Navigating the Complexities of Ship-from-Store Operations
Louis-Philippe Kerkhove
Founder & CTO at Crunch | Professor of Operations Management at UGent
In an era where the lines between digital and physical retail spaces are becoming increasingly blurred, the "Ship from Store" strategy has emerged as a powerful tool for retailers. Not only does this model increase the overall responsiveness of the business, but it also addresses the issue of "sleeping inventory" — unsold stock that sits in stores, contributing little to ROI. However, adopting this strategy isn't without its challenges. It necessitates complex decision-making and a data-centric approach.
UNPACKING THE COMPLEXITY: KEY DECISIONS YOU MUST NOT IGNORE
Before diving headlong into implementing a ship-from-store model, retailers need to confront some crucial questions.
1. Which stores to include
Selecting the number of stores that will partake in this strategy is your first crucial decision. It's tempting to engage all stores, but a selective approach often yields better results. It isn't about maxing out all stores but choosing the right ones based on a complex array of factors, including location, storage capacity, and staffing.
High-performing stores may seem like the obvious choice, but remember that these stores usually have fast inventory rotation. It's often the mid-to-low performing stores that stand to benefit the most, turning sluggish inventory into profit.
Equipping a store to be capable of processing shipments also incurs a cost. Infrastructure has to be adjusted, and daily collection efforts from your shipping partners have to be arranged. All this comes at a cost, which must be weighed against the benefit of more points of shipment.
2. To Split or Not to Split: The Order Dilemma
Sometimes it might be cost-effective to split orders between different locations. Knowing when to do this can save not only money but also time - another form of currency in the fast-paced retail world. Furthermore, shipping items to a centralized warehouse incurs both time and financial costs, which can affect the overall speed and efficiency of fulfilling an order.
However, the decision to split orders is not solely dictated by operational efficiency; it also heavily leans on customer preferences and store objectives. Customers generally dislike split orders due to the inconsistency it brings to their purchase journey. Yet, they value faster response times, creating a paradox that retailers must solve. Additionally, consider that orders may include hot-selling items that stores would prefer not to lose to online channels.
Striking the right balance in this complex decision matrix requires astute data analytics and perhaps even artificial intelligence to weigh the pros and cons. Ultimately, the decision to split or not to split an order should be a strategic one, considering both operational constraints and customer satisfaction.
3. Updating inventory management
Balancing inventory is an art form. A new inflow of demand from online sources means that the inventory needs of your stores may change. It is possible to take a purely reactive approach, allowing only orders that include surplus inventory at stores, but this is unlikely to yield favorable results.
4. Simple Rules vs Full Optimization: The Game-Changer
Many businesses rely on heuristic methods for decision-making. But at what point does a fully optimized AI-powered model outstrip these simple rules? And by how much? In a data-driven world, this could be your competitive edge. Inversely, it may be the case that simple rules come very close to the performance of a much more complex solution, in which case it is wise not to over-invest.
However, even then the question remains which are the rules that actually perform best?
THE DIGITAL TWIN AS DECISION SUPPORT
A simulation model, often referred to as a "digital twin," can serve as a testbed for answering these questions. This dynamic tool allows you to virtually recreate your retail ecosystem, complete with stores, warehouses, and even customer behaviors. What's particularly remarkable is that this digital replica lets you tweak a multitude of complex variables as if they were simple knobs on a control panel. You can rotate these knobs to simulate different scenarios, thereby gaining invaluable insights into how even minor changes can ripple through to yield significant impacts.
The following could be some interesting "knobs" to include in such a model:
Once the settings of such a digital twin are set in accordance to the real-life situation various tactics for ship-from-store can be tested. This avoids the need for expensive experiments in practice. Moreover, it avoids over- or under-investing in software.
By embracing the power of a digital twin, you're not just taking a leap into advanced operational strategy; you're stepping into the future of retail. This is where data meets action, complexity meets clarity, and challenges turn into opportunities. Welcome to the next level of data-driven retailing.
CHARTING THE COURSE AHEAD
Ship-from-store is more than an operational tactic; it's a data play. With the power of data analytics and artificial intelligence, you can unlock unprecedented efficiencies and customer satisfaction levels. You're not just managing stores or an online portal; you're orchestrating a symphony of inter-connected, data-driven channels that work in sublime harmony.