number of leading retailers have found in Machine Learning (ML) the solution to revolutionising their Supply Chain planning.
Fresh Food Retailing is, by all means, the higher percent of grocer’ revenue, as it has experienced a rapid diversification and an exponential growth. For instance, the consolidation of convenience-store chains, discounters, online players, fast food chains, whole food, organic food, gourmet food, and the list goes on. All these specialised Food Retailing has recognised the weight Fresh Food is quickly gaining to drive customer and to maintain loyalty to their products.
Machine Learning allows retailers to automate the once manual processes and to improve the accuracy of forecasts and orders. Retailers using ML technology for replenishment have seen its impact in many ways—for instance, considerable rising in out-of-stock rates, drops in days of manual inventory, and the rise of gross margin.
The customer demand and the offer of exotic-hard-to-find products are causing Fresh Food to become often complex and uncertain. Many retailers, to make it even harder , are offering "ultra fresh" food with the idea to stand out from competitors. This kind of food is offered as “being so fresh that they have a shelf life of no more than one or two days.
How do retailers manage to know the right amount of Fresh Food to supply?
As the constant-daily demand keeps fluctuating, and retailers have the need of placing orders quickly, they find it quite often difficult to make the right decisions. “Too much food will be waste; too little might make you lose customer’s loyalty.”
The advantages of Machine Learning in Replenishment
ML solutions can collect, analyse, and adjust large datasets from a wide range of sources, with lower investments in personnel. ML advanced algorithms currently used by leading retailers already examine an enormous amount of parameters:
- The parameter on each SKU in each store or each distribution centre on a daily basis.
- Also takes into account Supply Chain constraints, such as supplier delivery times and minimum or maximum order quantities.
- It generates order proposals for the entire product range every 24 hr, for product availability.
- It Minimises the risk of waste and markdowns.
- With best-in-class ML solutions, human intervention is significantly reduced.
- ML solutions are available as cloud-based, Software-as-a-service (SaaS) applications.
SaaS applications work making processes more flexible and faster to implement. These applications do not involve considerable investments in hiring new personnel; retailers can build its own staff’s proficiencies, get rid of a substantial amount of data-entry tasks, and redistribute staff time toward more value-added interests. ML can:
- Operate several food-processing plants.
- Integrate warehouse and manufacturing processes.
- Through just-in-time production reduce stock in the entire Supply Chain.
- Increase in-store product availability.
- Get fresher products on store shelves.
- Minimise workload at an individual store level due to a centralised and automated ordering process.
- Increased product availability for customers, while reducing write-offs.
How does ML do that?
Advanced ML algorithms build demand probability curves using sales and inventory data, making cost-benefit calculations, which assess the risk of waste against the risk of out-of-stocks.
The secret to smarter Fresh Food Replenishment is Machine Learning because it helps retailers determine optimal stock levels, taking into account both waste and lost sales, the store risks when having a discount or discard unsold units. In this situation, the algorithm identifies an optimal stock, demand probability, considers the value of costs for each stock level, taking into account potential loss of revenue due to out-of-stocks, as well as potential markdowns and waste.
The system can align individual ordering decisions with your strategic goals and Key Performance Indicators (KPIs); the algorithm will adjust decisions accordingly, and work toward improving several KPIs at the same time.
These developed algorithms can at once optimise pricing and replenishment, leading to even higher profit increases in fresh types; next, simulates changes in price and how it will affect demand. Then, the system will recommend more massive order quantities. When products are sold with a meaningful discount, the system would recommend smaller order quantities to minimise losses due to price cuts. Price flexibilities continuously change— oftenon a daily basis. ML can repetitively incorporate data on pricing and replenishment, making vast improvements in retailer’s value.
What do YOU have to do?
Retailers should take advantage to upgrade their business strategy for their Fresh Food departments concurrently, and turn that strategy into detailed assortment rules, for instance, defining the timing in which minor group and Stock Keeping Units (SKUs) should always be in stock.
You, as a retailer, must upgrade these business strategies for your Fresh Food departments and define suitable timing in which minor types and Stock-keeping Units (SKUs) should always be in stock.
It is advisable to assess the end-to-end Supply Chain planning processes continuously, both at the store level and at headquarters. Delivery frequencies should change, as well as the people processes, to ensure the store has a well-balanced replenishment process. The way the staff used to spend their time at work will be different and will need new performance management metrics and incentives.
Besides, retailers should make changes to capacity planning and labour scheduling in distribution centres. And finally, retailers have to re-evaluate the way they work with suppliers, negotiations of contracts, and possibly assisting suppliers to adjust their forecasting and ordering processes.
Final comments: When talking about short-life fresh products, automation achieved through Machine Learning will help you with your daily-changing demand orders and cope with Fresh Food Replenishment uncertainty. Supply Chain, Demand Planning and business strategy line up to bring you the better outcomes for your enterprise.
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