Hard Problems and Soft Information

Coen Suurmond

RBK Group, Keulenstraat 18, 7418 ET Deventer, The Netherlands

csuurmond@rbk.nl

Keywords: Business Processes, Process Logic, Organisational Semiotics, Sales & Operations Planning.

Abstract: Finished product planning is a key business process for companies. It is about finding the balance between

service levels and cost, and is therefore critical for the success of the company. In this paper the structure of

the problem will be analysed and compared with literature about sales & operations planning as well as ERP

solutions. In the analysis general process logic will be contrasted with idiosyncratic characteristics of the

individual company. The use of different kinds of information will be discussed, in combination with the

formal sign system of the computer and the social sign system of human communication.

1 INTRODUCTION

Finished product planning is a key business process

for companies. This planning decouples demand from

production and it is a highly determining factor both

for market volume and for profit margins. On the

market side customer service level and lead time are

key for market share while on the production side it

is crucial to keep down variable costs. In this paper a

specific product group (fresh meat) for a specific

market (multi-store retail chains) will be used to

analyse the business processes and information flows

that are in play, and it will be examined how this

problem is covered in literature and by ERP software.

In this paper the paired concepts of process logic

/ idiosyncrasy will play an important part. Process

logic will mark the necessary general structure

underlying the actual business processes of the

individual company. This structure will always be

present in every business in a particular market,

because certain structures are inherent to and

inevitable in operating with these products on those

markets. Alongside this there are the idiosyncratic

characteristics of the individual company. Companies

after all do differ, even though they operate with the

same products in the same market segment. The

individuality of the company is the foundation of the

existence of any company on the market. As John

Kay pointed out, the distinctive capabilities of the

company are what distinguishes the company from its

competitors and that form the foundation for the

success of the company. And, again as pointed out by

John Kay: “A firm can achieve added value only on

the basis of some distinctive capability – some feature

of its relationships which other firms lack, and cannot

readily reproduce” (Kay, 1993, p. 64).

The analysis of business processes in terms of

process logic and idiosyncratic characteristics has

two aims. Firstly, it is a method to map business

processes in a way that helps external consultants

(specialists in general patterns) and internal

employees (specialists in specific details) to come to

a mutual understanding and a common basis.

Secondly, it is about the awareness of the intangible,

the understanding that not everything can be reduced

to schemas and fixed rules. Information systems

should not be a goal onto themselves but rather serve

to improve the market position of the company, which

they must do by adequately supporting the business

processes. The process logic provides insight into the

general structures, while the idiosyncratic

characteristics provide insight into the way in which

the processes actually happen within the company

(including the often blurred boundaries between

processes). This approach should also help in getting

a feel for the distinctive capabilities of the company,

competitive strengths that must be preserved and

possibly enhanced in developing a new business

information system.

Besides the paired concepts of process logic /

idiosyncratic characteristics the analysis of the nature

of the information in business processes with relation

to formal and social sign systems used forms a second

pillar of this analysis. Computer systems are formal

sign systems, highly capable in processing declarative

information, but they have trouble with vague

Suurmond C.

Hard Problems and Soft Information.

DOI: 10.5220/0006221900320038

In Proceedings of the Sixth International Symposium on Business Modeling and Software Design (BMSD 2016), pages 32-38

ISBN: 978-989-758-190-8

boundaries, “unclean” categorisation and weighing

heterogeneous norms that cannot be fulfilled

simultaneously against each other (delivery in full but

slightly delayed or delivery on time but slightly less

than ordered? Delivery should be at the right time in

the right quantity with the right quality, which ‘right’

might be relaxed in which context?). Social sign

systems are much better in dealing with meaning in

context, modalities, and intentions (discussed in

Suurmond, 2015). In the creation of a business

information system awareness of the nature of the

information and the conscious choice for the right

sign systems is critically important for the

effectiveness and efficiency of the business

processes. Of the coordination mechanisms identified

by Mintzberg direct supervision and mutual

coordination are much more based on informal sign

systems, while the application of formal sign systems

presupposes standardisation (Mintzberg, 1979). The

effectiveness and efficiency of business processes is

of course dependent on correct, timely and complete

information (Starreveld, 1963), but also dependent on

the degree to which the available information is

relevant and accessible (Grice, 1989).

The paper consists of six sections. After a short

introduction to the problem area, the business

processes involved are analysed in terms of process

logic and idiosyncrasy. This is followed by an

analysis of the nature of the information used in the

business processes, and the role of formal and social

sign systems in conveying the information. Literature

about sales and operations planning is discussed in

section five, and the papers ends with a recapitulation

and conclusions.

2 PROBLEM AREA

This paper is concerned with the finished product

planning for the production unit for pre-packaged

meat in retail chains. A typical production unit

produces some 100 to 200 different fresh pre-

packaged meat products on a daily basis. Incoming

shop orders need to be made available ready-to-ship

on the loading dock for transport, delivery reliability

must be above 99.7%. The products have an internal

shelf life of at most two days. Everything is produced

from fresh ingredients each day. And, of course,

waste and production costs must be kept to a

minimum.

Many products have a fairly stable demand

pattern. Demand is however highly irregular in case

of promotions, product introductions (which do not

have a demand history) and a number of articles that

are weather-dependent. Products which have been

part of a promotion in the past weeks and seasonal

products also have demand irregularities.

Planning is generally done in a large Excel in

which the order history over the past weeks for each

distribution timeslot is recorded along with the

demand prognosis for promotions and in which the

planner records the amounts per product per day that

need to be produced. This last list is processed further

within the production planning to create production

orders as well as lists for the resource, man-hour and

line-hour needs. Each business within this sector has

developed its own particular solutions over time, the

common characteristic is the use of Excel with order

history as input and production lists as output. The

expected demand for promotions is mostly

determined in a separate process and then made

available to the planner.

3 PROCESS LOGIC &

IDIOSYNCRASY

3.1 Process Logic

Every company that produces for a market in which

the lead time between order time and delivery time is

less than the time necessary to produce the goods will

work with a (semi) finished product stock. This stock

must be sufficient to fulfil the orders within the

delivery reliability requirements in this market.

Further, the stock has to be as low as possible because

of warehouse costs and to minimise waste. Finally,

production will have requirements regarding the

frequency and size of the production batches. All of

this leads to the following three processes that will

always be found (but, more often than not, implicitly

rather than explicitly):

1. Determining expected demand

2. Determining the target stock level

3. Determining the production output

The distribution pattern defines loading times for

groups of orders, before the scheduled loading time

the orders must be picked and made available at the

loading docks. Therefore, distribution and production

is organised in timeslots, in each distribution timeslot

a set of orders is picked, and in each production

timeslot a set of products is produced. Shops have a

timeslot for ordering. Expected demand is specified

per distribution timeslot, which would correspond

with one or more ordering timeslots. The end times of

ordering timeslots and distribution timeslots are

Hard Problems and Soft Information

fixed, the end time of production timeslots is more

flexible.

Given the daily delivery to the shops in

combination with the internal shelf life of two days at

most, the full product range will be produced and

distributed in a 24 hour cycle. For each day and each

timeslot expected demand will be estimated, target

stock levels will be set and production output levels

will be set. The planning moments are:

1. After each ordering timeslot

2. Just before the production day

3. Just before the production week

4. A few weeks before the production week

5. About four to six weeks before the production

week

After each ordering timeslot the planning is checked

and adjusted where necessary (and possible), one day

in advance production orders are generated and fresh

raw materials are ordered, the week before the

production week production schedules are set and

suppliers will be informed about expected demand,

and the same applies for the planning moment a few

weeks before the production week (same information,

less certainty. In the first planning moment about four

to six weeks in advance of production promotions are

planned (because of the increased quantities it is

important to have agreements in place with suppliers

regarding price and expected demand of raw

materials).

3.2 Idiosyncrasies in Individual

Companies

Although the processes described above in the

process logic can be found in each company in this

market, in organisation and actual execution a great

variety exists. Often, boundaries between the

processes are blurred, both organisationally and in the

Excels used for planning. Much of the knowledge and

information is personal and non-coded (Boisot,

1998). Many problems are either ‘spirited away’ by

some creative and experienced old hands, or solved in

informal communication. Sometimes, this is a good

thing, because the problem solving capabilities of the

company are much greater than one would expect

from studying organisation charts and documents

about process flows. Sometimes, it is good but too

vulnerable because of the dependencies on one or two

key figures in the organisation. Sometimes, it is bad

because problems are not really solved, only moved

out of perception.

4 NATURE OF INFORAMTION &

SIGN SYSTEMS

4.1 Information for Determining

Expected Demand

In the process ‘determining expected demand’

(forecasting) the single information product appears

to be a list with the expected shop orders grouped by

timeslot and by saleable item, and the primary source

of information is demand history. However, although

extrapolation from history to expected demand may

lead to a convenient list of hard data, it does not tell

the whole story. Firstly, demand history is never the

only source of information. In case of promotions and

product introductions no relevant and reliable

demand history is available (although, the history of

promotions and product introduction might offer

useful indications), and demand for some products

might be dependent on future conditions (weather) or

situations (events, bank holidays). Other information

sources are needed, and the information from these

sources must be interpreted in context. The

interpretation of the weather forecast, especially in

case of a possible sudden change of the weather, is an

example. The assessment of the impact of publicity

(kind and scale) in case of promotions and product

introductions is another example. Secondly, expected

future demand is never a single value, but rather a

spread. If you were to discuss expected demand for a

certain product with a group of experts, they would

say that demand is expected somewhere in the range

between X and Y.

4.2 Information for Setting Stock

Levels

Given the outcome of the process ‘determining

expected demand’, the job of the stock planner is to

set the target levels such that stock will be sufficient

when demand is at the maximum level of the range,

and stock will not be wasted when demand is at the

minimum level. Depending on the volatility of

demand and on the allowed storage life of products in

stock, the planner can have an easy job (steady

demand, longer internal storage life) or an impossible

job (highly volatile demand, very short storage life).

Impossible situations are not primarily the problem of

the planner however, especially if this is a recurring

issue. It is a problem of setting realistic norms for

planning, probably for someone higher up in the

organisational hierarchy.

It gets interesting in the border area when the

Sixth International Symposium on Business Modeling and Software Design

planner has a difficult but doable job. In this area the

planner must be creative and use all his available

knowledge and information sources. The planner

might collect further information from experienced

demand experts in order to reduce the expectation

spread, or organise extra production capacity in order

to react quickly (quicker than normal production

schedules would allow) in case of impending stock

shortages. The planner will juggle with delivery risks

and production reaction times in order to find his

solution. Background information about what was

possible or impossible in comparable situations in the

past and informal discussions play a major role in this

kind of decisions.

4.3 Information for Setting Production

Levels

The third process must set production levels such that

production efficiency is optimised. Lot size and

production capacity are the primary determining

factors. Both factors represent discontinuity. Often,

products (or semi-finished products) are optimally

produced in fixed amounts, due to the capacity of

machinery. Optimal operation times for production

lines vary in units of x hours (8 hours is a typical

value), due to work schedules (people work in shifts).

The combination of lot size and optimal operation

times will lead to a production mix that satisfies the

minimum and maximum stock levels set in the

preceding process. In standard situations this can all

be calculated according to fixed patterns and decision

rules. In some situations not all requirements can be

met at the same time. In these cases additional

information is needed, either in finding ways to

squeeze a little more out of production, or in making

sure that some products will be produced in the

required quantity, or in assessing the weight of the

different norms in the given situation and accepting

the additional risk or additional cost.

4.4 Sign Systems

Considered from the viewpoint of the process logic as

analysed above, the information about expected

demand, stock level planning and production-level

planning seems pretty straightforward and a perfect

fit for the domain of formal sign systems. Essentially

it is about three consecutive datasets with the same

structure: date, timeslot, item code, quantity. In

demand this dataset represents an expectation, in

stock-level planning and in production-level planning

the dataset represents a target.

Considered from the more detailed analysis of the

information actually used in real planning situations,

it will be clear that other kinds of information and

other kinds of sign systems are involved. The

inaccuracies of demand expectation, actual stocks and

actual production output must be dealt with; the

planner works with patterns based on experience and

history; risks and possibilities are discussed between

planning, sales and production; and accompanying

instructions are given to the operators in the shop-

floor processes. Information takes the form of

background knowledge, consulting colleagues, oral

communication, and written notes.

4.5 Idiosyncrasies Revisited

Companies differ from each other in the way they

execute their key processes, and for the production of

fresh food for retailers the finished product planning

is such a key process. In this planning process success

on the market (delivery reliability, lead times) and

internal success (minimising production costs) come

together. The way formal sign systems and informal

sign systems are used to support this difficult and

critical process is highly characteristic for each

individual company. Depending on the distribution of

knowledge and experience in one company the ‘real’

decisions and adjustments might be made by

production management (using demand and stock

information), and the planner is no more than a rather

passive Excel-driver and data cruncher. In another

company, the planner plans and the shop floor

executes. In a third company, production and

management meet each day in order to prevent

upcoming problems and smooth out existing

problems. Each company can either flourish or be

ailing. It all depends on the quality and the fit of the

information to and from the planning process and on

the quality of the persons who make the decisions.

5 SALES & OPERATIONS

PLANNING

The subject of the case is the coordination between

production and expected demand using finished

product planning, an area termed Sales & Operations

Planning in the literature and for which ERP systems

provide support. It is then useful to look at what the

literature says about this and to what extent literature

can support the analysis of the case. First a definition

from internet: “Sales and Operations Planning

(S&OP) is an iterative business management process

that determines the optimum level of manufacturing

Hard Problems and Soft Information

output”. This definition fits the theme of this paper,

although the term ‘optimum’ in the definition is an

empty shell without criteria and it is a planning

process rather than a management process. The

definition proceeds to state that “The process is built

upon stakeholder agreement and an approved

consensus plan. To help stakeholders agree on a plan

of action based on real-time data, S&OP software

products include dashboards that display data related

to equipment, labour, facilities, material and finance.

The purpose of the dashboards is to provide the

stakeholders with a single, shared view of the data”.

This is not true in the situation considered here. The

planner has a delegated responsibility to solve the

planning problem within the set norms and to signal

when he is structurally unable to meet the norms.

Occasional deviations are permitted (and delivery

reliability prevails over costs), structurally both

norms need to be met. Determining tight but

achievable norms is a mutual undertaking in which all

stakeholders are involved and in which at least

commitment, if not consensus, needs to be achieved.

Operational planning is very different in nature. This

holds even more strongly if Sales & Operations

Planning is not done on a monthly basis, but, as in the

case, must be done on a weekly and daily basis.

The definition in the APICS dictionary, which

should be an authoritative source given the status of

APICS as an organisation (“the premier professional

organisational for supply chain management”,

according to its website, with over 43000 members

and more than 300 international partners), provides

even less of a guide. Because of the language used I

will cite the very long lemma in full:

(APICS Dictionary, 2008, p.121f)

“Sales and operations planning – a process to

develop tactical plans that provide management

the ability to strategically direct its business to

achieve competitive advantage on a continuous

basis by integrating customer-focussed marketing

plans for new and existing products with the

management of the supply chain. The plan brings

together all the plans for the business (sales,

marketing, development, manufacturing,

sourcing, and financial) into one integrated set of

plans. It is performed at least once a month and is

reviewed by management at an aggregate (product

family) level. The process must reconcile all

supply, demand, and new-product plans at both

the detail and aggregate levels and tie to the

business plan. It is the definitive statement of the

company’s plans for the near to intermediate term,

covering a horizon sufficient to plan the resources

and to support the annual business planning

process. Executed properly, the sales and

operations planning process links the strategic

plans for the business with its execution and

reviews performance measurements for

continuous improvement. See: aggragate

planning, production plan, production planning,

sales plan, tactical planning.”

This is a definition (or description) of everything and

therefore of nothing. Why should Sales & Operations

Planning not be about the common daily operational

practice of coordinating demand and availability and

about no more than that? What does something like

“a process to develop tactical plans that provide

management the ability to strategically direct the

business …” add to our understanding of the

problem?

Donald Sheldon writes in his World Class Sales

and Operations Planning (co-published with APICS)

“The S&OP process can have a major impact on the

management of inventory” (Sheldon, 2006, p. 29). He

then devotes chapters to “Creating the Demand Plan”

and to “Operations Planning for the S&OP Process”.

For Sheldon the S&OP process is the coordination

between the various subplans (“Stated in its simplest

terms, the S&OP process is a monthly planning cycle

where plans for both customer expectations and

internal operations are reviewed for accuracy, process

accountability, lessons learned, and future risk

management”, Sheldon, 2006, p. 2), where it should

be essentially about the planning process itself. Of

course there is an important role for higher level long

term planning in companies to coordinate market

developments, production capacities and resource

needs. In this kind of higher level coordination

operational norms must also be determined and

adjusted, and possible measures should be agreed

upon to ‘land’ changed norms with the relevant

internal and external stakeholders. Donald Sheldon

recognises the subordinate role of software: “All that

is needed is a spreadsheet and good problem-solving

tools and skills” (Sheldon, 2006, p. 15). The question

remains, however, where the information for this

problem solving will come from, and how to organise

the different kinds of information flows (both formal

via systems and informal via humans).

Robert Davis analyses what he calls the push-pull

hybrid for supply chain management. “This hybrid

model is based on the premise that you push produce

and pull distribute” (Davis, 2016, p39). This analysis

matches what was described above as the structure of

the problem. His further analysis concentrates on

what is happening in the supply chain as a whole. The

chapter about inventory optimisation discusses the

development of inventory policies that can be

Sixth International Symposium on Business Modeling and Software Design

translated into algorithms and executed

automatically. This approach does address the

problem of how to develop ways of coping with the

problem of inventory levels, but it does not address

the problem of the individual planner who uses

information and who makes decisions.

Shaun Snapp (S&OP in Software) describes the

standard S&OP process as follows: (1) review and

sign off the demand plan; (2) review and sign off the

supply plan, and (3) review and sign off the financial

plan. And he gives the time features as a planning

horizon between 1 and 5 years, a monthly planning

frequency, and a monthly planning bucket. This is not

quite the horizon the companies discussed here are

working with. Planning of inventory levels is not in

his list of plans to sign off, Snapp discusses dynamic

safety stock in the chapter entitled ‘How

Misunderstanding Service Level Undermines

Effective S&OP’. He writes: “Safety stock is often set

in companies by simply allowing individuals to

guesstimate what the safety stock value should be and

then provides them with the rights to make the safety

stock adjustments” (Snapp, 2016, p. 105. This is

followed by the remark that in his experience he never

saw this working well, and “Stock levels should not

be controlled by manually adjusting the safety stock.

Instead, safety stock should be dynamically

calculated and automated, and only changed as a

result of changes in the variability of supply or

demand” (Snapp, 2016, p. 107). The last part of

course is true (from ‘only changed as…’), but the first

part presupposes that variability of demand is

represented perfectly in the computer system, with all

relevant information taken into account. This clearly

cannot always be true. And, if manual adjustment of

demand forecast is allowed, planners very quickly

learn the trick how to adjust demand in order to get

the safety stock level they want.

The ERP systems of course offer solutions for

S&OP. Hamilton in his book about MS Dynamics

AX, paragraph 10.1: “You can automatically

calculate the safety stock requirement based on

variations in historical usage and the desired customer

service level” (Hamilton, 2016, p. 236). A bit further

in the same chapter, in paragraph 10.7: “When using

the min-max coverage code, you specify the item’s

minimum quantity and maximum quantity for each

relevant site/warehouse. The minimum quantity

represents the average daily usage multiplied by the

item’s lead time” (Hamilton, 2016, p. 253). So, you

either have safety stock, taking variation of demand

into account; or you have a min-max policy where

average demand represents the minimum stock

needed? Dickersbach gives in his book “Supply

Chain Management with SAP APO™” the following

structure of the demand planning process (somewhat

shortened in my representation): (1) Forecast; (2)

Check on plausibility of the forecast; (3) Production

planning. Inventory planning is not mentioned at all.

(Dickersbach, 2009)

A picture of confusing, vague and contradicting

terminology in combination with conceptual

weaknesses arises from the works mentioned above.

Neither in the literature nor in the software clear

structures of the problem area are defined. A concrete

example of the translation of this messy approach into

actual customer requirements is the following set of

questions for candidate software suppliers by a

company in the food industry:

 “How will stock adjustments automatically

influence production schedule?”

 “Sequence of production is determined by the

scheduling process. Disruptions in other

processes (up and down) lead to automatic

rescheduling of production capacity; manual

adjusting to schedule needs to be validated and

recorded as an exception”

 “How will the production schedule be adapted in

case of (1) late delivery of raw materials”; (2)

delays in production runs; (3) changes in available

stock caused by quality inspections; (4) rush

orders; (5) break down of production lines;

 “Reject of the output of finished product at the end

of the production line, how will the system

adapt?”

These questions show a model-based and reductionist

approach to the sales and operation planning process.

The production system is provided a forecast from the

central ERP system (just one value, no information

about spread), processes this to an optimal production

plan, and any deviation or disruption results in

adjustments to the production plan. A fully

deterministic production is assumed, as well as full

and real time information about the actual situation

on the shop floor.

This approach encounters a number of related

fundamental objections: (1) production is not

deterministic, as a result production results will

always deviate from planning which leads to the next

question: what constitutes a deviation? (2) product

registration never fully coincides with production

reality, the view on the computer screen is in the best

case an abstraction of the shop floor reality (details

are not available or omitted) and in other cases a

distortion of reality (for example by enforcing

classifications in search lists that have not been

sufficiently thought through). And, imagine what this

Hard Problems and Soft Information

approach would mean for the primary processes on

the shop floor: a continuous flow of changes in

production planning, which will go at the expense of

effectiveness and efficiency.

6 RECAPITULATION AND

CONCLUSIONS

The problem area is about how to combine agreed

service levels with minimising costs. The Sales &

Operations Planning literature clearly indicates the

comprehensive character of the problem, different

viewpoints have to be taken into account. In

structuring the problem field the literature is less

helpful, firstly because of the confusing use of

terminology, secondly because of the time horizon of

months and years, and thirdly because of the lack of

attention for the day-to-day work and challenges for

the people involved in planning. Software, as could

be expected, does not help either. It offers toolboxes

of statistical instruments, dashboards, and algorithms

without much notion of how to apply which

instrument in which situation. The fact that a planner

is responsible for his decisions, and that the planner

has to combine information from many sources in

order to deal with variance, inaccuracy and conflict of

norms is not discussed in the literature.

Essentially, the problem area is about two main

control loops decoupled by a third intermediate

control loop. The first main loop is about service

levels, available stock and expected demand. The

second main loop is about production output and

efficient production. The intermediate loop is about

stock control, firstly for decoupling variance of

demand from smooth and efficient production

processes, and secondly for dealing with all kinds of

disruptions, deviations and inaccuracies in both the

business processes itself and in available information

about the business processes.

The approach is about finding solutions that do

justice to both the structure of the problem (process

logic, formal sign systems) and to the intricacies of

the particular company that must organise its

information in such a way that its competitive power

and distinctive capabilities are enhanced (how it has

found its own ways of dealing with the challenges,

idiosyncratic characteristics, social sign systems).

Human judgement and human communication must

be combined with computing power.

In terms of the coordination mechanisms of

Mintzberg we see a combination of standardisation

(and formal sign systems) and mutual adjustment

(with social sign systems). Standardisation and rigid

definitions of data help to organise information flows

and to automate the processes of determining

demand, setting stock levels, and setting production

levels in standard situations. Dealing with conflicting

norms in non-standard situations, however, is about

mutual adjustment and human responsibilities.

Squeezing out that little bit extra is about human

creativity and problem solving.

To conclude with Kay: distinctive capabilities are

about the characteristics that distinguishes the

individual company from its competitors, and that can

not be readily copied. The challenge is to find the

right combination of formal and social sign systems

that addresses the needs of actual planners in actual

companies and that builds on the existing competitive

power of the company. The high level talk of the

literature nor the reductive approach of ERP software

helps here much.

REFERENCES

APICS, 2008. APICS Dictionary, APICS, Chicago IL.

Boisot, M.H., 1998. Knowledge Assets, Oxford University

Press, Oxford.

Davis, R.A., 2016. Demand Driven Inventory Optimization

and Replenishment, Wiley, Hoboken NJ.

Dickersbach, J.T., 2009. Supply Chain Management with

SAP APO

, Springer, Berlin.

Grice, P., 1989. Studies in the Ways of Words, Harvard

University Press, Cambridge.

Hamilton, S., 2016. Process manufacturing Using

Microsoft Dynamics AX, Scott Hamilton Press.

Kay, J., 1998. Foundations of Corporate Success, Oxford

University Press, Oxford.

Mintzberg, H., 1979. The Structuring of Organizations,

Prentice Hall, Englewood Cliffs NJ.

Sheldon, D.H., 2006. World Class Sales & Operations

Planning, J.Ross Publishing, Ft. Lauderdale FL

Snapp, S., 2016. Sales & Operations Planning in Software,

SCM Focus Press, Las Vegas NV.

Starreveld, R.W., 1963. Leer van de administratieve

organisatie, Samson, Alphen aan de Rijn.

Suurmond, C.P., 2015. Information Systems and Sign

Systems. In Information and Knowledge Management

in Complex Systems. Springer, Berlin.

Sixth International Symposium on Business Modeling and Software Design