Retailers work in distributed environment, where retail stores are located across geographies. Store performance is driven by multiple local factors – which are different at each location. While socio-economic diversity is a key factor, climate plays a different role in terms of Product Sales and Stock consumption. Similarly many different product and consumer attributes are important for in-season store sales performance.
However due to typical centralized buying business structure, Store specific requirements are often ignored by planning team. This is primarily due to large number of Style / SKU variation along with Number of Stores. Data volume and linked attributes plays a crucial role in selecting right product for right store. This is essentially important to ensure customer finds the right product at right time; which leads to increased customer satisfaction and build customer loyalty towards Retailer.
Point of Sales Data, Stock Consumption and Available Inventory at Store, Customer preferences, demographics, product choices and suppliers capability becomes almost important to track and manage. This is a Big Data processing and as on today, this takes place using BATCH MODE Data processing.
For example 600 retail Store company , managing 30000 SKU and take Sales as single attribute – the simple calculation results in 18 Million Combinations. However, we are discussing multiple Attributes and taking effective demand supply planning decision, which is critical for store financial performance as well as Customer expectation. Retailer have to meet up Local customer requirements and that is the Business Requirements of the day.
So 3 Key critical Issues for effective management of complex planning and effective demand supply:
– How do we plan effectively @ Store / SKU level?
– How do Store Team and Central/ Regional Operations / Planning team collaborate –Single View of truth ?
-Merchandise Planner and Store Manager have equal “SAY” in managing Product Performance well as Store Profitability ?
In today’s ERP Landscape POS Data including Supplies Data is available in BW and POSDM using Batch Job data extraction. Data analysis and initiating changes based on Sales Trends in real time basis at STORE / SKU becomes a Herculean Task or simply becomes impossible to achieve desired results quickly. Cluster Plan and Store Plan on SAP HANA based solution is specially build to take care of large data handling and predicting the store sales forecast / stock requirements on real time basis.
– Using trickle functionality POS data can be collected on real time transaction basis, Simultaneously using SAP HANA Sales and Stock data can be processed along with supply data at any attribute level and store specific demand can be predicted.
• Dynamic Cluster engine can be used to aggregate this demand on real time basis(end of Batch Job- thanks to SAP HANA) and subsequent supply chain decision can be taken either at Cluster level or store level.
• While planner making change at Cluster level – change using aggregation and distribution algorithm) can be seen at store level on real time basis.
• Plan balancing between Cluster Plan and Store plan can take place on real time basis – (no Batch Job required).
• Store Manager can review and suggest the change back to Central team on real time basis.
• Store Plan changes can be seen by Central Team instantaneously at Cluster level.
Store Plan and Actual data can be analyzed on real time basis on one side. On the other side, Cluster Plan can be aggregate (using custom aggregation algorithm) the Store plan/ Actual POS data at Cluster Level (Static Cluster – Region, State, City,
Market, Store Type, Store Class. Dynamic Cluster – Sales Through / STR / Sales Ranking / Stock Level based Store Cluster or Cluster Algorithms like Break Point clustering logic)
While Cluster Plan changes impact the Store plan, store Manager can also analyze the store performance along with change suggested by Merchandise Planner team. Store Manager can compare the store plan and offer suggestive changes back with central / regional team on real time basis.
Due to SAP HANA processing power 2 key aspects are going to be game changer for Retailers:
– Local Store Planning becomes essential for every Retail Store – hence store planning will be done on prevailing need to cater local customer effectively.
– Usage of dynamic Cluster Planning – going to be key for Retailers.
Finally Store Performance can be analyzed at cluster / store level by Merchandising Head & CFO on real time basis.
This makes the whole Cluster planning and Store Planning as interactive Productivity tool – rather than just a Retail Planning tool – Thanks to SAP HANA…
Based on SAP HANA Big Data performance – it is obvious that Retail Planning process will take a change fundamentally– where along with real time planning – collaboration across the distributed environment will be the key factor for Retailers success and this surely going to shorten the complete supply chain Time cycle. Accordingly stress and emphasis of effective / dynamic supply chain comes in play – very much.
Groupsoft SAP HANA Initiative – Thanks to SAP HANA Startup Program
Groupsoft is working on SAP HANA based Cluster / Store Planning solution since last 6 months, Groupsoft has build the SAP HANA competency on top of our SAP Retail competency and have Design and developed Cluster and Store Plan on SAP HANA. This solution was show cased in SAP Teched Las Vegas 2012. The offering will shortly be available on Amazon Cloud
Retail planning is considered as Science as well as ART. At this time Art is missing ! Watch out our next Blog ; Groupsoft team is working on some break through progress where Art will be embedded into SAP HANA solution.