your query. The tool will pull out digital data streams (DDS) in real-time which match your query so there is no scope for irrelevancy. There is a high level of speed, accuracy, reliability, and query-specific exhaustiveness in the reporting.

Problem 5: Unexpected surges in demand

For example, if you are manufacturing designer clothes, jewelry & accessories, corporate gift items, household appliances, decorative pieces & curios, to name but a few, you can predict a surge in demand during festivals and keep sufficient finished goods so that there are no delays in deliveries. But what happens when there is an unexpected surge in demand? Customers can sometimes be very unpredictable.

Trying to predict unprecedented surges in demand is not easy. It will be profitable for your company to rely on Data Analytics for that. Through social media likes, shares, comments, and other interactions, DA can predict whether there will be a sudden and unexpected demand for your products.

If you rely on DA reports on a regular basis you are being really smart. The report will give you sufficient time to procure more raw material, speed up the production process, keep the finished goods ready, and dispatch them to the regular suppliers well in time. So the digital data stream accessed by DA and the report generated will help you prevent delays, expand your customer base, and increase your profits.

Problem 6: Erratic marketing decision

Everyone is not an expert at marketing and sales whether it is traditional methods or SMM. When traditional methods are used, the data collected will be too vast for employees to sort and analyze. Also, even if the data mined is apparently the latest information, it is still not real-time information collected via Data Analytics.

So the marketing decisions based on this information will not always meet the demand, thus causing a delay in deliveries. In fact, it will result in erratic delivery schedules, loss of customer base, and loss of image as well. This will reduce the value of your brand and will cause long-term harm to the business. Re-building a reputation is harder than just building it!

Although your product is best among the best, if it is not delivered on time your reputation will definitely suffer. Using traditional methods of data mining is a tedious process. Due to the sheer volume of information, the production and sales decisions that you take may not always be the right ones to ensure timely delivery. Sometimes they may work and at times they may be completely off the mark! So there will be a certain level of uncertainty whether your strategies will succeed in retaining your customers or not.

With Analytics there is no room for error. However large the volume of the data collected (and it is not large, it is gargantuan!), the analysis and reporting will be absolutely accurate and will match your queries exactly.

Problem 7: Not target specific

You probably have an excellent social media marketing (SMM) strategy in place. Your YouTube videos are very good. The products images on all the E-commerce sites are very attractive. The prices are affordable. And yet you have not been able to reach the sales target for the year! You need something more to grab the attention of customers and gain a competitive edge.

A professional analysis of your SMM methods using Data Analytics is a surefire way to solve this problem. Are your products viewed by the audience from the right geographical region? Are your ads more product-oriented rather than focusing on the potential customers and their needs? Targeting the correct demographics and income groups is important but the geographical region of the potential buyers also plays an important role. There are two ways in which the zip code determines the success of your SMM techniques:

  • As an example, woolen overcoats are more in demand in very cold countries and cotton summer-wear is preferred in tropical nations so you have to reach out to the people from the right geographical region.
  • Do you have suppliers in the geographical region where your potential customers are? This is another important factor you need to consider.

With the help of Data Analytics, you can identify the reasons for the delays in deliveries. You can then take corrective measures to prevent delays in the future. These measures will promote the brand value of your products and help you to sustain the business despite strong competition from similar products.

To sum up, delayed deliveries are very, very bad for your reputation. They will kill the brand in no time because there are too many choices in the market. It is a customer-centric world out there! So in order to beat the competition, sustain your customer bases, and expand it at a global level, it is very important to know how Data Analytics prevents delayed deliveries.

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