Renovating Intelligent Operations in Supermarket Chains

Agostino G. Bruzzone

DIPTEM University of Genoa

Email agostino@itim.unige.it

URL st.itim.unige.it

Enrico Bocca

MAST Srl

Email enrico.bocca@mastsrl.eu

URL www.mastsrl.eu

Simonluca PoggiLiophant Simulation

Email simonluca.poggi@liophant.org

URL www.liophant.org

Abstract

This paper is focused on the development of

supermarket models for managing store resources; the

proposed approach is based on combining simulation,

time series analysis and data fusion for creating

forecasts able to support personnel organization. The

model operates receiving data provided by the

supermarket network; the time series data related to

sales, working hour, customers and material flows are

processed and then the forecasts are subjected to data

fusion and combined with simulation expectation of

workloads. The model provides forecasts of the total

operative workload for planning activities in

supermarkets; in addition the proposed approach

allows to estimate future values for target functions

such as sales and productivity and to compare

historical data in order to adopt predictive resource

and policy management. The proposed model is based

on a special architecture developed ad hoc by the

authors for managing retail networks.

This architecture has been implemented using Web

technologies providing an easy access for final users to

all the predictive models and algorithms. An important

added value of this research is related to the

identification and definition of new KPI (Key

Performance Index) devoted to validate and tune up

the model as well as to support management in

supermarket. This system was applied to a real case

study involving one of the biggest retail company in

Italy.

1. Introduction

Definition of workload is a problem involving

several aspect: aims of the company, customers

satisfaction, employee satisfaction and contractual

constrains[22]. In order to face all these aspects the

authors investigated an organizational model,

developed during early ’70 defined “island concept”

[1]. This model was applied initially in manufacturing,

Volvo was one of the first companies introducing tghis

concept, and this approach was tested after a while in

many cases, also in Italy (Olivetti, Pirelli, Laverda)

[19]. That model was created in order to reduce the

stress of employees that do repetitive jobs, varying

their jobs in a small environment called “work island”

[24]. Unfortunately this experience often don’t

provided good results, and sometime even further

increase the stress of the employees due to the request

to carry out concurrently different activities. Recently

this model was renovated in retail sector; by this

approach the companies try to combine customers,

employers and companies needs[13]. The critical point

it is to define a common reference baseline [20]; in fact

in retail, the customers are expecting good quality of

service (shelves full of goods and paying lanes quickly

served by many gates), employers are looking for self

organizing their time tables (i.e. having free time

consistently with their personal needs), while the

company looks for guarantee low costs and good

quality with high personnel flexibility (in order to

satisfy demand oscillations).

Island concept was reintroduced in large retailers

mostly for serving the cash barrier in mega markets; in

this context clusters of employers with alternative

expectations (i.e. mothers looking for being available

for child and singles interested in different free time

slots) for combining their coverage to the work load.

By this approach the company defines teams that are

requested to cover the cash barrier demand expected

by combining their internal needs in term of coverage

and availability[25].

2009 Third Asia International Conference on Modelling & Simulation

978-0-7695-3648-4/09 $25.00 © 2009 IEEE

DOI 10.1109/AMS.2009.98

425

Figure 1 - Work Load Estimation by RIO Models

In fact, the adoption of this approach only on pay gates

due to the single function requested to involved

employers strongly simplify the problem. The authors

was involved in further extend this experience to

medium and small stores where poly-functionality

requested to employers (i.e. serving at gates, filling

shelves, serving in different departments, accepting

goods) due to the reduced number of resources [3].

In addition these team are generated by smaller

employers pools so it is very difficult to find many

different social profiles for being integrated.

The success in applying this approach results to be

strongly related to much more sophisticated models

able to obtain reliable forecasts for such complex

multifunction workloads [5]. The authors aim,

presented in this paper is related to the developed of a

methodology that quantify the future workload able to

satisfy company and customer target functions and to

integrate in the organizational process where the island

coordinator combine team members requests in order

to finalize timetable for each employees guaranteeing

maximum mutual satisfaction. By this approach the

company target functions are safe based on model

performance baseline and at the same time the

customer expectations, while employers are free to self

manage the islands in order to comply with the defined

workloads [2].

2. Modeling store workloads

The definition of store workload is a critical issue

that traditionally is approached by retail operators

mainly by using the experience of the coordinators due

to the fact that many factors are sensitive (i.e. product

promotions, seasons, competitor behaviors, etc).

Simulation Team in DIPTEM had experiences in this

area since 2003 due to research projects devoted to

develop quantitative models [6]. The applied

methodology was based on Data Fusion techniques for

merging together different time series (i.e. workload,

ticket, sales) with operative procedures and contractual

constrains [26]; by this methodology it results possible

to elaborate a daily workload forecast, stepped every

15 minutes, for the next 50 days for each single

department in the store [4]. These results represent an

critical performance baseline to be used as starting

point by the store manager for finalizing the operative

timetables by applying, if necessary, changes due to

contingencies or personal evaluation [21].

The authors was involved in creating a solution in a

R&D project for implementing the model in retail

company processes by integrating them with the

information systems. The project was successful and

strongly supported the introduction of island concept

with major benefits in term of employers satisfaction

and motivation and with good results in term of quality

of customer service and company performances (i.e.

costs, productivity) [12]. However the experience in

this period put in evidence some open issue to be

solved to extend the effectiveness of these models. One

of the main problems in this development was related

to data processing and information sharing: in fact the

developed models require many data and the proposed

architecture of the whole system was based on batch

elaboration and FTP calls to transfer data from stores to

data warehouses, from different systems (i.e. Personnel

Management IT System, Sales Data from Cash Barrier,

Logistics Flows Data from ERP) in a common database

and then, from headquarters, back to the store users

[17].

This architecture was motivated mostly by the

dynamic evolution of the IT solutions in use in the final

user; obviously this solution was affected by several

problems in term of reliability and availability of data

due to the effect of the errors/problems encountered by

the different systems over a wide network of store [7];

in addition the maintenance of the historical data and

the sharing of the results obtained from the model was

426

restricting the effectiveness of the results; based on this

project can be used in order to have an overview of the

whole network.

For that reason the authors are currently involved in

new R&D project, presented in this study, that evolved

from the original models and that is devoted to achieve

the following main goals:

• Renewal of previous models with capabilities

in term of analyzing new processes effect on

the store (i.e. self check out, self scanning)

• Definition of a new architecture based on web

technologies for providing a net-centric

management of the Models and input/output

Data [10]

• Definition of KPI for validate and tune up the

model and for supporting management of the

point of sales and centralized control on store

network.

3. The architecture and conceptual model

The conceptual model that define the architecture of

the whole system can be represented by using the

following scheme

Rete Di

VenditaRete Di

VenditaRete Di

VenditaNetwork

Rete Di

VenditaRete Di

VenditaRete Di

VenditaNetwork

Appl

Server

Data Fusion

DB

Server

Database

Updates

Web Access

Forecast

History

•Work

•Ticket

•Sales

BW

Coordinator

Figure 2 – RIO Architecture

The data are collected for each single department of

each point of sale; these data include time series in use

are related to different variables:

• Sales [Euro/t]

• Tickets (representing paying customers) [tickets/t]

• Past Workload (representing employers use)

[people/t]

• Material Flow (representing the flow of goods)

[pallets/t]

Each of these variables is collected both in term of

daily values (t = 1 day) and in term of time frame

values (t = 15 minutes).

The data are transferred daily from the network to

the Central Data Base Server, through the existing ERP

(Enterprise Resource Planning) system, and then

accessed by the Application Server. On the Application

Server the “engine” update its input data and elaborate

forecasts of the workload for the next 50 days, in

addition the new model provides also sales, customers

and logistics flows estimation. Through the network

(LAN or WAN) each coordinator access and modify its

proposal using a web application [8]. This application

has been titled RIO as acronym of “Renovating

Intelligent Operations”.

Figure 3 - RIO Netcentric Management Framework

By analyzing the Workload expressed as working

hours, measured for every day/timeframe and for each

department of each point of sale, it becomes possible to

know how many employees are at work. In order to

know how many customer visit (and buy) the sales

point it is need to extract from the ERP system the

number of tickets emitted from the cashes (the data is

available for each departments of each sales point).

As anticipated all the variable above described are

sampled every 15 minutes in the time frame 0-24, is

also available the same data cumulated over the 24

hours in this case including corrections due to special

permissions and personnel re-assignement that are

introduced in the database the following week.

In addition to the number of customers is useful to

know how much are the sales (expressed in €) for each

department of each sales point. This variables are used

in the model in order to evaluate the flow of people in

the sales point in this way the model correct the

workload in order to improve the customers

satisfaction. In order to consider, inside the models, the

workload related to the operative procedures (such as

the loading of the shelves and the unloading of the

truck coming to the supplier) the authors decided to

collect the package delivered every day.

Using a Data Fusion approach that consider all the

variable described above the system calculate a

workload forecast for the next 50 days stepped by 15

minutes [9]. The variable are evaluated based on their

mean through a quantitative fuzzy analysis [27].

Another important feature of this new solutions is

represented by the possibility to directly access the

historical data (depending of the permission of each

user) for each store/department; in fact this provides a

very powerful investigation support to users and it

allows also to directly update, in real time, the

parameters of each sales point trough the network.

The RIO database contains different kind of data that

are mapped as proposed in the following table:

Table I – Data CharacteristicsInput

[Past]

Output

[Future]

Resol

ution

Past Horizon Future

Horizon

Time-

frame

Sales,

Customers,

Goods,

Workloads

Sales,

Customers,

Goods,

Workloads

15

Minutes

Last 3 Months plus

corresponding 3

Months of the last

year

50 Days

Day Sales,

Customers,

Goods,

Workloads

Sales,

Customers,

Goods,

Workloads

1 Day 2 Years 50 Days

427

Figure 4 – Target Function Forecasts by RIO

RIO provides services for navigating historical data

and forecasts and for completing analysis and

providing performance measurements, in respect with

the user profile authorization levels.

Figure 5 – Productivity by RIO

Figure 6 – Different Store Comparison by RIO

4. Models fine tuning

The RIO model has been applied to a real case study,

involving one of the major Retail Company in Italy.

During this phase, the researchers focused on a pilot

case involving over twelve Supermarkets, each one

including five department; for this pilot all the

supermarket resulted to be located in the North West of

Italy.

In order to setup the models, different KPIs (Key

Performance Index) was defined by the authors and

allowed to measure the difference between the forecast

of the model and the real data,.

Index J1 is used to measure the weekly seasonality

∑⋅

=⋅

−

=

m

h

hh

m

RIOREALJ

7

1

17

Where:

m number of weeks

REALh workload real value for h-th day

RIOh workload RIO forecast for h-th

day

Index J2 is used to measure weekly workload

∑⋅

=⋅

−=

m

h

hh

m

RIOREALJ

7

1

27

Where

m number of weeks

REALh workload real value for h-th day

RIOh workload RIO forecast for h-th

day

Index J3 is used to measure the daily periodicity

∑⋅

=⋅

−

=

n

k

kk

n

RIOREALJ

96

1

396

41

41

Where

n number day of the week

REAL 1/4h workload real value for the k-th

quarte

RIO1/4h workload RIO forecast for the k-th

quarter

The tune up of the model is made by combined index

that mix all the previous index, in that way is possible

to obtain the best fitting of the model using a mixed

target function that consider the different aspect

described above.

3322114 JkJkJkJ ++=

428

k1,k2,k3 weights for combing the overall

target function

The best fitting of the model is obtained minimizing

the index J4 with a fixed set of weights corresponding

to the users preferences [14]. In order to complete the

model fine tuning the authors adopted a stochastic

adaptive approach, modifying each store/department

parameter affecting the forecasts algorithms [23]. J4

behavior, as presented in the following figure, provides

the optimal tuning for a store/department configuration,

in this case the control variable tested is a parameter

used in the model to control the weekly workload that

obtain best fitting near to 10 value.

Figure 7 – Best Fitting respect Dept/Store

Alg.Param.

By using this target function on a set of data related to

an additional week keep out of the fine tuning just for

setting it becomes possible to measure the robustness

of the models respect the stochastic components; in the

pilot RIO was obtaining an average error in the bound

[3% - 10%]; these results represents the difference

between RIO forecasts and real data [16].

Some examples of the results obtained from model

tuning activity, are shown in the following graphs,

where red bars corresponds to real data, while blue bars

are RIO forecasts.

Figure 8 – Comparison of Real and RIO

Estimations

Figure 9 –Real DATA and RIO forecasts

Considering the far forward forecast of the workloads

provided by the models the measured errors are

considered by experts pretty satisfactory.

The validation and verification of RIO was

proceeding over an additional set of stores to extend

the island concept [18].

5. Results & Conclusions

The results obtained by RIO models was really

satisfactory and overpass the major open issues

affecting previous solutions; in fact the reliability of

the general architecture, by applying new algorithms

and web technologies, strongly improved [15]. Data

availability was much higher as well as the reliability

of the forecasts.

The performance of forecasts was very satisfactory

based on these more stable data set, corresponding in

each department/store to less than 10% error (respect to

resource use) on timeframe estimation within next 50

days; considering that a time frame corresponds in our

case to 15 minutes, the capability to define within this

tolerance how many person to assign to each

department in each store with seven weeks in advance

in each time slot of 15 minutes over the daily opening

of the point of sales it is very impressing. However it is

important to state that RIO is far away from being a

crystal ball, however statistically its forecasts are pretty

robust and provide a very good baseline for planning

store operations and controlling store resources. In

addition the new web based architecture allows to

introduce netcentric management of the stores, this

approach introduce the possibility to keep under

control key performance indexes in the store and to

measure and compare (respect baseline) the

performances of new departments/team [11].

Currently the authors are working forward on further

extensions of the existing models in order to cover

specific projects related to self-scanning and self-

check-out initiatives; these processes introduce new

activities, tasks and people assignment, however it is

very critical to proper measure the real performances

and requirements as well as to compare different stores

and different areas for identify critical issues.

429

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