How data-driven operations strategy changes warehousing businesses?

Lately, warehousing companies are increasing their investment funds for automation, robotics, technologies, and data management. In fact, according to Peerless Research Group's annual survey in 2018, the investments in innovations were steeply increasing, as 45% of the respondents claimed to be proceeding with such incentives (compared to only 28% in 2016). 

However, the growth in funding didn't end there and is even projected to reach peaks between 2019 and 2025. A study by "Global Market Insights" shows that the "North America data warehousing market is projected to dominate the global industry with a share of above 40% by 2025 due to high adoption of cloud services, robust ICT infrastructure, and rising popularity of big data analytics." 

As impressive as this forecast is, such data-based innovations have a significant impact on the organizations' operations strategy, converting it into a data-driven one, and implementing changes in every aspect of it: from resources to market requirements. 

Operations’ resources

Data changes the warehouses from inside. The evidence-based insights which become available to the management, due to generation of data from sensors, AI, deep learning, robots, etc., have the potential to improve the operations strategy, starting from its current resources. 

Assets

Data-driven warehouses are enabled to monitor the condition of their assets in real-time, detecting any technical problems, and estimating amortization pace. This way, the companies gain the opportunity to immediately interfere when an issue occurs and solve the appeared problem before it has caused delays and financial losses. 

As a result, the availability of asset data indirectly allows the warehouses to diminish the depreciation of their machinery, appliances, and devices, and lower the costs for their maintenance, repair, and eventual replacement.  

Processes

Effective and profitable performance is the main goal of every operating warehousing entity. Luckily, with a data-driven operations strategy, those organizations acquire knowledge based on evidence, which is used for decision-making on how to effectively allocate machinery and appliances to achieve the highest possible level of productivity.

This way, the warehousing plant can optimize its capacity, reduce the time for order processing, and simultaneously build a better connection between processes, such as receiving, storage, picking, distribution, handling, etc.

Capabilities

Integrated data resources have the potential to give valuable and complex answers to questions like: 

1. To what extent are we contributing to the organizational strategic objectives?
2. What is the demand projection for the following period?
3. Taking into account the demand forecast, what are the new practices, technologies, or incentives we should adopt to improve our operations?
4. What activities can we forego or freeze at the moment?
5. Should we optimize human capital by employing new experts or changing the specialization of the employees?

Market requirements 

Even though looking into and adjusting the internal warehousing processes is the main key for achieving high-grade productivity, and operational performance, it is crucial to pay attention to the market and the industry we operate in. 

The data-driven operational strategy enables us to maximize our financial performance, by identifying market gaps and consciously differentiating from the competition, taking into account the activities of our competitors, as well as the industrial trends, customer needs and expectations. 

Performance objectives 

Data integration from innovative warehousing technologies helps us to set the right corporate objectives based on organizational performance and market requirements. Managers take several factors into account in order to achieve a decision-making data-driven process. Those are:

• To what extent have we achieved our past objectives?
• What were our main trammels?
• What are our strengths and areas of improvement at the moment?
• What do our customers want?
• What activities are our competitors undertaking, that drives them customer satisfaction and financial profit?
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All those, together with instant progress tracking and implementing real-time changes in strategic and operational planning, stimulate the warehouses to set operational objectives that suit their future development in the best way possible. 

Quality 

Data-based operations strategy gives the business entities the opportunity to identify quality issues and solve them quickly to prevent future compromising of the set standards. 

With such real-time quality tracking, the warehouses are enabled to minimize the committed errors and enhance the number of perfect orders processed, shipped, and delivered to the customers. This way, the grade of CX is enhanced, and eventually, the loyal customer base is expanded, resulting in better financial performance and less returned orders. 

Speed

The warehousing industry is the best example that represents the issue of Time=Money. 

This applies mainly to delays in terms of order processing. When an order is placed, it should be dealt with in the fastest way possible, without passing through any unnecessary stages or conducting extra activities.

In that sense, delays usually result in increased costs due to low productivity levels (and fewer orders handled for a time period), as well as longer employee working hours and machine usage. 

Data helps organizations to speed up their order processing procedures, without compromising the quality of orders. The data resources give valuable insights about the focal point, and the goal, in terms of the optimal time needed for order handling, and provide answers for process organization. 

Flexibility 

Data-driven operations strategy allows the companies to become flexible in terms of continuous implementation of new trends and changes for better performance, competitive advantage, and constant improvement. This is key because when those organizations are flexible, they are easily adaptable to the new market tendencies and are eventually the ones that never step back from the leaders in the industry. 

Cost

The data-driven cost reductions are entirely motivated by the previous points mentioned in this article. As a result, the warehouses who have implemented high-quality data strategy achieve lower expenditures in terms of:

• Utilities. Because of the optimized plant capacity. 
• Machine maintenance. Due to the instant identification of technical issues.
• Human resources. Because of tasks and roles optimizations. 
• Property rent/ Tax. Due to plant size adjustments according to the needs of the company. 
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Conclusion 

Adopting a data-driven operations strategy comes with tons of changes in multiple areas of operation. In the beginning, such drastic business modification may be difficult, but the benefits it offers are not to be underestimated or neglected. This strategy not only enhances the efficiency of internal business practices but also drives competitive advantage, market opportunities, and financial return.