This document summarizes Remsoft's 20 years of experience operationalizing analytics in forestry planning. Key points: - Remsoft has evolved from a small software vendor to a solutions provider helping clients with short-term operational planning problems. - Operationalizing analytics involves making optimization tools accessible to non-experts through collaborative problem definition, reusable workflows, and fast turnaround of models. - Remsoft is working with Coillte, the Irish forestry board, on an operational planning solution using Remsoft technologies to schedule harvest crews while meeting resource and delivery demands. - Critical technologies developed over 20 years like Woodstock, the Allocation Optimizer, and model publishing capabilities now enable Remsoft's

1. Operationalizing Analytics in Forestry:
20 Years of Remsoft Experience
Karl R Walters
Senior Solutions Analyst
Remsoft Inc., Vancouver, WA, USA
Email: karl.walters@remsoft.com
Abstract
Remsoft has been in business over 20 years and has witnessed large-scale changes in technology
and the forest products industry over that time. From its beginning as a small software vendor
specializing in forest fire management software, Remsoft now licenses its forest management
applications to over 200 clients around the world. In the last 2 years, Remsoft has evolved into
more of a solutions provider than software vendor as clients have sought help with problems that
have short planning horizons and many, linked decisions. Implementing a solution to this kind of
problem can be described as “operationalizing analytics.”
Operationalizing analytics is basically about making operations research and other
analytics tools more accessible to decision-makers within an organization who are not subject
matter experts (non-modelers). Key to the process is a collaborative work environment, a reliable
modeling foundation, and a repeatable, industrial process. The latter requires 5 elements: a
systematic approach to data, workflows that support reuse and automation, engagement of less
technical users, fast turnaround and ongoing monitoring and management of models.
In its recent engagements with Coillte, the Irish Forestry Board, Remsoft has proposed a
solution to Coillte’s operational planning problem: the scheduling of harvest crews to felling
areas while ensuring that resource capabilities are met and deliveries of forest products meet
customer demands. The solution is based on a combination of Remsoft’s technologies developed
over the last 20 years that, together with technological advances in computing and mathematical
programming solver technologies, meets the criteria for operational analytics.
Walters, K.R. 2013. Operationalizing Analytics in Forestry: 20 Years of Remsoft Experience IN
Proceedings of the 15th
Symposium on Systems Analysis in Forest Resources, Quebec City, QC,
August 19-21, 2013.

2. 1 Introduction
Twenty years ago at the 1993 Symposium on Systems Analysis and Management Decisions in
Forestry in Valdivia, Chile, a paper was presented describing a new forest management modeling
system [1]. At the time, it was more of a curiosity than a commercial product, having no real user
base beyond one or two tinkerers willing to try something new and different. FORPLAN [2]
remained the required planning model of the US Forest Service, FOLPI [3] was widely used
throughout New Zealand and Australia, proprietary mainframe systems were still in use by the
large, integrated forest companies in the U.S and Canadian public lands agencies of the provinces
largely eschewed optimization in favor of inventory projection models. In the face of so many
competing alternatives, why develop yet another planning model?
Fast-forward 20 years and nearly everything just described has changed. FORPLAN and
FOLPI as modeling platforms are now largely historical footnotes; the North American
integrated forest products companies first consolidated into a few larger players and then
subsequently divested their timberlands to the investor class; public lands management in Canada
has evolved beyond a sole timber management emphasis to encompass real multi-objective
resource management; and the most recent version of that software first presented in 1993 is now
licensed by over 200 organizations across 6 continents who collectively manage more than 500
million hectares of forest land.
Over the last 20 years, as Remsoft has acquired clients around the globe, its developers have
had to adapt to new terminology, new planning concepts and indeed new problems that do not
arise in North America. This led to new feature sets that not only facilitated adoption in those
new markets, but also enhanced the product for existing users as well. Particularly in South
America where rotations are fractions of those in Canada, clients have challenged our developers
to not only broaden the scope of the core modeling platform, but to think beyond just the
conventional notion of forest planning as a tool for forest managers only. When rotations are less
than 10 years, there is a sense of urgency around strategic planning that just doesn't exist when
rotations are measured in decades. It is difficult for a Canadian forester in Northern Alberta to
fathom that planting the wrong clone in the wrong month can have a significant financial impact
in Brazil, when that full Brazilian rotation will elapse before the first determination of whether a
clearcut in the boreal forest is satisfactorily restocked. Yet, for one of Remsoft’s Brazilian clients,
Suzano, linking the time of harvest with the decision of what clone and what month to plant it in
was deemed critical, and doing so has yielded productivity gains of 5-10%.[4]
While Remsoft’s commercial success is no doubt highly correlated with its moves into new
markets and associated new insights, the huge (and more recent) interest in big data, analytics,
business intelligence and the corresponding investments in technology to capture, store and serve

3. data has made it more realistic for Remsoft to offer solutions at the tactical and operational
levels. Twenty years ago it wasn’t realistic for a company or organization to think about an
automated operational planning model because neither the data, nor the technology to transmit
that data was available. Now with improvements in computer processing power, software
development tools, mathematical programming solver technology and Remsoft’s efforts in
developing new application programming interfaces (APIs), much more automation can be
brought to bear on tactical and operational forest planning.
2 Operationalizing Analytics in Forestry
A recent white paper [5] provides a very good overview of operational analytics (OA) and how to
implement it:
“Operationalizing analytics has three elements: a collaborative environment and shared
framework for problem definition to ensure that the analytics created are solving the
right problem; a repeatable, industrial-scale process for developing the dozens or even
thousands of predictive analytic models needed; and a reliable architecture for deploying
and managing predictive analytic models in production systems… The solution to
operationalizing analytics involves the effective combination of a Decision Management
approach with a robust, modern analytic technology platform. Such a combination
focuses analytics on the right problems and effectively integrates analytical results
directly into operational systems for faster and more profitable decisions... An
industrialized process incorporates five key characteristics:
 A systematic approach to data management.
 A predictive analytics workbench environment that supports reuse,
automation and repeatability.
 Engagement of less technical users.
 High performance analytic architecture for fast model turn-around.
 Ongoing management and monitoring of models.”
Within much of the forestry sector, the decision management approach has been and remains
hierarchical forest planning using separate but linked models to address strategic, tactical and
operational planning problems. For many forestry organizations who have started along the path
to operational analytics, the analytic technology platform is Remsoft’s Woodstock Spatial
Optimization System (Woodstock) [6].
2.1 Operational Planner –OA being implemented at Coillte
Coillte is a state-owned forestry company in Ireland that manages over 400,000 ha of forest land
throughout the country. In addition to 2 pulp mills and an oriented strand-board mill that Coillte
operates, Coillte also supplies pulpwood, stakes and sawlogs to 15 additional private mills.

4. Remsoft has worked with Coillte since 2011, first to develop their strategic planning capabilities,
and more recently to help with production and delivery planning.
Once compartments are identified for harvest, Coillte managers undertake detailed
inventories of each one and decide potential harvest methods that can be employed on each. Each
harvest method specifies the type of machinery used to harvest and how the trees will be
merchandised, resulting in different log sorts for each method. Coillte employs a number of
harvest crews that are capable of carrying out different harvest methods, and the production rate
of each crew is known. The problem essentially is to minimize harvest and hauling cost by
allocating crews to compartments such that weekly volume demands of Coillte’s customers are
met, crew and trucking capacities are not exceeded and compartments are completely harvested
before moving on.
The planned approach is to formulate the problem as a mixed-integer programming
problem using Remsoft’s Woodstock and Allocation Optimizer (AO) software. Coillte maintains
corporate database systems for tracking weekly customer demands and prices, tracking deliveries
to mills, and issuing work orders to harvest and trucking crews. Because these systems are
housed in different databases, processes are needed to bring all the information together in one
place, called a DataMart. This shared data warehouse contains all the information required
throughout the Operational Analytics solution, and is populated via business processes that
source both Coillte's corporate database system and the operational model(s). It is important to
note that the DataMart is not a standard database that queries other systems, but rather a schema
and set of processes Remsoft helped Coillte develop that enables a systemic approach to data
management. With other clients, the DataMart is likely to be different since it is a result of an
elicitation process: ultimately it might be one database, a series of databases, or a cloud source –
and both the client and Remsoft need to populate it.
A standardized Woodstock model template (“foundation model”) was devised that
incorporates Coillte’s corporate naming conventions for felling units, harvest crews, harvest
methods, customer IDs, etc. within their database systems. Using Remsoft’s Integrator
technology, the foundation model can then be updated with the most recent prices, mill demands,
crew capacities, and available felling units from the DataMart to generate a new planning model
that simultaneously assigns crews to felling units and schedules the delivery of products to
various mills.
Once the model is solved, the results will be automatically published and made available
to regional managers for verification and/or modification through a Remsoft Analytics
application. Using an Excel interface, managers can examine the crew schedule (via tabular lists
or Gantt charts) and make any needed changes to the schedule (e.g., change the crew on a block,
change the cutting method on a block, advance or delay the harvest of the block, drop a block or

5. crew completely for that time period, or change destinations for products. Any changes are saved
and posted back to the DataMart. Presumably, if the changes are minor and do not violate any
constraints, the schedule can then be uploaded to corporate servers for issuing work orders.
Otherwise, if constraints are violated, the model will need to be updated with the changes, and re-
solved before final verification and posting back to the DataMart. Implementation of this system
is now underway.
The OP application fulfills the requirements of the industrialized process:
 there is a systematic approach to data management through the DataMart;
 the template model and Integrator supports automation and repeatability;
 the end-users of the Analytics application are crew managers, not modeling
experts;
 the system is run on a weekly basis or more;
 the underlying template is a Woodstock model that can be readily updated by
Coillte’s in-house Woodstock modeling experts using standard syntax.
Remsoft’s Chief Technology Officer opined that the recently-released operational
planning (OP) application would not have been viable without the development of several critical
technologies: the Woodstock modeling language, Allocation Optimizer (AO) module, the
Regimes module (RM), robust mixed-integer solvers, the model Publisher and analytics
framework API and Remsoft Integrator (RI) [7]. It is the combination of these six technologies,
developed over the last 20 years that has made Remsoft’s OP application possible.
2.2 Critical Path to Operational Planning
Remsoft was established in 1992 as a software company focused on providing software tools for
fire and forest management; Woodstock was the sole forest management software product and
the development team consisted of a single programmer. All software products were DOS-based
command line programs.
2.2.1 Woodstock (1993)
As presented in 1993, the initial release of Woodstock was a simulation model that supported
deterministic inventory projection modeling and binary search, as well stochastic modeling using
Monte Carlo simulation. The addition of linear programming extensions resulted in a generalized
Model II [8] structure but allowed the existing simulation functionality to remain intact. This
yielded a very flexible modeling framework that could appeal to advocates of both optimization
and simulation. In particular, the software’s modeling language is relatively easy to learn and
sufficiently flexible to address most any forestry application. In fact, it has been used in various
non-forestry applications over the years; one applied to transportation infrastructure maintenance
was a finalist for the Edelman Award in 2010.

6. In the U.S., many of the large integrated forest products companies had proprietary
mainframe systems in place and showed little interest in Woodstock. However, the Sustainable
Forestry Initiative (SFI) of 2004 changed all that: the spatial restrictions required by SFI were
more onerous than many in the US Southeast had anticipated. Remsoft’s Stanley software (now
marketed as Spatial Optimizer) provided an analytical tool to address them [9]; in order to use
Stanley, however, one needed a Woodstock harvest schedule. As a result, Remsoft was able to
gain a toehold in the U.S. market and slowly build on it. Later, as the forest products companies
began to divest their lands to the timberland investment management organizations (TIMOs), a
few of the consulting firms in the U.S. began to adopt Woodstock to service these TIMO clients.
As the TIMO land holdings increased, some of the largest entities which had rigorously
outsourced planning and management services, brought these in-house and adopted Woodstock
as their primary modeling platform for ongoing management and acquisitions/divestiture
analysis.
In Canada, early adopters in Alberta included both government and consultants working
on behalf of industry licensees of public land. The prairie provinces of Alberta, Saskatchewan
and Manitoba were the first government agencies to adopt Woodstock. Weyerhaeuser and
MacMillan-Bloedel also adopted Woodstock for Crown Licenses and fee lands, respectively.
Over time, as these government and private clients began to share their experiences with
counterparts in other provinces, adoption of Woodstock as a standard for public lands gradually
took hold across Canada.
In Australia and New Zealand, interest in Woodstock began after Remsoft ported it to the
32-bit Windows environment, while FOLPI remained a DOS-only product. Once early adopters
were convinced that Woodstock could work effectively with their inventory data and growth
models, others followed in quick succession to adopt the Remsoft platform.
In South America, as the large forest products companies, and later, TIMOs, began to
invest in loblolly pine plantations in Brazil, they introduced the Woodstock modeling
technologies that had been used successfully in the Southeastern United States. As interest in
Brazil and Uruguay for forestry investments expanded, so did local interest in Remsoft’s
technology. Remsoft first partnered with local service providers to support and deliver Remsoft
technology, but in the last year the company has hired a full-time employee to service this
growing market.
Over time, changes in forest land ownership have resulted in fluctuations in licensing.
For example, in 2000 when Champion International was acquired by International Paper (IP), the
Remsoft technology was dropped in favor of IP’s internally developed systems. Later, IP
reacquired licenses for Remsoft technology only to spin them off into Sustainable Forest
Technologies (SFT), a wholly-owned subsidiary charged with managing IP’s lands. In 2007, after

7. IP divested of its timberland, SFT was acquired by American Forest Management, a large land
management and consulting firm and existing Remsoft client.
2.2.2 Allocation Optimizer (2005)
In addition to one particular client in Canada, numerous clients in New Zealand and Brazil were
interested in constructing delivered price models: harvest schedules where decisions went beyond
just what to harvest and when to include where to deliver the harvested forest products. Simple
delivered price models were possible to construct in Woodstock but required a lot of coding
overhead; models with even a moderate number of products and destinations quickly became
impractical.
To respond to this need, Remsoft released Allocation Optimizer (AO) in 2005, an
optional add-on to Woodstock. Within the AO interface, the user specifies what landscape theme
determines the source (the basis for haul distance/cost calculations), what products are to be
produced, and what destinations (mills) are to be considered (products accepted, periodic
demands, mill yard capacity). AO products are linked to classic Woodstock outputs defined in
regular harvest scheduling model: there may be 1:1 correspondence, or processing can be
assumed to happen with a conversion factor (e.g. chipping from solid m3 to loose m3). Prices for
products at each destination and haul costs to each destination from each origin are specified in
table formats that the user specifies. AO generates all the constraints required for the resulting
transportation problem.
Although initial interest in AO was largely due to users in the Southern Hemisphere
(66% of all sales in the first year), once it was released AO gained in popularity with long-time
users in North America as well. Entry into the European market would not have been possible
without AO.
2.2.3 Framework API (2006-2007)
By 2006, Remsoft was a well-established vendor in the forest sector but the increasing
dominance of the investor class in forest ownership was creating new demands for information.
The forest products companies had acquired timberland as a means of securing supply of raw
materials at low cost, and even those that established timberlands as profit centers within the
corporation still required transfer prices that favored their own mills. When the lands were
divested, the new owners were truly interested in timberland ownership as a profit-making
enterprise, and as part of their fiduciary responsibilities to their investors, they wanted more
access to the drivers of value; in other words, they wanted access to the information captured in a
forest planning model, but they did not want to become modeling experts themselves to achieve
it.

8. Over the next two years, Remsoft developers began to conceptualize an Analytics
framework for developing focused, collaborative applications that work off a compiled
Woodstock model. The compiled model would be a compact representation of all the information
that comprises a Woodstock model: land characterization, area, activities, outputs, cost and yield
coefficients, and key performance indicators. New data structures for storing this information,
and quickly accessing it would be required. The goal was to produce a compiled model file small
enough to email, or at least quickly copy to a common server location where non-traditional users
(non-Woodstock modelers) could access it through a familiar application, whether that
application was a simple desktop application, a web interface or a third party application such as
Microsoft Excel or ESRI ArcGIS. Current Analytics applications are based on Framework
version 2.
2.2.4 Regimes Module (2008)
Since Woodstock was first released there remained a small but vocal group of modelers who had
previously used other forest planning models (Spectrum, FORPLAN, MaxMillion, etc.) that
employed a Model I [7] structure, and they asked for such a capability to be added to Woodstock
as well. This was not a trivial task because it had to be done without disabling or radically
changing the underlying architecture of the Woodstock modeling engine. Clients were also
beginning to ask about the potential for developing crew scheduling models with Woodstock.
In 2008, while the development of the Remsoft Framework continued, Remsoft also
developed and released the Regimes module (RM). Rather than abandon the generalized Model II
[7] structures of standard Woodstock, RM simply broadens the concept of decision variable to
span multiple periods. In a classic Woodstock formulation, all actions are assumed to occur in a
single planning period, and a transfer row is used to associate area treated with a new
development type following treatment in that same period. With a regime, the decision variable is
associated with the period when the regime is started, but transfer rows are associated with the
period when the regime completes. Internally, Woodstock must keep track of when development
types enter a regime prescription and when they emerge from it, so as to maintain the correct age
and area accounting.
The Regimes module simplifies the modeling of linked decisions that occur in different
planning periods. A simple example is a commercial thinning where marking occurs in the period
prior to the harvest of trees, and fertilization follows the harvest one period later. Thus, the
decision to conduct a commercial thinning is a linked decision spanning three planning periods,
but once the decision is made all three activities must be carried out in the specified sequence. In
a classic Woodstock formulation, all three activities could be represented as Woodstock actions
but actions are independent decision variables; additional constraints are necessary to enforce the

9. requirement that the same area be subject to all three activities in the subsequent planning periods
resulting in a very inefficient model formulation.
One of the basic assumptions of strategic (and most tactical) forest planning models is
that the time required to harvest a particular stand or felling area is much less than the length of a
planning period within the model. At the operational level, this assumption no longer holds:
when planning periods are weeks, a harvest block can require multiple planning periods to be
completely harvested. In a classic Woodstock model formulated using linear programming, the
harvest block may be harvested over multiple periods but because the harvest decisions are
necessarily independent decision variables, the result may be that the harvest block is not
completely harvested, or even if it is completely harvested at the end of the planning horizon, it
may not be harvested in adjacent planning periods. Using a RM formulation, these decisions can
span multiple periods, but be represented by single decision variable (binary if complete
harvesting is required). In 2009, RM was incorporated into the base Woodstock application.
2.2.5 Publisher and Remsoft Tactical Planner (2009)
In 2009, version 1 of the Remsoft Framework (RF) was completed and Remsoft released
Publisher and Remsoft Tactical Planner (RTP). Publisher is used to export, compress and store
the compiled data inherent to a Woodstock model and make it available to Analytics applications
via a single, compact file; RTP was the first application released in the Remsoft Analytics suite.
It was designed for use by field foresters (not modeling experts) to evaluate (and if necessary,
modify) spatially explicit harvest schedules. Using Woodstock (and optionally, Spatial
Optimizer), a candidate harvest schedule is allocated to stand polygons: each polygon scheduled
for harvest is assigned a treatment (harvest, thin), a treatment period, and a block identifier to
associate it with adjacent polygons also scheduled at the same time. Because there are many
variables that go into delineating a harvest block, it is likely that the initial allocation will need to
be adjusted. Field foresters interface with the schedule via a map where they can view polygons
and the schedule of activities (via color-coded maps). They can select polygons and make
modifications to them (change action, change harvest period, delete polygon from block, edit
polygon topology). Changes are saved and can be imported back into the Woodstock modeling
engine.
2.2.6 Gurobi solver
By 2011, mixed-integer programming (MIP) formulations were becoming increasingly common
and in response to customer requests for a recommended solver solution, Remsoft decided to
investigate different commercial solutions and choose one as a preferred vendor for bundling
with Woodstock. In addition to long-time vendors IBM (CPlex) and FICO (Xpress), Remsoft
evaluated solvers from smaller vendors, MOSEK and Gurobi. In the last few years, vast

10. improvements in MIP solver technology have occurred, making the types of problems that
Remsoft clients have wanted to explore finally tractable. Ultimately, Gurobi was chosen as an
affordable, high performance MIP solver to be bundled with Woodstock for clients demanding
MIP capability; for those who do not require MIP formulations, the very capable MOSEK solver
continues to be bundled with Woodstock.
2.2.7 Explorer and Analyst for Excel
In 2011, after the development of version 2 of RF, Remsoft released two additions to the
Analytics suite of applications: Remsoft Explorer (RE), a desktop application for exploring the
fundamentals of a Woodstock model and various scenarios of that model, and Remsoft Analyst
for Excel (RAE), an add-in to Microsoft Excel that allows analysts to access results from a
compiled Woodstock model and use the power of Excel to analyze results. Because Excel
spreadsheets are the lingua franca of the financial world, RAE has been particularly popular with
TIMO clients and the consultants who provide analytical services to them. Once a template
spreadsheet has been set up according to client specifications, multiple Woodstock scenarios can
be published to a compiled model file and accessed in Excel. Model results are automatically
updated when the user selects a different scenario.
Illustrating the power of published models and Analytic apps, RE and RAE were the first
applications licensed by Remsoft to clients who did not also have licenses to Woodstock. In other
words, the client knew nothing about Woodstock model syntax, but they knew what key
performance indicators should be included in their analyses. Rather than build models
themselves, these clients outsourced the forest planning work to consultants, who provided
results to the client via a Published model file rather than the more typical collection of report
documents and spreadsheets. In addition to the benefits of faster turnaround times, the clients
maintain their own spreadsheet templates and associated financial models with complete
confidentiality.
2.2.8 Remsoft Integrator (2012)
The Remsoft Integrator (RI) arose from humble beginnings: a syntactical replacement for
Woodstock FOR-loop structures that were developed when Woodstock was still a DOS
application. A Woodstock FOR-loop can be used to generate code based on a simple numerical
loop construct, similar to those in most programming languages. For example,
FOR xx := 1 to 10
Tractxx
ENDFOR.
The construct above will generate 10 lines of code, and on each line will read Tract1, Tract2…
Tract9, Tract10. The idea for the replacement was to broaden the scope of the loops to allow for
lists that could include character strings,

11. FOREACH xx in (Jan..Dec)
VolumeInxx
ENDFOR.
The new construct will generate 12 lines of code, and on each line will read VolumeinJan,
VolumeInFeb,…, VolumeInDec.
As the feature developed, the lists grew to include action codes defined in the Woodstock
model itself, the destinations defined in the AO section, the thematic attributes used to
characterize the forest. Eventually lists came to include database tables,
FOREACH xx in MillDemand.dbf
xx.Name xx.Month xx.Volume
ENDFOR
This construct will read a dBASE table and on each line will write the mill name, month and
volume demanded (e.g., Ste-Anne 6 35000). Eventually, the developers considered that if the
FOREACH loops could read external tables, could not a complete Woodstock model be
constructed, composed of nothing but a series of FOREACH loops reading database tables?
Additional development was required to provide for hooks to corporate databases and SQL
queries, but the fundamental basis for RI was largely complete. With this functionality, it was
possible to create a Woodstock foundational model that could be completely updated
automatically from database tables.
2.2.9 Operational Planner (2013)
In 2012, Remsoft began working closely with Coillte to draft a statement of work for developing
their forest operations planner system. During the elicitation process, Coillte managers were
asked about tasks and decisions, how schedules are conveyed from the central office to the
regions, and about how they would like to interact with the system in a meaningful way. Like
many organizations, Coillte managers use Excel to share production data, contract volumes and
crew schedules, and so Excel provides an interface that managers are both well-versed in, and
with which they are comfortable. Surprisingly, Coillte managers were not particularly interested
in mapped displays of harvests, instead favoring a Gantt chart display of crew schedules. Given
that RAE was already available, it became apparent that an Excel add-in that leveraged the power
of RAE would be cost-effective for development, rather than a new desktop application.
Much of the work required to implement OP for Coillte involved developing new
workflows, and determining how much of the underlying technology to expose to end-users.
Coillte’s template Woodstock model incorporated numerous constraints required certification
compliance, overall volume flow constraints, and so forth; Coillte management did not want end-
users changing these types of constraints. In the end, Coillte management decided on a very
limited set of changes that end-users are permitted to make, and these are accessible from a drop-
down list.

12. Using a template spreadsheet developed by Coillte, the operational schedule is published
from the Woodstock template and opened in Excel. End-users may interact with the schedule
using tabular data or Gantt chart representations. In tabular form, a manager may work from a list
of felling units, harvest crews or trucking crews. By double-clicking a particular entry in the
table, the manager can access all relevant information about that item. For example, by selecting
a particular felling unit, the manager sees what crew has been assigned, what harvest method has
been chosen, when the unit is to be harvested and where volumes are to be delivered. If changes
are necessary, the manager can simply type new values in the table fields. To interact with the
schedule graphically, a Gantt chart representation is used, with crews as rows and time periods as
columns, and felling units represented by bars that indicate time required to harvest using the
associated harvest method. A user can edit the schedule by double-clicking either a tabular field
or a Gantt chart bar, activating a dialog box. Using drop-down boxes, a felling unit can be
assigned to a different crew or it can be assigned a different starting period.
Instead of making the application prevent over-allocation of jobs to crews, Coillte
preferred to have the application report capacity requirements only; since the crews all have
similar production and capacities, it was deemed sufficient to report what resources are over-
committed, and then allow the managers to resolve the problem manually.
3 Lessons Learned
Remsoft’s work with Coillte on this project has provided a valuable opportunity to delve into the
business trends that are shaping the marketplace as a whole and to immerse itself in the
operational realities of the forestry business. In the past, and operating as a pure software vendor,
Remsoft has been something of an interested bystander as clients have implemented its software:
providing training and technical support but otherwise passively engaged in the development of
business practices. With this project, Remsoft has taken an active part in decision-making for
systems design (i.e., the data schema of the “DataMart”) and business processes (i.e., exposure of
underlying model, end-user interaction, process flows). In this environment, how a software
application is used is as important, if not more so, than the application itself.
Historically, Remsoft has provided analytics tools as stand-alone desktop applications,
but the Analytics Framework was conceptualized to work beyond the confines of the desktop, to
include add-ins with business applications like Microsoft Office or ESRI ArcGIS. As more
projects like the Operational Planning system come forward, Remsoft will need to consider not
just application platforms on the desktop, but also how to deliver analytics via mobile and tablet
applications. The consumerization of technology continues apace, and every year new workers
enter the workforce expecting to have ready access to their data and applications wherever they
are, whether by laptop, tablet or smartphone. The move to cloud computing is making it possible

13. to use these alternative devices within the workplace since the heavy processing can take place
on cloud servers, but the applications need to be more tailored with portable devices.
Cloud computing offers significant advantages for working with big data, but Remsoft
has to consider some of the potential drawbacks, such as data security for highly confidential
information. Historically, data security has been the responsibility of the client, but with cloud
computing the data may reside on a remote server, and thus be susceptible to hacking.
Alternatively, the application and data may reside on a secure server behind a firewall with an
MPS file uploaded to a cloud-based Gurobi solver; in fact, there are many alternatives for
implementing cloud computing and considerable forethought will be required before
implementing them.
Modeling the entire enterprise as a monolithic model is impractical and therefore
hierarchical planning with linked business models is the best alternative. Business trends and
operational realities are demanding better decisions throughout the supply chain to improve the
overall bottom line, and one way to deliver this is through analytics tools focused on increasingly
more particular planning problems. This is the gist of operationalizing analytics. Going forward,
the ongoing challenge for Remsoft will be how to maintain the open, flexible modeling
environment that has been the basis for the company’s success to date, while providing intuitive
working environments for non-modelers.
4 Conclusions
Remsoft has been in business over 20 years and continues to grow its customer base and software
offerings. Its success has come from a commitment to high quality software products, vigorous
reinvestment in research and development, and a willingness to listen to customer needs and
respond to them. Recent developments in technology (software and hardware) have finally made
possible the operational planning solutions that some Remsoft clients are starting to deploy. The
recent interest arising from the business community at-large in business intelligence, big data and
operational analytics will continue to generate the need for new solutions as planning problems
become increasingly closer to real-time.

14. References
[1] K. R. Walters, “Design and Development of a Generalized Forest Management Modeling
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