Fresh lab webinar: Automate fresh food replenishment and cut waste by 40% and more

RELEX Fresh Lab Webinar 1 21 January 2014

AUTOMATING FRESH FOOD REPLENISHMENT

Agenda

About fresh webinars and RELEX

Key elements of automated fresh replenishment

Cases: Stockmann and Tuko

RELEX agile implementation

Q & A

Fresh lab webinar themes

Webinar 2: Special situations in fresh

replenishment

30 January 2014

Reviewing the key elements for automated replenishment• Daily SKU-location level forecasts• Shelf-life management• Optimizations and workflow

Leveraging efficient base process and flexible reporting to control special situations:• Seasons• Campaigns• Holidays

Webinar 1:Key elements of fresh

replenishment

21 January 2014

RELEX in brief

• RELEX offers solutions for demand forecasting, automated replenishment, inventory optimisation, and supply chain analytics– Built especially to the requirements of grocery

retail: incorporates short life-cycles, seasonality, campaigns, assortment management and chain structure of delivery networks

• RELEX is the fastest growing supplier of SCM solutions in Europe– Highest ranked SCM solutions provider on the

Deloitte Technology Fast 500 EMEA list in 2011, 2012 and 2013

– 70 supply chain professionals working at offices in Finland, Sweden, Germany and the UK

RELEX solutions are used for managing and controling material flows at 50 customers in 12

European countries. These cover millions of SKUs worth billions of euros

RELEX references

Agenda

About RELEX and Fresh webinars

Key elements of automated fresh replenishment

Cases: Stockmann and Tuko

RELEX agile implementation

Q & A

The challenge and the potential in a single word – spoilage!

Harnessing accurate day level calculations and productshelf-life is the key to mastering spoilage automatically!

In order to get the balance right for the fresh replenishment in the firstplace, one must be clear on availability and service level goals and

priorities!

Day-level forecasts Day level safety stock parameters

Simulated spoilage Expected shelf-life factor in safety stock

FORECAST SAFETY STOCK

Identify weekly repeating demand patterns wisely and use them in your forecasts

• Finding the right calculation level for automatically calculated weekday profiles is essential

• One should have statistically significant data to identify the profile automatically

• Combine lines from different aggregate levels to achieve this:– in product dimension: upward in product hirarchy or– in location dimension: towards the chain

Example: Identifying weekday profile level (for Bread)

Product-location

Product

Product group

Build capability for day-level safety stocks

• Calculate day-level safety stocks for products with differing day-level forecast accuracy

• Take into account delivery patterns: long intervals provide the biggest risk of spoilage

Further embrace fresh specifics in replenishment byincorporating expected shelf-life into safety stock

• In practice, this means– expected shelf life parameter can be used to limit your safety stock

calculation to a max ‘x’% of expected shelf life forecast– Or there can be exception reports pointing out when safety stocks

are likely to creep over a set threshold of shelf life

Example: Incorporating expected shelf-life to safety stock parameter calculation

In addition, capitalize on forecasted spoilage

• In DC environments, the stock levels are typically maintained on batchlevel and last-sales-day information is systematically available

• In retail environments, we have forecasted spoilage by simulating the balance consumption by using a first-in-first-out assumption

Example: Anticipated spoilage in an outlet environment

In order to make all previous happen, the system should allow for flexible modification of low level data!

• Big results come from small improvements in countless SKU-Store -combinations.

• Ideally, the replenishment system should: – Support flexible drill-down and aggregation of all figures available– Enable modification of numbers on any aggregate levels– Allow for handling large data sets efficiently

…however, there are still exceptions… the next webinarwill shed light on these!

Webinar 2: Special situations in fresh

replenishment

30 January 2014

Leveraging efficient base process and flexible reporting to control special situations:• Seasons• Campaigns• Holidays

Agenda

About RELEX and Fresh webinars

Key elements of automated fresh replenishment

Cases: Stockmann and Tuko

RELEX agile implementation

Q & A

Case: Stockmann

• Goals and challenges– At the onset, Stockmann’s ordering process was manual and required a lot of effort.

The goal was to move store resources from ordering to customer service.– Successful automation would need to address the challenges presented by fresh

food, campaigns, as well as seasons and holidays

• Store replenishment is currently delivered as a service by Stockmann’s sourcing and logistics service provider – Implementation started in May 2011 with a pre-study and the replenishment

system was ready for production use in August 2011

• Main results attained by Stockmann– Improvement of on-shelf availability by more than 2 percentage points– 40 % less spoilage– Over 90% of the orders are accepted automatically, without any manual

interventions, which makes ordering less person-dependent and easier to manage during vacations

Stockmann is a leading chain of department stores. Stockmann’s department store concept includes large, high-end grocery departments. The turnover of the company’s grocery business in 2011 was 271 m€.

Case: Tuko Logistics

• Goals and challenges– Ambition to increase the level of automation in purchasing and to reduce inventories– Efficient replenishment of perishable products required more accurate and timely

forecasting– Customer chain level forecasts were needed to better account for impact of customers’

assortment changes– Need for better management of seasonal variation in demand as well as campaigns

• Implementation through piloting– RELEX’s solution was piloted in highly perishable product ranges with strong seasonal

variation– After successful pilot, a decision to switch from JDA E3 AWR to RELEX was taken

• Results attained by Tuko Logistics– The need for resources in purchasing has been reduced by 1/3– Inventory turnover has increased– Store replenishment and purchasing to the central warehouse are managed using the

same system, making it easier to manage campaigns, assortment changes and unusual delivery schedules

Tuko Logistics is a sourcing and logistics service provider serving more than 1000 retail outlets and approximately 4500 food service and HoReCa clients. Tuko Logistics’ Turnover in 2012 was 790 m€.

Agenda

About RELEX and Fresh webinars

Key elements of automated fresh replenishment

Cases: Stockmann and Tuko

RELEX agile implementation

Q & A

RELEX value promise is enabled by big data technology

RELEX Big Data Technology

• Columnar database• In-memory analytics• SaaS-based

RELEX Agile Projects

• Easy to integrate• Easy to pilot• Easy to scale

RELEX Value Promise

• Risk-free: pay as you go• Flexible: Adjust vision• Rapid and reliable: Quick

results

Thank you!

Any questions?