How to optimize your back office with Big Data

Published October 21, 2022

Supply chains are becoming increasingly complex and harder to predict the wider they expand. Without adequate access to the innermost workings of the inventory management process, there could be a considerable loss of efficiency and scalability of back-office processes.

Big data analytics through data science and data visualization could help back-office process optimization.

Not only is inventory optimization more important than ever for a grocery store to be a functioning part of the supply chain, but customers are also adopting higher expectations regarding product availability. Customer expectations for availability range depending on the type of retail service. With in-person shopping, customers expect immediate availability of products, while they might be willing to wait a few days for delivery.

Inventory optimization, at its most basic level, is having the right amount of inventory at hand to meet current and future needs through demand planning. It also requires having the right amount of incoming data through various phases of the supply chain.

Longer and more spread-out supply chains have a plethora of potential failure points, most of which are out of your control, making supply chain management difficult. Without real-time insight into the current state of the supply chain, along with educated prediction, it’s easy to either fall behind or overstock.

Why data analytics is changing inventory optimization


By introducing big data and predictive analytics into the inventory management software process, sellers and providers in the highly volatile and sensitive supply chain of grocery stores are able to work around incidents and accidents in the supply chain. Optimization through data-backed insights enables inventory managers and data analysts to act ahead of the curve, avoiding bottlenecks and delays at the supply chain leaders’ end.

Without supply chain analytics, suppliers will always be a step behind. Only relying on past trends and overdue updates, they could end up with severe shortages in supply or may order more than required. In the case of grocery stores inventory, a surplus in supply could spoil before demand reaches it.

Role of data analytics in smoothing inventory operations


The early 2020s were filled with numerous regional, national, and international events that directly impacted the global supply chain, especially for time-sensitive products like fresh produce. On average, on-shelf availability fell to 89% and the prices increased by 5.4%.

A single break in the supply chain is enough to paralyze and hinder it globally. In most cases, especially when it comes to worldwide emergencies, the weakest point of a supply chain sustains the most damage, taking longer than anticipated to recover, often with little to no affordable alternative.

Regarding grocery store supply chains, some examples include coffee bean shortages. Brazil, as one of the world’s largest exporters of beans, witnessed a decrease in crops due to drought. Furthermore, shipping containers and routes were affected by the low workforce and regional emergencies, contributing to the strain on shipping.

Role of data analytics in smoothing inventory analytics and operations


One way grocery store owners, inventory managers, and data analysts can hope to remain afloat through a highly volatile global supply chain incident is by relying on predictive analytics. Inventory management based on data mining and logistics analysis allows for demand estimation and improves forecast accuracy for the supply chain future. Sufficient data analysis could help predict incidents at any point of the supply chain that may hinder the supply of a particular product.

Big data analytics should also include customer experience, behavior, and trends in buying habits on both the local and global scales. Demand is an essential part of the supply chain like the supply and stock segments and target stock level. Locally and regionally sourced data can help you better manage inventory levels and capacity to guarantee customer satisfaction at all times.

Examples of the back-office capabilities of data analytics


Before seeking ways to optimize back-office activities, it’s important to know where the divide between the front and back-offices in your grocery business lies. Front-office processes are customer-facing, whether it’s the sale points, applications, employees, or aisles of fresh and packaged produce.

Back-office would include all the processes that allow the front-office to function. Front- to back-office integration enables administrators and managers to access and execute processes, applications, databases, and digital inventories from a single location. This approach is more sustainable in the long term and reduces the chances of miscommunication between departments and cross-system compatibility issues.

When it comes to deploying advanced analytics in the back-office for process optimization, the results can be drastically improved by implementing real-time analytics that suit your particular system.

Usual bottlenecks that arise with non-automatic systems could also appear when deploying analytics software. For better service that starts in the back-office, analytics need to be predictive and preventive in nature. Using both historical data and external data allows for more personalized results and updates on activities that focus on high-risk points in the supply chain.

Related: How retailers can use Big Data analytics to optimize operations

Examples of the back-office capabilities of data analytics

Predictive Analytics

With enough internal and external data used in business analytics, computer models could help with forecasting bottlenecks in a specific supply chain, or a surge or decrease in demand for a specific product or produce.

Process Optimization

Relying on thorough analysis of back-office activities over a long period of time, data analysts, alongside specialized software, could offer insights and suggestions to optimize processes.

Diagnostic Analytics

The same data can be used to determine weak points in how a back-office is run. Unlike optimization capabilities, diagnostic analytics allows you to pinpoint the reasons for previous bottlenecks and failures in the supply chain.

How you can begin leveraging data analytics for inventory optimization


The first step before leveraging a data analytics solution for your inventory is making sure your grocery store is ready for one. It’s important to have well-structured and stable front- and back-office systems with a source of data that can then be used in analytics.

Understanding own personalized experiences through data analytics, you can instruct the inventory management software to focus on pre-identified weak points within the system. Harnessing external data and introducing it to the system would, over time, allow for a more nuanced approach to the role your inventory management plays.

The more internal, accurate, and detailed the data used in advanced analytics, the more reliable the generated insights. Then, you can make informed decisions when it comes to inventory analytics. Combined and compared to data from the entirety of the supply chain, you can use data analytics to remain ahead of the curve and not get overtaken by a shortage or breakage in the chain.

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