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Solver for complex Bundles

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Hey fellow redditors, I'm developing a solution to automate the calculation of goods input for retailers. My company supplies various bundles, which change yearly and can include up to eight different items. To tackle this, I'm collecting data in a pivot table for easy access and creating a main sheet that combines last year’s output with bundle details. While I have a table set up for 100 stores and 25 bundles, I'm struggling with the main sheet format.

In the evolving landscape of retail inventory management, the challenge of accurately forecasting and fulfilling demand is increasingly complex. The recent inquiry by a Reddit user highlights a common scenario: navigating the intricacies of bundle-based product offerings while striving to optimize stock levels. This situation is not unique; many professionals face similar hurdles. As articulated in our previous articles, such as Setting up pivot tables properly for inventory tracking purposes and Manipulating large data sets in an Inventory management scenario, the ability to manage and analyze data effectively is paramount to driving productivity and profitability in this sector.

The Redditor's plan to utilize a pivot table to collate data across multiple stores and products is a strategic approach. However, the real crux of the challenge lies in the complexity that arises from a diverse array of bundles—each containing various items that may overlap with others. This layering of products necessitates not only meticulous organization of the data but also a keen understanding of how to format that data for optimal solver functionality. Clear, accessible presentation of information is critical; the right format can significantly reduce cognitive load and facilitate better decision-making. By focusing on user outcomes, the Redditor is on the right path toward simplifying a convoluted process.

As the user explores potential solutions, it’s essential to consider how the structure of their main sheet can enhance their experience. A well-organized sheet that clearly delineates bundles, item arrangements, and store-specific data will not only support the solver's calculations but also contribute to the user's mental clarity. Adding auxiliary rows or columns for calculated values may be beneficial, particularly for tracking performance against last year's figures. This kind of proactive planning can empower users to leverage their data more effectively, leading to improved forecasting accuracy and inventory management.

Looking ahead, the integration of AI and machine learning into spreadsheet technology holds transformative potential for professionals grappling with similar challenges. As retailers increasingly adopt these advanced tools, the future of data management will likely shift toward more intuitive, user-friendly solutions that automate complex calculations and provide actionable insights. This evolution will not only ease the burden on users but also elevate the overall efficiency of inventory management processes. Retailers must remain vigilant in adopting innovative technologies that simplify their workflows while enhancing productivity.

Ultimately, the question remains: how can professionals continuously adapt their strategies to leverage these emerging tools effectively? As we observe the ongoing developments in AI and data management, staying informed and open to new methods will be key to navigating the complexities of retail inventory management in the years to come.

Hey fellow redditors,

The situation:

I am currently working on a solution to automatically calculate the input of goods in retail.

I am working for a company that delivers goods to said retailer and want to create an offer based on last years Input.

The challenge is that we provide a large variety of bundles/displays (consisting of up to 8 different items), which change from year to year.

My Plan:

- collect all data needed in a pivot for easy access

- create a main sheet, which combines the list of all bundles with last years output (on store level inclunding out of stock compensation).

- Use a solver that gets me the best possible outcome (~ Not more/less than 1.2 times of last years output, while still managing a positive development compared to last year).

What have I completed:

- A table containing: storenumber (~100 Stores), EAN, output, additional Input based on last years out of stock.

- A pivot table containingh to data from above (to use the getpivot function)

What I am Stuck on:

- Finding the right format for the main sheet

What needs to be in there:

- I do need to have each bundle including the arrangement of items.

- A row/ column for each store, which holds the amount of bundles/displays that the solver suggests

Would not be as hard if there werent a lot of bundles containing items that are present in others.

How would you arrange said sheet to make it as easy as possible for the solver and my mental health. Do I Need an extra column/ row to calculate?

We are talking about:

~ 100 stores

~ 25 different bundles

~ 80 different items

Edit: Fixed some auto correction errors

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